Advances in Clinical Chemistry

VOLUME EIGHTY ONE

ADVANCES IN CLINICAL CHEMISTRY

This page intentionally left blank

VOLUME EIGHTY ONE

ADVANCES IN CLINICAL CHEMISTRY

Edited by

GREGORY S. MAKOWSKI Hartford HealthCare Laboratories Hartford, CT Hartford Hospital Hartford, CT

Academic Press is an imprint of Elsevier 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States 525 B Street, Suite 1800, San Diego, CA 92101-4495, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 125 London Wall, London, EC2Y 5AS, United Kingdom First edition 2017 © 2017 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-12-812074-3 ISSN: 0065-2423 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Zoe Kruze Acquisition Editor: Poppy Garraway Editorial Project Manager: Shellie Bryant Production Project Manager: Vignesh Tamil Cover Designer: Mark Rogers Typeset by SPi Global, India

CONTENTS Contributors Preface

vii ix

1. Microparticles in Chronic Heart Failure

1

Alexander E. Berezin 1. Introduction 2. Definition, Classification, Structure, and Regulation of MPs 3. Biological Role and Function of MPs 4. Measurement of MP 5. MPs in Cardiovascular Disease 6. Diagnostic and Predictive Value of Circulating MPs in HF 7. MPs and Therapeutic Aspects 8. Conclusion Acknowledgments References

2 4 8 14 20 26 28 30 31 31

2. Peptide Antibodies in Clinical Laboratory Diagnostics

43

Nicole H. Trier and Gunnar Houen 1. Introduction 2. Generation of Peptide Antibodies 3. Peptide Antibodies in Clinical Laboratory Diagnostic 4. Conclusion References

3. Measurement and Clinical Utility of βCTX in Serum and Plasma

44 49 63 83 84

97

Stephen A.P. Chubb and Samuel D. Vasikaran 1. Introduction 2. Analysis of βCTX Concentration 3. Clinical Utility of CTX 4. Conclusion References

98 99 115 125 126

v

vi

Contents

4. Human Papillomavirus and Its Testing Assays, Cervical Cancer Screening, and Vaccination

135

Yusheng Zhu, Yun Wang, Julie Hirschhorn, Kerry J. Welsh, Zhen Zhao, Michelle R. Davis, and Sarah Feldman 1. Introduction 2. Molecular Biology, Pathogenesis, and Epidemiology of HPV 3. Principles and Methods for HPV Testing 4. Cervical Cancer Screening 5. HPV Vaccines and the Impact on Cervical Cancer Screening Acknowledgments References

5. Physical Exercise and DNA Injury: Good or Evil?

136 137 153 157 174 180 181

193

Elisa Danese, Giuseppe Lippi, Fabian Sanchis-Gomar, Giorgio Brocco, Manfredi Rizzo, Maciej Banach, and Martina Montagnana 1. Introduction About Physical Activity and Health 2. Physical Exercise and ROS Generation 3. Considerations About the Methods Used for Assessing DNA Injury 4. Physical Exercise and DNA Injury 5. Conclusions References

194 197 214 217 223 225

6. Bulky DNA Adducts, Tobacco Smoking, Genetic Susceptibility, and Lung Cancer Risk

231

Armelle Munnia, Roger W. Giese, Simone Polvani, Andrea Galli, Filippo Cellai, and Marco E.M. Peluso 1. Introduction 2. Bulky DNA Adduct Analysis 3. Exposure Data 4. Chemical Carcinogenesis 5. Smoking-Related Bulky DNA Adducts 6. Nature and Nurture Susceptibilities 7. Bulky DNA Adducts and Carcinogen Exposure 8. Bulky DNA Adducts and Lung Cancer 9. Conclusion Acknowledgments References Index

232 233 237 238 240 249 256 262 266 267 267 279

CONTRIBUTORS Maciej Banach WAM University Hospital in Lodz, Medical University of Lodz, Lodz, Poland Alexander E. Berezin State Medical University of Zaporozhye, Zaporozhye, Ukraine Giorgio Brocco Research Institute of the Hospital 12 de Octubre (i + 12), Madrid, Spain Filippo Cellai Cancer Risk Factor Branch, Regional Cancer Prevention Laboratory, ISPO-Cancer Prevention and Research Institute, Florence, Italy Stephen A.P. Chubb PathWest Laboratory Medicine WA, Fiona Stanley Hospital, Murdoch; School of Pathology and Laboratory Medicine, School of Medicine and Pharmacology, University of Western Australia, Nedlands, WA, Australia Elisa Danese Section of Clinical Biochemistry, University of Verona, Verona, Italy Michelle R. Davis Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States Sarah Feldman Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States Andrea Galli Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy Roger W. Giese Bouve College of Health Sciences, Barnett Institute, Northeastern University, Boston, MA, United States Julie Hirschhorn Medical University of South Carolina, Charleston, SC, United States Gunnar Houen Statens Serum Institut, Copenhagen, Denmark Giuseppe Lippi Section of Clinical Biochemistry, University of Verona, Verona, Italy Martina Montagnana Section of Clinical Biochemistry, University of Verona, Verona, Italy Armelle Munnia Cancer Risk Factor Branch, Regional Cancer Prevention Laboratory, ISPO-Cancer Prevention and Research Institute, Florence, Italy

vii

viii

Contributors

Marco E.M. Peluso Cancer Risk Factor Branch, Regional Cancer Prevention Laboratory, ISPO-Cancer Prevention and Research Institute, Florence, Italy Simone Polvani Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy Manfredi Rizzo University of Palermo, Palermo, Italy Fabian Sanchis-Gomar Research Institute of the Hospital 12 de Octubre (i + 12), Madrid, Spain Nicole H. Trier Statens Serum Institut, Copenhagen, Denmark Samuel D. Vasikaran PathWest Laboratory Medicine WA, Fiona Stanley Hospital, Murdoch, WA, Australia Yun Wang Medical University of South Carolina, Charleston, SC, United States Kerry J. Welsh National Institute of Health, Bethesda, MD, United States Zhen Zhao National Institute of Health, Bethesda, MD, United States Yusheng Zhu Pennsylvania State University Hershey Medical Center, Hershey, PA, United States

PREFACE The fourth volume of the Advances in Clinical Chemistry series for 2017 is presented. In Chapter 1, the role of circulating microparticles in the diagnosis and prognosis of heart failure is reviewed. Microparticles, a heterogeneous subpopulation of extracellular vesicles containing markers derived from their cell of origin, are continuing to stimulate considerable study and research as diagnostic and potentially therapeutic tools in many disease processes including heart failure. In Chapter 2, the importance of peptide antibodies in clinical laboratory diagnostics is explored. Due to their high specificity and sensitivity, these multifunctional molecules are indispensable in the generation of novel clinical assays for the identification and quantification of disease markers. In Chapter 3, biomarkers of bone turnover are highlighted with emphasis on the c-terminal cross-linked telopeptide for type I collagen. Accurate assessment of bone status remains a continuing clinical problem due to the potential for catastrophic nontraumatic fracture in the growing elderly population. In Chapter 4, testing for human papilloma virus, the causative agent in cervical cancer, is reviewed. The molecular basis of this virus, its pathogenesis, and the epidemiology of infection will be discussed. Guidelines for cervical cancer screening and treatment will also be considered. In Chapter 5, the role of exercise in physiology and pathophysiology will be explored. Although low-intensity physical activity is considered beneficial, strenuous exercise may enhance inflammation and trigger the generation of free radicals, thus mediating damage to intracellular targets including DNA. In Chapter 6, damage to nucleic acid via the generation of bulky chemical complexes, i.e., adducts, is reviewed with emphasis on smoking and lung cancer. The generation of large reactive electrophiles during detoxification increases adduct risk and can lead to mutations in oncogenes/tumor suppressor genes, thus promoting carcinogenesis. I thank Volume 81 contributors and colleagues for their peer review. I extend thanks to Shellie Bryant and Vignesh Tamilselvvan for expert editorial support.

ix

x

Preface

I hope the fourth volume for 2017 will be enjoyed. Comments and feedback from the readership are always appreciated. I would like to dedicate Volume 81 to Chris on the occasion of his 40th birthday. GREGORY S. MAKOWSKI

CHAPTER ONE

Microparticles in Chronic Heart Failure Alexander E. Berezin1 State Medical University of Zaporozhye, Zaporozhye, Ukraine 1 Corresponding author: e-mail addresses: [email protected]; [email protected]

Contents 1. 2. 3. 4.

Introduction Definition, Classification, Structure, and Regulation of MPs Biological Role and Function of MPs Measurement of MP 4.1 Flow Cytometry 4.2 Nanoparticle Tracking Analysis 4.3 Western Blot Analysis 4.4 Nanoparticles—Surface Plasmon Resonance-Based Imaging Microscopy 4.5 Highly Sensitive Fluorescent Microscopy 4.6 Surface-Assisted Laser Desorption/Ionization Mass Spectrometry 4.7 Micronuclear Magnetic Resonance Technique 4.8 Raman Microspectroscopy 4.9 Small-Angle X-Ray Scattering 4.10 Future Perspectives for MP Detection 5. MPs in Cardiovascular Disease 5.1 Erythrocytes-Derived MPs 5.2 Leukocyte-Derived MPs 5.3 Platelet-Derived MPs 5.4 Endothelial Cells-Derived MPs 6. Diagnostic and Predictive Value of Circulating MPs in HF 7. MPs and Therapeutic Aspects 8. Conclusion Acknowledgments References

2 4 8 14 15 16 17 17 18 18 18 18 19 19 20 20 21 22 23 26 28 30 31 31

Abstract Heart failure (HF) continues to have a sufficient impact on morbidity, mortality, and disability in developed countries. Growing evidence supports the hypothesis that microparticles (MPs) might contribute to the pathogenesis of the HF development playing a pivotal role in the regulation of the endogenous repair system, thrombosis, coagulation, inflammation, immunity, and metabolic memory phenomenon. Therefore, there is a

Advances in Clinical Chemistry, Volume 81 ISSN 0065-2423 http://dx.doi.org/10.1016/bs.acc.2017.01.001

#

2017 Elsevier Inc. All rights reserved.

1

2

Alexander E. Berezin

large body of data clarifying the predictive value of MP numerous in circulation among subjects with HF. Although the determination of MP signature is better than measurement of single MP circulating level, there is not yet close confirmation that immune phenotype of cells produced MPs are important for HF prediction and development. The aim of the chapter is to summarize knowledge regarding the role of various MPs in diagnosis and prognosis of HF. The role of MPs as a delivery vehicle for drugs attenuated cardiac remodeling is considered.

ABBREVIATIONS BNP brain natriuretic peptide CV cardiovascular EVs extracellular vesicles HF heart failure HFpEF chronic HF with preserved ejection fraction HFrEF chronic HF with reduced ejection fraction HSP heart shock protein ICAM intracellular adhesion molecule MI myocardial infarction MPs microparticles PGF placental growth factor PLGA poly(lactic-co-glycolic acid) STEMI ST-segment elevation myocardial infarction VCAM vascular cell adhesion molecule VEGF vascular endothelial growth factor

1. INTRODUCTION Heart failure (HF) continues to have a sufficient impact on morbidity, mortality, and disability in developed countries [1]. However, within last decades, the prevalence of HF have been progressively decreased predominantly HF with reduced left ventricular ejection fraction (HFrEF) [2]. In contrast, frequency of novel cases of HF with preserved left ventricular ejection fraction (HFpEF) appears to be raised [3]. These changes in epidemiology of HF depend in particularly on the implementation of contemporary strategy regarding early diagnosis, prevention, treatment of HF [4], as well as resulting in effect of aging, sex, socioeconomic status, and comorbidities [5–8]. Cardiac dysfunction that accompanies various types of HF development is a complex and rather controversial issue and results from the trophic effects

Microparticles in Chronic Heart Failure

3

of pure mechanical overload, and susceptibility factors (i.e., ischemia, inflammation, overload, dysmetabolic reasons) and the neurohormonal reaction [9]. There are current available data regarding the role of cardiac remodeling, worsening of adrenergic signaling mechanisms in the cardiac response, catecholamines toxicity, inflammation, thrombosis, worsening of endothelial integrity, and endothelium injuries are common for HF onset and development beyond etiology [9–12]. Indeed, there are evidence regarding the important role of dysregulation of sympathetic nervous system and renin–angiotensin–aldosterone system (RAAS) in the HF [9,13]. To our knowledge, adrenal signaling and RAAS overdrive accompany noncardiovascular (non-CV) pathologies (i.e., hyperglycemia and diabetes mellitus, obesity and metabolic syndrome, obstructive sleep apnea, and renal disease) with cardiac impairment [12,13]. Moreover, dysregulation of intracellular signaling mechanisms in HF is considered a determined higher risk of arrhythmias and cardiac remodeling contributing to worsen the prognosis of this disease [13]. In this context, the investigations regarding the underlying molecular mechanisms of failing heart could be promised in the discovery of novel diagnostic tools and predictive biomarkers in several phenotypes of HF. Nevertheless, male gender, current smoker status, increased highly sensitive troponin T, and previous myocardial infarction (MI) were associated with new onset HFrEF, whereas female gender, history of atrial fibrillation, increased urinary albumin excretion, and cystatin C were conferred new onset HFpEF [14]. However, higher age, obesity, and increased N-terminal pro-B-type natriuretic peptide (NT-proBNP) increased the risk for both HFpEF and HFrEF [14]. Although improving the management of HF remains a priority for health care services, the outcome of HF patients remains poor despite modern pharmacological and none-pharmacological therapies including established devices, i.e., cardiac resynchronization therapy devices and implantable defibrillator/cardioverters [8,15]. Furthermore, the clinical outcomes of both phenotypes of HF have been occurred similar or at least not sufficiently distinguished [16] that is important challenge for contemporary medical care service. There is growing awareness of the role of several predictive tools reflecting various pathophysiological stages of cardiac dysfunction development for risk stratification of the patients with various of HF. Most studies have described the utility of biological markers in HF for diagnosis,

4

Alexander E. Berezin

prediction, and even biomarker-guided therapy, but by now natriuretic peptides, soluble ST2, galectin-3, and high-sensitive cardiac specific troponins were validated only [4,17]. As expected, the routine use of biomarkers on diagnosis of HF might help to stratify the patients at higher risk of death and clinical outcomes. In fact, both 2012 European Society of Cardiology (ESC) Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure and 2013 American College of Cardiology Foundation/ American Heart Association (ACCF/AHA) Guideline for the Management of Heart Failure are well accepted by many clinicians regarding diagnosis and prognosis of HFrEF. In contrast, diagnosis and prediction of HFpEF with biomarkers is still challenging for practitioners [18]. However, there was not a large body of evidence regarding perspectives to may provide clinically useful prognostic information both concerning the future risk of HFpEF/ HFrEF manifestation in asymptomatic subjects, the risk of fatal events, and primary/readmissions in the hospital in individuals for those have already established symptomatic acute, acutely decompensated/advanced, and chronic stable HF related to ischemic and nonischemic causes [19]. It is suggested that multimorbidity in HF may limit the diagnostic and predictive utility of biomarkers [20–22]. HF may closely associate with release of newly detectable circulating biomarkers currently called microparticles (MPs) [23,24]. The aim of the chapter is to summarize knowledge regarding the role of various MPs in diagnosis and prognosis of HF.

2. DEFINITION, CLASSIFICATION, STRUCTURE, AND REGULATION OF MPs MPs are defined a heterogeneous subpopulation of extracellular vesicles (EVs) with diameter average from 100 to 1000 nm originated from plasma membranes of mother’ cells (Table 1). EVs are phospholipid-based endogenously produced particles (30–1000 nm in diameter), which contain cell-specific collections of proteins, glycoproteins, lipids, nucleic acids, and other molecules. Abundant cells including cardiomyocites, blood cells, endothelial cells, immune cells, and even tumor cells are capable to secrete MPs of different size and compositions [25]. Depending on their origin EVs are graduated to follow subsets, i.e., the exosomes (30–100 nm in diameter), the microvesicles (50–1000 nm in diameter), ectosomes (100–350 nm in diameter), small-size MPs (40

944

Australia

Y

100–700

[68]

35–45

226

Shanghai

Y

100–612

[67]

50–92

660

Spain

Y

69–760

[71]

70–89

298

Australia

Y

117–740

[72]

35–39d

113

Germany

Nb

110–780

[69]

Germany

b

50–610

[69]

Men

IDS iSYS

70–74

d

85

N

a

Included 4.9% premenopausal women. Collection time 08:00–20:00. c Collection time 12:00–15:00. d Reference intervals for men aged 25–79 years in 5 year strata were published. b

women. Among men, in whom βCTX values fall with age until about 50 years [68,69], there is more variation depending on the age of the studied cohort. Reference intervals for men by the ELISA assay have not been determined formally. 2.3.3 Harmonized Reference Intervals Under the auspices of the Australasian Association of Clinical Biochemists Harmonization of Reference Intervals project, the published literature was surveyed and a single set of reference intervals for βCTX (and PINP) measured by the Roche system in adults were agreed for all laboratories in Australia and New Zealand [73]. A similar process is under way in the United States [74].

Measurement and Clinical Utility of βCTX

111

Fig. 4 Typical reference intervals for βCTX in boys and girls throughout childhood. Redrawn from M. Rauchenzauner, A. Schmid, P. Heinz-Erian, K. Kapelari, G. Falkensammer, A. Griesmacher, et al., Sex- and age-specific reference curves for serum markers of bone turnover in healthy children from 2 months to 18 years, J. Clin. Endocrinol. Metab. 92 (2007) 443–449.

2.3.4 βCTX Reference Intervals in Children Four studies have examined reference intervals for βCTX in children [17,75–77]. Three of these used the ELISA, but there is variation between studies in the inclusion of nonfasting samples and time of day of sample collection. Throughout childhood, βCTX concentrations are considerably higher than in adults. All studies show a rise during puberty with a fall toward adult values in late adolescence that parallels changes in other BTMs and growth (Fig. 4). The two studies using the ELISA and morning samples gave intervals that agree well [75,77]. The third study measured samples collected through the day—this may explain why the intervals obtained are rather lower than the later studies using the same assay [17]. Upper reference values for the morning samples measured in the Roche assay are lower than in the ELISA [76], in line with our understanding of between method differences in results (see later).

2.4 Agreement of Results From Different Methods Nomination of βCTX as the reference bone resorption marker by the International Osteoporosis Federation/International Federation of Clinical Chemistry and Laboratory Medicine Bone Marker Standards Working Party [3] presupposes that all studies reporting this parameter will give equivalent results. That there may be differences in the results reported by the ELISA and the Roche assays were indicated by a study that derived a lower reference interval for premenopausal women in the Roche assay [64]. However, formal method comparison studies of the three commercially available assays

112

Stephen A.P. Chubb and Samuel D. Vasikaran

have only recently become available; the original evaluation of the Roche βCTX assay used the original One-Step ELISA with results in pmol/L as the comparator assay, so it was not possible to assess numerical agreement of the results [19]. Comparison of the Roche and ELISA assays using contemporary formulations showed the Roche assay to give results approximately 20% lower [78], which accounted for the lower reference interval by this method in the study previously mentioned [64]. Regarding the IDS iSYS assay, results have given variable outcomes. In comparison to the ELISA, the iSYS assay gave reasonable agreement, with approximately 6% lower results than the ELISA in one study [20] but a more complex pattern, with both systematic and proportional bias in another [78]. Again, in comparison with the Roche assay, the iSYS assay has given different outcomes, but tends to have a positive proportional bias [78–80] ranging from 12% [79] to 60% [78]. In our hands, the assay also had a significant negative systematic bias which was not shown in other studies [78]. A method of harmonizing the results from the different assays may be needed to reduce the impact of method-based variation on results of studies of βCTX and fracture risk. One approach to this would be use of a common calibrator, but data from one study suggested that there was limited commutability of the calibrators in the currently available reagent kits between assays [78]. An alternative interim measure is to develop equations that allow the results to be interconverted. Robust studies carried out on multiple instruments from each manufacturer, probably in multicenter studies, would be needed to generate such data. One feature that has not been well studied is the functional sensitivity, or lower limit of quantitation, of the βCTX assays. This is usually defined as that concentration above which the between run imprecision is 1.0 above is defined as normal. Based on the WHO definition of osteoporosis, 30% of postmenopausal Caucasian women have osteoporosis at the hip, spine, or forearm based on a T-score below 2.5 [91]. Fracture is the major outcome of osteoporosis of clinical consequence. Patients with minimal trauma fractures are considered to have osteoporosis, regardless of their BMD since their future fracture risk has been shown to be high [93,94]. Osteoporosis is a major public health

116


problem with over 2 million fractures in the United States being attributed to it [95]. However, hip fracture is associated with the worst outcomes, up to half of those with that fracture not returning to normal life and a quarter dying within 12 months [96]. BMD is not the only factor which determines fracture risk. The risk of fracture increases with age in postmenopausal women and in older men. In addition, since osteopenia is much more prevalent than osteoporosis, more fractures occur in subjects with osteopenia than those with osteoporosis [97]. These considerations have led to the development of tools for absolute fracture risk calculation [98,99]. In addition to age, sex, BMD, past, and family history of fracture, other risk factors such as glucocorticoid therapy, smoking, body mass index, and secondary causes of osteoporosis are included in the fracture risk calculations. However, BTMs are currently not included due to lack of data for their inclusion.

3.2 The Role of BTM in the Management of Osteoporosis 3.2.1 BTM in Assessment of Fracture Risk BTM measurement, compared to imaging techniques, is safe, noninvasive, inconveniences the patient less, cheaper, and less labor intensive. BTMs may potentially have a role in the following situations in osteoporosis management: fracture risk prediction, prediction of response to treatment, and monitoring treatment. The use of BTM is most accepted is in the monitoring of osteoporosis treatment. BTMs predict fracture risk independent of BMD. A recent metaanalysis examined the performance characteristics of βCTX (as well as PINP) in fracture risk prediction from systematic literature searches [88]. Prospective cohort studies of βCTX measured at baseline in untreated middle aged or older men and women where primary outcome was the first incident fracture were included in that metaanalysis. Cross-sectional and case–control studies, and studies that did not provide separate data for men and women or on hip fractures were excluded. The results are summarized in Table 4. The results of the metaanalysis showed that the hazard ratio for fracture per SD increase in βCTX was 1.18 (95% CI: 1.05–1.34), i.e., each SD increment in βCTX was associated with an 18% increase fracture risk. When the four studies that examined women only were examined the HR per SD was 1.19 (95% CI: 1.05–1.34) [88]. Four of the studies reported the association between βCTX and the risk of hip fracture (Table 4). The merged hazard ratio for hip fracture per SD βCTX was 1.23 (95% CI: 1.04–1.47), i.e., each SD increment in βCTX was associated with a 23% increase in the risk of hip fracture. The analysis notes that this gradient of risk is substantially


117

Table 4 Results of a Metaanalysis Examining the Relationship Between βCTX and Fracture in Untreated Patients [88] FU Age HR for Hip Study (Year) (Years) Sex (Years) Fracture Outcome HR per SD # per SD

Bauer et al. [100]

4.6

M

>65

Hip and nonspine 1.16 1.41 (0.99–1.37) (1.02–1.95)

Chapurlat et al. [60]

4.9

F

>75

Hip

Dobnig et al. 2 [101]

F

>70

Hip, nonvertebral 1.10 1.07 (0.93–1.32) (0.79–1.45)

Garnero et al. 5 [102]

F

50–89 Osteoporotic (vertebral + appendicular)

Gerdhem et al. [103]

6.5

F

75

Any, hip, clinical 1.10 1.01 vertebral (0.88–1.38) (0.65–1.55)

Meier et al. [104]

6.3

M

>70

Low-trauma

Merged result

1.48 1.48 (1.03–2.12) (1.03–2.12)

1.75 (1.13–2.71)

1.20 (0.94–1.54) 1.18 1.23 (1.08–1.29) (1.04–1.47)

lower than those seen with reductions in BMD. The gradient of risk for BMD is age dependent; for example at age 80, it is 1.62 and falls progressively to 1.19 at the age of 50 [105]. The metaanalysis was not able to determine to what extent fracture risk prediction by BTMs was independent of BMD [88]. BTMs would be useful for inclusion in fracture risk calculations if they were independent predictors of fracture risk. Published studies have given inconsistent results on this point with some reporting that BTM predict fracture independent of BMD while others do not [14,100,106] (Fig. 5). Also of note, the relationship between BTM and fracture risk is the highest at the start of the follow-up period and becomes weaker with time [108]. 3.2.2 Change in CTX Following Osteoporosis Treatment BTMs that have been found to be of use in osteoporosis including CTX in serum and in urine show large and rapid responses to the treatments used for this condition. The pattern and direction of change depend on the medication and its mode of action. Antiresorptive agents such as selective estrogen receptor modulators (SERMs), bisphosphonates, and denosumab initially inhibit osteoclastic bone resorption leading to a reduction in bone

118


Fig. 5 The impact of uβCTX, bone mineral density (BMD), and prior fracture on the 10-year hip fracture probability based on the EPIDOS data [106] applied to women from Sweden [107]. BMD refers to a T-score of 2.5 SD at the femoral neck and uβCTX to a urinary value that exceeds the upper limit of normal for premenopausal women. Reproduced with permission of Springer from S. Vasikaran, R. Eastell, O. Bruyere, A.J. Foldes, P. Garnero, A. Griesmacher, et al., Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards, Osteoporos. Int. 22 (2011) 391–420.

resorption, which is reflected in a reduction in markers of bone resorption such a CTX. Owing to coupling of bone formation to bone resorption, this is followed by a later reduction in bone formation. The degree of reduction in BTMs is dependent on the potency of the antiresorptive agents, with denosumab being the most potent, and SERMS the least. These changes in BTMs following antiresorptive treatments lead to an increase in BMD and reduction in fracture risk [109]. There are also differences in BTM changes following treatment with different bisphosphonate, alendronate, and ibandronate cause greater reduction in BTM than does risedronate and these changes are reflected in differential BMD changes as demonstrated by the TRIO study, although fracture reduction is not different between these bisphosphonates, shown in Fig. 6 [38,110]. The changes in βCTX are greater than the changes seen with uNTX an alternative bone resorption marker. The speed of change in BTMs is in part dependent on the route of administration with parenteral administration leading to a reduction in resorption markers within days and oral administration taking weeks for the reduction in resorption markers (Fig. 7).


20

Ibandronate % change from baseline

% change from baseline

20

119

0 –20 –40 –60 –80 –100

Alendronate

0 –20 –40 –60 –80 –100

012

4

12

48

96

012

4

12

20 % change from baseline

48

96

Week

Week Risedronate

CTX NTX

0 –20 –40 –60 –80 –100 012

4

12

48

96

Week

Fig. 6 The percentage change from baseline (mean and standard error of the mean) for βCTX and uNTX following treatment with three oral bisphosphonates (ibandronate, alendronate, and risedronate) over 2 years. Reproduced with permission of Springer from K.E. Naylor, R.M. Jacques, M. Paggiosi, F. Gossiel, N.F. Peel, E.V. McCloskey, et al., Response of bone turnover markers to three oral bisphosphonate therapies in postmenopausal osteoporosis: the TRIO study, Osteoporos. Int. 27 (2016) 21–31. S-βCTX (ng/mL) 0.5 Alendronate 70 mg

0.4

Zoledronate 5 mg 0.3

0.2

0.1

0 0

4

8

12

16

20

24

Time (weeks)

Fig. 7 The time course of βCTX (mean SEM) following treatment with the bisphosphonate alendronate given weekly by mouth and zoledronic acid given as a single intravenous dose. Reproduced with permission of Springer from S. Vasikaran, R. Eastell, O. Bruyere, A.J. Foldes, P. Garnero, A. Griesmacher, et al., Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards, Osteoporos. Int. 22 (2011) 391–420.

120


The decrease in βCTX is maintained for the duration of therapy with bisphosphonates and reverts toward baseline values when therapy is ceased. The offset is rapid after cessation of oral risedronate but in the case of alendronate and zoledronic acid residual reduction may be seen for years afterward. Subjects who received 5 years of oral alendronate in the fracture intervention trial (FIT), when followed up for a further 5 years in the placebo arm of the FIT long-term extension trial still had BTMs lower than at baseline [111]. The decrease in βCTX following a single intravenous zoledronic acid infusion persists for up to 5 years after the infusion [112]. With denosumab therapy, the offset of effect is not only rapid after cessation of therapy, there is a rebound increase in βCTX together with a rapid reduction in BMD [113] (Fig. 8). The changes following the use of the anabolic agent teriparatide are different. There is initially an increase in bone formation markers following initiation of therapy with teriparatide, followed by a smaller increase in bone resorption markers such as βCTX (Fig. 9) [114,115]. These changes are also associated with a significant increase in bone density and reduction in fracture risk.

100%

1000

0

sCTX concentration (ng/L)

Percentage change in bCTX from baseline

Change in bCTX over 48 months with denosumab therapy for 24 months

0

–100% 0 1

6

12

24

30

48

Study month

Fig. 8 Change in βCTX over 48 months with denosumab therapy for 24 months. Percentage change from month 0 is presented as median and interquartile range (left vertical axis). Approximate corresponding concentrations are presented for reference (right vertical axis). Redrawn from H.G. Bone, M.A. Bolognese, C.K. Yuen, D.L. Kendler, P.D. Miller, Y.C. Yang, et al., Effects of denosumab treatment and discontinuation on bone mineral density and bone turnover markers in postmenopausal women with low bone mass, J. Clin. Endocrinol. Metab. 96 (2011) 972–980.


121

Change in bCTX following treatment Mean (SE) +400

Teriparatide

bCTX (ng/L)

Risedronate +200

0

–200 0

3

6

12

18

Months

Fig. 9 Mean (SE) changes from baseline for βCTX at 3, 6, and 18 months following treatment with teriparatide and with risedronate. Redrawn from P. Farahmand, F. Marin, F. Hawkins, R. Moricke, J.D. Ringe, C.C. Gluer, et al., Early changes in biochemical markers of bone formation during teriparatide therapy correlate with improvements in vertebral strength in men with glucocorticoid-induced osteoporosis, Osteoporos. Int. 24 (2013) 2971–2981.

Not all medications fit into the two above categories. Strontium ranelate treatment results in a small increase in bone formation as well as a small decrease in bone resorption; its mechanism of action in reducing fracture risk is unclear [116,117]. Odanacatib, a cathepsin K inhibitor that is currently in development seems to inhibit bone resorption leading to a decrease in βCTX without reducing osteoclast numbers [118]. 3.2.3 The Role of βCTX in Monitoring Osteoporosis Treatment There is evidence that the reduction in BTM following antiresorptive therapy determines in part the antifracture efficacy of these therapies [119]. The aim of treatment in osteoporosis is to reduce the risk of fracture. Early changes in BTM following initiation of treatment with raloxifene and with bisphosphonates have been shown in some studies to predict fracture risk reduction, and these results form the basis for the use of BTM as a surrogate marker of treatment efficacy [62,120–122]. The alternative monitoring strategy using BMD has some drawbacks as the changes are much smaller and slower than those seen with BTM. In addition, some studies suggest that BTM changes following treatment explain a greater proportion of fracture reduction effect than the change in BMD [123,124]. These considerations

122


have led to use of biochemical markers of bone remodeling in monitoring patients on treatment for osteoporosis [125,126]. However, the benefits of monitoring in improving fracture outcomes or in adherence to oral therapies have not been established [127,128]. There are no fracture data for BTM in patients treated with denosumab or teriparatide. The latter being an anabolic agent, CTX may not be the optimum marker for monitoring; a bone formation marker such as PINP is mostly studied [129]. 3.2.4 Goals of Treatment Optimum BTM targets for fracture risk reduction are not known. In the VERT study of risedronate therapy, the fracture risk posttreatment decreased with lower levels of uCTX to a plateau when the uCTX level was equivalent to the mean value for premenopausal women (i.e., a T-score of 0). This supported the use of the lower half of the reference interval for premenopausal women as the goal of antiresorptive treatment [62,130]. The uNTX data in this study suggested a threshold of T-score 1.5, equivalent to 21 nmol CE/L, but this was not confirmed in the reanalysis which did not exclude further reductions in fracture risk at lower levels [62,130]. Using this threshold, we have extrapolated a βCTX equivalent of around 250 ng/L as an optimum threshold as a goal of bisphosphonate treatment [81]. Not all subjects on treatment with risedronate may achieve this threshold, and premenopausal mean may be used as an adequate target with risedronate therapy [62,130]. The results of the FIT study of alendronate demonstrated that progressively lower BTM postalendronate treatment were associated with a lower risk of spine, hip, and nonspine fracture with no evidence of a threshold [121]. These relationships were more consistent for bone alkaline phosphatase (ALP), than βCTX. However, βCTX results were confounded, since 80% of the baseline samples and most posttreatment samples were obtained in the nonfasting state [121]. Changes greater than the RCV confirm drug effect and may also be useful in monitoring therapy. Since the direction of change is known, a pffiffiffi one-sided probability of 0.05 may be used: 2 1:65 total CV ¼ 2:33 CV which is 28% for βCTX (see earlier). The changes in BTM following denosumab therapy are more marked than for bisphosphonates. βCTX decreases within days and reaches a nadir at month with a median reduction of 89% from baseline; continued suppression from baseline of 63%–88% was maintained through the 36-month treatment period of the FREEDOM trial of denosumab treatment [131].


123

Median bCTX following denosumab therapy

100 bCTX (ng/L)

90

Placebo

50

20

Denosumab

0 0

1

6

12

24

Months

Fig. 10 Median values of serum: pβCTXβCTX, over 24 months in the FREEDOM trial. Horizontal dotted lines represent the premenopausal reference interval (200–900 ng/L). pβCTX βCTX was measured by ELISA (Nordic Bioscience Diagnostics A/S, Herlev, Denmark). Drawn based on J.A. Clowes, N.F. Peel, R. Eastell, The impact of monitoring on adherence and persistence with antiresorptive treatment for postmenopausal osteoporosis: a randomized controlled trial, J. Clin. Endocrinol. Metab. 89 (2004) 1117–1123.

Fig. 10 shows the absolute pβCTX values (by the ELISA assay) seen in the FREEDOM trial [127]. Clearly βCTX (and other BTM) is maintained at well below the premenopausal mean during the treatment period with denosumab. βCTX remained below the lower reference limit for premenopausal women for the duration of the treatment in nearly half the subjects [132]. While direct correlation between BTM changes and fracture reduction is unavailable, the 6-month change in BTMs was related to the 3-year change in BMD; i.e., subjects with the largest decreases in BTMs at 6 months had the largest increases in BMD at 3 years [132]. The increase in BMD in this study was shown to be associated with significant reductions in the risk of vertebral, nonvertebral, and hip fractures. These results suggest that the goal of treatment with denosumab would be to maintain βCTX around or even below the lower reference limit for premenopausal women; 8 years of follow-up with denosumab therapy has not shown any evidence of adverse outcomes due to oversuppression [133]. However, being a medication that is delivered by subcutaneous injection by a healthcare worker at six monthly intervals, monitoring of BTM may not be required to ensure compliance

124


with treatment. The rate of prevalence of treatment failure is unknown, if that is to be used as a reason for monitoring therapy with denosumab. 3.2.5 Managing Drug Holidays The concerns with the possibility of increased incidence of the rare conditions osteonecrosis of the jaw and atypical femoral fractures due to prolonged suppression or “oversuppression” of bone remodeling especially with the use of alendronate and zoledronic acid have led to the introduction of the concept of “drug holidays” for patients who are not considered at high risk of fracturing after 5 years of continuous alendronate therapy or after three annual infusions of zoledronic acid. The theoretical possibility of the use of BTM to identify patients who may need to continue treatment in these instances due to ongoing bone loss has been canvassed but studies have not demonstrated the ability of BTM to identify patients who would go on to fracture if medication is ceased after long-term use [134,135]. βCTX was not examined in these studies in any case. The use of βCTX for this purpose is, therefore, not evidence based and decision thresholds do not exist for βCTX for this purpose.

3.3 Predicting the Risk of Development of Osteonecrosis of the Jaw Following Dental Procedures The measurement of βCTX has been suggested as a marker of bone suppression and a useful tool for the clinician to stratify risk of development of ONJ following dental procedures in patients on bisphosphonate therapy [136]. βCTX < 100 ng/L was suggested as representing high risk and CTX > 150 ng/L as representing minimal risk, with values in-between representing moderate risk. However, these suggestions are based on small studies; a recent publication that is the largest study to examine βCTX in the setting of dental extraction in patients on bisphosphonate therapy for osteoporosis showed that a threshold of Basal 2/3 to full thickness of epithelium

Little to none

Human Papillomavirus Screening and Vaccination

149

binding and abrogating transactivation of p53-responsive genes [68]. The E6 proteins also inhibit p53-independent extrinsic and intrinsic apoptotic pathways through the interaction with serval proteins involved in death receptors signaling pathway (e.g., TNFR-1, FADD, TRAIL) [69–71] and proapoptotic Bcl2 members (e.g., Bak and Bax) [71,72], respectively. The oncogenic activities of the E6 proteins can also be attributed to the increase of telomerase activity via inducing the expression of telomerase reverse transcriptase to ensure infinite proliferative life span of infected cells, the degradation of proteins involved in maintaining chromosomal stability, as well as the increased tolerance of genomic instability. On the other hand, the hrHPV E7 proteins can efficiently induce postmitotic epithelial cells to bypass the G1 phase and enter the S phase via stimulating proteasomemediated degradation of retinoblastoma tumor suppressor (pRB), which subsequently activates E2F-mediated transcription of S phase-specific genes and potentiates the proapoptotic activity of p53 [73,74]. Additionally, the E7 proteins have been shown to retain the DNA synthesis competent state in differentiated keratinocytes via abrogating cyclin-dependent kinase inhibitors (p21 and p27) to maintain the activity of CDK2/cyclin A complex [75]. Intriguingly, the E7 proteins can either trigger or inhibit apoptosis dependent on the cell and viral types as well as the specific stages of the virus life cycle [71,76,77]. In addition, the E7 proteins have been reported to facilitate vDNA integration [78], induce mitotic aberrations, and abrogate cell cycle checkpoints to induce genomic instability [79], and compromise cellmediated HPV-specific immune responses [80], which collectively contribute to the malignant progression of cervical lesions. Recent studies also suggest that the induction of epigenetic alteration and aberrant expression of miRNA by the E6 and E7 proteins may contribute to the cervical carcinogenesis [81]. Compared to E6 and E7, the E5 protein is a weak oncogenic protein, but it plays important roles in multiple events during the early stage of HPVassociated cervical cancer by enhancing the oncogenic activities of the E6 and E7 proteins, although it does not directly contribute to the malignant progression and the maintenance of transformed phenotype [82]. E5 has been reported to induce the formation of tetraploid cells with increased chromosomal instability, particularly in the presence of cell cycle checkpoint inhibitors like HPV-16 E6 and E7 [83]. Additionally, the E5 protein downregulates MCH I- and II-mediated antigen presentation which enables infected cells to escape from immunosurveillance and subsequent viral persistence, and thus facilitates the induction of cell transformation by the E6

150

Yusheng Zhu et al.

and E7 proteins and increases the probability of malignant progression [59,84]. Moreover, the E5 protein favors the proliferation of infected cells via stimulating DNA synthesis in differentiated epithelial cells [85], enhancing the activation of EFGR in keratinocytes [86], and downregulating the gap-junction-mediated growth suppression of transformed cells by adjacent normal cells [87]. Finally, the E5 protein also facilitates virus integration and episomal loss that are critical events in the early stage of HPV-associated cervical cancer. It has been shown that an endogenous antiviral response through the stimulation of IRF-1 and IFN-β can be activated by the E5 protein, which leads to the loss of episomal E2 DNA and impaired inhibitory regulation of oncogene expression [88].

2.3 Epidemiology of HPV Infection and HPV-Associated Cancers HPV infections are the most common sexually transmitted infections in the world, and the treatment and prevention cost incurred represents significant economic burdens in many countries [89]. Approximately 70% of sexually active men and women will encounter at least one infection during their lifetime [89]. HPVs have been established as the principal cause of cervical cancer and also suggested as a relevant factor for a growing incidence of other anogenital cancers and a subset of head and neck cancers worldwide [89]. Multiple risk factors are responsible for HPV infection and progression and contribute to the geography, age, and sex-specific incidence patterns of HPV-associated cancers. 2.3.1 Risk Factors for HPV Infection, Persistence, and Malignant Progression Many risk factors collaboratively contribute to the HPV infection, persistence, and carcinogenesis. HPV infections are primarily related to sexual behavior, especially the number of recent or lifetime partners and the age at sexual debut [90]. Circumcision and regular use of condom were associated with reduced risk for oncogenic HPV infection and HPV-associated cancer in both men and their sexual partners [91,92]. Additionally, individuals with compromised immune system, such as patients with HIV or receiving immunosuppressive treatment, are at greater risk of HPV infection [93]. The risks of developing persistent HPV infection and HPV-associated cancer are mostly associated with the HPV type. Infections with low-risk HPVs (HPV types 6, 11, etc.) usually clear up without any intervention


151

or develop only benign or low-grade cervical cell abnormalities, genital warts, or laryngeal papillomas [67]. In contrast, hrHPVs (HPV types 16, 18, etc.) often cause persistent infection which can progress to invasive cancer if left untreated [67]. hrHPV types are detected in 99% of cervical cancer patients with about 50% associated with HPV type 16. The rate can increase up to 70% when coinfected with HPV type 18 [94]. The global HPV prevalence in women with normal cervical cytology is 11%–12% according to hybrid capture and PCR-based HPV DNA detection, which increases in proportion to the severity of the cervical lesion [95]. Furthermore, other environmental factors, including multiparty [96], long-term oral contraceptives [97], tobacco use [98], and coinfection with other sexually transmitted agents [99,100], are consistently identified as cofactors likely to influence the risk of progression from cervical HPV infection to high-grade CIN and invasive cervical cancer. Additionally, HPV-associated cancers also exhibit some socioeconomic status and race/ethnic-related geography disparity as described later. 2.3.2 Geography-, Age-, and Sex-Related Incidences of HPV-Associated Cancers In 2008, a total of 610,000 cancers are attributable to HPV infection worldwide with 86.9% are cervical cancers [89,101]. Cervical cancer is the fourth most common cancer among women worldwide, and it is the second most common female cancer in women with ages between 15 and 44 years in the world with an estimated 527,624 new cases and 265,672 deaths in 2012 [89]. The majority of cervical cancer cases are squamous cell carcinoma developed from high-grade CIN [102]. Additionally, growing evidence suggests a strong association between HPV infection and cancers of the anus, vulva, vagina, penis, and oropharynx [103]. More importantly, the incidence of HPV-associated cancers displays geographic location-, age-, and sex-specific distribution patterns. A distinct geographical distribution of HPV-associated cancers has been attributed independently to socioeconomic and race disparities [104]. In general, lower education and higher poverty are associated with higher incidence of penile, cervical, and vaginal invasive cancers, whereas higher education is associated with higher incidence of vulvar cancer, anal cancer, and oropharyngeal cancers [104]. According to the 2016 Human Papillomavirus and Related Diseases Report [105], the incidence of cervical cancer is significantly higher in less developed countries than that in developed countries. The highest age-standardized rate (ASR) of cervical cancer was

152

Yusheng Zhu et al.

observed in Africa and the lowest was in Oceania [105]. Not surprisingly, the incidences of penile cancer strongly correlate with those of cervical cancer, which are higher in developing countries, accounting for up to 10% of male cancers in some parts of Africa, South America, and Asia [105]. Similar to cervical cancer, the majority of vaginal cancer cases (68%) occur in less developed countries [105]. Conversely, about 60% of all vulvar cancer cases occur in developed countries located in Europe and America and Australia has the highest ASR of anal cancer [105]. In addition to socioeconomic status, sexual behavior, innate genetic differences, or circulating intratypic HPV variants may contribute to the differences in HPV infection and associated diseases across racial/ethnic groups in the United States and other countries [106,107]. It has been reported that Hispanic women have higher rates of cervical cancer compared to non-Hispanic women [108]. On the contrary, the incidence of vulvar, anal, and oropharyngeal cancers in Whites and non-Hispanics was higher than that in other racial/ethnic groups. Rates of vaginal cancer were the highest among Blacks [109]. Asian and pacific island race had the lowest incidence of HPV and exhibited a lower probability of acquiring new HPV infections [107]. The incidence of HPV-associated cancers is generally higher in sexually active population. However, some of these cancers may exhibit an agespecific distribution pattern. The cervical cancer risk is strongly related to age with higher incidence in women between the late teens and mid1930s in the United States [110]. A bimodal peak pattern of HPV-associated cervical cancer has also been reported in some countries, with a second peak among individuals older than 45 years, possibly due to immunosenescence, hormonal changes at menopause, and reactivation of latent infections [111–113]. In contrast, invasive vaginal cancer is diagnosed rarely in women under 45 years with the peak incidence of carcinoma in situ observed between ages 55 and 70 [114]. The two types of vulvar cancer, basaloid/ warty lesions and keratinizing, are more common in young women and older women, respectively [115]. Penile cancer is rare and most confined to uncircumcised men between 50 and 70 years of age, indicating that circumcision might have strong protective effect against HPV infection-related penile caner [116]. In addition, a sexual preference has been observed in HPV-associated cancer at specific locations related to sexual activities. Women generally have higher incidences of anal cancer than men, although elevated incidence is observed in men who have sex with men and/or immunodeficiency disorders [117]. On the contrary, higher incidence and mortality of pharyngeal


153

cancer occur in men [118]. Therefore, implementation of specific preventive strategies is necessary to effectively reduce the burden of HPVassociated cancers in targeted populations.

3. PRINCIPLES AND METHODS FOR HPV TESTING There are over about 200 different genotypes of HPV with around 40 of these types able to infect the anogenital mucosa of humans. Of these 40 types, 14 (types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68) are considered high risk (HR) for the development of cervical cancer and/or precursor legions. HPV-16 is associated with approximately 60% of cervical cancers, while HPV-18 is associated with 15% of cervical cancers. In 2002, guidelines began inclusion of recommendations for testing HR HPV types as a screening tool for patient management [119,120]. In 2012, the guidelines of a variety of organizations across the county including American College of Obstetricians and Gynecologists (ACOG), American Society for Clinical Pathology (ASCP), American Cancer Society (ACS), and American Society for Colposcopy and Cervical Pathology (ASCCP) were updated to be consistent in recommending routine Pap and HPV cotesting in women 30 years of age or greater [121]. The indicated use for HPV DNA testing and genotyping was indicated for two scenarios starting with the first approval in 1999. The first indication is as a screening tool for patients observed to have atypical squamous cells of undetermined significance (ASCUS) Pap test results. However, it should be noted that results from HPV screening were not at the time intended to prevent women from continuing to colposcopy. The second indication was for women 30 years of age or greater to evaluate for the presence of hrHPV types. The results of the HPV test in combination with cytology results and clinical assessment were used to guide patient management. These remained the indicated guidelines for all of the Food and Drug Administration (FDA)-approved assays (Table 4) until April 2014, when the Roche Cobas® 4800 HPV test was FDA-approved HPV test for women 25 and older to be used alone to assess the need to undergo additional diagnostic testing for cervical cancer. The features of all the FDA-approved current HPV assays are summarized in Table 4 and discussed in details later.

3.1 Qiagen Hybrid Capture 2 High-Risk HPV DNA Test In 1999, Qiagen Corporation was the first to obtain FDA approval for a hrHPV DNA test [123]. The Qiagen Hybrid Capture 2 High-Risk HPV

154

Yusheng Zhu et al.

Table 4 Features of the High-Risk HPV FDA-Cleared Assays Feature

Qiagen HC2

Cervista

Cobas

Aptima

FDA approval

2003

2009

2011

2011

Technology

DNA:RNA hybrid probe, signal amplification

Invader® chemistry, signal amplification

Target amplification, real-time PCR

Target amplification, TMA

Nucleic acid DNA starting material

DNA

DNA

RNA

Target(s)

L1, E6, E7

L1

E6, E7

Sample type ThinPrep (4 mL); (volume) STM

ThinPrep (2 mL)

ThinPrep (1 mL)

ThinPrep (1 mL)

Requires prealiquot

No

No

Yes

No

Options for automation

Semiautomated and automated

Manual and automated

Automated

Automated

Internal control

None

Human Human genomic genomic (HIST2H2BE) (β-globin)

Multigene

Process only

HPV-16/18 None genotype identification

Yes (separate reaction)

Yes (integrated) Yes, plus HPV-45 (separate reaction)

LOD at clinical cutoff

5000 Copies/ reaction

1250–7500 Copies/ reaction

300–2400 Copies/mL

Cross reactivity with LR HPV types

6, 11, 42, 53, 54, 55, 67, 70 58, 61, 62, 66a, 67, 69, 70, 73, 81, 82/82v, 84, 86

Intended use ASCUS, cotesting

Expanded test menu available a

ASCUS, cotesting

CT/NG (Chlamydia No trachomatis/Neisseria gonorrhoeae)

20–240 Copies/ reaction

6, 42, 54, 55, 62, 26, 62, 67, 70, 89 82

ASCUS, ASCUS, cotesting, cotesting primary screening (approved 2014) CT/NG, HSV (Herpes simplex virus) 1/2

CT/NG, TV (Trichomonas vaginalis)

Studies involving the classification of the HPV-66 type are limited and conflicting [122]; starting around 2005 HPV-66 was reclassified from low-risk to high-risk in a number of national guidelines.


155

DNA test (HC2 HR HPV) utilizes a cocktail of RNA probes recognizing HPV HR types that will then hybridize to the DNA material obtained from a woman’s cervical cells. The DNA:RNA hybrid molecules are then captured on the surface of an antibody-coated microplate. Numerous alkaline phosphatase-conjugated antibodies can bind to each DNA:RNA hybrid molecule resulting in signal amplification that is then detected with a chemiluminescent substrate. The intensity of the light emitted is detected by a luminometer, and the intensity of the light indicates the presence or absence of the HPV target. The package insert describes 13 high-risk (HR) types detected by this test including 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68. The Qiagen assay does not include an internal control. The assay can be semiautomated using the Rapid Capture® System or fully automated using the QIAensemble™ system. The Qiagen assay remained the only FDA-approved test on the market for the next 6 years.

3.2 Cervista HPV HR and HPV 16/18 Genotyping Test In 2009, the FDA-approved two tests manufactured by Third Wave Technologies (Hologic), the Cervista® HPV HR and Genfind® DNA extraction kit, and the Cervista® HPV-16/18 genotyping test. Both of these tests utilize the Invader® chemistry for the detection of 14 hrHPV types. The Invader® chemistry uses two isothermal reactions to detect the HPV DNA target. The first isothermal reaction consists of an Invader oligonucleotide and DNAspecific target probe that both bind at the same time to the target DNA sequence. The binding of both probes results in a structure that is then recognized by the Cleavase® enzyme, which is a proprietary enzyme. Recognition by Cleavage causes a cut in an overlapping flap of the target-specific probe that is then released. Each overlapping flap that is released binds to a FRET cassette creating the second isothermal reaction. The binding of the overlapping flap to the FRET cassette creates a structure again recognized by Cleavase® that will emit a fluorescent signal. Each copy of target HPV DNA results in a total of 106- to 107-fold signal amplification per hour due to the two isothermal reactions. The same 13 HR types are looked as with the Qiagen assay, but HPV-66 was added. The Cervista® HPV HR test cannot distinguish between the 14 HPV types present. Use of the Cervista® HPV16/18 genotyping test is able to determine the presence of the 16, 18, or both 16 and 18 genotypes as a separate reaction. The human genomic internal control used for the Cervista assay is the human histone 2 gene (HIST2H2BE). The assay can be semiautomated or fully automated using the Cervista high-throughput automation system.

156

Yusheng Zhu et al.

3.3 Cobas HPV Assay In 2011, the Cobas® HPV assay, manufactured by Roche, was FDA approved. This assay in a single reaction provides results for the HPV-16, HPV-18, and a pooled result of the 12 additional HR HPV types. Primers are designed to amplify HR HPV DNA from the L1 region of the HPV genome and with detection of HR HPV by fluorescent probes binding to HPV DNA. The assay uses four fluorescent dyes for the probes with the dyes recognizing HPV-16, HPV-18, the pool of 12 other HR HPV types, and finally the β-globin gene. The β-globin target serves as the human genomic internal control used for the Cobas® assay. Based on real-time PCR technology, complementary probes bind to the target DNA and the probes are cleaved due to the 5ʹ–3ʹ nuclease activity of the polymerase. Once the reporter dye is separated from the quencher (probe), it is free to emit fluorescence when excited by the proper spectrum of light. Full automation of the assay is accomplished using the Cobas® 4800 instrument. In 2014, the Cobas® 4800 HPV test became the first HPV test approved by the FDA as a first-line screen for cervical cancer risk in women 25 and older.

3.4 Aptima HPV Assay and Aptima HPV 16 18/45 Genotype Assay Also in 2013, the Aptima HPV assay and Aptima HPV-16/18/45 genotype assays manufactured by Hologic were approved by the FDA. The three steps involved in this assay are performed in a single reaction tube and focus on amplification of the E6/E7 viral oncogenes of HPV. The first step involves target capture. The second step utilizes transcription-based NA amplification (TMA) method. Reverse transcription involving two enzymes MMLV reverse transcriptase and T7 RNA polymerase generates a DNA copy of the target mRNA sequence containing a promoter sequence for the T7 RNA polymerase. The T7 RNA polymerase can then bind and product many copies of the HPV RNA amplicon from the DNA copy template. The third step is detection by hybridization protection assay, which uses a singlestranded DNA probe with chemiluminescent labels complementary to the amplicon. A selection reagent can differentiate between hybridized and unhybridized probes by inactivating the label on the unhybridized probes. Light is emitted from the RNA:DNA hybrid and results are determined based on the analyte signal to cutoff. Collected in ThinPrep liquid cytology specimens. Detects messenger RNA overexpressed by the E6 and E7 oncogenes in 14 hrHPV types. Full automation of these assays is accomplished on


157

the TIGRIS DTS system and later approved for the Panther System in 2013. The APTIMA HPV-16/18/45 genotype assay is a separate reaction used to specifically identify the HPV-16, HPV-18, or HPV-45 type.

3.5 Clinical Performance of HPV Assays An analysis of the last 2 years of CAP Proficiency surveys for the hrHPV ThinPrep methodology reveals that an average of 7% of laboratories use the Qiagen HC2 assay, 10% use Cervista®, 37% use Cobas®, and 46% use the Aptima® assay. There is higher use today of the Cobas® and Aptima® assays. One reason may be comparable levels of sensitivity across the assays, but increased specificity with the newer assays. A large contributor to the increased specificity of the Cervista®, Cobas®, and Aptima® assays has to do with lower levels of cross-reactivity with low-risk HPV types [124–128]. Cross-reactivity between low-risk and high-risk HPV genotypes is highest in the Qiagen HC2 assay with an estimated 5%–10% false-positive result due to cross-reactivity with low-risk HPV genotype(s) [129,130]. Another potential reason that users have switched toward the Cobas® and Aptima® assays is the required sample volume, which in the newer assays is 1 mL from the ThinPrep vial vs 4 or 2 mL.

4. CERVICAL CANCER SCREENING Historically cervical cancer screening recommendations have changed relatively rapidly since the introduction of the Pap test reflecting emerging data and understanding of the pathogenesis of cervical cancer. Prior to 1980 the ACS recommended a Pap test “as part of a regular checkup” [119]. From 1980 to 1987 the recommendation for cervical cancer screening was for annual Pap smears with cervical cytology for women over the age 20 (younger if sexually active) and if two negative Pap tests, this could be spaced to every 3 years. This was revised in 1987 to recommend yearly Pap testing for women 18 years and older with spacing of screening at the discretion of the provider. Up to this point, the majority of screening recommendations were based on expert opinion. In 2002, following the ASCUS–LSIL triage group randomized control trial adding reflex HPV testing for abnormal cytology, screening parameters again changed, increasing in complexity with new agebased variations with the addition of HPV cotesting in women over the age of 30 [119,131,132]. In 2012, the ACS, ASCCP, and the ASCP developed a set of guidelines with the goal of providing unified, evidence-based recommendations aimed

158

Yusheng Zhu et al.

at detecting precancerous lesions while simultaneously reducing the risk of overtreatment [131]. Despite general agreement among 11 national and international organizations around these guidelines, there remain challenges in uptake and adherence. In a study by Teoh et al. looking at provider adherence to the 2012 guidelines, in a cross-sectional survey, 12.1% of providers were not aware of the changes made in these guidelines and only 5.7% were able to answer questions correctly regarding the information in the 2012 guidelines [133]. Despite evidence-based recommendations, providers and patients have been slow to change practice, often opting to continue annual testing. Since that time, additional changes have been made to screening recommendations after the FDA approved the Cobas HPV test in April of 2014 for use in primary HPV testing [134]. This section will review the most up to date recommendation from major organizations as well as the evidence behind these recommendations to guide clinical screening practice. This section will include recommendations and evidence for onset, interval, modality, and duration of screening and will also address screening in special populations. Finally, it will review areas in need of further study to improve screening programs to further reduce the incidence of cervical cancer.

4.1 The Current Cervical Cancer Screening Guidelines Although screening is a vital part of a successful prevention program, a complete program should include primary prevention with vaccination as well as management of abnormal screens with diagnostic testing such as colposcopy and biopsies and treatment of high grade or persistent abnormalities with ablative or excisional procedures. While management of screening abnormalities will not be discussed in this chapter, it is important to note that proper triage and treatment of abnormal results are critical to an effective prevention program. The goal of screening is to optimize the detection of precancerous lesions in healthy individuals at a time when the disease is treatable while limiting the harm of overtreating benign disease. Ten prominent organizations have published guidelines in the last 5 years to guide clinicians and improve screening practices. This has been led by the updated guidelines released by the ACS, ASCCP, and the ASCP. The ACS/ASCCP/ASCP guidelines were developed with the intent to provide an evidenced-based optimal screening strategy. These guidelines employed a rigorous process laid out by the Institute of Medicine to perform an unbiased review of the literature


159

using the Grading Recommendations Assessment, Development, and Evaluation (GRADE) system. They also sought public evaluation and comment prior to submission to add strength and transparency to the guidelines. Because of the relative rarity of cervical cancer, in these guidelines, “benefit” was defined as a higher detection of high-grade dysplasia or higher (CIN3+) and a reduction in CIN3+ at subsequent rounds of screening. “Harm” was defined as an increased number of colposcopies. At the time of publication of the majority of the guidelines in 2012, primary screening with HPV testing was not recommended, based on the limitation number of studies with long-term follow-up, data on actual cancer prevention, and no clear recommendations for triaging an abnormal test [131,134–141]. Estimates of the number of colposcopies performed if all positive HPV tests were triaged to evaluation suggest an absolute increase in the number of colposcopies by 4%, such that the harm would outweigh the benefit [131]. However, following the publication of the ATHENA trial in 2015, experts from Society of Gynecologic Oncology (SGO)/ASSCP state that primary HPV may be an appropriate screening alternative in women ages 25–65 if managed according to the algorithms followed in the ATHENA trial [134]. In 2016 ACOG published an update to their guidelines now including primary HPV screening as an alternate screening strategy [135]. Table 5 outlines the screening recommendations currently available for each organization. 4.1.1 Screening Modalities In regards to modality of screening, there are currently five FDA-approved technologies available: conventional cytology, liquid-based cytology, cotesting with liquid-based cytology and HPV testing, HPV genotyping for triage, and primary hrHPV testing. The details of these assays have been discussed in Section 3. Conventional cytology uses cervical cells which are applied directly to a microscope slide following collection and subsequently interpreted. Liquid-based cytology utilizes cervical cells which are collected and then are suspended in a preservative media to allow for subsequent interpretation in a laboratory setting. In a meta-analysis by Arbyn et al. conventional cervical cytology and liquid-based cytology demonstrated similar sensitivities and specificities in individual studies though liquid-based cytology had a slightly lower pooled specificity when using atypical squamous cells of undetermined significance (ASCUS) as a cutoff [143]. A subsequent randomized control trial using CIN as a cutoff found no significant difference between detection of CIN1, 2, or 3 and found similar adjusted positive

Table 5 Cervical Cancer Screening Guidelines From Major Organizations Onset of Screening

Preferred Method of Screening

Definition of High-Risk Cessation of Screening Patients

Screening for High-Risk Patientsa Prior Hysterectomy

Organization

Date

ASCCP/ ASCP/ACS [131]

2012 21

65—if adequate prior Age 21–29: cytology q3yrs negative screeninga Age 30–65: – Cytology with HPV cotesting q5yrs – Alternative q3yr cytology

NCCN guidelines panel for cervical cancer screening [139]

2012 21

65—if adequate prior – Age 21–29: – Cytology q3yrs with reflex HPV screeninga (and no hx – (ASCUS, HPV+ colpo, of prior abnormal – ASCUS, HPV negative rescreen cytology) with cytology in 3 years) – Continue screening – for women with high-risk features Age 30–65: – Cytology with HPV cotesting – May discontinue in women with q5yrs is preferred – Cytology w3yr is also acceptable life-threatening conditions – Screening with HPV alone is not recommended

USPSTF [136]

2012 21

High-grade precancerous Excluded 65 Age 21–65: lesions or cervical cancer Recommend screening with q3yr – Caveat: after 65 may be indicated – DES exposure cytology if no prior screening – Immunocompromised – If desired lengthening of women screening interval may consider or for high-risk patients HPV cotesting q5yrs “as a reasonable alternative” after age 30 – Recommends against primary HPV or HPV cotesting in women < 30 years

– – – –

Hx of CIN2 or more Excluded Hx of cervical cancer DES exposure Immunocompromised women

HIV infection Immunocompromised women DES exposure Women with prior treatment for CIN2 or greater

HPV Vaccination

Continue routine No screening screening as per Caveat: age-specific 1. Retention of guidelines cervix 2. Hx CIN2 or more requires 20 years of screening

Continue routine HIV, solid-organ transplant, or No screening screening as per long-term steroid use may need Caveat: age-specific 1. Retention of more frequent screening guidelines cervix – HIV: q6months for 1 year 2. Hx CIN2 or more after diagnosis then annual requires 20 years of screening (does not specify screening cytology vs HPV) – DES exposure: recommend more frequent screening, usually annually as determined by their physician Continue routine No screening screening as per Caveat: age-specific 1. Retention of guidelines cervix 2. Hx CIN2 or more requires 20 years of screening

AMAa

2014 21

65—if adequate prior Age 21–29: negative screeninga Cytology q3yr Age 30–65: – Cytology with HPV cotesting q5yrs is preferred – Cytology q3yrs is an alternative

Not addressed – HIV infection – Immunocompromised women – DES exposure – Women with prior treatment for CIN2 or greater

No screening

For women with HIV: screen at Not addressed diagnosis – If negative test q3yrs – If treated for precancer lesion follow up in 1 year

WHO [140] 2014 30

49 (or determined by Women with HIV Adolescent to age 30: infection – Primary prevention with HPV national standards) vaccination and education Age 30–49: Screen and treat at least once: – Options include – HPV testing and treatment for positive results (with or without triage) – If negative for HPV rescreen in minimum 5 years – Visual inspection with acetic acid (VIA) in women who have a visible transformation zone – Cytology – If negative for VIA or cytology rescreen in 3–5 years – Concomitant screening for HIV in endemic areas

ACP [138]

No screening Excluded 65—if adequate prior – High-grade Age 21–65: Cytology q3yrs or Caveat: precancerous lesions or HPV cotesting q5yrs beginning in negative screeninga 1. Retention of – American Society of cervical cancer women age 30–65 – Ending screening cervix Nephrology recommends prior to age 65 in – DES exposure against screening women with – Immunocompromised women with end-stage renal disease on women (including life-limiting dialysis with limited life HIV) comorbid expectancy conditions is reasonable (limited evidence)

2015 21

Not addressed

Continue routine screening as per age-specific guidelines

Continue routine screening as per age-specific guidelines

Continued

Table 5 Cervical Cancer Screening Guidelines From Major Organizations—cont’d Organization

Date

Onset of Screening

Preferred Method of Screening

Definition of High-Risk Cessation of Screening Patients

Screening for High-Risk Patients

Prior Hysterectomy

HPV Vaccination

Not addressed

Not addressed

Not addressed

Not addressed

65—if adequate prior Not addressed Age 21–25: negative screeninga Cytology q3yrs Age 25–65: – Option for q3yr primary HPV testing (triage for positive test: hr genotyping 16/18 1. If positive ! colposcopy 2. If “other high-risk types” ! cytology, if ASCUS + colposcopy 3. If negative ! repeat in 1 year Age 30–65: Cytology with HPV cotesting q5yr or q3yr cytology or primary HPV testing as above

Not addressed

Not addressed

Not addressed

ACOG US 2015 Not Alternative screening methods in Not addressed Pacific Islandaddressed resource poor settings: PB 624 [142] – VIA with subsequent cryotherapy if abnormal – HPV testing followed by treatment with cryotherapy if positive SGO/ ASCCPinterim clinical guidance [134]

2015 21

ACOG-PB 157 [135]

65—if adequate prior 2016 21 except Age 21–29: cytology q3yrs negative screeninga Age 30–65: women with HIV – Cytology with HPV cotesting q5yrs preferred – Q3 yr cytology is acceptable – Alternative screening strategy of primary hrHPV testing q3yrs beginning at age 25 if performed according to SGO/ASCCP interim guidance algorithms – Regardless of screening interval, annual well women visits recommended

Continue routine No screening – Recommend for HIV and – HIV infection screening as per ageimmunosuppression: initiate Caveat: – Immunocompromised specific guidelines screening at the time of sexual 1. Retention of women (i.e., solidcervix debut (regardless of mode of organ transplant) 2. Hx CIN2 or more HIV transmission) and no – DES exposure recommendation later than 21 – Women with prior q3yr cytology for treatment for CIN2 + – Annual cytology for 3 years 20 years following diagnosis: if all negative may lengthen interval to q3yrs – For women 30 years + screening may be with cytology or cotesting. If one negative cotest may screen q3yrs – Low-grade abnormalities (LSIL or ASCUS/HPV +) should be evaluated with immediate colposcopy – For DES, recommend annual cytology

a Adequate prior negative screening is defined as three consecutive negative cytology results or two negative cotests in the last 10 years with the most recent evaluation in the last 5 years. For women with a history of CIN2 or higher, routine screening should be continued for 20 years after regression or treatment even if extending beyond age 65. ASCCP, American Society for Colposcopy and Cervical Pathology; ASCP, American Society for Clinical Pathology; ACS, American Cancer Society; ACOG, American Congress of Obstetrics and Gynecology; SGO, Society of Gynecologic Oncology; AMA, American Medical Association; NCCN, National Comprehensive Cancer Network; USPSTF, United States Preventive Services Task Force; ACP, American College of Physicians; WHO, World Health Organization.

164

Yusheng Zhu et al.

predictive value ratios between conventional and liquid-based cytology, suggesting they are essentially equivalent modalities [143,144]. Several HPV tests have been approved to be used in combination with cytology for cotesting, and HPV genotyping for high-risk subtypes 16 and 18 has been approved following cotesting in the setting of negative cytology and a positive HPV cotest [138]. The most recently approved testing modality is hrHPV testing in which cervical cells are collected in a similar liquid media and DNA testing for hrHPV subtypes is performed. The FDA-approved Cobas HPV test detects HPV type 16 and 18 as well as 12 other high-risk subtypes in a pooled analysis. To date, the Cobas HPV test is the only FDAapproved HPV test to be used in primary HPV screening [134,138]. Any of the FDA-approved modalities may be used as outlined later based on clinic access, availability of pathologic analysis, and provider comfort with test interpretation. 4.1.2 Onset of Screening Among the 10 organizations, there is consensus among 9 of the organizations that screening should begin at the age of 21 regardless of sexual debut [131,134–141]. The incidence of cervical cancer in women under 20 is 1–2 cases per 1,000,000 females, and further, screening may not be preventative in this population as the incidence of cervical cancer in adolescents has remained unchanged despite initiation of screening, unlike the remainder of the population which has shown a 60% reduction in cervical cancer following the initiation of screening [131,135,138]. Furthermore, the incidence of HPV is highest following the initiation of sexual intercourse, but has been shown to resolve spontaneously in 85%–90% of young women within 2 years with the majority clearing within 8 months [135]. Thus, many organizations, in particular the US Preventive Service Task Force (USPSTF) and American College of Physicians (ACP), cite the increased harm of overtreatment in this age group and the associated pain, anxiety, cost of treatment, as well as the risk of preterm delivery from multiple cervical treatments [136,138]. The USPSTF also notes that the prevalence of CIN3 in women under 21 is estimated at 0.2%, while the false-positive cytology rate is reported at 3.1% again emphasizing the potential harm of early screening [136,137]. Overall, the consensus for adolescents is to focus on primary prevention with education and universal vaccination [131,134,137–140]. The updated ACOG guidelines only recommend screening prior to age 21 for women with HIV, regardless of the mode of transmission [135].


165

4.1.3 Screening Modality and Interval for Women Age 21–29 All US organizations recommend screening women age 21–29 with cervical cytology (either with conventional or with liquid-based cytology) every 3 years [131,134,135,137–140]. While the 2012 guidelines recommend against HPV testing in this population either as primary testing or as cotesting [131,134,137–140], updated guidelines from ACOG reflecting the interim recommendations from SGO and the ASCCP suggest that primary hrHPV testing every 3 years with the FDA-approved Cobas test may be used as an alternative screening strategy for women age 25 and older [134,135]. The evidence for the screening interval of 3 years in this population comes from modeling studies [131,137]. The estimated lifetime cervical cancer risk in the absence of screening is 31–33 per 1000 women [137]. Modeling studies compared the lifetime cervical cancer risk between annual, every 2 years, and every 3 years screening interval and found that while the lifetime risk of cancer diagnosis is slightly decreased with annual screening (3 per 1000 for annual vs 4–6 per 1000 for every 2-year vs 5–8 per 1000 for every 3-year screening, respectively), the predicted lifetime risk of death due to cervical cancer is essentially unchanged at 0.03 vs 0.05 vs 0.05 per 1000 women [131,137,145]. In contrast, the risk of harm was significantly higher in the annual screening model with more than double the colposcopies compared to every 3 years [131]. There was no significant difference in the odds ratio of the risk of invasive cancer following the last negative cytology between a 2- and 3-year interval (OR 1.2, CI 0.65–2.21); however, there was a rise in cancer risk at intervals over 3 years, suggesting that a 3-year screening interval is the optimal balance between benefit and harm in this age group [131,146]. The available evidence regarding HPV testing has consistently demonstrated an improved sensitivity as compared to cytology (95% vs 40%–70%), a slightly lower rate of CIN3 following a negative test, but also a lower specificity (94% vs 97%, respectively) [131,147–149]. The 2012 guidelines noted that the harm of HPV testing in this population outweighed the benefit, suggesting that higher rates of largely transient infections with a higher sensitivity with HPV testing would lead to unnecessary procedures. The majority of data on primary HPV testing have been from large, European studies; however, the publication of the ATHENA trial in 2015 leads to the consideration of primary HPV as a viable screening option in the United States [147,150–152]. In the ATHENA trial, Wright and colleagues analyzed over 40,000 women over the age of 25 who received both primary HPV and cytology testing. The triaging strategy proposed in the ATHENA trial

166

Yusheng Zhu et al.

was for repeat screening in 3 years for HPV-negative patients, immediate colposcopy for HPV-16 or -18 positive patients, and for women with other HPV genotype positivity, reflex cytology was recommend with colposcopy if the results were ASCUS or greater. Women with negative triage cytology would have a repeat cotest in 1 year. At baseline, 10.5% of women were HPV positive with 6.4% demonstrating cytology abnormalities. The 3-year cumulative incidence rate for CIN3+ with a negative test was lowest with cotesting at 0.3% vs 0.38% with primary HPV vs 0.8% with cytology. HPV also improved detection of cancers, as well as adenocarcinoma in situ, compared to cytology alone [150]. While HPV was more prevalent in women 25–29, they also found an increased sensitivity for detection of CIN3+ over cytology in this age group. Both the hybrid cotesting strategy and primary HPV were associated with absolute increased number of colposcopies; however, there were a similar number of colposcopies per case of CIN3+ detected at 12.8 compared to 10.8 for cytology [150]. These data led to the FDA approval of the Cobas test (hrHPV test utilized in the ATHENA trial) and led to the updates in current recommendations to include primary hrHPV screening as an alternate screening strategy. Some important caveats are that there is no data supporting other HPV tests for primary testing, the exact triage algorithms must be followed as outlined in the ATHENA trial, as explained in the SGO/ASCCP interim guidance, and there are no cost-effectiveness or long-term follow-up studies to determine applicability and efficacy in clinical practice. 4.1.4 Screening Modality and Interval for Women Age 30–65 The ACS/ASCCP/ASP, ACOG, SGO, National Comprehensive Cancer Network (NCCN), and American Medical Association (AMA) recommend screening every 5 years with cervical cytology and HPV cotesting as the preferred method for women 30–65. Screening with cytology every 3 years is recognized as an acceptable alternative and as of 2016; ACOG, SGO, and the ASCCP recognize primary hrHPV testing every 3 years as another acceptable alternative [134,135]. There is a body of evidence that suggests that the addition of HPV testing results in an increased sensitivity and only slightly decreased specificity, resulting in an increased detection of CIN3 while providing a similar or lower cancer risk than screening cytology alone every 3 years [131,141,147,148]. Four European randomized control trials have compared cotesting to cytology screening, and in each trial, the cotesting arm showed an absolute increase in detection of CIN3 and an absolute decrease in cancer in the second round of screening [131,141,151–155]. The


167

ARTISTIC trial, which included a longer follow-up period up to 6 years after the initial screen, found the cumulative rate of CIN2+ was 1.41% for negative cytology and 0.87% for negative HPV [154–156]. In a pooled analysis by Dillner et al. of seven European studies screening over 24,000 women, the cumulative incidence rate (CIR) of CIN3+ at 6 years following a negative baseline HPV test was 0.27% (CI 0.12%–0.45%) and was lower compared to the CIR of CIN3+ at 3 years following a negative baseline cytology 0.51% (0.23%–0.77%) [147]. This evidence supports the recommendation for increased screening intervals with cotesting and may even suggest the possibility of extended intervals with negative primary HPV testing though more data are needed [131,135,141,147,157,158]. Cotesting also has improved detection of adenocarcinoma compared to cytology and in the posttreatment surveillance of ACIS; hrHPV positivity has been shown to be the most significant independent predictor of recurrent or progressive disease [157,158]. A modeling study further supported increased screening intervals with HPV cotesting, demonstrating that over a 10-year study period, there was only a modest decrease in lifetime cancer risk (0.39%) with cotesting every 3 years compared to every 5 years (0.61%), while there was a significant increase in harm [131,137]. In a United States population-based study by Katki et al. looking at over 330,000 women, the 5-year cumulative incidence of cancer was 3.2 per 100,000 for negative cytology with HPV cotesting vs 7.5 per 100,000 with negative cytology alone [148]. These data suggest that with the added sensitivity of HPV cotesting, an extended screening interval allows for a minimal risk while decreasing the harm of increased colposcopies with shorter screening intervals. The USPSTF, on the other hand, recommends cytology every 3 years as the preferred modality of screening with HPV cotesting only for those wishing to extend the screening internal [136]. For the development of their guidelines the USPSTF performed a decision analysis to clarify screening intervals as well as to address the benefits and harm of over- vs underscreening [136]. While it is recognized that both modalities demonstrate a comparable balance between benefit and harm, the USPSTF suggests that HPV cotesting may prolong surveillance for women nearing the end of screening who test positive for HPV with otherwise negative cytology resulting in increased harm with minimal benefit [136]. This is based on data that upward of 11% of women age 30–34 years and 2.6% of women age 60–65 years will fall into the category of cytology negative, HPV positive who then require repeat evaluation in 1 year, potentially extending

168

Yusheng Zhu et al.

screening intervals [136,159]. At the time of publication, the USPSTF has not addressed the addition of primary hrHPV testing in screening practices. The ACP recognizes all published guidelines and suggests that either cytology every 3 years or cytology with HPV cotesting every 5 years are viable options for women age 30–65 [138]. The ACP does address the cost of screening, citing a lower cost with cytology but a cost benefit with increased screening intervals [138,160]. The ACP also warns against the significant increased cost of annual screening in a low-risk population [138]. Goldie et al. reviewed the cost-effectiveness and reduction in cancer risk of varying screening models in cytology alone every 1, 2, 3, or 4 years and cytology with HPV cotesting every 1, 2, 3, or 4 years and found that cotesting every 3 or 4 years had a 89%–91.3% reduction in cancer risk with a slightly higher incremental cost-effectiveness ratio than cytology alone every 3 years [160]. This study does not provide cost-effectiveness data for cotesting every 5 years as recommended in the guidelines, but ultimately the extended screening interval for cotesting provides a balance between the benefit of improved detection of dysplasia, cost-effectiveness, and harm of screening. In 2014 in response to the guidelines released by the USPSTF the AMA also petitioned for third-party payers to amend metrics to reflect these recommendations (as seen in Table 5). 4.1.5 Cessation of Screening In regards to exiting from screening, all US organizations recommend the discontinuation of screening at age 65 with adequate prior negative screening [131,135–139,141]. Adequate prior negative screening is defined as three consecutive negative cytology or two consecutive negative HPV results in the last 10 years with the most recent test within the last 5 years. All US guidelines agree that women with a history of CIN2+ should continue routine screening for at least 20 years following the initial increased period of surveillance even if this extends beyond age 65 as these women retain a 5- to 10-fold increase risk of cervical cancer compared to the general population [131,135–141]. The evidence for discontinuation of screening is based primarily on a single modeling study with a model of continued screening up to age 90 [137]. A prolonged screening model only resulted in the reduction of 1.6 cancer cases and 0.5 cancer deaths per 1000 women compared to an additional 127 colposcopies per 1000 women [131,137]. ACOG also suggests that vulvovaginal atrophy contributes to a higher rate of false-positive cytology which is supported by data from Sawaya et al. who reported that only 1 out of 110 postmenopausal women with abnormal


169

cytology following a previously normal screen had dysplasia on biopsy (PPV 0.9%) [135,141,161]. The ACP does address the possibility of early discontinuation for women with life-limiting comorbidities given an estimated 10 years for disease progression, though evidence is limited [138]. The 2016 ACOG guidelines stress the importance of discontinuation of all screening modalities including primary HPV testing in women with prior negative screening. It has been reported that 19.6% of the new cases of cervical cancer are in women over the age of 65; however, most cases are in women who are unscreened or underscreened reflective of the slow disease progression [135]. Taking an accurate and thorough screening history is paramount prior to the discontinuation of screening to avoid underscreening patients with a history of prior abnormalities who may benefit from ongoing evaluation. 4.1.6 Screening Following Hysterectomy All US guidelines are in agreement recommending the discontinuation of screening, regardless of age, for a woman undergoing hysterectomy for benign disease without a history of CIN2+ [131,135–139,141]. These patients do not require adequate prior negative screening because the risk of vaginal cancer is so low (reported at 0.18 per 100,000 women), and additionally, the positive predictive value for vaginal cytology is poor. In a systematic review of 19 studies of patients undergoing total hysterectomy both with and without a history of CIN, for women without CIN, 1.8% had abnormal cytology and 0.12% had vaginal intraepithelial neoplasia (VAIN) on biopsy compared to women with a history of CIN2+, of whom 14.1% had abnormal cytology, 1.7% had VAIN, and one patient had vaginal cancer [135,141,162,163]. A patient who undergoes a supracervical hysterectomy should continue routine screening, and diagnostic cytology should still be performed for all symptomatic patients. The updated ACOG recommendations recommend against primary HPV testing in women without a cervix. 4.1.7 Screening for High-Risk Populations One of the limitations of the majority of the guidelines, including the ACS/ ASCCP/ASCP, AMA, SGO, the ACP, and the USPSTF, is they were developed specifically to guide screening for the general population and do not address screening for high-risk populations defined in these studies as patients with immunosuppression, diethylstilbestrol (DES) exposure, or patients with prior abnormal cytology with CIN2+ [131,136,138]. In the

170

Yusheng Zhu et al.

updated ACOG guidelines they do cite updated recommendations from the Panel on Opportunistic Infections in HIV-infected adults and adolescents on screening for women with HIV (Table 5) [135]. Immunosuppression increases the risk of persistent HPV infection in women with HIV and has been shown to expedite the progression to invasive cervical carcinoma from 15.7 years in the general population to 3.2 years in women with acquired immunodeficiency syndrome, and in a large North American cohort study, the incidence of invasive cervical cancer was 26 per 100,000 person-years in HIV-positive women compared to 6 per 100,000 person-years in their HIV-negative counterparts [164,165]. In regards to screening modality, a multicenter International study evaluating cytology, colposcopy, and HPV testing for HIV-positive women: the sensitivities of baseline cytology, HPV testing, cotesting, and colposcopy were similar for the detection CIN2+ at 93.3%, 91.3%, 86.8%, and 98.0%, while HPV testing demonstrating a significantly lower specificity at 47.7% compared to other modalities ranging from 62.9% to 76.7% [166]. While small studies in resource poor settings demonstrate the feasibility of self-collection of hrHPV for women with HIV, there is limited data currently available to support the use of primary HPV testing and primary HPV testing has not been approved or validated in high-risk populations [135]. Data suggest that following three negative annual cytology results for HIV-positive women with normal range CD4 counts, the 3-year risk of high-grade dysplasia is 1% making it reasonable to consider extended screening intervals up to 3 years which is reflected in the newest ACOG and CDC recommendations [167,168]. Screening recommendations for women with HIV have been extrapolated to apply to all women who are immunocompromised, but there are no evidence-based guidelines to direct this care. The American Society of Transplantation recommends annual cytology and pelvic examination for women with a history of solid-organ transplant, though these recommendations are based on limited evidence [169]. The updated ACOG guidelines recommend screening similar to women with HIV, and the NCCN suggests that women with immunosuppression may require more frequent screening, though an optimal interval is left to the discretion of the provider [135,139]. Data show an increased rate of cervical cancer following kidney transplant with a standardized incidence ratio of 2–5 [164,169]. The data regarding cervical cancer risk with autoimmune diseases remain mixed, though overall there does appear to be an increased risk of dysplasia. In a population-based cohort study of over 130,000 women with all


171

autoimmune diseases, the crude incidence rates of severe dysplasia were highest with SLE and lowest in psoriasis, though still elevated compared to the general population [170]. SLE appears to confer a substantially elevated risk of high-grade dysplasia, with risks ranging from 1.5- to more than 8-fold higher than the general population [170,171]. A meta-analysis including eight studies on women with IBD on varying immune therapies demonstrated a modest increased risk of CIN2+ (OR 1.34, CI 1.23–1.46) [172]. These data are limited by the heterogeneity of the studies with varying immunosuppressive agents, though five of the studies independently suggest a small but statistically significant increase in cervical cancer with IBD [172]. No recommendations are made on the role of HPV testing in this population in any of the above guidelines. 4.1.8 Screening in a Resource Poor Setting Several of the guidelines discuss the challenges and barriers to screening; however, the World Health Organization (WHO) and ACOG specifically outline recommendations for screening in a resource poor setting [140,142]. More than 80% of cervical cancers occur in developing nations, and cervical cancer mortality is predicted to rise by 25% over the next 10 years in low- to middle-income countries [140,142]. ACOG produced a committee opinion in 2015 to help guide care, specifically in areas such as the US-affiliated Pacific Islands where only 55% of women had received cervical cancer screening in the 5 years prior to the development of the guidelines [142]. They suggest alternate options for screening when access, cost, and available personnel for test interpretation and treatment are limited. The WHO has produced evidence-based guidelines, endorsed in this committee opinion, for a “screen and treat” model for secondary prevention [140]. The WHO recommends initiation of secondary prevention with screening from age 30 to 49 with HPV testing or visual inspection with acetic acid (VIA). For primary HPV testing, when available, it is recommended either to triage HPV-positive tests with VIA or to cytology [140]. Success rates with VIA and treatment with cryotherapy have been reported up to 70% for eradication of CIN3 [55]. In regards to primary HPV testing in this population, a prospective study in rural India demonstrated a 50% reduction in cervical cancer mortality and advanced stage cervical cancer diagnosis with a single lifetime HPV test compared to controls [142,173]. This was reproduced in a South African study where HPV testing with subsequent cryotherapy showed a 77% reduction in CIN3+ compared to a 38% reduction with VIA and cryotherapy [142,174]. ACOG addresses some

172

Yusheng Zhu et al.

limitations of using primary HPV including increased cost compared to VIA and the role of triaging positive tests and increased visits which can be a major challenge in this population. It has been proposed that this may be circumvented with patient-collected HPV where promising data have suggested equivalent sensitivities to provider-collected samples [142].

4.2 Future Directions of Cervical Cancer Screening Although the guidelines are evidence based and generally in agreement on screening average-risk women—with onset at age 21, screening every 3–5 years if all normal results until age 65, there are several important areas of future investigation to optimize screening practices and further reduce the risk of cervical cancer. An effective strategy to prevent cervical cancer is multifactorial including primary prevention with vaccination, optimization of screening for average-risk women and proper utilization of new screening technologies, triage of patients risk for future dysplasia, and appropriate treatment of abnormal results. Data from 2015 have demonstrated a role for HPV testing as an alternative primary screening strategy which is supported by current guidelines. While the data on primary HPV testing appear promising in detecting and reducing CIN3+, without cost-effective data and long-term followup on the incorporation of triaging algorithms into clinical practice, generalization of the data remains limited. Primary HPV testing is an appropriate alternate option; though, ongoing research is needed on long-term outcomes and translation into a clinical practice. Several recent studies have compared cervical cancer screening approaches—specifically comparing primary cytology, primary HPV testing, and cotesting. Blatt et al. compared the efficacy of cotesting vs primary HPV and primary cytology among a retrospective cohort of greater than 250,000 women whose specimens were processed by Quest diagnostics [175]. They found that the negative predictive and positive predictive values were almost identical among the three options, although the sensitivity of cotesting at 98.8% was better than HPV alone (94%) over one round of testing. Since the 4% improvement in sensitivity resulted from double the number of initial tests (since two tests were by definition done on all patients), and presumably an increase number of downstream colposcopies, the cost of this approach must be considered as well by studying a programmatic approach over time which considers consecutive screening rounds.


173

Choi et al. studied 1000 cervical cytology samples at Korea University Ansan Hospital which were obtained for screening. These samples were tested using liquid-based cytology, Hybrid Capture 2, and real-time HPV genetic PCR. Patients were then evaluated by colposcopy using standard clinical algorithms, and pathology results were used to calculate the clinical performance of each option. They found that “primary HPV screening alone was equivalent to that of cotesting for CIN2+.” They noted that primary HPV screening can be performed economically and with a simple algorithm, but due to its lower specificity may result in an increased referral for colposcopy for a subgroup of patients [176]. More data are needed in real clinical settings over time to provide overall programmatic costeffectiveness, especially as increased rates of HPV vaccination may alter the underlying prevalence of HPV and thus the specificity of primary HPV testing. There are also limited data guiding screening practices in “high-risk” women, in particular, women who are immunosuppressed. Data suggest that there is an increased risk of cervical cancer in women with HIV, and there appear to be a spectrum of risk associated with autoimmune diseases: specifically, women with SLE and IBD on immunomodulator therapy appear to have a modest increased risk for high-grade dysplasia and cervical cancer compared to the general population; however, the impact of duration and strength of immunomodulator therapy on cancer risks is not well defined. Optimal screening intervals and modalities are still not clear from the current literature in this population and warrants further investigation. Compliance with screening remains a major barrier to care across many populations, but in particular, in those high-risk populations as seen in women with HIV, solid-organ transplant, and in underserved communities. Also, while rapid advancements in research provide improved knowledge on prevention of cervical cancer, it is often difficult for providers across multiple specialties to remain abreast to changes and to educate their patients about the most current recommendations. A recent article by Kim et al. using population-based data from a state-wide registry in New Mexico studied the cost-effectiveness of “current screening practice” which includes both over- and underscreening women in real practice and found current practice to be less cost-effective than improved compliance with screening at 3-year intervals [177]. Similarly, the low vaccination rates in the United States preclude significant changes in screening recommendation for this population which is a barrier to care. Improving compliance with primary

174

Yusheng Zhu et al.

prevention, as has been suggested internationally, likely would optimize screening programs and further investigation is needed. Ultimately provider and patient education and improved compliance in prevention, screening, and treatment are critical for the uptake of evidence-based practices and the reduction of cervical cancer.

5. HPV VACCINES AND THE IMPACT ON CERVICAL CANCER SCREENING HPV vaccines consist of type-specific HPV L1 proteins that are capable of self-assembly into VLPs. VLPs do not contain vDNA, and thus are noninfectious. The FDA currently licenses three HPV vaccines. Cervarix (GlaxoSmithKline Biologicals, Rixensart, Belgium) is a bivalent vaccine that provides protection against HPV types 16 and 18, which are responsible for approximately 70% of cervical cancer cases. A quadrivalent HPV vaccine, Gardasil (Merck, Kenilworth, NJ) adds protection against HPV types 6 and 11, which cause the majority of genital warts, in addition to types 16 and 18. A 9-valent HPV vaccine, Gardasil 9 (Merck) was licensed by the FDA in 2014, and covers five additional strains of HPV to include HPV-31, -33, -45, -52, and -58. The addition of these five HPV types in the 9-valent vaccine is expected to increase cervical cancer protection to approximately 90% [178]. The vaccine VLP components are each generated and purified individually and then combined [28]. Cervarix uses the adjuvant AS04, which consists of aluminum hydroxide and monophosphoryl lipid A. The aluminum adjuvant aluminum hydroxyphosphate sulfate is used for the quadrivalent and 9-valent vaccines. Aluminum salt adjuvants promote strong antibody responses with generation of a primarily T-helper 2 immune response [179]. Monophosphoryl lipid A is an agonist of toll-like receptor 4 that promotes formation of antigen-specific interferon-gamma-producing CD4+ T cells, thus skewing toward a Th1 immune response; therefore, the AS04 adjuvant used in Cervarix has the potential to promote both Th1 and Th2 responses [180].

5.1 Immunogenicity of HPV Naturally acquired HPV genital infections are cleared due to cell-mediated immune responses. Antibodies against the type-specific HPV viral capsids may be generated; however, this only occurs in approximately 60% of women with incident HPV infection [51]. This is likely because natural


175

HPV infection does not cause viremia, and viral particles do not localize to the vasculature and lymph nodes where acquired immunity is generated [181]. Unfortunately, the humoral immune responses that do get generated by natural HPV infection result in low antibody titers that may not persist. The specific role of humoral responses induced by naturally acquired HPV infection in preventing additional HPV infections is uncertain [51]. The intramuscular delivery of HPV vaccine VLPs results in antigen distribution to lymph nodes, enabling the induction of acquired immunity [181]. VLPs administered intramuscularly alone without adjuvant are immunogenic and induce high-specific antibody titers [181,182]. Several studies have been conducted examining antibody titers in clinical trial patients administered the vaccine. Nearly 100% of patients administered either the bivalent or the quadrivalent vaccine seroconvert with a significant increase in the geometric mean titers after the third dose [180,183–186]. It has been noted that the two companies manufacturing the vaccine used different assays for measuring antibody titers, and thus antibody titer results from the main clinical trials are not directly comparable [187]. However, higher mean geometric antibody titers have been reported for Cervarix in studies directly comparing it to the quadrivalent vaccine [188,189], a finding attributed to the enhanced immune activation caused by the adjuvant AS04 [187]. Immunogenicity of the 9-valent vaccine has also been evaluated. The 9-valent vaccine administered in three doses to 3066 boys and girls ages 9–15 years resulted in seroconversion and increases in titers to all of the HPV types included in the vaccine [190].

5.2 Vaccine Efficacy Proof of concept that a vaccine against hrHPV types could protect against type-specific HPV infection was first demonstrated by administration of a HPV-16 VLP vaccine or placebo to 2392 women ages 16–23 years, which showed a reduction in incident HPV-16 infection and CIN in women administered the vaccine [191]. Follow-up of this population for 8.5 years demonstrated that vaccine recipients did not develop HPV-16 infection or its associated cervical lesions [192]. Several phases II and III studies with multiple subanalyses have since been performed [28,187,193]. The major phase III trials are summarized in Table 6. Cervarix was evaluated by the Costa Rica Vaccine Trial (CVT) and Papilloma Trial Against Cancer in Young Adults (PATRICIA) trials, while Females United to Unilaterally Reduce Endo/Ectocervical disease (FUTURE) I and II evaluated Gardasil.

176

Yusheng Zhu et al.

Table 6 Summary of Phase III Clinical Trials of the HPV Vaccine Vaccine Trial Evaluated Participants Endpoint

CVT

Cervarix

7466

Development of persistent HPV-16/18

FUTURE I

Gardasil

5455

HPV-6/11/16/18 + genital warts and CIN1–3

FUTURE II

Gardasil

12,167

HPV-16/18 CIN2 +

PATRICIA

Cervarix

18,644

HPV-16/18 CIN2 +

V503-001

9vHPV

14,215

High-grade cervical, vulvar, or vaginal disease of any HPV type

CIN, cervical intraepithelial neoplasia; CVT, Costa Rica HPV trial; FUTURE, Females United to Unilaterally Reduce Endo/Ectocervical disease; PATRICIA, Papilloma Trial Against Cancer in Young Adults [194–198].

The FUTURE I, FUTURE II, and PATRICIA trials placed limitations on the number of lifetime sexual partners; however, prevalent infections with HPV determined by DNA detection, prior exposure demonstrated by serology, and abnormal cervical cytology at enrollment were not exclusion criteria. These criteria allowed the investigators to minimize the numbers of women with active infections or genital lesions, but still permit evaluation of vaccine efficacy in patients with current or prior HPV infection [187]. The trial vaccines were administered at times 0, 1–2 months, and at 6 months. The PATRICIA and FUTURE II trial endpoints included CIN grades II or III (CIN2+), adenocarcinoma in situ, or cervical cancer due to the vaccine subtypes [194,195]. FUTURE I also included vaccine subtype CIN1+, genital warts, and vaginal and vulvar intraepithelial neoplasia [196]. CVT had an endpoint of HPV-16 or -18 infection that was persistent after 1 year [197]. All four trials showed high efficacy against the HPV types targeted by the vaccine including persistent infection and cervical cytology in women who had never been infected by the HPV subtype targeted by the vaccine [194–197]. A recent study compared the 9-valent HPV vaccine to the quadrivalent vaccine and found that the 9-valent HPV vaccine prevented infection and associated cervical, vaginal, and vulvar disease caused by HPV types 31, 33, 45, 52, and 58 while preserving immune responses to types 6, 11, 16, and 18 [198]. However, the vaccines did not provide an effect for prevalent infection or cervical lesions present at the time of enrollment. Thus, the vaccine is considered most efficacious if administered prior to HPV infection. Given that HPV infection is usually


177

acquired shortly after initiation of sexual activity, the series should ideally be administered before onset of sexual activity [28]. While the quadrivalent HPV vaccine has been demonstrated to have efficacy in women over age 26 [199], the public health benefit of vaccinating older women is limited due to the higher frequency of prevalent HPV infections in older women that are not impacted by vaccination. Analysis of HPV vaccine clinical trial data has demonstrated effectiveness at sites other than the cervix. Vaccinated women in the CVT trial had a 50% decrease in vulvar infection as well as an 84% reduction of anal infection due to HPV types 16 and 18 [200,201]. A vaccine efficacy of 93% was found for oral infection of HPV-16/18 4 years after vaccination [202]. The quadrivalent vaccine was shown to significantly reduce vaginal, vulvar, and perianal disease regardless of the associated HPV type [196], and has an efficacy of 99% for genital warts caused by vaccine subtypes in women ages 16–26 [183]. Vaccine efficacy has also been demonstrated in males. A trial of 4065 males age 16–26 administered the quadrivalent vaccine or placebo showed prevention of infection with the vaccine HPV subtypes and genital lesions [203]. The rates of anal intraepithelial neoplasia were significantly reduced in men who have sex with men in a trial of 602 men who were administered the quadrivalent vaccine or placebo [203]. Another study showed similar antibody responses in mid-adult men ages 27–45 compared to younger men, a finding of potential importance because men acquire new HPV infections with increasing age and HPV-associated cancers occur at an older age compared to women [204]. The HPV vaccine is recommended for administration in three doses scheduled at 0, 1–2, and 6 months for adolescents aged 11–12 years, although the vaccine series can be initiated at 9 years of age [135]. Catchup vaccination is recommended for females up to age 26, age 21 for most males, and age 26 for immunocompromised males or men who have sex with men. Immunogenicity studies and analyses of clinical trial patients who received less than three doses of the vaccine have been conducted. The antibody responses induced by two vaccine doses given at least 6 months apart were similar to those raised by three doses [205–207]; however, longevity of antibody responses with only two doses has not been sufficiently evaluated. HPV infection with vaccine subtypes is similar among the clinical trial participants who received three doses and one or two doses [200,208,209]. However, an investigation by Pollock et al. [210] did not find a significant decrease in CIN in partially immunized patients compared to unvaccinated patients. Others investigations concluded that all three doses

178

Yusheng Zhu et al.

of vaccine were needed to reduce the risk of high-grade histological abnormalities, although some protection was still obtained with fewer doses [211,212]. It was noted that the women who received fewer doses were older and more likely to be HPV infected compared to fully vaccinated patients, suggesting demographic and behavioral characteristics may be associated with partial vaccination [211,212]. Thus, the true effectiveness of fewer vaccine doses remains unknown. Cost-effectiveness studies have concluded that two-dose HPV vaccine schedules are the most cost-effective approach [213]. Indeed, two-dose vaccination schedules are recommended for girls ages 9–14 in certain countries and the WHO in part because of the cost-effectiveness [213]. Additional studies explored coadministration of the 9-valent vaccine with other vaccines including the meningococcal, Tdap (diphtheria, tetanus toxoids, and acellular pertussis), and poliomyelitis vaccines with good tolerance and no impact on antibody responses, which would allow for reduced clinic visits [190,214]. Unfortunately, HPV vaccination in the United States is significantly lower compared to other vaccines typically given to adolescents. Thirty-five percent of boys and 57% of girls had received at least one dose by 2013; however, only approximately 14% of boys and 38% of girls received all three of the recommended doses [215]. While HPV vaccine uptake has been improving since surveillance began in 2007, it is still substantially lower than the over 75% coverage for meningococcal conjugate, Tdap (tetanus toxoids, diphtheria, and pertussis), and varicella vaccines [215]. Barriers to HPV vaccination include the absence of a school requirement and the need for multiple doses. A number of strategies have resulted in increased HPV vaccination include school-based initiatives, selected marketing to target populations, physician education, and reminder systems [216]; however, the increase in vaccination rates by these various strategies was generally small. Caskey et al. [217] advocate for a “less is more” strategy for HPV vaccination by treating this vaccine in a similar manner to other adolescent vaccines; specifically, a recommendation should be made to vaccinate against HPV without a detailed discussion that possibly gives parents the notion that the HPV vaccine is different from other vaccines.

5.3 Potential Impact of HPV Vaccine on Cervical Cancer Screening HPV vaccination may potentially alter the epidemiology of cervical pathology that may eventually lead to changes in the strategy for cervical cancer screening. A reduction in the prevalence of hrHPV types is expected to


179

cause a reduction in the positive predictive value of cervical cancer screening, potentially resulting in more abnormal results being false positives and overtreatment of patients [218]. Various strategies have been proposed to circumvent this problem, including initiation of screening at a later age and less frequent screening [216]. Shifting to HPV DNA testing as a firstline screening test has also been suggested since a decrease in the prevalence of hrHPV will result in fewer positives by this method [219]. However, a change in the epidemiology of cervical cancer from vaccination is not expected to occur for many decades due to the very long lag time between acquisition of HPV infection and development of malignancy [218]. Furthermore, vaccine uptake in the United States is currently suboptimal. Certain countries such as Australia have achieved high levels of HPV vaccination that has impacted the epidemiology of HPV infection and cervical lesions. Australia began offering a national HPV vaccination program in 2007 targeting girls ages 12–13 and catch-up vaccination to girls ages 14–26 [212,220]. Vaccination was offered in schools and community-based settings, and over 70% of the targeted women have received all three doses of the vaccine [212]. A reduction in the prevalence of infections with HPV vaccine subtypes was observed in both women administered the vaccine and an indication of herd immunity suggested by a lower infection prevalence in unvaccinated women [221,222]. Declines in high-grade cervical abnormalities were observed 3 and 5 years after implementation of the Australia national vaccination program [212,220]. Significant declines in genital warts were also observed in both women and men in the postvaccination time period; the decrease in warts in unvaccinated men is attributed to herd immunity [165]. Other countries with national HPV vaccine programs and high vaccine uptake such as Denmark and the United Kingdom have shown reduced prevalence of HPV vaccine subtype infection prevalence, genital warts, and cervical lesions [223–225]. A reduction in high-grade cervical lesions has even been demonstrated in the United States after vaccine introduction, where vaccine uptake remains suboptimal and there is a lack of immunization and screening registries [216]. The ACOG recommends cytology alone every 3 years for women ages 21–29 and combined cytology and HPV testing every 5 years for women ages 30–65 years [135]. Combined testing for HPV is specifically not recommended by ACOG in women under age 30 due to the high prevalence of hrHPV types that ultimately have a low overall risk of progression to cervical cancer [135]. Australia, which has at least 70% HPV vaccine coverage, is introducing a cervical cancer screening approach that will be based on

180

Yusheng Zhu et al.

HPV DNA testing at age 25 regardless of a woman’s vaccination status [211,226]. The basis of this recommendation is the declining prevalence of hrHPV in Australia and is expected to result in fewer referrals for colposcopy [226]. Current ACOG guidelines do not recommend a separate screening strategy for women who have been vaccinated [135]. First, the vaccine is relatively new and it is possible that the vaccine efficacy decreases with advancing age. Second, many women may have received catch-up vaccination after initiation of sexual activity, which does not protect against HPV infection acquired prior to vaccination. Finally, complete vaccination history may not always be available. Confusion with other vaccines may occur and patients may not receive all of the recommended doses. Thus, a modified screening approach for vaccinated women is not currently recommended at this time [135]. In summary, the clinical trial results demonstrate that the available HPV vaccines show high efficacy in preventing HPV vaccine subtype persistent infection, cervical lesions, genital lesions, and oral HPV infection. However, no protection is provided for prevalent HPV infections at the time of vaccination. Thus, the vaccine is ideally administered prior to initiation of sexual activity and has limited public health benefit in women over age 26 due to the high prevalence of HPV infection in older women. An effect on cervical cancer epidemiology by HPV vaccination is not anticipated for many years due to the long lag period between the acquisition of HPV infection and cervical cancer. However, countries with high vaccine uptake have demonstrated a reduction in infections due to HPV vaccine subtypes and associated cervical lesions that are anticipated to ultimately impact cervical cancer epidemiology. Changes to cervical cancer screening are not recommended in the United States at this time; however, certain countries such as Australia are shifting to HPV DNA testing as a sole screening mechanism due to a reduction in the prevalence of hrHPV subtypes. Future directions include evaluating the effectiveness of fewer doses to improve compliance and improving vaccine uptake.

ACKNOWLEDGMENTS Section 4, “Cervical Cancer Screening,” is adapted from a prior published article in Current Treatment Options in Oncology by M.R.D. and S.F. entitled “Making Sense of Cervical Cancer Screening” [227] with permission of Springer.


181

REFERENCES [1] M.J. Conway, C. Meyers, Replication and assembly of human papillomaviruses, J. Dent. Res. 88 (4) (2009) 307–317. [2] R.L. Finnen, et al., Interactions between papillomavirus L1 and L2 capsid proteins, J. Virol. 77 (8) (2003) 4818–4826. [3] J.W. Wang, R.B. Roden, Virus-like particles for the prevention of human papillomavirus-associated malignancies, Expert Rev. Vaccines 12 (2) (2013) 129–141. [4] W.G. Hubert, T. Kanaya, L.A. Laimins, DNA replication of human papillomavirus type 31 is modulated by elements of the upstream regulatory region that lie 5ʹ of the minimal origin, J. Virol. 73 (3) (1999) 1835–1845. [5] T. Chong, W.K. Chan, H.U. Bernard, Transcriptional activation of human papillomavirus 16 by nuclear factor I, AP1, steroid receptors and a possibly novel transcription factor, PVF: a model for the composition of genital papillomavirus enhancers, Nucleic Acids Res. 18 (3) (1990) 465–470. [6] B. Gloss, et al., Clusters of nuclear factor I binding sites identify enhancers of several papillomaviruses but alone are not sufficient for enhancer function, Nucleic Acids Res. 17 (9) (1989) 3519–3533. [7] V. Gunasekharan, G. Hache, L. Laimins, Differentiation-dependent changes in levels of C/EBPbeta repressors and activators regulate human papillomavirus type 31 late gene expression, J. Virol. 86 (9) (2012) 5393–5398. [8] T. Kanaya, S. Kyo, L.A. Laimins, The 5ʹ region of the human papillomavirus type 31 upstream regulatory region acts as an enhancer which augments viral early expression through the action of YY1, Virology 237 (1) (1997) 159–169. [9] M.J. O’Connor, et al., YY1 represses human papillomavirus type 16 transcription by quenching AP-1 activity, J. Virol. 70 (10) (1996) 6529–6539. [10] F. Stubenrauch, H.B. Lim, L.A. Laimins, Differential requirements for conserved E2 binding sites in the life cycle of oncogenic human papillomavirus type 31, J. Virol. 72 (2) (1998) 1071–1077. [11] A. Piccini, et al., Regulation of human papillomavirus type 16 DNA replication by E2, glucocorticoid hormone and epidermal growth factor, J. Gen. Virol. 78 (Pt. 8) (1997) 1963–1970. [12] L. Yang, et al., The E1 protein of bovine papilloma virus 1 is an ATP-dependent DNA helicase, Proc. Natl. Acad. Sci. U.S.A. 90 (11) (1993) 5086–5090. [13] C.M. Sanders, A. Stenlund, Mechanism and requirements for bovine papillomavirus, type 1, E1 initiator complex assembly promoted by the E2 transcription factor bound to distal sites, J. Biol. Chem. 276 (26) (2001) 23689–23699. [14] M. Lehoux, et al., Inhibition of human papillomavirus DNA replication by an E1-derived p80/UAF1-binding peptide, J. Virol. 86 (7) (2012) 3486–3500. [15] J. Barsoum, et al., Mechanism of action of the papillomavirus E2 repressor: repression in the absence of DNA binding, J. Virol. 66 (6) (1992) 3941–3945. [16] R. Kurg, et al., Characterization of the functional activities of the bovine papillomavirus type 1 E2 protein single-chain heterodimers, J. Virol. 80 (22) (2006) 11218–11225. [17] M. Nasseri, et al., A human papilloma virus type 11 transcript encoding an E1–E4 protein, Virology 159 (2) (1987) 433–439. [18] Q. Wang, et al., Phosphorylation of the human papillomavirus type 16 E1Ê4 protein at T57 by ERK triggers a structural change that enhances keratin binding and protein stability, J. Virol. 83 (8) (2009) 3668–3683. [19] J. Doorbar, et al., Analysis of HPV-1 E4 gene expression using epitope-defined antibodies, EMBO J. 7 (3) (1988) 825.

182

Yusheng Zhu et al.

[20] C.E. Davy, et al., HPV16 E1–E4 protein is phosphorylated by Cdk2/cyclin A and relocalizes this complex to the cytoplasm, Virology 349 (1) (2006) 230–244. [21] S. Roberts, et al., Identification of an arginine-rich motif in human papillomavirus type 1 E1Ê4 protein necessary for E4-mediated inhibition of cellular DNA synthesis in vitro and in cells, J. Virol. 82 (18) (2008) 9056–9064. [22] G.L. Knight, et al., A cyclin-binding motif in human papillomavirus type 18 (HPV18) E1Ê4 is necessary for association with CDK–cyclin complexes and G2/M cell cycle arrest of keratinocytes, but is not required for differentiation-dependent viral genome amplification or L1 capsid protein expression, Virology 412 (1) (2011) 196–210. [23] H. Griffin, et al., E4 antibodies facilitate detection and type-assignment of active HPV infection in cervical disease, PLoS One 7 (12) (2012) e49974. [24] P.B. McIntosh, et al., Structural analysis reveals an amyloid form of the human papillomavirus type 16 E1Ê4 protein and provides a molecular basis for its accumulation, J. Virol. 82 (16) (2008) 8196–8203. [25] R. Ghittoni, et al., The biological properties of E6 and E7 oncoproteins from human papillomaviruses, Virus Genes 40 (1) (2010) 1–13. [26] M. M€ uller, et al., Human papillomavirus E5 oncoprotein: function and potential target for antiviral therapeutics, Futur. Virol. 10 (1) (2015) 27–39. [27] X. Wang, et al., Construction of a full transcription map of human papillomavirus type 18 during productive viral infection, J. Virol. 85 (16) (2011) 8080–8092. [28] C.B. Buck, et al., Arrangement of L2 within the papillomavirus capsid, J. Virol. 82 (11) (2008) 5190–5197. [29] S. Iyengar, et al., Self-assembly of in vitro-translated human papillomavirus type 16 L1 capsid protein into virus-like particles and antigenic reactivity of the protein, Clin. Diagn. Lab. Immunol. 3 (6) (1996) 733–739. [30] K.F. Richards, et al., Multiple heparan sulfate binding site engagements are required for the infectious entry of human papillomavirus type 16, J. Virol. 87 (21) (2013) 11426–11437. [31] S. DiGiuseppe, et al., Topography of the human papillomavirus minor capsid protein L2 during vesicular trafficking of infectious entry, J. Virol. 89 (20) (2015) 10442–10452. [32] R. Yang, et al., Interaction of L2 with beta-actin directs intracellular transport of papillomavirus and infection, J. Biol. Chem. 278 (14) (2003) 12546–12553. [33] K. Kondo, et al., Nuclear location of minor capsid protein L2 is required for expression of a reporter plasmid packaged in HPV51 pseudovirions, Virology 394 (2) (2009) 259–265. [34] S. Mamoor, et al., The high risk HPV16 L2 minor capsid protein has multiple transport signals that mediate its nucleocytoplasmic traffic, Virology 422 (2) (2012) 413–424. [35] N. Kajitani, et al., Productive lifecycle of human papillomaviruses that depends upon squamous epithelial differentiation, Front. Microbiol. 3 (2012) 152. [36] T. Letian, Z. Tianyu, Cellular receptor binding and entry of human papillomavirus, Virol. J. 7 (2010) 2. [37] A.A. McBride, Replication and partitioning of papillomavirus genomes, Adv. Virus Res. 72 (2008) 155–205. [38] K. M€ unger, et al., Mechanisms of human papillomavirus-induced oncogenesis, J. Virol. 78 (21) (2004) 11451–11460. [39] M.A. Ozbun, C. Meyers, Characterization of late gene transcripts expressed during vegetative replication of human papillomavirus type 31b, J. Virol. 71 (7) (1997) 5161–5172. [40] D. Bzhalava, C. Eklund, J. Dillner, International standardization and classification of human papillomavirus types, Virology 476 (2015) 341–344.


183

[41] M. Leto, et al., Human papillomavirus infection: etiopathogenesis, molecular biology and clinical manifestations, An. Bras. Dermatol. 86 (2) (2011) 306–317. [42] K. Hellner, K. Munger, Human papillomaviruses as therapeutic targets in human cancer, J. Clin. Oncol. 29 (13) (2011) 1785–1794. [43] S. Nalliah, B. Karikalan, K. Kademane, Multifaceted usage of HPV related tests and products in the management of cervical cancer—a review, Asian Pac. J. Cancer Prev. 16 (6) (2015) 2145–2150. [44] T. Omland, et al., Recurrent respiratory papillomatosis: HPV genotypes and risk of high-grade laryngeal neoplasia, PLoS One 9 (6) (2014) e99114. [45] N. Bhatia, et al., Understanding genital warts: epidemiology, pathogenesis, and burden of disease of human papillomavirus, J. Cutan. Med. Surg. 17 (Suppl. 2) (2013) S47–S54. [46] E.M. de Villiers, et al., Classification of papillomaviruses, Virology 324 (1) (2004) 17–27. [47] H.U. Bernard, et al., Classification of papillomaviruses (PVs) based on 189 PV types and proposal of taxonomic amendments, Virology 401 (1) (2010) 70–79. [48] L. Mariani, A. Venuti, HPV vaccine: an overview of immune response, clinical protection, and new approaches for the future, J. Transl. Med. 8 (2010) 105. [49] S.C. Fausch, D.M. Da Silva, W.M. Kast, Differential uptake and cross-presentation of human papillomavirus virus-like particles by dendritic cells and Langerhans cells, Cancer Res. 63 (13) (2003) 3478–3482. [50] H.M. Koskimaa, et al., Human papillomavirus 16 E2-, E6- and E7-specific T-cell responses in children and their mothers who developed incident cervical intraepithelial neoplasia during a 14-year follow-up of the Finnish Family HPV cohort, J. Transl. Med. 12 (2014) 44. [51] J.J. Carter, et al., Comparison of human papillomavirus types 16, 18, and 6 capsid antibody responses following incident infection, J. Infect. Dis. 181 (6) (2000) 1911–1919. [52] L. Eckhart, et al., Cell death by cornification, Biochim. Biophys. Acta 1833 (12) (2013) 3471–3480. [53] M.A. Stanley, Immune responses to human papillomaviruses, Indian J. Med. Res. 130 (3) (2009) 266–276. [54] R.W. Tindle, Immune evasion in human papillomavirus-associated cervical cancer, Nat. Rev. Cancer 2 (1) (2002) 59–65. [55] M. Stanley, Immune responses to human papillomavirus, Vaccine 24 (Suppl. 1) (2006) S16–S22. [56] S.J. Lee, et al., Both E6 and E7 oncoproteins of human papillomavirus 16 inhibit IL-18-induced IFN-gamma production in human peripheral blood mononuclear and NK cells, J. Immunol. 167 (1) (2001) 497–504. [57] L. Pacini, et al., Downregulation of toll-like receptor 9 expression by beta human papillomavirus 38 and implications for cell cycle control, J. Virol. 89 (22) (2015) 11396–11405. [58] J.C. Guess, D.J. McCance, Decreased migration of Langerhans precursor-like cells in response to human keratinocytes expressing human papillomavirus type 16 E6/E7 is related to reduced macrophage inflammatory protein-3alpha production, J. Virol. 79 (23) (2005) 14852–14862. [59] B. Zhang, et al., The E5 protein of human papillomavirus type 16 perturbs MHC class II antigen maturation in human foreskin keratinocytes treated with interferon-gamma, Virology 310 (1) (2003) 100–108. [60] M.H. Hibma, The immune response to papillomavirus during infection persistence and regression, Open Virol. J. 6 (2012) 241–248. [61] M. Anderson, et al., Current views on cervical intraepithelial neoplasia, J. Clin. Pathol. 44 (12) (1991) 969.

184

Yusheng Zhu et al.

[62] V. Kumar, R.S. Cotran, Robbins’ basic pathology, Arch. Pathol. Lab. Med. 118 (2) (1994) 203. [63] T.C. Wright, et al., Precancerous lesions of the cervix, in: R.J. Kurman, L.H. Ellenson, B.M. Ronnett (Eds.), Blaustein’s Pathology of the Female Genital Tract, sixth ed, Springer, New York, 2011, pp. 193–252. [64] R. Nayar, D. Wilbur, The Bethesda System for Reporting Cervical Cytology: Definitions, Criteria, and Explanatory Notes, Springer, New York, 2015. [65] G.A. Mishra, S.A. Pimple, S.S. Shastri, An overview of prevention and early detection of cervical cancers, Indian J. Med. Paediatr. Oncol. 32 (3) (2011) 125–132. [66] A.S. Kadish, et al., Regression of cervical intraepithelial neoplasia and loss of human papillomavirus (HPV) infection is associated with cell-mediated immune responses to an HPV type 16 E7 peptide, Cancer Epidemiol. Biomarkers Prev. 11 (5) (2002) 483–488. [67] R.P. Insinga, et al., Progression and regression of incident cervical HPV 6, 11, 16 and 18 infections in young women, Infect. Agent Cancer 2 (2007) 15. [68] D. Martinez-Zapien, et al., Structure of the E6/E6AP/p53 complex required for HPV-mediated degradation of p53, Nature 529 (7587) (2016) 541–545. [69] M. Filippova, et al., The human papillomavirus 16 E6 protein can either protect or further sensitize cells to TNF: effect of dose, Cell Death Differ. 12 (12) (2005) 1622–1635. [70] K. Kabsch, A. Alonso, The human papillomavirus type 16 E5 protein impairs TRAILand FasL-mediated apoptosis in HaCaT cells by different mechanisms, J. Virol. 76 (23) (2002) 12162–12172. [71] T.O. Garnett, M. Filippova, P.J. Duerksen-Hughes, Accelerated degradation of FADD and procaspase 8 in cells expressing human papilloma virus 16 E6 impairs TRAIL-mediated apoptosis, Cell Death Differ. 13 (11) (2006) 1915–1926. [72] M.P. Underbrink, et al., E6 proteins from multiple human betapapillomavirus types degrade Bak and protect keratinocytes from apoptosis after UVB irradiation, J. Virol. 82 (21) (2008) 10408–10417. [73] S. Chellappan, et al., Adenovirus E1A, simian virus 40 tumor antigen, and human papillomavirus E7 protein share the capacity to disrupt the interaction between transcription factor E2F and the retinoblastoma gene product, Proc. Natl. Acad. Sci. U.S.A. 89 (10) (1992) 4549–4553. [74] S. Cheng, et al., Differentiation-dependent up-regulation of the human papillomavirus E7 gene reactivates cellular DNA replication in suprabasal differentiated keratinocytes, Genes Dev. 9 (19) (1995) 2335–2349. [75] W. He, et al., Direct activation of cyclin-dependent kinase 2 by human papillomavirus E7, J. Virol. 77 (19) (2003) 10566–10574. [76] X. Liu, et al., Structure of the human papillomavirus E7 oncoprotein and its mechanism for inactivation of the retinoblastoma tumor suppressor, J. Biol. Chem. 281 (1) (2006) 578–586. [77] M. Zimmermann, et al., TNF-like weak inducer of apoptosis (TWEAK) and TNFalpha cooperate in the induction of keratinocyte apoptosis, J. Allergy Clin. Immunol. 127 (1) (2011) 200–207. 207 e1–10. [78] S. Jeon, B.L. Allen-Hoffmann, P.F. Lambert, Integration of human papillomavirus type 16 into the human genome correlates with a selective growth advantage of cells, J. Virol. 69 (5) (1995) 2989–2997. [79] S.A. Heilman, et al., Abrogation of the postmitotic checkpoint contributes to polyploidization in human papillomavirus E7-expressing cells, J. Virol. 83 (6) (2009) 2756–2764. [80] U. Hasan, Human papillomavirus (HPV) deregulation of Toll-like receptor 9, Oncoimmunology 3 (1) (2014) e27257.


185

[81] H. Jimenez-Wences, O. Peralta-Zaragoza, G. Fernandez-Tilapa, Human papilloma virus, DNA methylation and microRNA expression in cervical cancer (Review), Oncol. Rep. 31 (6) (2014) 2467–2476. [82] E. Krawczyk, et al., Koilocytosis: a cooperative interaction between the human papillomavirus E5 and E6 oncoproteins, Am. J. Pathol. 173 (3) (2008) 682–688. [83] P. Gao, J. Zheng, High-risk HPV E5-induced cell fusion: a critical initiating event in the early stage of HPV-associated cervical cancer, Virol. J. 7 (2010) 238. [84] G.H. Ashrafi, et al., E5 protein of human papillomavirus 16 downregulates HLA class I and interacts with the heavy chain via its first hydrophobic domain, Int. J. Cancer 119 (9) (2006) 2105–2112. [85] F. Fehrmann, D.J. Klumpp, L.A. Laimins, Human papillomavirus type 31 E5 protein supports cell cycle progression and activates late viral functions upon epithelial differentiation, J. Virol. 77 (5) (2003) 2819–2831. [86] B. Zhang, D.F. Spandau, A. Roman, E5 protein of human papillomavirus type 16 protects human foreskin keratinocytes from UV B-irradiation-induced apoptosis, J. Virol. 76 (1) (2002) 220–231. [87] I. Oelze, et al., Human papillomavirus type 16 E5 protein affects cell-cell communication in an epithelial cell line, J. Virol. 69 (7) (1995) 4489–4494. [88] V. Muto, et al., Human papillomavirus type 16 E5 protein induces expression of beta interferon through interferon regulatory factor 1 in human keratinocytes, J. Virol. 85 (10) (2011) 5070–5080. [89] L. Bruni, et al., Human Papillomavirus and Related Diseases Report, ICO Information Centre on HPV and Cancer, L’Hospitalet de Llobregat, 2014. [90] A. Baudu, et al., Prevalence and risk factors of human papillomavirus infection types 16/18/45 in a cohort of French females aged 15-23 years, J. Epidemiol. Glob. Health 4 (1) (2014) 35–43. [91] B.Y. Hernandez, et al., Circumcision and human papillomavirus infection in men: a site-specific comparison, J. Infect. Dis. 197 (6) (2008) 787–794. [92] S. Tokudome, et al., Condom use promotes regression of cervical intraepithelial neoplasia and clearance of human papillomavirus: a randomized clinical trial, Int. J. Cancer 112 (1) (2004) 164. author reply 165. [93] L.A. Denny, et al., Human papillomavirus, human immunodeficiency virus and immunosuppression, Vaccine 30 (Suppl. 5) (2012) F168–F174. [94] S. Ramakrishnan, S. Partricia, G. Mathan, Overview of high-risk HPV’s 16 and 18 infected cervical cancer: pathogenesis to prevention, Biomed. Pharmacother. 70 (2015) 103–110. [95] P. Guan, et al., Human papillomavirus types in 115,789 HPV-positive women: a meta-analysis from cervical infection to cancer, Int. J. Cancer 131 (10) (2012) 2349–2359. [96] M. Hinkula, et al., A population-based study on the risk of cervical cancer and cervical intraepithelial neoplasia among grand multiparous women in Finland, Br. J. Cancer 90 (5) (2004) 1025–1029. [97] M.M. Madeleine, et al., Human papillomavirus and long-term oral contraceptive use increase the risk of adenocarcinoma in situ of the cervix, Cancer Epidemiol. Biomarkers Prev. 10 (3) (2001) 171–177. [98] J.A. Fonseca-Moutinho, Smoking and cervical cancer, ISRN Obstet. Gynecol. 2011 (2011) 847684. [99] J.S. Smith, et al., Herpes simplex virus-2 as a human papillomavirus cofactor in the etiology of invasive cervical cancer, J. Natl. Cancer Inst. 94 (21) (2002) 1604–1613. [100] J.S. Smith, et al., Chlamydia trachomatis and invasive cervical cancer: a pooled analysis of the IARC multicentric case-control study, Int. J. Cancer 111 (3) (2004) 431–439.

186

Yusheng Zhu et al.

[101] D. Forman, et al., Global burden of human papillomavirus and related diseases, Vaccine 30 (Suppl. 5) (2012) F12–F23. [102] International Collaboration of Epidemiological Studies of Cervical Cancer, Comparison of risk factors for invasive squamous cell carcinoma and adenocarcinoma of the cervix: collaborative reanalysis of individual data on 8,097 women with squamous cell carcinoma and 1,374 women with adenocarcinoma from 12 epidemiological studies, Int. J. Cancer 120 (4) (2007) 885–891. [103] A.R. Giuliano, et al., Epidemiology of human papillomavirus infection in men, cancers other than cervical and benign conditions, Vaccine 26 (Suppl. 10) (2008) K17–K28. [104] V.B. Benard, et al., Examining the association between socioeconomic status and potential human papillomavirus-associated cancers, Cancer 113 (10 Suppl.) (2008) 2910–2918. [105] L. Bruni, et al., Human Papillomavirus and Related Diseases report, ICO Information Centre on HPV and Cancer (HPV Information Centre). Human Papillomavirus and Related Diseases in the World, 2016. [106] G.O. Akogbe, et al., Race and prevalence of human papillomavirus infection among men residing in Brazil, Mexico and the United States, Int. J. Cancer 131 (3) (2012) E282–E291. [107] M.B. Schabath, et al., Racial differences in the incidence and clearance of human papilloma virus (HPV): the HPV in men (HIM) study, Cancer Epidemiol. Biomarkers Prev. 22 (10) (2013) 1762–1770. [108] K. Mendez, et al., Cervical human papillomavirus infection in a sample of Hispanic women living in Puerto Rico: comparison with cervical cytology reports, P. R. Health Sci. J. 32 (1) (2013) 3–7. [109] X. Wu, et al., Descriptive epidemiology of vaginal cancer incidence and survival by race, ethnicity, and age in the United States, Cancer 113 (10 Suppl.) (2008) 2873–2882. [110] V.B. Benard, et al., Cervical carcinoma rates among young females in the United States, Obstet. Gynecol. 120 (5) (2012) 1117–1123. [111] P. Gonzalez, et al., Behavioral/lifestyle and immunologic factors associated with HPV infection among women older than 45 years, Cancer Epidemiol. Biomarkers Prev. 19 (12) (2010) 3044–3054. [112] P.E. Gravitt, et al., A cohort effect of the sexual revolution may be masking an increase in human papillomavirus detection at menopause in the United States, J. Infect. Dis. 207 (2) (2013) 272–280. [113] D.R. Brown, B. Weaver, Human papillomavirus in older women: new infection or reactivation? J. Infect. Dis. 207 (2) (2013) 211–212. [114] A.K. Sinno, et al., Human papillomavirus genotype prevalence in invasive vaginal cancer from a registry-based population, Obstet. Gynecol. 123 (4) (2014) 817–821. [115] B. Leonard, et al., A clinical and pathological overview of vulvar condyloma acuminatum, intraepithelial neoplasia, and squamous cell carcinoma, Biomed. Res. Int. 2014 (2014) 480573. [116] B.J. Morris, et al., The strong protective effect of circumcision against cancer of the penis, Adv. Urol. 2011 (2011) 812368. [117] J.M. Palefsky, Human papillomavirus-related disease in men: not just a women’s issue, J. Adolesc. Health 46 (4 Suppl.) (2010) S12–S19. [118] T. Ramqvist, T. Dalianis, Oropharyngeal cancer epidemic and human papillomavirus, Emerg. Infect. Dis. 16 (11) (2010) 1671–1677. [119] T.C. Wright Jr., et al., 2001 Consensus guidelines for the management of women with cervical cytological abnormalities, JAMA 287 (16) (2002) 2120–2129.


187

[120] D. Saslow, et al., American Cancer Society guideline for the early detection of cervical neoplasia and cancer, CA Cancer J. Clin. 52 (6) (2002) 342–362. [121] D. Saslow, et al., American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer, Am. J. Clin. Pathol. 137 (4) (2012) 516–542. [122] M. Schiffman, G. Clifford, F.M. Buonaguro, Classification of weakly carcinogenic human papillomavirus types: addressing the limits of epidemiology at the borderline, Infect. Agent Cancer 4 (2009) 8. [123] Digene, Hybrid Capture 2 High-Risk HPV DNA Test [Package Insert, 5199-1220], Digene Corporation, Gaithersburg, MD, 2004. [124] M.L. Lindemann, et al., Analytical comparison of the cobas HPV test with hybrid capture 2 for the detection of high-risk HPV genotypes, J. Mol. Diagn. 14 (1) (2012) 65–70. [125] J. Dockter, et al., Clinical performance of the APTIMA HPV Assay for the detection of high-risk HPV and high-grade cervical lesions, J. Clin. Virol. 45 (Suppl. 1) (2009) S55–S61. [126] Hologic Inc., Cervista HPV HR [Package Insert, PRD-00804, Revision 204], Hologic, San Diego, CA, 2015. [127] Roche Molecular Systems Inc., Cobas HPV Test [Package Insert, 05641268001, Rev. 1.0], Roche, Indianapolis, IN, 2010. [128] Gen-Probe, Aptima HPV Assay [Package Insert, 50218EN, Rev. A], Hologic, San Diego, CA, 2001. [129] M. Poljak, et al., Hybrid Capture II HPV test detects at least 15 human papillomavirus genotypes not included in its current high-risk probe cocktail, J. Clin. Virol. 25 (Suppl. 3) (2002) S89–S97. [130] K. Seme, et al., Resolving repeatedly borderline results of Hybrid Capture 2 HPV DNA test using polymerase chain reaction and genotyping, J. Virol. Methods 134 (1–2) (2006) 252–256. [131] D. Saslow, et al., American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer, CA Cancer J. Clin. 62 (3) (2012) 147–172. [132] The ASCUS-LSIL Triage Study (ALTS) Group, A randomized trial on the management of low-grade squamous intraepithelial lesion cytology interpretations, Am. J. Obstet. Gynecol. 188 (6) (2003) 1393–1400. [133] D.G. Teoh, et al., Adherence to the 2012 national cervical cancer screening guidelines: a pilot study, Am. J. Obstet. Gynecol. 212 (1) (2015) 62.e1–62.e9. [134] W.K. Huh, et al., Use of primary high-risk human papillomavirus testing for cervical cancer screening: interim clinical guidance, Gynecol. Oncol. 136 (2) (2015) 178–182. [135] Practice Bulletin No. 157: cervical cancer screening and prevention. American College of Obstetricians and Gynecologist, Obstet. Gynecol. 127 (1) (2016) e1–e20. [136] V.A. Moyer, Screening for cervical cancer: US Preventive Services Task Force recommendation statement, Ann. Intern. Med. 156 (12) (2012) 880–891. [137] S.L. Kulasingam, et al., Screening for Cervical Cancer: A Decision Analysis for the U.S. Preventive Services Task Force, AHRQ Publication No. 11-05157-EF-1. Agency for Healthcare Research and Quality, Rockville, MD, 2011. [138] G.F. Sawaya, et al., Cervical cancer screening in average-risk women: best practice advice from the Clinical Guidelines Committee of the American College of Physicians, Ann. Intern. Med. 162 (12) (2015) 851–859. [139] E.E. Partridge, et al., Cervical Cancer Screening, National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology, NCCN, Fort Washington, PA, 2012.

188

Yusheng Zhu et al.

[140] N. Broutet, et al., Comprehensive Cervical Cancer Control: A Guide to Essential Practice, WHO, Geneva, Sweden, 2014. [141] American College of Gynecologist and Obstetricians, Screening for cervical cancer. Clinical management guidelines for obstetricians-gynecologist, Pract. Bull. 131 (2012) 1–18. [142] American College of Obstetricians and Gynecologists Committee on Health Care for Underserved Women, Committee Opinion No, 624: cervical cancer screening in low-resource settings, Obstet. Gynecol. 125 (2) (2015) 526–528. [143] M. Arbyn, et al., Liquid compared with conventional cervical cytology: a systematic review and meta-analysis, Obstet. Gynecol. 111 (1) (2008) 167–177. [144] A.G. Siebers, et al., Comparison of liquid-based cytology with conventional cytology for detection of cervical cancer precursors: a randomized controlled trial, JAMA 302 (16) (2009) 1757–1764. [145] N.K. Stout, et al., Trade-offs in cervical cancer prevention: balancing benefits and risks, Arch. Intern. Med. 168 (17) (2008) 1881–1889. [146] M.G. Miller, et al., Screening interval and risk of invasive squamous cell cervical cancer, Obstet. Gynecol. 101 (1) (2003) 29–37. [147] J. Dillner, et al., Long term predictive values of cytology and human papillomavirus testing in cervical cancer screening: joint European cohort study, BMJ 337 (2008) a1754. [148] H.A. Katki, et al., Cervical cancer risk for women undergoing concurrent testing for human papillomavirus and cervical cytology: a population-based study in routine clinical practice, Lancet Oncol. 12 (7) (2011) 663–672. [149] M.-H. Mayrand, et al., Human papillomavirus DNA versus Papanicolaou screening tests for cervical cancer, N. Engl. J. Med. 357 (16) (2007) 1579–1588. [150] T.C. Wright, et al., Primary cervical cancer screening with human papillomavirus: end of study results from the ATHENA study using HPV as the first-line screening test, Gynecol. Oncol. 136 (2) (2015) 189–197. [151] G. Ronco, et al., Efficacy of HPV-based screening for prevention of invasive cervical cancer: follow-up of four European randomised controlled trials, Lancet 383 (9916) (2014) 524–532. [152] M. Arbyn, et al., European guidelines for quality assurance in cervical cancer screening—summary document, Ann. Oncol. 21 (3) (2010) 448–458. [153] D.C. Rijkaart, et al., Human papillomavirus testing for the detection of high-grade cervical intraepithelial neoplasia and cancer: final results of the POBASCAM randomised controlled trial, Lancet Oncol. 13 (1) (2012) 78–88. [154] H.C. Kitchener, et al., ARTISTIC: a randomised trial of-human-papillomavirus (HPV) testing in primary cervical screening, Health Technol. Assess. 13 (51) (2009) 1. [155] P. Naucler, et al., Human papillomavirus and Papanicolaou tests to screen for cervical cancer, N. Engl. J. Med. 357 (16) (2007) 1589–1597. [156] H.C. Kitchener, et al., A comparison of HPV DNA testing and liquid based cytology over three rounds of primary cervical screening: extended follow up in the ARTISTIC trial, Eur. J. Cancer 47 (6) (2011) 864–871. [157] H.A. Katki, et al., Five-year risk of recurrence following treatment of CIN2, CIN3, or AIS: performance of HPV and Pap cotesting in post-treatment management, J. Low. Genit. Tract Dis. 17 (5 Suppl. 1) (2013) S78. [158] S. Costa, et al., Human papillomavirus (HPV) test and PAP smear as predictors of outcome in conservatively treated adenocarcinoma in situ (AIS) of the uterine cervix, Gynecol. Oncol. 106 (1) (2007) 170–176. [159] S.D. Datta, et al., Human papillomavirus infection and cervical cytology in women screened for cervical cancer in the United States, 2003–2005, Ann. Intern. Med. 148 (7) (2008) 493–500.


189

[160] S.J. Goldie, J.J. Kim, T.C. Wright, Cost-effectiveness of human papillomavirus DNA testing for cervical cancer screening in women aged 30 years or more, Obstet. Gynecol. 103 (4) (2004) 619–631. [161] G.F. Sawaya, et al., Advancing age and cervical cancer screening and prognosis, J. Am. Geriatr. Soc. 49 (11) (2001) 1499–1504. [162] H. Stokes-Lampard, et al., Vaginal vault smears after hysterectomy for reasons other than malignancy: a systematic review of the literature, BJOG 113 (12) (2006) 1354–1365. [163] K.F. Pearce, et al., Cytopathological findings on vaginal Papanicolaou smears after hysterectomy for benign gynecologic disease, N. Engl. J. Med. 335 (21) (1996) 1559–1562. [164] M.L. Nguyen, L. Flowers, Cervical cancer screening in immunocompromised women, Obstet. Gynecol. Clin. North Am. 40 (2) (2013) 339–357. [165] A.G. Abraham, et al., Invasive cervical cancer risk among HIV-infected women: a North American multi-cohort collaboration prospective study, J. Acquir. Immune Defic. Syndr. 62 (4) (2013) 405. [166] H. Kitchener, et al., Colposcopy is not necessary to assess the risk to the cervix in HIVpositive women: an international cohort study of cervical pathology in HIV-1 positive women, Int. J. Cancer 121 (11) (2007) 2484–2491. [167] L.S. Massad, et al., Negative predictive value of pap testing: implications for screening intervals for women with human immunodeficiency virus, Obstet. Gynecol. 120 (4) (2012) 791. [168] T.G. Harris, et al., Incidence of cervical squamous intraepithelial lesions associated with HIV serostatus, CD4 cell counts, and human papillomavirus test results, JAMA 293 (12) (2005) 1471–1476. [169] W.S. Asch, M.J. Bia, Oncologic issues and kidney transplantation: a review of frequency, mortality, and screening, Adv. Chronic Kidney Dis. 21 (1) (2014) 106–113. [170] S.C. Kim, et al., Risk of high-grade cervical dysplasia and cervical cancer in women with systemic inflammatory diseases: a population-based cohort study, Ann. Rheum. Dis. 74 (7) (2015) 1360–1367. [171] E. Zard, et al., Increased risk of high grade cervical squamous intraepithelial lesions in systemic lupus erythematosus: a meta-analysis of the literature, Autoimmun. Rev. 13 (7) (2014) 730–735. [172] J.R. Allegretti, E.L. Barnes, A. Cameron, Are patients with inflammatory bowel disease on chronic immunosuppressive therapy at increased risk of cervical high-grade dysplasia/cancer? A meta-analysis, Inflamm. Bowel Dis. 21 (5) (2015) 1089. [173] R. Sankaranarayanan, et al., HPV screening for cervical cancer in rural India, N. Engl. J. Med. 360 (14) (2009) 1385–1394. [174] L. Denny, et al., Human papillomavirus-based cervical cancer prevention: long-termresults of a randomized screening trial, J. Natl. Cancer Inst. 102 (2010) 1557–1567. [175] A.J. Blatt, et al., Comparison of cervical cancer screening results among 256,648 women in multiple clinical practices, Cancer Cytopathol. 123 (5) (2015) 282–288. [176] J.W. Choi, et al., The clinical performance of primary HPV screening, primary HPV screening plus cytology cotesting, and cytology alone at a tertiary care hospital, Cancer Cytopathol. 124 (2) (2016) 144–152. [177] J.J. Kim, et al., Inefficiencies and high-value improvements in US cervical cancer screening practice: a cost-effectiveness analysis, Ann. Intern. Med. 163 (8) (2015) 589–597. [178] P. Van Damme, et al., Use of the nonavalent HPV vaccine in individuals previously fully or partially vaccinated with bivalent or quadrivalent HPV vaccines, Vaccine 34 (6) (2016) 757–761.

190

Yusheng Zhu et al.

[179] E.B. Lindblad, Aluminium compounds for use in vaccines, Immunol. Cell Biol. 82 (5) (2004) 497–505. [180] A.M. Didierlaurent, et al., AS04, an aluminum salt- and TLR4 agonist-based adjuvant system, induces a transient localized innate immune response leading to enhanced adaptive immunity, J. Immunol. 183 (10) (2009) 6186–6197. [181] M. Stanley, L. Gissmann, D. Nardelli-Haefliger, Immunobiology of human papillomavirus infection and vaccination—implications for second generation vaccines, Vaccine 26 (Suppl. 10) (2008) K62–K67. [182] C.D. Harro, et al., Safety and immunogenicity trial in adult volunteers of a human papillomavirus 16 L1 virus-like particle vaccine, J. Natl. Cancer Inst. 93 (4) (2001) 284–292. [183] N. Bhatla, et al., Immunogenicity and safety of human papillomavirus-16/18 AS04adjuvanted cervical cancer vaccine in healthy Indian women, J. Obstet. Gynaecol. Res. 36 (1) (2010) 123–132. [184] D.R. Brown, et al., The humoral response to Gardasil over four years as defined by total IgG and competitive Luminex immunoassay, Hum. Vaccin. 7 (2) (2011) 230–238. [185] M.H. Einstein, et al., Comparative immunogenicity and safety of human papillomavirus (HPV)-16/18 vaccine and HPV-6/11/16/18 vaccine: follow-up from months 12-24 in a phase III randomized study of healthy women aged 18-45 years, Hum. Vaccin. 7 (12) (2011) 1343–1358. [186] E.A. Joura, et al., HPV antibody levels and clinical efficacy following administration of a prophylactic quadrivalent HPV vaccine, Vaccine 26 (52) (2008) 6844–6851. [187] J.T. Schiller, X. Castellsague, S.M. Garland, A review of clinical trials of human papillomavirus prophylactic vaccines, Vaccine 30 (Suppl. 5) (2012) F123–F138. [188] M.H. Einstein, et al., Comparison of the immunogenicity and safety of Cervarix and Gardasil human papillomavirus (HPV) cervical cancer vaccines in healthy women aged 18-45 years, Hum. Vaccin. 5 (10) (2009) 705–719. [189] M.H. Einstein, et al., Comparison of long-term immunogenicity and safety of human papillomavirus (HPV)-16/18 AS04-adjuvanted vaccine and HPV-6/11/16/18 vaccine in healthy women aged 18-45 years: end-of-study analysis of a phase III randomized trial, Hum. Vaccin. Immunother. 10 (12) (2014) 3435–3445. [190] P. Kosalaraksa, et al., An open-label, randomized study of a 9-valent human papillomavirus vaccine given concomitantly with diphtheria, tetanus, pertussis and poliomyelitis vaccines to healthy adolescents 11-15 years of age, Pediatr. Infect. Dis. J. 34 (6) (2015) 627–634. [191] L.A. Koutsky, et al., A controlled trial of a human papillomavirus type 16 vaccine, N. Engl. J. Med. 347 (21) (2002) 1645–1651. [192] A. Rowhani-Rahbar, et al., Longer term efficacy of a prophylactic monovalent human papillomavirus type 16 vaccine, Vaccine 27 (41) (2009) 5612–5619. [193] L.A. Koutsky, D.M. Harper, Chapter 13: current findings from prophylactic HPV vaccine trials, Vaccine 24 (Suppl. 3) (2006). S3/114-21. [194] F.I.S. Group, Quadrivalent vaccine against human papillomavirus to prevent highgrade cervical lesions, N. Engl. J. Med. 356 (19) (2007) 1915–1927. [195] J. Paavonen, et al., Efficacy of a prophylactic adjuvanted bivalent L1 virus-like-particle vaccine against infection with human papillomavirus types 16 and 18 in young women: an interim analysis of a phase III double-blind, randomised controlled trial, Lancet 369 (9580) (2007) 2161–2170. [196] S.M. Garland, et al., Quadrivalent vaccine against human papillomavirus to prevent anogenital diseases, N. Engl. J. Med. 356 (19) (2007) 1928–1943.


191

[197] R. Herrero, et al., Rationale and design of a community-based double-blind randomized clinical trial of an HPV 16 and 18 vaccine in Guanacaste, Costa Rica, Vaccine 26 (37) (2008) 4795–4808. [198] E.A. Joura, et al., A 9-valent HPV vaccine against infection and intraepithelial neoplasia in women, N. Engl. J. Med. 372 (8) (2015) 711–723. [199] N. Munoz, et al., Safety, immunogenicity, and efficacy of quadrivalent human papillomavirus (types 6, 11, 16, 18) recombinant vaccine in women aged 24-45 years: a randomised, double-blind trial, Lancet 373 (9679) (2009) 1949–1957. [200] A.R. Kreimer, et al., Efficacy of a bivalent HPV 16/18 vaccine against anal HPV 16/18 infection among young women: a nested analysis within the Costa Rica Vaccine Trial, Lancet Oncol. 12 (9) (2011) 862–870. [201] K.A. Lang Kuhs, et al., Reduced prevalence of vulvar HPV16/18 infection among women who received the HPV16/18 bivalent vaccine: a nested analysis within the Costa Rica Vaccine Trial, J. Infect. Dis. 210 (12) (2014) 1890–1899. [202] R. Herrero, et al., Reduced prevalence of oral human papillomavirus (HPV) 4 years after bivalent HPV vaccination in a randomized clinical trial in Costa Rica, PLoS One 8 (7) (2013) e68329. [203] A.R. Giuliano, et al., Efficacy of quadrivalent HPV vaccine against HPV infection and disease in males, N. Engl. J. Med. 364 (5) (2011) 401–411. [204] A.R. Giuliano, et al., Immunogenicity and safety of Gardasil among mid-adult aged men (27-45 years)—The MAM Study, Vaccine 33 (42) (2015) 5640–5646. [205] S.R. Dobson, et al., Immunogenicity of 2 doses of HPV vaccine in younger adolescents vs 3 doses in young women: a randomized clinical trial, JAMA 309 (17) (2013) 1793–1802. [206] E. Lazcano-Ponce, et al., Overcoming barriers to HPV vaccination: non-inferiority of antibody response to human papillomavirus 16/18 vaccine in adolescents vaccinated with a two-dose vs. a three-dose schedule at 21 months, Vaccine 32 (6) (2014) 725–732. [207] B. Romanowski, et al., Immunogenicity and safety of the HPV-16/18 AS04adjuvanted vaccine administered as a 2-dose schedule compared with the licensed 3-dose schedule: results from a randomized study, Hum. Vaccin. 7 (12) (2011) 1374–1386. [208] A.R. Kreimer, et al., Efficacy of fewer than three doses of an HPV-16/18 AS04adjuvanted vaccine: combined analysis of data from the Costa Rica Vaccine and PATRICIA trials, Lancet Oncol. 16 (7) (2015) 775–786. [209] R. Sankaranarayanan, et al., Immunogenicity and HPV infection after one, two, and three doses of quadrivalent HPV vaccine in girls in India: a multicentre prospective cohort study, Lancet Oncol. 17 (1) (2016) 67–77. [210] K.G. Pollock, et al., Reduction of low- and high-grade cervical abnormalities associated with high uptake of the HPV bivalent vaccine in Scotland, Br. J. Cancer 111 (9) (2014) 1824–1830. [211] B. Barbaro, J.M. Brotherton, Measuring HPV vaccination coverage in Australia: comparing two alternative population-based denominators, Aust. N. Z. J. Public Health 39 (4) (2015) 326–330. [212] D.M. Gertig, et al., Impact of a population-based HPV vaccination program on cervical abnormalities: a data linkage study, BMC Med. 11 (2013) 227. [213] M. Jit, et al., Comparison of two dose and three dose human papillomavirus vaccine schedules: cost effectiveness analysis based on transmission model, BMJ 350 (2015) g7584. [214] A. Schilling, et al., Coadministration of a 9-valent human papillomavirus vaccine with meningococcal and Tdap vaccines, Pediatrics 136 (3) (2015) e563–e572.

192

Yusheng Zhu et al.

[215] S. Reagan-Steiner, et al., National, regional, state, and selected local area vaccination coverage among adolescents aged 13-17 years—United States, 2014, MMWR Morb. Mortal. Wkly Rep. 64 (29) (2015) 784–792. [216] S. Hariri, et al., Reduction in HPV 16/18-associated high grade cervical lesions following HPV vaccine introduction in the United States—2008–2012, Vaccine 33 (13) (2015) 1608–1613. [217] R. Caskey, S. Andes, S.M. Walton, HPV vaccine: less is more, Vaccine 34 (16) (2016) 1863–1864. [218] L.S. Massad, et al., The impact of human papillomavirus vaccination on cervical cancer prevention efforts, Gynecol. Oncol. 114 (2) (2009) 360–364. [219] E.L. Franco, et al., The expected impact of HPV vaccination on the accuracy of cervical cancer screening: the need for a paradigm change, Arch. Med. Res. 40 (6) (2009) 478–485. [220] J.M. Brotherton, et al., Early effect of the HPV vaccination programme on cervical abnormalities in Victoria, Australia: an ecological study, Lancet 377 (9783) (2011) 2085–2092. [221] S.N. Tabrizi, et al., Fall in human papillomavirus prevalence following a national vaccination program, J. Infect. Dis. 206 (11) (2012) 1645–1651. [222] S.N. Tabrizi, et al., Assessment of herd immunity and cross-protection after a human papillomavirus vaccination programme in Australia: a repeat cross-sectional study, Lancet Infect. Dis. 14 (10) (2014) 958–966. [223] B. Baldur-Felskov, et al., Incidence of cervical lesions in Danish women before and after implementation of a national HPV vaccination program, Cancer Causes Control 25 (7) (2014) 915–922. [224] M. Blomberg, et al., Strongly decreased risk of genital warts after vaccination against human papillomavirus: nationwide follow-up of vaccinated and unvaccinated girls in Denmark, Clin. Infect. Dis. 57 (7) (2013) 929–934. [225] D. Mesher, et al., Reduction in HPV 16/18 prevalence in sexually active young women following the introduction of HPV immunisation in England, Vaccine 32 (1) (2013) 26–32. [226] J.M. Brotherton, et al., HPV vaccine impact in Australian women: ready for an HPVbased screening program, Med. J. Aust. 204 (5) (2016) 184. [227] M. Davis, S. Feldman, Making sense of cervical cancer screening guidelines and recommendations, Curr. Treat. Options Oncol. 16 (12) (2015) 55.

CHAPTER FIVE

Physical Exercise and DNA Injury: Good or Evil? Elisa Danese*, Giuseppe Lippi*,1, Fabian Sanchis-Gomar†, Giorgio Brocco†, Manfredi Rizzo{, Maciej Banach§, Martina Montagnana* *Section of Clinical Biochemistry, University of Verona, Verona, Italy † Research Institute of the Hospital 12 de Octubre (i + 12), Madrid, Spain { University of Palermo, Palermo, Italy § WAM University Hospital in Lodz, Medical University of Lodz, Lodz, Poland 1 Corresponding author: e-mail addresses: [email protected]; [email protected]

Contents 1. Introduction About Physical Activity and Health 2. Physical Exercise and ROS Generation 3. Considerations About the Methods Used for Assessing DNA Injury 3.1 The COMET Assay 3.2 8-Hydroxy-20-Deoxyguanosine 3.3 Phosphorylation of Histone Protein H2AX 4. Physical Exercise and DNA Injury 4.1 Clinical Studies Using the COMET Assay 4.2 Clinical Studies Using 8-Hydroxy-20-Deoxyguanosine 4.3 Clinical Studies Using Phosphorylation of Histone Protein H2AX 5. Conclusions References

194 197 214 215 216 217 217 217 221 221 223 225

Abstract Regular, low-intensity physical activity is currently advocated for lowering the risk of developing many acute and especially chronic diseases. However, several lines of evidence attest that strenuous exercise may enhance inflammation and trigger the generation of free radical-mediated damage, thus overwhelming the undisputable benefits of regular, medium-intensity physical activity. Since reactive oxygen species are actively generated during high-intensity exercise, and these reactive compounds are known to impact DNA stability, we review here the current evidence about strenuous exercise and DNA injury. Despite the outcome of the various studies cannot be pooled due to considerable variation in design, sample population, outcome, and analytical techniques used to assess DNA damage, it seems reasonable to conclude that medium- to high-volume exercise triggers a certain amount of DNA injury, which appears to be transitory and directly proportional to exercise intensity. This damage, reasonably attributable to direct effect of free radicals on nucleic acids, is efficiently repaired in vivo within Advances in Clinical Chemistry, Volume 81 ISSN 0065-2423 http://dx.doi.org/10.1016/bs.acc.2017.01.005

#


193

194

Elisa Danese et al.

24–72 h. Therefore, physical exercise should not bear long-term consequences for athlete’s health provided that an appropriate time of recovery between volumes of high-intensity exercise is set. Regular exertion, with a step-by-step increase of exercise load, also seems to be the most safe approach for eluding DNA instability.

ABBREVIATIONS ACSM American College of Sports Medicine AMP adenosine monophosphate APs apurinic sites CAT catalase CBMN cytokinesis-block micronucleus CE capillary electrophoresis cORP capacity oxidation–reduction potential ENDO III endonuclease III ELISA enzyme-linked immunosorbent assay FPG formamidopyrimidine glycosylase GC–MS gas chromatography–mass spectrometry GPx glutathione peroxidase γ-H2AX histone protein H2AX HIDT high-intensity discontinuous training HPLC-ECD high-performance liquid chromatography-electrochemical detection MDA malondialdehyde MN micronucleus MnSOD manganese-dependent superoxide dismutase NADPH nicotinamide adenine dinucleotide phosphate NF-κB nuclear factor kappa B 8-OHdG 8-hydroxy-20-deoxyguanosine PC protein carbonylation ROS reactive oxygen species SBs strand breaks SCGE single-cell gel electrophoresis SOD superoxide dismutase sORP static oxidation-reduction potential TAC total antioxidant capacity TAS total antioxidant status TBARS thiobarbituric acid-reactive substances %tDNA percentage of DNA in tail TEAC trolox-equivalent antioxidant capacity

1. INTRODUCTION ABOUT PHYSICAL ACTIVITY AND HEALTH Although the concepts of physical activity, physical exercise, and physical fitness are often used interchangeably to define a bodily activity

Physical Exercise and DNA Injury

195

aimed to enhance or maintain physical fitness and overall health and wellness, these terms outline different concepts. Physical activity has been defined as bodily movements, generated by skeletal muscle contraction, associated with energy expenditure and measurable in kilocalories [1]. According to this definition, physical activity entails many different activities, such as sports, conditioning, household, and many other tasks requiring active muscle contraction. Physical exercise can hence be categorized as a “subset” of physical activity, entailing clear characteristics such as scheduling, structuring, and repeatability, ultimately aimed at producing an improvement (or a maintenance) of physical fitness. Physical fitness is finally defined as the capability to perform daily activities or meet unforeseen emergencies without unwarranted fatigue [1]. It is now undeniable that regular physical activity is one of the most important aspects for preserving health and fitness [2,3]. The many advantages of physical activity include disease prevention (and sometimes treatment), weight control, mood, and sleep improvement, along with energy boost. Due to these favorable effects, regular performance of physical activity has now been endorsed, and often advocated, for preventing development, or mitigating the effects, of a kaleidoscope of human disorders such as cardiovascular disease [4], Alzheimer’s disease [5], cancer [6], diabetes [7,8], sarcopenia and/or frailty [9,10], osteoporosis [11], and fractures [12], as well as for lowering the risk of all-cause mortality in the general population [13–16]. Despite the fact that the relationship between physical activity and health is virtually incontestable and that the individual amount of exercise should be tailored according to individual performance, physical function, health status, and response to exercise and goals, the average amount of exercise needed for obtaining biologic and metabolic benefits still challenges many scientific organizations [17]. As clearly shown in Table 1, the recommended minimum levels of physical activity vary slightly among the different organizations (i.e., not less than 150 min of aerobic physical activity per week is usually recommended), but considerations about the additional health benefits attainable with performance of physical activity above the minimum recommended levels are heterogeneous or even lacking. The results of a recent meta-analysis including 54 studies and 3 other articles from online sources have convincingly concluded that elite athletes (i.e., those performing high-performance participation in sports) live between 4 and 8 years longer than the general population [18]. Likewise, a lower rate of all-cause and cardiovascular mortality was reported in professional athletes compared to the general population [19]. Notably, while also endorsing that the benefits of exercise far outweigh the risks in most adults, the American College

196

Elisa Danese et al.

Table 1 Recommendations of Some National and International Organizations About Physical Activity and Heath Statement About Exercise Above Minimum Recommended References Organization Recommendations

World Health Organization

At least 150 min of moderate-intensity aerobic physical activity per week or at least 75 min of vigorous-intensity aerobic physical activity per week

[15] Adults may increase their moderate-intensity aerobic physical activity to 300 min per week or engage in 150 min of vigorous-intensity aerobic physical activity per week for obtaining additional health benefits

American College of Sports Medicine and American Heart Association

At least 30 min of moderate-intensity aerobic physical activity on 5 days each week or at least 20 min of vigorous-intensity aerobic activity on 3 days each week

Aerobic and muscle-strengthening physical activities above minimum additional health benefits and results in higher levels of physical fitness

[4]

American Diabetes Association

At least 150 min of moderate-intensity physical activity per week

None

[8]

Centers for Disease Control and Prevention

At least 150 min of moderate-intensity aerobic activity per week

None

[14]

Canadian Society for Exercise Physiology

At least 150 min of moderate- to vigorous-intensity aerobic physical activity per week in bouts of 10 min or more

More physical activity [16] provides greater health benefits


197

of Sports Medicine (ACSM) emphasizes that the risk of coronary heart disease and musculoskeletal complications may transitorily increase during strenuous physical activity [20]. The thrombotic complications have been mostly attributed to the development of an acute and transient hypercoagulability state during and immediately after strenuous exercise, especially in untrained individuals. The most important hemostatic abnormalities include enhanced thrombin generation, platelet hyperreactivity, and increased activity of coagulation factors [21], whereas muscle injury is seemingly due to oxygen radical-mediated damage. The first information that physical exercise may generate both localized and diffuse free radical-mediated injury was brought to the public’s attention nearly 40 years ago and has been then upheld by the outcomes of a large number of studies investing the relationship between exercise and oxidative stress [22]. Although one of the most known and important consequences of an enhanced oxidative stress is the potential emergence of nucleic acid injury, especially DNA damage [23], definitive conclusions about the existence of a clerical and hierarchical connection between physical exercise, free radical generation, and DNA injury are lacking. Therefore, the aim of this narrative review is to provide an overview of the relationship between physical exercise and free radical generation and, in turn, between physical exercise and DNA injury.

2. PHYSICAL EXERCISE AND ROS GENERATION The oxidant–antioxidant balance plays a pivotal role during physical exercise and appears to be finely tuned [24]. Muscle activity triggers an increased generation of reactive oxygen species (ROS) such as superoxide, hydrogen peroxide, and hydroxyl radical [25,26]. The main mechanisms underlying free radical production include a remarkable increase of oxygen consumption due to enhanced muscular demand, activation of inflammatory cells due to muscle tissue injury (especially in strenuous or long-lasting exercise), ischemia and/or hypoxia/reoxygenation damage, activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase(s) expressed by skeletal muscle with generation of superoxide, degradation of adenosine monophosphate (AMP), loss of calcium homeostasis in stressed muscles, enhanced cytokine production, activation of nuclear factor kappa B (NF-κB), and catecholamine autooxidation [27–29]. The relationship between exercise and oxidative stress is multifaceted and depends on many variables such as age, sex, type (i.e., aerobic vs

198

Elisa Danese et al.

Table 2 Biomarkers Useful to Estimate Oxidative Stress Status

Oxidative stress biomarkers

Measures of lipid peroxidation • Expired pentane and ethane • Malondialdehydes (MDA) • Lipid hydroperoxides • Isoprostanes • Conjugated dienes Measures of protein and DNA oxidation • Protein carbonylation (PC) • Urinary 8-hydroxydeoxyguanosine (8-OHdG)

Antioxidative biomarkers

• Reduced vs oxidized glutathione • • • • • • •

(GSH/GSSG) ratio Total antioxidant capacity (TAC) Superoxide dismutase (SOD) Catalase (CAT) Glutathione peroxidase (GPx) Glutathione reductase (GR) Vitamins A, C, and E Uric acid

anaerobic), intensity, and duration of exercise. The oxidative stress status can be actually mirrored by a number of biomarkers (the most important are summarized in Table 2). Reliable evidence was brought that strenuous physical exercise may trigger a broad range of adverse reactions, including increased lipid peroxidation, glutathione oxidation, protein oxidation, and damage of nucleic acids, which are mostly attributable to a disproportionate oxidative stress eventually overwhelming the scavenging capacity of the antioxidant systems [29,42]. Exercise may also regulate epigenetic mechanisms such as methylation of the DNA, changes in the posttranslational modifications of histones, and micro-RNA expression [43]. In some circumstances, such as in young or well-trained athletes, the oxidative stress can also induce an adaptive (somehow favorable) physiological response [44]. This is mainly attributable to the activation of specific redox-sensitive transcriptional pathways, which promote increased expression of endogenous antioxidants enzymes such as mitochondrial manganese-dependent superoxide dismutase (MnSOD), which finally turn into a decreased oxidative damage or an enhanced repair [45–51]. Several lines of evidence now attest that the antioxidant response is rapidly activated by oxidative stress during exhaustive exercise [30]. This is

Table 3 Epidemiological Studies Investigating the Relationship Between Exercise and Oxidative Stress in Athletes Markers of Markers of Antioxidant References Population Physical Activity Oxidative Stress Capacity

Aguilo´ et al. [30]

– N¼8 professional cyclists participating in the “Setmana Catalana 2000,” a 5-day competition for professional cyclists held near Barcelona (Spain)

A cycling mountain stage (171 km) of the “Setmana Catalana 2000”

– SOD activity in erythrocytes – CAT activity in erythrocytes – GPx-1 and -2 activities in erythrocytes – GR activities in erythrocytes – Vitamin E in plasma – Uric acid in plasma – Oxidized glutathione in plasma

Main Results

– The mountain cycling stage induced a significant increase (30%) in catalase activity which returned to basal values after 3 h of recovery – GPx-1 and -2 activity decreased significantly (11.3% and 17.6%, respectively) after the stage, but returned to basal levels after the 3 h of recovery – GR increased after stage – No changes were observed in SOD activity – Blood oxidized glutathione and serum uric acid rose after the stage – Plasma vitamin E increased after the stage but dropped to below basal values after 3 h of recovery Continued

Table 3 Epidemiological Studies Investigating the Relationship Between Exercise and Oxidative Stress in Athletes—cont’d Markers of Antioxidant Markers of Capacity Main Results References Population Physical Activity Oxidative Stress

Bloomer et al. [31]

N ¼ 131 (24 4 years) – N ¼ 89 men (74 exercise trained and 15 untrained) – N ¼ 42 women (22 exercise trained and 20 untrained)

Trained individuals: – PC in blood structured exercise for – MDA in blood a minimum of 3 h per – 8-OHdG in week for at least six blood consecutive months prior to participation Untrained individuals: no structured exercise during the 6 months prior to participation

Falone et al. [32]

– N ¼ 33 nonprofessional regular aerobic runners (4 1 h/week, 30 5 km/ week) – N ¼ 25 untrained sedentary individuals

A maximal treadmill – TBARS in test: seven stages serum (3 min each) with – PC in serum increasing speeds (2.74–8.05 km/h) and grades (0%–18%) of the treadmill

– PC did not differ significantly between trained men and women or between untrained men and women – Trained participants had significantly lower plasma PC (measured in nmol mg/ protein) (mean [SEM] 0.0966 [0.0055]) than did untrained participants (0.1036 [0.0098]) (P < 0.05) – MDA levels (measured in μmol/L), but not 8-OHdG, were significantly lower in trained women (0.4264 [0.0559]) compared with trained men (0.6959 [0.0593]) – TAC in serum

– Trained subjects showed significantly higher levels of total antioxidant capacity, with respect to untrained individuals (P < 0.05), in both pre- and postexercise samples

– Trained group exhibited significantly higher levels of lipid peroxidative damage, with respect to sedentary subjects (P < 0.001), in both pre- and postexercise samples – Trained individuals showed significantly lower PC, as compared to the untrained group (P < 0.05), in both pre- and postexercise samples Soares et al. [33]

– N ¼ 31 individuals submitted to 16 weeks of combined physical exercise training – N ¼ 26 individuals who did not undergo any physical activity

Exercise training over – MDA in plasma – TAC in plasma 16 weeks, with three – TBARS in sessions per week, of plasma 60–75 min, on nonconsecutive days. Each session was divided into three components: 25–30 min of aerobic exercise, 30–35 min of strength exercise, and 5–10 min of stretching and cool down

– Increase in antioxidant activity and decrease in lipid peroxidation levels after physical exercise training

Continued


Marin et al. [34]

– N ¼ 10 professional handball players (25 4.5 years, 95.3 9.8 kg, 187 6.6 cm, 11.3 3.1% body fat, 51.9 2.1 mL/ kg/min VO2max, 15 0.5 training hours per week)

Three 8-week training periods (T2, T3, and T4). The training loads increased incrementally in the preparatory phase to early-season (T1–T3) and decreased during the competitive phase (T4). The training sessions consisted of specific skills, speed, muscular power, agility, and endurance training common to handball game. The first to third training periods (T1–T3) included five sessions per week that lasted 2–3 h each. During the play-off period (T4), the athletes usually played two matches each week

– Ferric-reducing – TBARS in ability of plasma plasma and (FRAP assay) erythrocyte based on a – MDA in plasma single-electron – Intracellular transfer reaction O2• content – Intracellular between plasma hydrogen antioxidants and peroxide Fe3+ utilized as oxidant – SOD activity in erythrocytes – CAT activity in erythrocytes – GPx activity in erythrocytes – GR activity in erythrocytes

– Plasma TBARS levels increased 4.4-fold following T3, 3.2-fold following T4, in parallel to a marked decrease in plasma thiols groups in T3 (42%) and in T4 (47%) – Erythrocyte TBARS levels showed a transitory increase following T2 (85%), returned to baseline levels following T3, and had a significant reduction following T4 (79%) compared to T1 – Total erythrocyte superoxide dismutase activity increased 4-fold following T2, 8.5-fold following T3, and 14.7-fold following T4. Similarly, erythrocyte catalase was significantly increased 4.6fold following T2, 3.2-fold following T3, and 5.1-fold following T4. Erythrocyte

glutathione peroxidase increased 3.2-fold following T3, and 2.8-fold following T4 compared to T1. Erythrocyte glutathione reductase was significantly increased only after T4 (85%) – Lymphocytes from athletes showed a significant decrease in intracellular superoxide anion production only in T4 (43%) as compared to T1. Hydrogen peroxide production by lymphocytes demonstrated a biphasic response by decreasing following T2 (41%) and increasing following T3 (61%) and T4 (43%) compared to T1. Intracellular superoxide anion production by athlete’s neutrophils presented a significant decrease after T2 (26%) compared to T1. On the Continued

Table 3 Epidemiological Studies Investigating the Relationship Between Exercise and Oxidative Stress in Athletes—cont’d Markers of Markers of Antioxidant References Population Physical Activity Oxidative Stress Capacity Main Results

other hand, under baseline conditions (unstimulated cells) we found that neutrophils produced higher levels of intracellular superoxide anion following T3 (37%) and T4 (66%) than T1. There was a significant increase in hydrogen peroxide production by neutrophils following T3 (34%) and T4 (59%) compared to T1. Mrakic-Sposta – N ¼ 46 experienced ultra-marathon runners et al. [35] (males, age 45.04 8.75 years): 25 runners who finished the race (FR) and 21 runners who dropped out of the race

– ROS Mountain Tor des – Antioxidant production in Geants® capacity by ultra-marathon capillary blood electrochemistry (330 km trail-run in – 8-OHdG in in capillary blood Valle d’Aosta, capillary blood 24,000 m of positive – 8-isoPGF2α in and negative elevation capillary blood changes) Three test sessions for capillary blood and urine samples collection: the first

– Starting from not significantly different basal levels, the ROS production significantly increased at middle race (P < 0.01) in both the FR (1.87 0.18 vs 1.65 0.22 μmol/min) and the NFR (2.13 0.29 vs 1.62 0.28 μmol/min) groups. – In the FR, ROS level was found significantly

(prerace) was performed 1–2 days preceding the race in Courmayeur, the second (middle race, 148.7 km) at an almost intermediate point in Donnas, the last (postrace) immediately followed the end of the race in Courmayeur

increased at the end of the race with respect to both prerace (P < 0.0001, postrace level: 2.20 0.27 μmol/min) and middle race (P < 0.001) – In the FR group, with respect to the level measured at prerace (154.50 26.12 nW), this parameter results significantly increased at middle race (172.80 39.98 nW, P < 0.05), while decreased at postrace (144.30 32.26 nW, P < 0.001). At middle race, the parameters were found significantly increased (P < 0.05) in the NFR group too (pre- vs middle-race levels: 147.40 30.97 vs 179.00 30.17 nW) – No significant differences of 8-OHdG and 8-isoPGF2α were found between the Continued


levels calculated for the FR and NRF groups at prerace. At postrace in FR, both markers attain a significantly greater level (P < 0.05): 8-OHdG/creatinine (postvs prerace levels 6.32 2.38 vs 4.16 1.25 ng/mg) and for 8-isoPGF2α/creatinine (postvs prerace levels 1404.0 518.30 vs 822.51 448.91 pg/mg) Ferreira et al. [36]

– N ¼ 32 adult elite kayakers 4-min KE test consists – Lipoproteins of a 5-min free joint (total warm up, plus a cholesterol, 5-min warm up on a LDL, HDL, kayak-ergometer and with 40 W load, triglycerides) in followed by a 2-min plasma passive interval

– All lipoproteins concentrations increased post-KE test: total cholesterol (+16.52%), LDL cholesterol (+8.08%), HDL cholesterol (+24.29%), triglycerides (+27.04%), P < 0.05 for all

Park and Kwak [37]

– N ¼ 20 ET: competitive endurance athletes (i.e., distance runners, cyclists, triathletes)

– No significant differences in resting MDA values between ET, RT, and UT (0.79 0.8, 0.78 0.1, and 0.83 0.2 μmol/L, respectively)

Subjects warmed up – MDA in plasma – TAC in plasma on the treadmill at a – PC in plasma self-selected walk/jog pace at 3% elevation for 3 min. The treadmill grade was

– N ¼ 20 RT: resistance trained athletes (i.e., football players, power lifters), and – N ¼ 20 UT: untrained individuals, meaning they were not engaging in regular endurance or resistance training

then lowered and the subjects ran at a moderately fast self-selected pace. The treadmill elevation was increased 1.5% every minute until the subject indicated to stop the test

– Resting PC levels were not different in these distinct groups – Both MDA and PC levels significantly increased following GXT in UT but not in ET and RT (MDA vs ET, 0.79 0.8 μmol/L vs 0.78 1 μmol/L; MDA vs RT, 0.78 0.1 μmol/L vs 0.75 0.2 μmol/L; MDA vs UT, 0.83 0.2 μmol/L vs 0.98 1 μmol/L; PC vs ET, 0.45 0.3 nM/mg protein vs 0.50 0.2 nM/ mg protein; PC vs RT, 0.43 0.2 nM/mg protein vs 0.48 0.3 nM/mg protein; PC vs UT, 0.51 0.4 nM/mg protein vs 0.72 0.5 nM/mg protein, P < 0.05) – Resting TAC levels were not different in ET, RT, and UT – TAC levels significantly decreased following the GXT in all groups (TAC vs Continued


ET, 3.21 0.12 mM vs 1.24 0.22 mM; TAC vs RT: 3.38 0.13 mM vs 1.31 0.21 mM; TAC vs UT, 3.41 0.16 mM vs 0.81 0.24 mM, P < 0.05, respectively) – UT showed significantly lower post-TAC levels compared to ET and RT (1.24 0.22 mM vs 1.31 0.21 mM vs 0.81 0.24 mM, P < 0.05, respectively) Mena et al. [38] – Sedentary subjects, amateur bicycle racers, and professional bicycle racers

Exercise of 22 km in 5 h

– SOD, CAT, GPx – Under resting conditions activities in the SOD activity was higher erythrocytes (P < 0.01) in cyclists than in controls – The activities of CAT and GPx were higher (P < 0.05 and P < 0.01, respectively) under resting conditions in professional cyclists vs measured in both sedentary subjects and amateur cyclists

– The enzyme activities were not modified significantly in professional cyclists after a bout of exercise of 22 km in 5 h, but the SOD activity was increased (P < 0.05) and CAT activity reduced (P < 0.05) after 2800 km in 20 days Ypatios et al. [39]

– N ¼ 12 adult male runners (age 41.1 3.2 years)

Mountain – PC in plasma ultra-marathon race – TBARS in (103 km) named plasma “Olymphus Mythical – sORP in Trail 2015” plasma

– TAC in plasma – GSH levels in erythrocytes – CAT activity in erythrocytes – cORP in plasma

– sORP marker representing the current redox status increased significantly (P < 0.05) by 14.29% at 72 h postrace compared with prerace samples – cORP marker exhibited a significant decrease (P < 0.05) at 24 and 72 h postrace by 22.26% and 23.29%, respectively, compared with prerace samples – GSH levels in erythrocytes exhibited a significant decrease (P < 0.05) at 24, 48, and 72 h postrace by 26.78%, 29.04%, and Continued


23.19%, respectively compared with prerace – None of the other oxidative stress markers (i.e., PC, TBARS, TAC, CAT) exhibited a significant change (P < 0.05) at any time postrace Finaud et al. [40]

– N ¼ 17 professional rugby players from the same team playing in the top French professional rugby championship and in the top European professional rugby championship (age 27.0 3.4 years)

Measurements carried – Conjugated four times in 1 year dienes after periods of oxidation in different volumes and plasma intensities of training (T1–T4): – T1: during the beginning of the competitive period of the season – T2: during the most important period of competition

– Vitamin E in – Intense periods of training plasma and competition (T1 and – Uric acid in plasma T4) induced a significant – TAC in plasma higher maximum rate of – Lag phase in conjugated dienes oxidation plasma (+67.2% in T1 and +40.6% in T4) compared to those observed at the reference time (T3). – Intense periods of training and competition (T1 and T4) induced a significant increase in uric acid (+6.9% and 3.2%),

– T3: at the beginning of the season – T4: during the beginning of the competitive period of the season Palazzetti et al. [41]

– N ¼ 9 male triathletes – N ¼ 6 male sedentary subjects

Before and after a 4-week overloaded training, triathletes exercised for a duathlon

– Vitamin E (8.7% in T1) and lag phase (23.0% and 14.7%) were lower during these periods

– TBARS in plasma

– GSH/GSSG in – In rest conditions, blood overloaded training induced – SOD activity in plasma GPx activity increase erythrocytes and plasma TAS decrease – GPx in plasma and (both P < 0.05) erythrocytes – In exercise conditions, – TAS in plasma overloaded training resulted in higher exercise-induced variations of blood GSH/ GSSG ratio, TBARS level (both P < 0.05) and decreased TAS response (P < 0.05)

CAT, catalase; cORP, capacity oxidation–reduction potential; FR, runners who finished the race; GR, glutathione reductase; GSH, reduced glutathione; GSSG, oxidized glutathione; GPx, glutathione peroxidase; GXT, graded exercise test; 8-OHdG, 8-hydroxydeoxyguanosine; KE, kayak-ergometer; MDA, malondialdehyde; NFR, group of runners who dropped out of the race; PC, protein carbonylation; ROS, reactive oxygen species; SOD, superoxide dismutase; sORP, static oxidation-reduction potential; TAC, total antioxidant capacity; TAS, total antioxidant status; TBARS, thiobarbituric acid-reactive substances.

212

Elisa Danese et al.

mirrored by a notable increase (up to 30%) of catalase (CAT) activity, an antioxidant enzyme that converts hydrogen peroxide to water and oxygen. This effect, typically observed immediately after exhaustive exercise, is transitory, since the antioxidant activity was shown to return to baseline values after 3 h of recovery [30]. The oxidative stress status at rest is usually lower in athletes than in sedentary individuals [31,32]. Bloomer and Fisher-Wellman [31] measured protein carbonylation (PC) and malondialdehyde (MDA) in 89 men exercise trained and 15 untrained and 42 women (22 exercise trained and 20 untrained) at rest, showing that trained individuals had significantly lower plasma PC than untrained participants. Interestingly, the values of plasma MDA were also found to be significantly lower in trained women than in trained men [31]. Falone et al. measured the serum concentration of typical lipid peroxidation-related by-products and oxidatively damaged proteins (i.e., trolox-equivalent antioxidant capacity [TEAC], thiobarbituric acid-reactive substances [TBARS], and PC in 33 nonprofessional regular runners and 25 untrained sedentary individuals) [32]. In regularly trained individuals the concentration of TEAC and TBARS (i.e., total antioxidant capacity, TAC) as well as the degree of oxidative damage to the proteins (i.e., PC) was significantly higher compared to untrained individuals, both before and after maximal treadmill-based ergometric test. High-intensity exercise may trigger oxidative stress-induced DNA damage and may also be associated with inefficient DNA repair, thus potentially promoting DNA injury and mutations [52–54]. Unlike strenuous exercise, regular physical activity at moderate intensity produces an opposite effect, enhancing the expression of DNA repair genes [29], a mechanism likely attributable to upregulation of some DNA repair enzyme activity such as the oxoguanine DNA glycosylase [55]. Soares et al. measured an MDA and TAC in 31 individuals undergoing 16 weeks of combined physical exercise training and 26 resting subjects [33] and showed that physical exercise training was associated with increased antioxidant activity and decreased lipid peroxidation. Several studies showed that an increased generation of ROS and a decreased antioxidant capacity are frequently observed during or after ultra-endurance muscle activity (Table 3). Palazzetti et al. studied nine overloaded triathletes exercising for a duathlon and six male sedentary subjects [41] and observed that overloaded training caused an exerciseinduced increase of reduced vs oxidized glutathione (GSH/GSSG) ratio,


213

TBARS and CK-MB, whereas total antioxidant status (TAS) was found to be globally decreased. Finaud et al. assessed oxidative stress by means of conjugated dienes oxidation and antioxidant status (vitamin E, uric acid, TAC, and lag phase) in 17 professional rugby players throughout a competitive season [40] and reported that the oxidative stress was substantially higher during intense periods of training and competition than in those of less intensive physical activity. Marin et al. studied 10 male elite handball athletes who were monitored four times every 6 weeks throughout the competitive season [34] and demonstrated that plasma indices of oxidative stress (as reflected by increased TBARS and decreased thiol values) were significantly increased during the more intense periods of training and competitions. Conversely, a decrease in TBARS and PC and an increase of erythrocyte antioxidant enzyme activities were observed during exercise training. Mrakic-Sposta et al. studied 25 athletes performing the mountain Tor des Geants® ultra-marathon (330 km trail-run in Valle d’Aosta, 24,000 m of positive and negative elevation changes) [35] and reported a significant postrace increase of ROS production rate and oxidative damage biomarkers (mirrored by an increase of 8-OHdG and 8-isoPGF2α), along with a reduction of antioxidant capacity. The same research group investigated the effects on oxidant–antioxidant balance of a 6-week high-intensity discontinuous training (HIDT) characterized by repeated variations of intensity and variations of redox potential in 16 master swimmers [56], observing that HIDT increased peak oxygen consumption by 11% and antioxidant capacity by 13%, while ROS production was found to be significantly decreased both at rest (20%) and after incremental arm-ergometer exercise (25%). Lipid peroxidation markers were measured by Ferreira et al. in 32 adult elite kayakers in response to a maximum 4-min work paddling on an ergometer with maximum muscle power throughout the test [36]. The concentration of all atherogenic lipoproteins was found to be increased after intensive exercise, whereas that of uric acid, a well-known antioxidant endogenous compound [57], was found to be significantly decreased. More recently, Ypatios et al. studied 12 athletes performing a 103 km ultra-marathon mountain race [39]. Several traditional biomarkers such as PC, TBARS, and TAC in plasma, as well as GSH levels and CAT activity in erythrocytes, and the two novel markers static oxidation–reduction potential marker (sORP) and capacity oxidation–reduction potential (cORP) were measured in plasma pre- and 24, 48, and 72 h postrace, concluding that oxidative stress was enhanced, and remained so for up to 3 days after strenuous exercise.

214

Elisa Danese et al.

Interestingly, no major differences seemingly exist between aerobic and anaerobic exercise on oxidative stress and antioxidant defense. Park and Kwak measured resting and postexercise plasma MDA and PC in 60 healthy young males comprising aerobically trained athletes (i.e., distance runners, cyclists, triathletes), anaerobically trained athletes (i.e., football players, power lifters), and untrained individuals [37], failing to find statistically significant differences between the differently trained groups. Interestingly, MDA and PC were significantly increased following a graded exercise test in untrained individuals, whereas the total antioxidant capacity decreased after graded exercise test in both trained and untrained groups. Additional studies have suggested that both aerobic [38] and anaerobic endurance training may significantly increase the antioxidant capacity of trained or untrained individuals. In particular, Mena et al. observed an increase in the erythrocyte activities of the main free radical scavenger enzymes, superoxide dismutase (SOD), CAT, and glutathione peroxidase (GPx) under resting conditions in professional bicycle racers. Only one study by Selamoglu et al. reported a different effect of aerobic and anaerobic exercise on oxidative/antioxidative status to the best of our knowledge [58]. The authors observed that aerobic training increased erythrocytes GPx activity with a subsequent decrease in plasma TBARS levels, whereas anaerobic training had no significant effect.

3. CONSIDERATIONS ABOUT THE METHODS USED FOR ASSESSING DNA INJURY A number of studies have investigated the influence of exercise on DNA damage so far, but the heterogeneity in the experimental protocols, in the samples matrices, and in the methods used precludes pooling the data. The currently available scientific literature is mainly based on the effect of habitual exercise or low- or moderate-intensity exercise on different parameters of DNA damage, assuming (with little doubts) that exhaustive high-intensity endurance exercises triggers DNA damage. Therefore, we present here a summary of published studies, which have evaluated the effect of ultra-endurance and endurance competitive exercise on DNA damage. We will especially focus on studies using the SCGE assay (single-cell gel electrophoresis) and 8-hydroxy-20-deoxyguanosine (8-OHdG) measurement as reference techniques.


215

3.1 The COMET Assay The SCGE assay is a simple, rapid, and sensitive method for measuring DNA-strand breaks in eukaryotic cells [59]. Since its original description in the 1980s [60,61], the method has been modified to detect a variety of other biochemical injuries, such as oxidized bases. Therefore, it is now widely used for monitoring occupational exposure to mutagens, testing dietary supplementation with antioxidants, or checking levels of oxidative stress in relation to diverse diseases. The key principle of the method is based on migration of damaged DNA in an electrical field, forming comet-shaped images (hence the nickname “COMET” assay). The relative amount of DNA in the tail represents the frequency of DNA-strand breaks [62]. The standard version is performed under alkaline conditions, enabling detection of DNA single- and double-strand breaks (SBs) and apurinic sites (APs). In implemented versions, the use of injury-specific enzymes endonuclease III (ENDO III) and formamidopyrimidine glycosylase (FPG) allows detecting oxidized purines and pyrimidines, respectively [63]. Results can be reported as tail lengths of the comets, percentage of DNA in tail (%tDNA), and tail moment. Despite the increasing use of the COMET assay in biomonitoring studies, and albeit some authors proposed the value of %tDNA as the most suitable primary end-point, no consensus has been reached as yet on the best parameter accurately reflecting the extent of DNA damage [64]. Tail length is considered informative only at relatively low damage levels, since it reaches a plateau relatively quickly, so that it cannot be used for biomonitoring purposes. Tail moment, an index entailing both migration of genetic material and relative amount of tail DNA, can be estimated in many ways. The most widely used is the Olive tail moment which, expressed as the product of % tail DNA and tail length, is plagued by a substantial bias attributable to assessment of the end of the tail with different image analysis systems [65]. Tail moment is also measured in arbitrary units, which makes results barely comparable across studies. The %tDNA, namely tail DNA or tail intensity, is linearly related to DNA damage over a wide range of damage (from 0% to 100%) and also depends on DNA break frequency. Furthermore, this parameter is less variable across studies because it provides an immediate, unambiguous, and objective indication of comet appearances [66]. For all these reasons, %tDNA is increasingly regarded as the preferred metric of DNA-strand breakage in the COMET assay [67]. However, the %tDNA can be calculated using different fluorescent dyes and can be excited by different light sources with different intensities, and the images

216

Elisa Danese et al.

can be captured and analyzed using different image analysis systems and a different software. Therefore, the comparison of results across different studies is extremely challenging. Finally, attention should be paid to the statistical methods used by different research groups for the analysis of COMET data, since there is no simple statistical/mathematical distribution that would explain the observed distributions, thus making rather intricate a statistical analysis using individual cell scores. Recently, a range of parametric tests including T-tests and ANOVA and their nonparametric equivalents have been considered appropriate for analyzing single experiments [65]. Although the low throughput and the laborious sample workup have been for long considered the major drawbacks of the COMET assay, some recent efforts have been made to automate this technique. More specifically, Karbaschi and Cooke have developed an innovative approach entailing multiple conventional slide processing, followed by batch electrophoresis (i.e., 25 samples at the same time) [68]. This innovation has enabled to reduce the turnaround time of the assay by 60%, also ensuring a major quality of the test due to the use of smaller footprint, more uniform orientation of gels during electrophoresis, and lower risk of damage to the slides.

3.2 8-Hydroxy-20-Deoxyguanosine The 8-OHdG is currently regarded as the most commonly measured index of oxidative DNA damage. This compound is generated by oxidative modification of guanine and can be assessed both in urine and in leukocytes. The measured urinary 8-OHdG is thought to be the result of repair of DNA lesions, excretion into the plasma and subsequently into urine [54]. Therefore, although urinary 8-OHdG is widely accepted as marker of “whole-body” oxidative DNA damage, it does not necessarily reflect the steady state of unrepaired DNA damage nor is specific for DNA damage in leukocytes. Karpouzi et al. recently confirmed that plasma measurement appears to be more sensitive to exercise-induced 8-OHdG changes than urine assessment, thus representing a more appropriate medium for assessing oxidative DNA damage [69]. Many analytical methods have been developed to measure 8-OHdG. Both high-performance liquid chromatographyelectrochemical detection (HPLC-ECD) and gas chromatography–mass spectrometry (GC–MS) are recommended for measuring modified bases. Other available methods include the HPLC-tandem MS, 32P-postlabeling, capillary electrophoresis (CE), enzyme-linked immunosorbent assays (ELISAs), and cell-based indirect immunofluorescence. Particular attention


217

should be given in interpretation of 8-OHdG values due to methodological drawbacks and discrepancies among the aforementioned heterogeneous approaches. Notably, it was also emphasized that ELISA 8-OHdG is prone to interference from high molecular weight compounds found in plasma and serum, thus potentially generating biased results.

3.3 Phosphorylation of Histone Protein H2AX The onset of DNA double-strand breaks is associated with detrimental consequences on cell survival and genomic stability. Among the various indices of DNA double-strand breaks, the phosphorylation of histone protein H2AX at Ser 139 (i.e., γ-H2AX) was shown to be profuse, rapid, and strongly related to DNA injury, so that its assessment has been proposed for identifying the potential DNA injury due to a kaleidoscope of various exogenous or exogenous causes along with the DNA repair [70]. The many parameters γ-H2AX foci analysis can then be objectively assessed using a pattern recognition algorithm implemented in an automated fluorescence interpretation system [71].

4. PHYSICAL EXERCISE AND DNA INJURY Four research groups used the COMET assay for investigating the relationship between competitive ultra-endurance and DNA injury [72–75], whereas five research groups used this same assay for assessing DNA injury after endurance exercise [76–80] (Table 4).

4.1 Clinical Studies Using the COMET Assay From the four studies on competitive ultra-endurance exercise, it appears clear that, when applying the standard SCGE method and %tDNA parameter evaluation, the number of DNA-strand breaks increases at some time points during or soon after the race, then returning to baseline values few days postrace. This kinetics, quite consistent across the different studies, seemingly indicates that DNA may be actively injured by strenuous exercise, but the DNA damage would not persist for long (i.e., less than 72 h) after strenuous prolonged exercise. Unlike these findings, contradictory data emerged from the three studies evaluating the effect of the Ironman triathlon race on the ENDO III-sensitive sites and the FPG-sensitive sites. Studies on competitive endurance exercise are unfortunately less homogeneous with

Table 4 Studies Investigating the Effect of Competitive Exercise on DNA Damage by COMET Assay Reference Experimental Protocol Subjects Matrix End-Point Parameters

Main Results

Competitive ultra-endurance exercise (>4 h)

Mastaloudis Ultra-marathon (50 km) et al. [72]

Reichhold et al. [73]

Neubaeur et al. [74]

Ironman triathlon race (3.8 km swim, 180 km cycle, 42 km run)

Ironman triathlon race (3.8 km swim, 180 km cycle, 42 km run)

%tDNA, proportion of cells (of 50) with tail moment

% DNA: " at mid-race Proportion of cell with tail moment: " at mid-race; back to baseline 2 h after race

Lymph SBs, AP

%tDNA

" 1 day after race; back to initial levels 5 days after race

Lymph FPG-ss

%tDNA

$

Lymph ENDO III-ss

%tDNA

" 5 days after race compared to 1 day after race; back to initial levels 19 days after race

Lymph SBs, AP

%tDNA

# immediately after the race " 1 day postrace Back to basal levels 5 days after the race

Lymph FPG-ss

%tDNA

$

Lymph ENDO III-ss

%tDNA

$

22 runners (11 M, 11 F)

Leu

28 well-trained triathletes (M)

42a nonprofessional, well-trained triathletes (M)

SBs, AP

Wagner et al. [75]

Ironman triathlon (3.8 km 28b nonprofessional swimming, 180 km well-trained cycling, and 42 km triathletes (M) running)

Lymph SBs, AP

%tDNA

# immediately after the race, " 1 day postrace # 5 days after the race until 19 days

Lymph FPG-ss

%tDNA

# immediately after the race, " 1 day after the race until 19 days after the race

Lymph ENDO III-ss

%tDNA

# immediately after the race, " 5 days postrace (compared to 1 day after the race) and # until 19 days postrace

Leu

SBs, AP

Tail moment

" 1 day until 5 days after race

Leu

FPG-ss

Tail moment

$ immediately after race

SBs, AP

Tail length

" 1 day after race

Competitive endurance exercise

Hartmann et al. [76]

Short-distance triathlon Six trained (3 M, 3 F) competition (1.5 km swim, 40 km cycle, 10 km run)

Niess et al. [77]

Half-marathon (21.1 km)

12 moderate trained (M)

Leu

Tsai et al. [78]

Marathon (42 km)

14 runners (M)

PBMC SBs, AP

%tDNA

" 1 day after race; still " 14 days after race

PBMC FPG-ss

%tDNA

" immediately until 24 h after race

PBMC ENDO III-ss

%tDNA

" immediately after race; still " 14 days after race Continued

Table 4 Studies Investigating the Effect of Competitive Exercise on DNA Damage by COMET Assay—cont’d Reference Experimental Protocol Subjects Matrix End-Point Parameters

Briviba et al. [79]

Ryu et al. [80]

a

Half-marathon (21.1 km), marathon (42.2 km)

%tDNA

$ immediately after races

% tDNA

$ immediately after races

Lymph ENDO III-ss

%tDNA

" after races

Lymph SBs

%tDNA, tail length, tail moment

%tDNA and tail length $ Tail moment " after the races. Tail moment at post42.2 km is higher than those post-10 km and post-21 km

10 well-trained Lymph SBs, AP half-marathon hobby runners (5 M, 5 F) 12 well-trained marathon hobby runners (10 M, 2 F) Lymph FPG-ss

10 km, 21 km, and 42.2 km 30 male amateur runners races randomly assigned to 10 km, 21 km, and 42.2 km groups

Main Results

Out of the entire study group (48 subjects) the SCGE was performed for 42 participants. Out of the entire study group (48 subjects) the SCGE was performed for 28 participants. AP, apurinic/apyrimidinic sites; %tDNA, percentage of DNA in tail; ENDO III-ss, endonuclease III-sensitive sites; F, female; FPG-ss, glycosylase-sensitive sites; Leu, leucocyte; lymph, lymphocyte; M, male; PBMC, peripheral blood mononuclear cells; SBs, strand breaks. Effects are described as no significant change ($), significant increase ("), and significant decrease (#). b


221

respect to the matrix and the parameters evaluated by different authors. Two studies performed the COMET assay on total leukocytes, two others on lymphocytes, and the latter on peripheral blood mononuclear cells. Similarity, two studies reported results as tail moment, two other as %tDNA, one as tail length, and one as both %tDNA and tail moment. Regardless of the different approaches used to assess the DNA damage, all these studies (Table 4) found an increase of at least one end-point measure immediately or 1 day after the race, thus clearly indicating the existence of some effects of strenuous and competitive endurance exercise on DNA damage.

4.2 Clinical Studies Using 8-Hydroxy-20-Deoxyguanosine The five studies evaluating competitive exercise-induced DNA damage by means of 8-OHdG measurement are summarized in Table 5 [76,78,81–83]. Since a relationship between oxygen consumption and urinary excretion of 8-OHdG has been suggested [84], concomitantly increased values of this modified base in the urine of athletes after strenuous exercise are predictable. However, this assumption has not been completely supported by experimental data so far. Although four out of five studies described a consistent increase of oxidized base after competitive strenuous exercise, a clear conclusion cannot be reached due to methodological limitations. Major issues raising from data comparison are as follows: (i) only two out of five studies measured the 8-OHdG levels through a gold standard method (i.e., HPLC-ECD); (ii) no one study evaluated the marker in the matrix of choice (i.e., serum or plasma); (iii) each study reported a different timing for urine collection; and (iv) three out of five studies corrected the levels of 8-OHdG for creatinine excretion. As regards the last issue, it has been suggested that the use of creatinine correction method should be avoided in sport medicine, due to the well-known alteration of creatinine clearance during exercise [85–87].

4.3 Clinical Studies Using Phosphorylation of Histone Protein H2AX Two recent studies independently measured γ-H2AX foci after physical exercise. In the former investigation, Heydenreich et al. studied eight moderately trained healthy male athletes, whose blood was collected in two separate days after a mean period of 24 min of vigorous physical activity or exercise training [88]. Despite the leading drawback of the study, i.e., the lack of a baseline measurement of DNA double-strand breaks (preexercise

Table 5 Studies Investigating the Effect of Competitive Exercise on DNA Damage by Measuring 8-Hydroxy-20-Deoxyguanosine (8-OHdG) Measure Reference Experimental Protocol Subjects Matrix Method Units Main Results Competitive ultra-endurance exercise (>4 h)

" after the first race (day 1); back to initial levels after the fourth race (day 4)

Rada´k et al. [81]

4 days supramarathon (93, 120, 56, and 59 km; road running)

Five well-trained athletes (M)

Miyata et al. [82]

Ultra-marathon (2 days) (90 km)

95 nonprofessional Urine HPLC-ECD μg/g " at mid-race (1st day, well-trained (79 M, (spot) creatinine 40 km); back to initial levels 16 F) after race (2nd day, 90 km)

Urine ELISA (12 h)

ng/mL

Competitive endurance exercise

Hartmann et al. [76]

Short-distance triathlon competition (1.5 km swim, 40 km cycle, 10 km run)

Six trained (3 M, 3 F)

Urine HPLC-ECD μmol/ $ 1 day until 4 days after race (24 h) mol creatinine

Tsai et al. [78]

Marathon (42 km)

14 runners (M)

Urine ELISA (8 h)

μmol/ " immediately after race; still mol " 14 days after race creatinine

Eight heptathlon athletes (F)

Urine ELISA (spot)

nmol/L

Samia and Youssef [83] Day 1: 100 m run, high jump, shot put, and 200 m run Day 2: long jump, javelin throw, and 800 m run

" after both days of competition

ELISA, enzyme-linked immunoassay; F, female; HPLC-ECD, high-performance liquid chromatography with electrochemical detection; M, male. Effects are described as no significant change ($), significant increase ("), and significant decrease (#).


223

value), the γ-H2AX foci were found to be considerably higher compared to those conventionally observed at rest. In the latter study, Lippi et al. investigated γ-H2AX foci in 15 adults and trained athletes performing four separate trials characterized by increasing running distance (5, 10, 21, and 42 km) [89]. Interestingly, all values of the γ-H2AX foci were found to be significantly increased at the end of all trials, with differences paralleling the increase of running distance. More specifically, a minor increase of DNA double-strand breaks was observed at the end of the 5- and 10-km trials, whereas the increase occurring after longer running distances (i.e., 21 and 42 km) was considerably higher. Even more importantly, the increase of the different γ-H2AX foci parameters was found to be independently associated with both running distance and average intensity, thus underscoring that the intensity of physical exercise may be a major determinant of the extent of DNA injury in elite athletes.

5. CONCLUSIONS It is conventionally acknowledged that regular moderate physical activity reduces the risk of developing many diseases and is now part of nonpharmacological therapy and lifestyle changes especially for the effective therapy of cardiovascular disease [90]. However, several lines of evidence also attest that strenuous physical exercise may enhance inflammation and trigger the generation of free radical-mediated damage, thus overwhelming the undisputable benefits of regular, medium-intensity physical activity. As specifically regards the potential DNA damage consequent to strenuous or exhaustive physical exercise, uncertainty still looms for data interpretation due to heterogeneity of the study populations, sample size, analytical techniques, type and intensity of physical exercise, and type of DNA alterations [91,92]. In particular, the use of a wide range of methods for quantification of exercise-induced DNA damage has made it difficult to compare results among different studies. As specifically regards the analytical techniques, the most frequently used methods for detection of DNA damage during and after physical activity include SCGE (i.e., COMET) assay, micronucleus (MN) assay, and its implemented version, along with the cytokinesis-block micronucleus (CBMN) assay. Some assays have also been developed to quantify the DNA-based oxidation product 8-OHdG in plasma and urine. Recently, the analysis of γ-H2AX has emerged as an innovative and promising approach to accurately measure DNA double-strand breaks [70] and has been successfully used to evaluate exercise-induced

224

Elisa Danese et al.

DNA damage by two independent groups, which showed rather comparable results. Irrespective of these limitations, few doubts remain that medium- to high-volume exercise can trigger a certain amount of DNA injury and release of free DNA into the circulation [93], which appear to be transitory and directly proportional to exercise intensity (Fig. 1). This damage, reasonably attributable to the direct effects of ROS on double-strand DNA, is efficiently repaired in vivo within 24–72 h. Therefore, high-intensity exercise should not seemingly bear long-term consequences for athlete’s health, provided that an appropriate time of recovery is set. Intuitively appealing through this hypothesis, repeated bulks of exhaustive exercise, without appropriate periods of recovery, may overwhelm the activity of the endogenous antioxidant systems and the efficiency of DNA repair armamentarium (Fig. 1). Support to this assumption comes from recent evidence that the oxidative damage in elite athletes can be substantially attenuated by supplementation with antioxidant substances [94].

DNA injury

DNA injury

Adequate recovery Complete DNA repair Adequate recovery Complete DNA repair DNA injury

DNA injury

Double strain breaks

DNA injury

Inadequate recovery Partial DNA repair

Inadequate recovery Partial DNA repair

Fig. 1 Influence of high-volume physical exercise and recovery on DNA damage.


225

Therefore, in agreement with the ancient Latin mantra that “in medio stat virtus,” we can conclude that high-intensity exercise may trigger a higher oxidative stress-induced damage than moderate physical exercise, and it should always be accompanied by appropriate periods of recovery to enable repair of oxidative injury on nucleic acids and other important biological structures. Regular exertion, with a step-by-step increase of exercise load, also seems the most safe approach for eluding DNA instability. Conflict of interest: All authors declare no conflict of interest.

REFERENCES [1] C.J. Caspersen, K.E. Powell, G.M. Christenson, Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research, Public Health Rep. 100 (1985) 126–131. [2] F. Sanchis-Gomar, C. Fiuza-Luces, A. Lucia, Exercise as the master polypill of the 21st century for the prevention of cardiovascular disease, Int. J. Cardiol. 181 (2015) 360–361. [3] C. Fiuza-Luces, N. Garatachea, N.A. Berger, A. Lucia, Exercise is the real polypill, Physiology (Bethesda) 5 (2013) 330–358. [4] W.L. Haskell, I.M. Lee, R.R. Pate, K.E. Powell, S.N. Blair, B.A. Franklin, et al., Physical activity and public health: updated recommendation for adults from the American college of sports medicine and the American heart association, Circulation 116 (2007) 1081–1093. [5] A. Santos-Lozano, H. Pareja-Galeano, F. Sanchis-Gomar, M. Quindo´s-Rubial, C. Fiuza-Luces, C. Cristi-Montero, et al., Physical activity and Alzheimer disease: a protective association, Mayo Clin. Proc. 91 (2016) 999–1020. [6] F. Sanchis-Gomar, A. Lucia, T. Yvert, A. Ruiz-Casado, H. Pareja-Galeano, A. Santos-Lozano, et al., Physical inactivity and low fitness deserve more attention to alter cancer risk and prognosis, Cancer Prev. Res. (Phila.) 8 (2015) 105–110. [7] American Diabetes Association, Foundations of care and comprehensive medical evaluation, Diabetes Care 39 (Suppl. 1) (2016) S23–S35. [8] American Diabetes Association, Prevention or delay of type 2 diabetes, Diabetes Care 39 (Suppl. 1) (2016) S36–S38. [9] N. Montero-Fernańdez, J.A. Serra-Rexach, Role of exercise on sarcopenia in the elderly, Eur. J. Phys. Rehabil. Med. 49 (2013) 131–143. [10] N. Garatachea, H. Pareja-Galeano, F. Sanchis-Gomar, A. Santos-Lozano, C. Fiuza-Luces, M. Morań, et al., Exercise attenuates the major hallmarks of aging, Rejuvenation Res. 18 (2015) 57–89. [11] J.J. Armstrong, I.B. Rodrigues, T. Wasiuta, J.C. MacDermid, Quality assessment of osteoporosis clinical practice guidelines for physical activity and safe movement: an AGREE II appraisal, Arch. Osteoporos. 11 (2016) 6. [12] M.K. Karlsson, A. Nordqvist, C. Karlsson, Physical activity, muscle function, falls and fractures, Food Nutr. Res. 52 (2008). [13] H. L€ ollgen, A. B€ ockenhoff, G. Knapp, Physical activity and all-cause mortality: an updated meta-analysis with different intensity categories, Int. J. Sports Med. 30 (2009) 213–224.

226

Elisa Danese et al.

[14] Centers for Disease Control and Prevention, Physical Activity and Health, 2016. Available at: https://www.cdc.gov/physicalactivity/basics/pa-health/. Last access: 19 August. [15] World Health Organization, Physical Activity and Adults, 2016. Last access: 19 August. Available at: http://www.who.int/dietphysicalactivity/factsheet_adults/en/. [16] Canadian Society for Exercise Physiology, Canadian Physical Activity Guidelines for Adults, 2016. Available at: http://www.csep.ca/CMFIles/Guidelines/CSEP_ PAGuidelines_adults_en.pdf. Last access: 19 August. [17] J. Myers, Cardiology patient pages. Exercise and cardiovascular health, Circulation 107 (2003) e2–e5. [18] S. Lemez, J. Baker, Do elite athletes live longer? A systematic review of mortality and longevity in elite athletes, Sports Med. Open 1 (2015) 16. [19] N. Garatachea, A. Santos-Lozano, F. Sanchis-Gomar, C. Fiuza-Luces, H. Pareja-Galeano, E. Emanuele, A. Lucia, Elite athletes live longer than the general population: a meta-analysis, Mayo Clin. Proc. 89 (2014) 1195–1200. [20] C.E. Garber, B. Blissmer, M.R. Deschenes, B.A. Franklin, M.J. Lamonte, I.M. Lee, et al., Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise, Med. Sci. Sports Exerc. 43 (2011) 1334–1359. [21] G. Lippi, N. Maffulli, Biological influence of physical exercise on hemostasis, Semin. Thromb. Hemost. 35 (2009) 269–276. [22] S.K. Powers, M.J. Jackson, Exercise-induced oxidative stress: cellular mechanisms and impact on muscle force production, Physiol. Rev. 88 (2008) 1243–1276. [23] A. Barzilai, K. Yamamoto, DNA damage responses to oxidative stress, DNA Repair (Amst.) 3 (2004) 1109–1115. [24] Z. Radak, Z. Zhao, E. Koltai, H. Ohno, M. Atalay, Oxygen consumption and usage during physical exercise: the balance between oxidative stress and ROS-dependent adaptive signaling, Antioxid. Redox Signal. 18 (2013) 1208–1246. [25] C. Leeuwenburgh, J.W. Heinecke, Oxidative stress and antioxidants in exercise, Curr. Med. Chem. 8 (2001) 829–838. [26] L. Packer, E. Cadenas, K.J.A. Davies, Free radicals and exercise: an introduction, Free Radic. Biol. Med. 44 (2008) 123–125. [27] K. Bedard, K.H. Krause, The NOX family of ROS-generating NADPH oxidases: physiology and pathophysiology, Physiol. Rev. 87 (2007) 245–313. [28] J. Vinã, M.C. Gomez-Cabrera, A. Lloret, R. Marquez, J.B. Minãna, F.V. Pallardo´, et al., Free radicals in exhaustive physical exercise: mechanism of production, and protection by antioxidants, IUBMB Life 50 (2000) 271–277. [29] S.S. Sachdev, K.J.A. Davies, Production, detection, and adaptive responses to free radicals in exercise, Free Radic. Biol. Med. 44 (2008) 215–223. [30] A. Aguilo´, P. Tauler, E. Fuentespina, J.A. Tur, A. Co´rdova, A. Pons, Antioxidant response to oxidative stress induced by exhaustive exercise, Physiol. Behav. 84 (2005) 1–7. [31] R.J. Bloomer, K.H. Fisher-Wellman, Blood oxidative stress biomarkers: influence of sex, exercise training status, and dietary intake, Gend. Med. 5 (2008) 218–228. [32] S. Falone, A. Mirabilio, A. Pennelli, M. Cacchio, A. Di Baldassarre, S. Gallina, et al., Differential impact of acute bout of exercise on redox- and oxidative damage-related profiles between untrained subjects and amateur runners, Physiol. Res. 59 (2010) 953–961. [33] J.P. Soares, A.M. Silva, M.M. Oliveira, F. Peixoto, I. Gaivaõ, M.P. Mota, Effects of combined physical exercise training on DNA damage and repair capacity: role of oxidative stress changes, Age (Dordr.) 37 (2015) 9799. [34] D.P. Marin, A.P. Bolin, T.R. Campoio, B.A. Guerra, R. Otton, Oxidative stress and antioxidant status response of handball athletes: implications for sport training monitoring, Int. Immunopharmacol. 17 (2013) 462–470.


227

[35] S. Mrakic-Sposta, M. Gussoni, S. Moretti, L. Pratali, G. Giardini, P. Tacchini, et al., Effects of mountain ultra-marathon running on ROS production and oxidative damage by micro-invasive analytic techniques, PLoS One 10 (2015) e0141780. [36] H.R. Ferreira, P.G. Ferreira, J.P. Loures, J. Fernandes Filho, L.C. Fernandes, H.S. Buck, et al., Acute oxidative effect and muscle damage after a maximum 4 min test in high performance athletes, PLoS One 11 (2016) e0153709. [37] S.Y. Park, Y.S. Kwak, Impact of aerobic and anaerobic exercise training on oxidative stress and antioxidant defense in athletes, J. Exerc. Rehabil. 12 (2016) 113–117. [38] P. Mena, M. Maynar, J.M. Gutierrez, J. Maynar, J. Timon, J.E. Campillo, Erythrocyte free radical scavenger enzymes in bicycle professional racers. Adaptation to training, Int. J. Sports Med. 12 (1991) 563–566. [39] S. Ypatios, S. Dimitrios, O. Marina, G. Nikolaos, B.O. David, S. Demetrios, et al., Variations in oxidative stress levels in three days follow-up in ultra-marathon mountain race athletes, J. Strength Cond. Res. (2016), http://dx.doi.org/10.1519/ JSC.0000000000001584. Jul 25 [Epub ahead of print]. [40] J. Finaud, V. Scislowski, G. Lac, D. Durand, H. Vidalin, A. Robert, et al., Antioxidant status and oxidative stress in professional rugby players: evolution throughout a season, Int. J. Sports Med. 27 (2006) 87–93. [41] S. Palazzetti, M.J. Richard, A. Favier, I. Margaritis, Overloaded training increases exercise-induced oxidative stress and damage, Can. J. Appl. Physiol. 28 (2003) 588–604. [42] S. Lee, M. Kim, W. Lim, T. Kim, C. Kang, Strenuous exercise induces mitochondrial damage in skeletal muscle of old mice, Biochem. Biophys. Res. Commun. 461 (2015) 354–360. [43] F. Sanchis-Gomar, J.L. Garcia-Gimenez, C. Perez-Quilis, M.C. Gomez-Cabrera, F.V. Pallardo, G. Lippi, Physical exercise as an epigenetic modulator: eustress, the “positive stress” as an effector of gene expression, J. Strength Cond. Res. 26 (2012) 3469–3472. [44] F. Sanchis-Gomar, H. Pareja-Galeano, M.C. Gomez-Cabrera, J. Candel, G. Lippi, G.L. Salvagno, et al., Allopurinol prevents cardiac and skeletal muscle damage in professional soccer players, Scand. J. Med. Sci. Sports 25 (2015) e110–e115. [45] Z. Radak, H.Y. Chung, S. Goto, Systemic adaptation to oxidative challenge induced by regular exercise, Free Radic. Biol. Med. 44 (2008) 153–159. [46] M.J. Jackson, A. McArdle, Role of reactive oxygen species in age-related neuromuscular deficits, J. Physiol. 594 (2016) 1979–1988. [47] M.C. Gomez-Cabrera, E. Domenech, J. Vina, Moderate exercise is an antioxidant: upregulation of antioxidant genes by training, Free Radic. Biol. Med. 44 (2008) 126–131. [48] A. Boccatonda, R. Tripaldi, G. Davı`, F. Santilli, Oxidative stress modulation through habitual physical activity, Curr. Pharm. Des. 22 (2016) 3648–3680. [49] P. Steinbacher, P. Eckl, Impact of oxidative stress on exercising skeletal muscle, Biomolecules 5 (2015) 356–377. [50] L.L. Ji, Modulation of skeletal muscle antioxidant defense by exercise: role of redox signaling, Free Radic. Biol. Med. 44 (2008) 142–152. [51] V. Coffey, J. Hawley, The molecular bases of training adaptation, Sports Med. 37 (2007) 737–763. [52] H. Poulsen, Oxidative DNA modifications, Exp. Toxicol. Pathol. 57 (2005) 161–169. [53] W. Knez, J. Coombes, D. Jenkins, Ultra-endurance exercise and oxidative damage: implications for cardiovascular health, Sports Med. 36 (2006) 429–441. [54] H. Poulsen, A. Weimann, S. Loft, Methods to detect DNA damage by free radicals: relation to exercise, Proc. Nutr. Soc. 58 (1999) 1007–1014. [55] Z. Radak, P. Apor, J. Pucsok, I. Berkes, H. Ogonovszky, G. Pavlik, et al., Marathon running alters the DNA base excision repair in human skeletal muscle, Life Sci. 72 (2003) 1627–1633.

228

Elisa Danese et al.

[56] S. Mrakic-Sposta, M. Gussoni, S. Porcelli, L. Pugliese, G. Pavei, G. Bellistri, et al., Training effects on ROS production determined by electron paramagnetic resonance in master swimmers, Oxid. Med. Cell. Longev. 2015 (2015) 804794. [57] G. Lippi, M. Montagnana, M. Franchini, E.J. Favaloro, G. Targher, The paradoxical relationship between serum uric acid and cardiovascular disease, Clin. Chim. Acta 392 (2008) 1–7. [58] S. Selamoglu, F. Turgay, B.M. Kayatekin, S. G€ onenc, C. Yslegen, Aerobic and anaerobic training effects on the antioxidant enzymes of the blood, Acta Physiol. Hung. 87 (2000) 267–273. [59] G.W. Davison, Exercise and oxidative damage in nucleoid DNA quantified using single cell gel electrophoresis: present and future application, Front. Physiol. 7 (2016) 249. [60] O. Ostling, K.J. Johanson, Microelectrophoretic study of radiation-induced DNA damages in individual mammalian cells, Biochem. Biophys. Res. Commun. 123 (1984) 291–298. [61] N.P. Singh, M.T. McCoy, R.R. Tice, E.L. Schneider, A simple technique for quantitation of low levels of DNA damage in individual cells, Exp. Cell Res. 175 (1988) 184–191. [62] R.R. Tice, E. Agurell, D. Anderson, B. Burlinson, A. Hartmann, H. Kobayashi, et al., Single cell gel/comet assay: guidelines for in vitro and in vivo genetic toxicology testing, Environ. Mol. Mutagen. 35 (2000) 206–221. [63] K.J. Angelis, M. Dusinska´, A.R. Collins, Single cell gel electrophoresis: detection of DNA damage at different levels of sensitivity, Electrophoresis 20 (1999) 2133–2138. [64] A.R. Collins, The comet assay for DNA damage and repair: principles, applications, and limitations, Mol. Biotechnol. 26 (2004) 249–261. [65] D.P. Lovell, T. Omori, Statistical issues in the use of the comet assay, Mutagenesis 23 (2008) 171–182. [66] A. Hartmann, E. Agurell, C. Beevers, S. Brendler-Schwaab, B. Burlinson, P. Clay, et al., Recommendations for conducting the in vivo alkaline Comet assay, Mutagenesis 18 (2003) 45–51. [67] A. Collins, G. Koppen, V. Valdiglesias, M. Dusinska, M. Kruszewski, P. Møller, et al., ComNet Project, The comet assay as a tool for human biomonitoring studies: the ComNet project, Mutat. Res. Rev. Mutat. Res. 759 (2014) 27–39. [68] M. Karbaschi, M.S. Cooke, Novel method for the high-throughput processing of slides for the comet assay, Sci. Rep. 4 (2014) 7200. [69] C. Karpouzi, S. Nikolaidis, A. Kabasakalis, G. Tsalis, V. Mougios, Exercise-induced oxidatively damaged DNA in humans: evaluation in plasma or urine? Biomarkers 21 (2016) 204–207. [70] A. Willitzki, S. Lorenz, R. Hiemann, K. Guttek, A. Goihl, R. Hartig, et al., Fully automated analysis of chemically induced γH2AX foci in human peripheral blood mononuclear cells by indirect immunofluorescence, Cytometry A 83 (2013) 1017–1026. [71] R. Runge, R. Hiemann, M. Wendisch, U. Kasten-Pisula, K. Storch, K. Z€ ophel, et al., Fully automated interpretation of ionizing radiation-induced γH2AX foci by the novel pattern recognition system AKLIDES, Int. J. Radiat. Biol. 88 (2012) 439–447. [72] A. Mastaloudis, T.W. Yu, R.P. O’Donnell, B. Frei, R.H. Dashwood, M.G. Traber, Endurance exercise results in DNA damage as detected by the comet assay, Free Radic. Biol. Med. 36 (2004) 966–975. [73] S. Reichhold, O. Neubauer, C. Hoelzl, B. Stadlmayr, J. Valentini, F. Ferk, et al., DNA damage in response to an ironman triathlon, Free Radic. Res. 43 (2009) 753–760. [74] O. Neubauer, S. Reichhold, L. Nics, C. Hoelzl, J. Valentini, B. Stadlmayr, et al., Antioxidant responses to an acute ultra-endurance exercise: impact on DNA stability and indications for an increased need for nutritive antioxidants in the early recovery phase, Br. J. Nutr. 104 (2010) 1129–1138.


229

[75] K.H. Wagner, S. Reichhold, C. H€ olzl, S. Knasm€ uller, L. Nics, M. Meisel, et al., Well-trained, healthy triathletes experience no adverse health risks regarding oxidative stress and DNA damage by participating in an ultra-endurance event, Toxicology 278 (2010) 211–216. [76] A. Hartmann, S. Pfuhler, C. Dennog, D. Germadnik, A. Pilger, G. Speit, Exercise-induced DNA effects in human leukocytes are not accompanied by increased formation of 8-hydroxy-2’-deoxyguanosine or induction of micronuclei, Free Radic. Biol. Med. 24 (1998) 245–251. [77] A.M. Niess, M. Baumann, K. Roecker, T. Horstmann, F. Mayer, H.H. Dickhuth, Effects of intensive endurance exercise on DNA damage in leucocytes, J. Sports Med. Phys. Fitness 38 (1998) 111–115. [78] K. Tsai, T.G. Hsu, K.M. Hsu, H. Cheng, T.Y. Liu, C.F. Hsu, et al., Oxidative DNA damage in human peripheral leukocytes induced by massive aerobic exercise, Free Radic. Biol. Med. 31 (2001) 1465–1472. [79] K. Briviba, B. Watzl, K. Nickel, S. Kulling, K. B€ os, S. Haertel, et al., A half-marathon and a marathon run induce oxidative DNA damage, reduce antioxidant capacity to protect DNA against damage and modify immune function in hobby runners, Redox Rep. 10 (2005) 325–331. [80] J.H. Ryu, I.Y. Paik, J.H. Woo, K.O. Shin, S.Y. Cho, H.T. Roh, Impact of different running distances on muscle and lymphocyte DNA damage in amateur marathon runners, J. Phys. Ther. Sci. 28 (2016) 450–455. [81] Z. Rada´k, J. Pucsuk, S. Boros, L. Josfai, A.W. Taylor, Changes in urine 8-hydroxydeoxyguanosine levels of super-marathon runners during a four-day race period, Life Sci. 66 (2000) 1763–1767. [82] M. Miyata, H. Kasai, K. Kawai, N. Yamada, M. Tokudome, H. Ichikawa, et al., Changes of urinary 8-hydroxydeoxyguanosine levels during a two-day ultramarathon race period in Japanese non-professional runners, Int. J. Sports Med. 29 (2008) 27–33. [83] B.A. Samia, G.A. Youssef, Changes in urinary 8-hydroxydeoxyguanosine levels during heptathlon race in professional female athletes, J. Hum. Kinet. 41 (2014) 107–111. [84] S. Loft, A. Astrup, B. Buemann, H.E. Poulsen, Oxidative DNA damage correlates with oxygen consumption in humans, FASEB J. 8 (1994) 534–537. [85] M. Almar, J.G. Villa, M.J. Cuevas, J.A. Rodrı´guez- Marroyo, C. Avila, et al., Urinary levels of 8-hydroxydeoxyguanosine as a marker of oxidative damage in road cycling, Free Radic. Res. 36 (2002) 247–253. [86] G. Lippi, F. Schena, G.L. Salvagno, C. Tarperi, M. Montagnana, M. Gelati, et al., Acute variation of estimated glomerular filtration rate following a half-marathon run, Int. J. Sports Med. 29 (2008) 948–951. [87] F. Sanchis-Gomar, G. Lippi, Physical activity—an important preanalytical variable, Biochem. Med. (Zagreb) 24 (2014) 68–79. [88] J. Heydenreich, C. Otto, F. Mayer, A. Carlsohn, Reliability of a fully automated interpretation of γ-H2AX foci in lymphocytes of moderately trained subjects under resting conditions, J. Nutr. Metab. 2014 (2014) 478324. [89] G. Lippi, R. Buonocore, C. Tarperi, M. Montagnana, L. Festa, E. Danese, et al., DNA injury is acutely enhanced in response to increasing bulks of aerobic physical exercise, Clin. Chim. Acta 460 (2016) 146–151. [90] J.N. Booth 3rd, L.D. Colantonio, G. Howard, M.M. Safford, M. Banach, K. Reynolds, et al., Healthy lifestyle factors and incident heart disease and mortality in candidates for primary prevention with statin therapy, Int. J. Cardiol. 207 (2016) 196–202. [91] S. Reichhold, O. Neubauer, A.C. Bulmer, S. Knasm€ uller, K.H. Wagner, Endurance exercise and DNA stability: is there a link to duration and intensity? Mutat. Res. 682 (2009) 28–38.

230

Elisa Danese et al.

[92] O. Neubauer, S. Reichhold, A. Nersesyan, D. K€ onig, K.H. Wagner, Exercise-induced DNA damage: is there a relationship with inflammatory responses? Exerc. Immunol. Rev. 14 (2008) 51–72. [93] A. Pokrywka, A. Zembron-Lacny, K. Baldy-Chudzik, J. Orysiak, D. Sitkowski, M. Banach, The influence of hypoxic physical activity on cfDNA as a new marker of vascular inflammation, Arch. Med. Sci. 11 (2015) 1156–1163. [94] F. Sanchis-Gomar, H. Pareja-Galeano, C. Perez-Quilis, A. Santos-Lozano, C. Fiuza-Luces, N. Garatachea, et al., Effects of allopurinol on exercise-induced muscle damage: new therapeutic approaches? Cell Stress Chaperones 20 (2015) 3–13.

CHAPTER SIX

Bulky DNA Adducts, Tobacco Smoking, Genetic Susceptibility, and Lung Cancer Risk Armelle Munnia*, Roger W. Giese†, Simone Polvani{, Andrea Galli{, Filippo Cellai*, Marco E.M. Peluso*,1

*Cancer Risk Factor Branch, Regional Cancer Prevention Laboratory, ISPO-Cancer Prevention and Research Institute, Florence, Italy † Bouve College of Health Sciences, Barnett Institute, Northeastern University, Boston, MA, United States { Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy 1 Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Bulky DNA Adduct Analysis 2.1 DNA Adduct Detection 2.2 32P-DNA Postlabeling Technique 3. Exposure Data 3.1 Epidemiology 3.2 Tobacco Smoke Aromatic Carcinogens 4. Chemical Carcinogenesis 5. Smoking-Related Bulky DNA Adducts 5.1 DNA Adducts by 32P-Postlabeling 5.2 DNA Adducts by Gas Chromatography–Mass Spectrometry 6. Nature and Nurture Susceptibilities 6.1 DNA Adduct Variability 6.2 DNA Adducts and Diet 6.3 DNA Adducts and Multiple DNA Polymorphisms 7. Bulky DNA Adducts and Carcinogen Exposure 7.1 Dose–Response Relationships 7.2 Meta-Analysis of Cross-Sectional Studies 8. Bulky DNA Adducts and Lung Cancer 9. Conclusion Acknowledgments References

232 233 233 234 237 237 237 238 240 240 247 249 249 250 251 256 256 258 262 266 267 267

Abstract The generation of bulky DNA adducts consists of conjugates formed between large reactive electrophiles and DNA-binding sites. The term “bulky DNA adducts” comes from early experiments that employed a 32P-DNA postlabeling approach. This Advances in Clinical Chemistry, Volume 81 ISSN 0065-2423 http://dx.doi.org/10.1016/bs.acc.2017.01.006

#


231

232

Armelle Munnia et al.

technique has long been used to elucidate the association between adducts and carcinogen exposure in tobacco smoke studies and assess the predictive value of adducts in cancer risk. Molecular data showed increased DNA adducts in respiratory tracts of smokers vs nonsmokers. Experimental studies and meta-analysis demonstrated that the relationship between adducts and carcinogens was linear at low doses, but reached steady state at high exposure, possibly due to metabolic and DNA repair pathway saturation and increased apoptosis. Polymorphisms of metabolic and DNA repair genes can increase the effects of environmental factors and confer greater likelihood of adduct formation. Nevertheless, the central question remains as to whether bulky adducts cause human cancer. If so, lowering them would reduce cancer incidence. Pooled and meta-analysis has shown that smokers with increased adducts have increased risk of lung cancer. Adduct excess in smokers, especially in prospective longitudinal studies, supports their use as biomarkers predictive of lung cancer.

1. INTRODUCTION The generation of bulky DNA adducts consists of conjugates formed between large reactive electrophiles and DNA-binding sites [1,2]. The term “bulky DNA adducts” comes from early experiments that employed a 32 P-DNA postlabeling approach for damage analysis and refers to all the relatively large aromatic and nonpolar carcinogen DNA adducts, such as those induced from polycyclic aromatic hydrocarbons (PAH) and aromatic amines [3–6]. These are well-known carcinogens present in cigarette smoke and motor vehicle exhaust and fumes from industrial processes and residential heating [7–9]. Interest in the analysis of the bulky DNA adducts is derived from early experimental animal models that demonstrated that DNA damage is necessary but insufficient for cancer development [2,10,11]. At the beginning of the 21st century, most evidence supported the notion that exposure to environmental carcinogens [3,5,12–28], including cigarette smoke [1,3, 5,29–35], resulted in alterations to the structural integrity of DNA, i.e., bulky DNA adducts and oxidative DNA damage [1,12,19,36–46]. Unless repaired, DNA lesions may lead to mutations in oncogenes and tumor suppressor genes [42,47], thereby initiating carcinogenesis [48]. As such, bulky DNA adducts are widely considered as biomarkers that reflect carcinogen exposure [1,15,30,37,49–53] and cancer risk [1,29,33,50,54–60]. This chapter focuses on measurement of bulky DNA adducts by 32 P-postlabeling, an assay long used to assess the association between genetic damage and carcinogen exposure and the predictive value of adducts for

Bulky DNA Adducts in Tobacco Smokers

233

Fig. 1 From carcinogen exposure, including industrial fumes and cigarette smoking, to bulky DNA adducts as well as to TP53 mutations, and, ultimately, to lung cancer development, with parameters that may influence adduct generation. The figure of TP53 is reproduced from the study of A. Eldar, et al., Structural studies of p53 inactivation by DNA-contact mutations and its rescue by suppressor mutations via alternative protein-DNA interactions, Nucleic Acids Res. 41 (18) (2013) 8748–8759.

cancer risk (Fig. 1). Included is a description of the cigarette smoke constituents responsible for the bulky DNA adduct generation. Underlying mechanisms by which tobacco smoke carcinogens are metabolically activated to reactive electrophiles capable of interacting with DNA are also discussed. The chapter subsequently focuses on the history of chemical carcinogenesis and the description of 32P-postlabeling studies as well as the influence of inherited and acquired susceptibilities, especially dietary habits and DNA polymorphisms. Finally, the dose–response relationship with lung cancer, including by pooled and meta-analysis, are reviewed.

2. BULKY DNA ADDUCT ANALYSIS 2.1 DNA Adduct Detection Over time, methods to assess DNA damage have been developed for experimental animal and human studies [1,3,20,61–64]. Historically, administration of labeled chemicals was the method of choice whereas the study of DNA adducts was extremely rare. Chronic carcinogen exposure was difficult to model due to the high cost of synthetic radiolabeled (3H and 14C) compounds and the complexity of procedures used to assess DNA damage. In 1981, Randerath et al. [65] developed the 32P-DNA postlabeling technique which made it possible to measure DNA damage in experimental animals and humans. Mainly in the early days, other techniques such as

234


immunoassay and immunohistochemistry, gas chromatography–mass spectrometry (GC–MS), and fluorescence spectroscopy have also been used [3,20,50,61–64,66–69]. Immunohistochemistry, using mono- and polyclonal antibodies, facilitated the localization of carcinogen-modified adducts in human tissue [70]. Unfortunately, staining intensity lacked specificity due to cross-reactivity, i.e., antibodies to benzo(a)pyrene (B(a)P) DNA adducts also detected other PAH DNA adducts [70]. GC–MS can provide high sensitivity for detection of DNA damage [71,72]. Structural information and exact mass measurement has been achieved by use of matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) [45,73]. As can be expected, detection limits for these techniques vary considerably, i.e., one adduct per 107 to one adduct per 109–10 nucleotides [3,20,50,61–63,66–68]. Use in human studies is dependent on the amount of DNA required, e.g., 50–100 μg for immunoassays and immunohistochemistry, 100 μg for fluorescence spectroscopy, 100 μg for GC–MS, 100 μg for HPLC with fluorescence detection, and 1–5 μg for 32 P-postlabeling [3,20,50,61–63,66–68].

2.2

32

P-DNA Postlabeling Technique

Over the last decades, the 32P-postlabeling assay has emerged as a major tool for measuring bulky DNA adducts in humans exposed to complex mixtures of environmental carcinogens [6,8,9]. This method is based on the enzymatic hydrolysis of nonradioactive carcinogen-modified DNA to 30 -phosphonucleosides, subsequent to the [32P]-phosphorylation at the free 50 -OH group by the [γ-32P]-ATP and the action of the T4 polynucleotide kinase (Fig. 2), as well as on the separation of DNA adducts from normal nucleotides usually by thin-layer chromatographic [50,67,68,74]. Several

Fig. 2 DNA is enzymatically digested to 30 mononucleotides, and the postlabeling is then achieved by transfer of 32P-orthophosphate from [γ-32P]-ATP to the 50 -OH position of the deoxyribonucleotide adduct. This reaction is mediated by polynucleotide kinase.

235


Fig. 3 Description of the nuclease P1 modification of the technique.

32

P-DNA postlabeling

modifications of the original technique have been developed to increase sensitivity [50,67,68]. The most frequently used is based on the enzymatic treatment of DNA digests with the nuclease P1 to enrich bulky DNA adducts [75] (Fig. 3). The nuclease P1 treatment causes the

236


dephosphorylation of normal nucleotides, but not bulky adducted nucleotides, and deoxyribonucleosides cannot serve as substrates of the T4 polynucleotide kinase for the transfer [32P]-phosphate from [γ-32P]-ATP [50]. Chromatographic resolution and thereby sensitivity of the 32 P-postlabeling method was substantially improved by reducing sample load thereby limiting diffusion [66]. This approach effectively reduced the value of height equivalent per theoretical plate by increasing theoretical plate number with a more efficient spot resolution. The 32P-postlabeling method has many advantages. It requires only a few micrograms of DNA, is ultra-sensitive (detection limit of one adduct per 109–10 nucleotides), and is applicable to a large spectrum of aromatic carcinogens [50,67,68]. Furthermore, this approach is also highly reproducible [76]. Unfortunately, 32P-postlabeling uses of radioactive phosphorus and does not provide any structural information. Characterization of adducts is limited by the presence of complex patterns of single/mixed spots within the diagonal radioactive zone (DRZ) after autoradiography (Fig. 4) [50].

Fig. 4 Four representative autoradiographic maps (A–D) of the 32P-postlabeled digests (nuclease P1 enrichment) of nonsmoker police officers. Reproduced from the study of M. Peluso, et al., 32P-postlabeling detection of aromatic adducts in the white blood cell DNA of nonsmoking police officers, Cancer Epidemiol. Biomarkers Prev. 7 (1) (1998) 3–11.


237

Although DRZ results for complex mixtures of aromatic and hydrophobic adducts are generally consistent [50,66–68], discrepant results have also been reported [77]. Munnia et al. [66], showed that human adducts are likely to be induced by aromatic carcinogens. In this study, a complex pattern of bulky DNA adducts was detected using two chromatographic systems. Higher specificity was obtained when 32P-postlabeling was combined with internal standards [16,17,68] or coupled with other techniques, i.e., GC/ electron capture MS or electrospray tandem MS [78–81].

3. EXPOSURE DATA 3.1 Epidemiology In the past century, lung cancer was extremely rare and represented 50%) in lung tumor [54]. The presence of special mutation patterns might be indicative of the mutagens that have caused them [104]. Theoretically, colocalization of bulky DNA adducts and mutational hotspots in TP53 might be used for causality inference. Nevertheless, Thilly et al. has shown that mutation spectra can vary with dose and are thereby limited in their ability to define origin [105]. Also, people are always exposed to mixtures of environmental carcinogens [105]. It is currently accepted that most human cancers are initiated by activation based on the interplay of various genes that require malignant transformation of hyperplastic cells. The ability of cancer cells to migrate and spread result from subsequent genetic and epigenetic changes. Cell transformation, however, does not occur with every interaction between a carcinogen and DNA. It is likely that a relatively small proportion of these adduct affect critical DNA sites. As such, exposure may increase cancer risk only.

5. SMOKING-RELATED BULKY DNA ADDUCTS 5.1 DNA Adducts by 32P-Postlabeling In 1986, Randerath et al. [106], demonstrated the production of bulky DNA adducts in various tissues of smokers by 32P-postlabeling. The occurrence of genotoxic events with smoking habits prompted the investigation of bulky DNA adducts in the respiratory tract of smokers [1], i.e., the main site of cigarette smoking-related cancer [8,9]. A large number of studies (n ¼ 25) has been performed thus far (Table 1) [31,33,55,77,80,81,107–126]. Of these, 21 were performed in lung [77,80,81,107–124], two in larynx [31,125], and two in nose [33,55,126]. Most (n ¼ 21) compared DNA damage in smokers vs nonsmokers. Four used exsmokers for comparison. Mean number of cigarette smoked per day ranged from 13.3 to 31.9. Chromatography revealed the presence of the DRZ in smokers and exsmokers, while fainter DRZ or spot-patterns were detected in nonsmokers (Fig. 6). DRZ profile broadly reflected exposure to PAH and other aromatic compounds contained in tobacco smoke [66] and suggested that adduct patterns were predominantly due to exposure of complex mixtures of large aromatic nonpolar compounds. The study of Culp et al. [81],

Table 1 Twenty-Five 32P-Postlabeling Studies That Analyzed the Levels of Bulky DNA Adducts (Expressed Such as Per 108 Normal Nucleotides) in the Upper Respiratory Tract of Smokers Cigarette Smoked Per Day Bulky DNA Adducts Smoking Status

Disease Status

Tissue

Nonsmokers

Lung cancer

Bronchial tissue

Mean SD

Exsmokers 20.7 9.2

Smokers Nonsmokers

Lung cancer

Bronchial tissue

Exsmokers 31.9 16

Smokers Nonsmokers

Lung and no cancer Bronchial tissue

Exsmokers 27.1 9.5

Smokers Nonsmokers

Lung cancer

Bronchial tissue

Exsmokers 13.8 7.4

Smokers Exsmokers Smokers

Lung cancer

N

Mean SD/SE or Range

5

2.4 0.6 (SD)

7

9 5.7 (SD)

3.7

17

11 8.4 (SD)

4.7

2

0.3 0.2 (SD)

3

1.5 0.9 (SD)

5

7

7.2 3.0 (SD)

24

8

3.4 2.1 (SD)

8

3.3 1.9 (SD)

0.9

37

5.5 2.1 (SD)

1.6

5

2.5 2.3 (SD)

6

6.0 4.2 (SD)

2.4

8

9.3 6.5 (SD)

3.7

Ratioa References

[107]

[108]

[109]

[110]

11 12 (SD)

Bronchial tissue 19.3 15.3

8

11 6.3 (SD)

[111] 0.9 Continued

Table 1 Twenty-Five 32P-Postlabeling Studies That Analyzed the Levels of Bulky DNA Adducts (Expressed Such as Per 108 Normal Nucleotides) in the Upper Respiratory Tract of Smokers—cont’d Cigarette Smoked Per Day Bulky DNA Adducts Smoking Status

Exsmokers

Disease Status

Tissue


No cancer

N

22 26 12.4

Smokers Nonsmokers

Mean SD

Bronchial tissue

Exsmokers Smokers

15

Mean SD/SE or Range

36 (5–263)

Ratio

b

88 (26–232)

[112] b

2.4

b

10

25 (4–88)

18

22 (4–129)b

0.9 b

13

73 (32–132)

4

1.1 0.8 (SD)

19

5.4 3.2 (SD)

14

3.2 4.0 (SD)

Exsmokers

7

12 8.3 (SD)

5.2

Smokers

16

7.3 3.9 (SD)

2.3

4

5.7 3.5 (SD)

Exsmokers

4

3.4 1.3 (SD)

Smokers

6

6.9 4.2 (SD)

0.6

14

2.2 1.1 (SD)

1.2

Exsmokers

Lung and no cancer Bronchial tissue 26 11.7

Smokers Nonsmokers

Nonsmokers

Exsmokers



Lung cancer

Bronchial tissue

References

2.9 [113] 4.9 [114]

[115]

[116]

17.1 6.8

Smokers

49

10 7.9 (SD)

4.8

All subjects

Lung cancer

Bronchial tissue

31

1.2 1.0 (SD)

[117]

Nonsmokers

Lung cancer

Bronchial tissue

2

9.1 1.0 (SD)

[80]

10

52.4 37.3 (SD)

5.7

Exsmokers Smokers

11.2 2.8

15

19.1 9.9 (SD)

2.1

Heavy smokers

25.5 11

9

31.9 13.9 (SD)

3.5

11

1.3 1.0 (SD)

14

4.1 3.1 (SD)

23

6.2 3.8 (SD)

Long-term exsmokers

20

5.8 4.0 (SD)

0.9

Short-term exsmokers

25

11 4.1 (SD)

1.7

Smokers

82

7.8 4.0 (SD)

1.2

45

6.6 6.2 (SD)

Exsmokers


Smokers Nonsmokers

Nonsmokers and exsmokers


Lung cancer

Bronchial tissue

120 8.8 4.7 (SD)

Smokers Nonsmokers (women) Smokers (women) Nonsmokers (men)

Lung cancer

Bronchial tissue 13.3 5.4

13

1.4 0.6 (SD)

29

15 9.5 (SD)

24

4.1 2.1 (SD)

[81] 3.1 [118]

[119] 1.3 [120] 11 Continued

Table 1 Twenty-Five 32P-Postlabeling Studies That Analyzed the Levels of Bulky DNA Adducts (Expressed Such as Per 108 Normal Nucleotides) in the Upper Respiratory Tract of Smokers—cont’d Cigarette Smoked Per Day Bulky DNA Adducts Mean SD

N

Mean SD/SE or Range

Ratio

16.5 7.4

93

12 8.1 (SD)

2.9

73

19.0 18 (SE)

33

16 8.5 (SE)

38

49.3 30 (SE)

32

49.0 37 (SE)

11

2.2 2.2 (SD)

13

11.2 7.8 (SD)

29

4.7 3.0 (SD)

35

11.0 6.9 (SD)

3

2.2

Exsmokers

22

2.2 0.3 (SD)

1

Smokers

24

3.2 0.4 (SD)

1.5

Smoking Status

Disease Status

Tissue

Smokers (men) Nonsmokers

No cancer

Bronchial tissue

Smokers Nonsmokers

Lung cancer

Bronchial tissue

Smokers Nonsmokers

Lung cancer

Bronchial tissue

Smokers Nonsmokers

Lung cancer

Bronchial tissue

Smokers Nonsmokers

Lung cancer

Bronchial tissue

References

[121] 0.8

0.9 [122] 5.0 [123] 2.3 [77]

All subjects

Lung cancer

Bronchial tissue

135 0.9 0.1 (SD)

[124]

Nonsmokers

Larynx cancer

Laryngeal tissue

4

5.7 3.0 (SD)

[125]

Smokers (first group)

30

4.8 5.0 (SD)

0.8

Smokers (second group)

9

6.7 7.3 (SD)

1.2

9

4.4 1.3 (SE)

21

6.1 1.0 (SE)

14

6.8 2.9 (SD)

6

17 4.5 (SD)

10

1.4 0.2 (SE)

8

2.5 0.4 (SE)

1.8

24

5.5 0.5 (SE)

3.9

Nonsmokers

Larynx cancer

Laryngeal tissue 22.2 12.2

Smokers Nonsmokers

No cancer

Nasal tissue 16.7 6.1

Smokers Nonsmokers

No cancer

Nasal tissue

Exsmokers Smokers

15.4 9.5

Ratio ¼ adduct levels of smokers or exsmokers/adduct levels of nonsmokers or exsmokers, as appropriated. Geometric means. Study characteristics, i.e., smoking habits, number of cigarettes smoked per day, cancer status, DNA source, are listed.

a

b

[31] 1.4 [126] 2.5 [33,55]

246


Bulky DNA adducts per 108 total nucleotides

1.00

.50

.00

−.50

−1.00 −10

0

10

20

30

40

Daily number of cigarettes

Fig. 6 Dose–response relationship between bulky DNA adducts and the number of cigarettes smoked per day in the nasal epithelium of smokers. Reproduced from the study of M.E.M. Peluso, A. Munnia, DNA adducts and the total sum of at-risk DNA repair alleles in the nasal epithelium, a target tissue of tobacco smoking-associated carcinogenesis, Toxicol. Res. 3 (2014) 42–49.

identified a single adduct-spot emerging from the DRZ such as a DNA adduct induced from the carcinogen, 4-aminobiphenyl. In general, adducts were increased in smoker lung vs non- and/or exsmokers, with the exception of the studies of Van Schooten et al. [111] and Chen et al. [127] (Table 1). The highest ratios (adducts in smokers/ adduct in nonsmokers or exsmokers) were 24 and 11 by Randerath et al. [108] and Mollerup et al. [120], respectively. The latter found that DNA damage was gender associated (women > men). Wiencke et al. [117] demonstrated that peripheral blood was a valid surrogate to estimate adduct burden in respiratory tissue. Unfortunately, Wiencke et al. [117] and Lee et al. [124] did not report on bulky DNA adducts stratified for smoking habit. When restricted to adduct studies reporting significant association with smoking habits, bulky DNA adducts were 5.4–31.9 in smokers vs 0.3–3.2 adducts per 108 nucleotides in nonsmokers (Table 1). Increased adducts (1.1–52.4 adducts per 108 nucleotides) were also found in exsmoker lungs. Bulky DNA adducts were correlated to tobacco smoking intensity in three 32 P-postlabeling studies [107,109,112]. Laryngeal DNA adducts were associated with smoking by Munnia et al. [31], but not by Szyfter et al. [125] (Table 1). Strong associations with smoking were found in nasal epithelium [55,126]. A significant linear correlation has been reported for nasal DNA adducts and smoking intensity (Fig. 7) [55].


247

Fig. 7 Diagonal radioactive zone found in the nasal epithelium of (A) smokers and (B) nonsmokers (license number 3853610527390). Reproduced from the study of M. Peluso, et al., Detection of DNA adducts in human nasal mucosa tissue by 32P-postlabeling analysis, Carcinogenesis 18 (2) (1997) 339–344.

The major strength of the studies of Zhao et al. [126] and Peluso et al. [29,55] was the choice of nasal epithelium as the source of DNA. Nasal epithelium constitutes one of the main entry points for air carcinogens. This tissue is composed of 75% columnar surface epithelium, 14% other epithelial cells, 11% neutrophils, 0.07% eosinophils, and 0.2% lymphocytes [126]. In addition to its value for investigations of tobacco smoke carcinogen exposure [29,30,55,126], this tissue was used to assess industrial pollutant exposure [19,39,43,128]. The amount and complexity of bulky DNA adducts in the respiratory tract of smokers far exceeds that of nonsmokers (Table 1). These findings corroborate the epidemiologic data supporting the association of lung cancer with smoking habit. The persistence of DNA adducts in exsmokers may be a consequence of the slow clearance of carcinogen containing tar and particulate deposits in the respiratory tract potentially leading to metabolic activation despite cessation [108].

5.2 DNA Adducts by Gas Chromatography–Mass Spectrometry The 32P-postlabeling detection of bulky DNA adducts in smoker tissues has been confirmed by GC–MS [69,80,129–131]. In those studies, B(a)P DNA adducts were analyzed in lung and other tissues, including placenta, cervical epithelium, and peripheral blood. Derivatization was performed to enhance GC volatility and/or MS response. GC–MS measurement of DNA adducts has been the subject of several reviews [69,129–131].

248


Two GC–MS systems have been employed with respect to ion source. The first, due its high sensitivity, uses an electron capture negative ion (ECNI) source, i.e., wherein the derivatized analyte efficiently captures a low energy electron in the gas phase. The resulting anion radical is detected or the product formed following break up. This source sometimes is given alternative names such as negative ion chemical ionization. The second ion source is electron impact (EI). Using this approach, the analyte encounters a high-energy electron forming a cation radical (or subsequent cation) for detection. GC–MS analytes need to be thermally stable and volatile. As such, adducts need to be isolated in nucleobase form or released from DNA prior to derivatization via acid hydrolysis. For B(a)P DNA adducts, derivatization with trimethylsilylchloride or methyl iodide may be used. Moderate sensitivity was achieved for B(a)Ptetrahydrotetrol due to poor derivatization. Three studies reported B(a)P DNA adducts in smokers by GC-ECNI-MS [132–134]. In each case, DNA was acid hydrolyzed to release isomeric B(a)Ptetrahydrotetrols (B(a)Ptetrols). In the earliest study, Manchester et al. [132], used GC-ECNI-MS to verify B(a)P diol-epoxide DNA adducts in human placenta following their tentative identification by HPLC with off-line (fraction collection) synchronous fluorescence spectroscopy (HPLC-SFS). Of 28 placentas tested (15 active cigarette smokers and 13 nonsmokers), 10 tested positive by HPLC-SFS. Interestingly, no correlation was found with smoking history. Eight of the positive DNA samples were pooled for analysis by GC-EI-MS which confirmed the presence of B(a)P diol-epoxide DNA adducts. To detect adducts in placental DNA, the following steps were applied: (1) partial digestion to oligomers, (2) immunoaffinity chromatography to concentrate oligonucleotide adducts, (3) acid hydrolysis (pH 1.5, 90°C, 3 h) to release B(a)Ptetrols, (4) conversion to tetratrimethylsilyl derivatives, and (5) analysis. Melikian et al. [134] reported B(a)P DNA adducts in the epithelial cervical tissues of smokers (3.5 1.1 adducts per 108 nucleotides; n ¼ 8) were nearly double those in nonsmokers (1.8 0.9 per 108 nucleotides; n ¼ 9). In corresponding stromal tissues the values in smokers (1.8 0.9 per 108 nucleotides) vs nonsmokers (1.4 1.1 per 108 nucleotides) were also higher. In this study, B(a)Ptetrahydrotetrols were released by acid hydrolysis (0.1 N HCl, 80°C, under vacuum), permethylated with methyl iodide in the presence of methyl sulfinyl carbanion, purified by HPLC, and injected into a GC-ECNI-MS. In a prior study using mice, they learned that conducting the acid hydrolysis under vacuum (or nitrogen) vs ambient air increased tetrahydrotetrol yield by 25% [135]. Also,


249

derivatization with perfluorinated reagents yielded poorer results, i.e., the tetrahydrotetrol isomers tended to aromatize or oxidize, thus generating more than one product. Four authentic tetrahydrotetrols of B(a)P were used to optimize test conditions. Pastorelli et al. [133] measured B(a)P DNA adducts in blood lymphocytes of 44 male smokers with concurrent lung cancer. This method used acid hydrolysis (0.3 N HCl, 60 min, 100°C), trimethylsilylation derivatization and GC-ECNI-MS analysis. Adducts were detected in 18 individuals with a mean adduct concentration of 0.07 adducts per 108 nucleotides. The use of GC-ECNI-MS for measurement of DNA adducts has been supplanted by LC-MS. Nevertheless, the former may reemerge due to the high resolution of GC and very high sensitivity of ECNI-MS for strong electrophores. These characteristics make this method useful as a confirmatory technique especially when high resolution MS is employed [136].

6. NATURE AND NURTURE SUSCEPTIBILITIES 6.1 DNA Adduct Variability High variability in bulky DNA adducts was found among persons exposed to similar amounts of carcinogens. Variation was associated with various parameters including gender, age, genetic susceptibilities, and dietary habit [21,22,32,52,116,120,137–145]. An earlier age of onset of smoking was reported to influence the increase and the persistence of adducts in exsmokers [117]. Polymorphisms in the 15q25 locus was linked with the generation of bulky DNA adducts [146]. Certain polymorphisms in genes encoding metabolic enzymes were associated with bulky DNA adducts [37,49,55,138,147–157]. Adduct production was found to be dependent on cytochrome P450, microsomal epoxide hydrolase, myeloperoxidase and N-acetyl-transferase enzymes, and lack of glutathione S-transferase Mu 1 (GSTM1) genotype [12,119, 124,142,143,147,158–161]. These studies provided indirect evidence that bulky DNA adducts were partially caused by PAH exposure. Adduct generation was affected by repair enzyme coding [49,55,119,143,148–151,157–159,162,163]. For example, bulky DNA adducts were increased in cancer patients and healthy relatives vs controls [162]. The role of the X-ray repair cross complementing1 (XRCC1), the XRCC group3 (XRCC3), and the xeroderma pigmentosum-D (XPD) genes, representing the base excision repair, the nucleotide-excision repair and the double strand break repair pathways, respectively, was reported in the Italian

250


cohort of the European “Prospective Investigation into Cancer and Nutrition” (EPIC) investigation [164]. All variants were associated with deficient DNA repair and increased adduct formation. DNA damage was also linked to indoor heating in Poland during winter [165,166] and photochemical smog in Italy during summer [52,167]. Transformation reactions occurring in the troposphere during photochemical episodes [168] may induce a number of oxidized degradation products, including B(a)P-lactones and B(a)P-quinones that are capable of binding DNA and forming adducts without metabolic activation [52].

6.2 DNA Adducts and Diet The generation of bulky DNA adducts may be influenced by exposure to PAH in charcoal-broiled food [22,169] and in food due to environmental pollution [170,171]. Ingestion of grilled meat and fried products has been linked to increased adducts [172]. In contrast, diets rich in antioxidants and vitamins may be associated with the opposite effect. In fact, large cohort studies have shown that adduct levels were inversely related to fruit and/or vegetable intake [141,167,173]. The Mediterranean diet, a diet rich in cereals, fresh fruits, raw, or cooked vegetables, and with low saturated fat intake, was inversely associated to adduct generation [137,141,145]. A protective nutrient pattern, characterized by high intake of antioxidant vitamins, including ascorbic acid, beta-carotene, and alpha-tocopherol, and fatty acids mostly derived from olive and other vegetable oils, such as monounsaturated and linoleic acid was also identified [22,137,138,145,174,175]. The protective effect of the Mediterranean diet was partially related to increased intake of vegetables containing flavonoids [176–179]. These compounds that may protect against adduct production by inhibiting metabolic activation and induce DNA repair activity [176]. Recently, DNA adducts were shown to be influenced by methylenetetrahydrofolate reductase polymorphisms and low folate intake [163]. Both supportive and discrepant findings were reported in those studies that directly examined vitamin levels [139,180–182]. In the “Population Exposure to PAH Study” [181], an inverse association was found between bulky DNA adduct levels and vitamin A, C, and E. Adduct generation was independently and inversely associated with both vitamin A and C in nonsmokers, but not in smokers. In a Czech Republic study, a protective effect of vitamin A, but not of vitamin C, was found in nonsmokers only [182].


251

In 2010, a pooled analysis was conducted from Ragin et al. [183] to evaluate the association of bulky DNA adducts with dietary vitamins directly by measurement in plasma or indirectly from dietary questionnaire. Data were combined from seven 32P-postlabeling studies having 2758 subjects [33,137,181,182,184–186]. Pooled-analysis showed that adduct levels were inversely associated with vitamin E in both smokers than nonsmokers, but with vitamin A in smokers only. The protective effect of specific dietary constituents was correlated to specific genetic traits. Subjects with low plasma dietary antioxidants have greater amounts of adducts, if they do not have the ability to detoxify carcinogens via the GSTM1 pathway [138,139,175]. Mooney et al. [175] reported an inverse association between retinol, β-carotene and α-tocopherol, and DNA adducts in subjects lacking the GSTM1 detoxification gene. Adducts were inversely correlated with vitamin E and C in individuals with the GSTM null genotype [187]. In the EPIC study, stratification by the GSTM1 genotype showed an inverse relationship of adducts to increased intake of leafy vegetables and raw leafy vegetables and increased β-carotene, vitamin E, niacin, and potassium [138,139]. It should be noted, however, that discrepant findings with the GSTM1 pathway have also been reported [183].

6.3 DNA Adducts and Multiple DNA Polymorphisms Various studies suggested a role for genetic susceptibility in DNA adduct generation, but some were inconsistent with metabolizing [37,49,55,124, 138,147–156] and DNA repair genes [49,55,119,143,148–151,158,159, 162]. As such, the study of multiple polymorphisms on the generation of bulky DNA adduct generation has received considerable attention [37, 49,151,153–156,161]. Matullo et al. [151] showed the combined effect of multiple gene variants of DNA repair genes may be more important than single nucleotide polymorphisms in repair capacity (Fig. 8). The combination of three DNA repair genes, including the XRCC1-Arg399Gln, the XRCC3-Thr241Met, and the XPD-DLys751Gln genes, was associated with increased production of bulky DNA adducts [164]. Peluso et al. [37], showed that lung cancer at-risk alleles involved in activation and detoxification of carcinogens and defense against oxidative stress and apoptosis, was associated with increased adducts in workers exposed to industrial air pollution. In an Iranian population at high risk of esophageal

252


Fig. 8 Average levels of bulky DNA adducts according to multiple DNA polymorphism in DNA repair genes. Reproduced from the study of G. Matullo, et al., Combination of DNA repair gene single nucleotide polymorphisms and increased levels of DNA adducts in a population-based study, Cancer Epidemiol. Biomarkers Prev. 12 (7) (2003) 674–677.

squamous cell carcinoma, the variability in the generation of bulky DNA adducts was explained by a combination of polymorphisms in phase I genes and nucleotide-excision repair capacity [156]. Multiple DNA polymorphisms in PAH-metabolizing genes were also shown to influence adducts in smokers [153]. The combination of multiple DNA polymorphisms in metabolic and DNA repair pathways was recently considered [49]. The relationship between bulky DNA adducts and multiple lung cancer at-risk alleles was examined in environmentally exposed workers and smokers [37,49,153–155]. This study evaluated if overall genetic damage in these subjects was influenced by combined action of multiple genetic polymorphisms, despite possible null effects in single DNA polymorphisms [37,49,153–155]. The effects of these DNA polymorphisms on lung cancer risk and on enzyme and/or protein activities are shown (Table 2). Study characteristics used by Peluso et al. [49] are shown for comparison (Table 3). In the Netherlands, Ketelslegers et al. [153] measured DNA damage in cigarette smokers (26 9 cigarettes per day for at least 10 years) and assessed at-risk alleles involved in detoxification. DNA adducts in smokers with multiple at-risk alleles were increased relative to those not bearing these genetic traits. The Italian study of Peluso et al. [154] examined the relationship of

Table 2 Influence of Selected DNA Polymorphisms on Lung Cancer Risk and Estimated Effects on Enzyme/Protein Functions and Molecular Pathways From the Study of Peluso et al. [49] Lung Cancer Estimated Effect of the at-Risk Allele Lung on Enzyme/Protein Activity and Point Cancer At-Risk Allele Biological Processes Gene Abbreviation ID Polymorphism Mutation Risk

Metabolic activation of carcinogens Cytochrome P450, CYP1A1*2A rs4646903 Msp1 family 1, member A1*2A

T/C

+

C

CYP1, member A1*2C

CYP1A1*2C rs1048943 Ile462Val

A/G

+

G

CYP1, member A1*4

CYP1A1*4

rs1799814 Thr461Asn

C/A

+

A

CYP1, member B1*3

CYP1B1*3

rs1056836 Leu432Val

C/G

+

G

Epoxide hydrolase 1

EPHX1

rs2234922 His139Arg

A/G

+

G

Higher catalytic activity; increased metabolic activation of carcinogens.

Detoxification of reactive metabolites and defense against oxidative stress NAD(P)H quinone oxidoreductase 1

NQO1

rs1800566 Pro187Ser

C/T

+

T

Epoxide hydrolase 1

EPHX1

rs1051740 Tyr113His

C/T

+

T

Deficient enzyme activity; decreased detoxification of reactive metabolites.

Continued

Table 2 Influence of Selected DNA Polymorphisms on Lung Cancer Risk and Estimated Effects on Enzyme/Protein Functions and Molecular Pathways From the Study of Peluso et al. [49]—cont’d Lung Estimated Effect of the at-Risk Allele Cancer Lung on Enzyme/Protein Activity and Cancer At-Risk Point Biological Processes Allele Gene Abbreviation ID Polymorphism Mutation Risk

Glutathione peroxidase 1

GPX1

rs1050450 Pro198Leu

C/T

+

T

Less efficient final GSH peroxidase complex; higher oxidative stress.

Glutathione-Stransferase M1

GSTM1

Deletion

Ins/Del

Del

+

Del

GST Theta1

GSTT1

Deletion

Ins/Del

Del

+

Del

Lack of enzyme activity; decreased detoxification of reactive metabolites.

Manganese superoxide dismutase 2

MnSOD2

rs1799725 Val16Ala

T/C

+

C

Deficient transport of enzyme into mitochondria membrane; higher oxidative stress.

DNA repair, cell-cycle, and apoptosis regulations Apurinic/ apyrimidinic endonuclease 1

APE1

rs3136820 Asp148Glu

T/G

T

Decreased activation of transcriptional factors; higher survival of damaged cells.

X-ray repair cross complementing group 1

XRCC1

rs1799782 Arg194Trp

C/T

+

T

rs25487

Arg399Gln

G/A

+

A

Altered protein–protein interactions; decreased DNA repair.

XRCC group 3

XRCC3

rs861539

Thr241Met

C/T

C

Table 3 Relationships Between the Levels of Bulky DNA Adducts (Expressed Such as per 108 Nucleotides) and Cumulative DNA Polymorphisms in Five 32P-Postlabeling Studies of Environmental Exposure and Tobacco Smoking [49] Control Susceptible At-Risk Alleles Exposure N Population Na Subjects Na N Subjects Nb Ratioc %d References

Allele C for CYP1A1*2°, G for CYP1A1*2C, A for CYP1A1*4, G for CYP1B1*3, T for GPX1, Del for GSTM1, and GSTT1

Urban air pollution

47

Urban residents

18 0.9

Allele G for EPHX1, T for NQO1, T for MnSOD2, and T for APE1

Mix air pollution

69

Urban residents

22 0.7 0.5 0–2 4 (SE)

Industrial air pollution

77

Industrial workers

2

8

1.0 0.3 (SE)

5–7 1.5

20 0.7 0.5 0–2 11 1.8 1.6 (SD) (SD)

5–7 2.6

14.2

Smoking 120 Smokers (11 4.9 cig./day for 4 years)

36 0.7 0.1 0–2 13 1.7 0.4 (SE) (SE)

5–7 2.5

10.8 [49]

Allele T for GPX1, T for EPHX1, and Del for GSTM1

Smoking (26 9 cig./day for 10 years)

63

Smokers

10 0.8 0.5 0 (SD)

4

2.5

19.0 [153]

Allele T for XRCC1, A for XRCC1, and C for XRCC3

Smoking (28 18 cig./day for 40 years)

23

Smokers

5

3–4 2.2

39.1 [154]

12 2.0 0.1 (SD)

1.3 1.1 0–1 9 (SD)

2.9 1.3 (SD)

1.4

4.2 [155]

1.1

a

4

Number of at-risk alleles. Number of susceptible subjects carrying the higher number of at-risk alleles. Ratio ¼ adduct levels of susceptible subjects with the higher number of at-risk alleles/adduct levels of referents without the genetic traits in exam. d Percentage of individuals carrying at-risk alleles ¼ [(n of susceptible subjects/n of the studied population) 100]. Study characteristics, i.e., DNA polymorphism, carcinogen exposure, study population, and number of at-risk alleles, are listed. b c

5.8 [37]

256


bulky DNA adducts to at-risk DNA repair alleles in lung cancer. An effect was observed in long-term smokers (28 18 cigarettes per day for at least 40 years) and multiple genetic polymorphisms. The Mexican study of Garcia-Suastegui et al. [155] analyzed DNA adducts with respect to at-risk alleles of various metabolic pathway genes. Bulky DNA adducts were associated to the total sum of at-risk alleles in subjects heavily exposed to air carcinogens. A subsequent study by Peluso et al. [37] evaluated the relationship between adducts and multiple genetic susceptibility in industrial workers and nearby residents in Map Ta Phut, Thailand. A correlation was detected in industrial workers carrying multiple at-risk alleles who were heavily exposed to the industrial air pollutants. Peluso et al. [49] also explored the effects of multiple DNA polymorphisms on adducts in smokers in the aforementioned investigations [12,38]. In that study, adducts in current smokers having 4 or 5 DNA polymorphisms were significantly greater than those not possessing these genetic traits. A pattern emerges from studies of DNA adducts and DNA polymorphisms in subjects exposed to environmental pollutants and tobacco smoke carcinogens [37,49,153–155] (Table 3). Smokers who carry multiple at-risk alleles tended to have increased bulky DNA adducts with respect to environmental exposure. Specifically, in the context of the susceptible individuals, the level of DNA damage showed differences between the long-term heavy smokers and the urban residents, with intermediate amounts in the industrial exposed workers. Ratio analysis showed increased values in smokers and industrial workers indicating that the expression of these traits might be affected by various genes as well as the environment. It appears that certain combinations of DNA polymorphisms might increase the effect of environmental risk factors and confer a greater likelihood of having increased adduct levels after environmental exposure, especially tobacco smoke [49]. Obviously, certain at-risk alleles might need to be present with respect to environmental exposure before manifesting biologic activity. It is plausible, however, that concomitant changes in enzyme activity as well as subtle alterations of critical pathways might play a key role in cellular response.

7. BULKY DNA ADDUCTS AND CARCINOGEN EXPOSURE 7.1 Dose–Response Relationships Dose–response is a concept fundamental to our understanding of chemical carcinogenesis. Paracelsus, a great medieval toxicologist, told that it “is the


257

dose that makes the poison” establishing that all the chemicals have toxic properties. Until the development of highly sensitive methods, such as 32 P-postlabeling in 1981 [65], chronic carcinogen exposure in experimental model studies has been difficult to perform due to the high cost of radiolabeled chemicals. Despite this, a number of long-term studies have been performed with B(a)P, 4-methylnitrosamino-1-3-pyridyl-1-butanone and 4-aminobiphenyl [4]. Those investigations demonstrated an early increase in the accumulation of DNA damage followed by steady state. The relationship between DNA adducts and cigarette smoking intensity was analyzed by 32P-postlabeling in smoker respiratory tracts [55,107, 109,112]. A linear association was found between bulky DNA adducts and cigarettes smoked per day. Peluso et al. [55], showed that dose–response was linear up to 10 cigarettes per day and reached a steady state concentration in heavy smokers thereafter (Fig. 9). This observation supports earlier

Fig. 9 Dose–response relationship between frequency ratios (FR) of bulky DNA adducts, e.g., adduct levels of exposed subjects/adduct levels of controls, and external benzo(a)pyrene (B(a)P) concentrations from the study of Peluso et al. [18]. The inset shows the curve at low B(a)P exposure doses (license number 3853611335937).

258


findings with B(a)P exposure in rodents [188]. Lewtas and coworkers [189,190] suggested that industrial workers exposed to high PAH levels showed a leveling-off phenomenon. DNA damage per unit of exposure was lower than that detected at environmental exposure among highly exposed populations.

7.2 Meta-Analysis of Cross-Sectional Studies In 2001, the dose–response relationship between adducts and PAH exposure was analyzed by a meta-analysis [18]. Data from 13 32P-postlabeling occupational exposure studies to different levels and types of air pollutants were examined (Table 4) [13,14,23,74,191–200]. Cohorts included coke oven workers, aluminum plant workers, foundry workers, as well as truck terminal workers, mechanics, bus garage workers, bus, and taxi drivers. Increased adducts were found in heavily exposed industrial workers as well as in urban workers. This study demonstrated a linear relationship between adduct formation and B(a)P up to 4.5 ng/m3 B(a)P reaching steady-state in heavily exposed industrial workers. Interesting, this relationship was mathematically related to the Michaelis–Menten equation: V0 ¼

Vmax ½S ð½S + Km Þ

wherein V0 is initial reaction velocity, [S] is substrate concentration, and Km is the Michaelis–Menten constant. It is likely that most reactive metabolites are eliminated by cytoplasmic nucleophiles, e.g., glutathione, and by conjugating/detoxifying enzyme activities, e.g., sulfotransferases and hydrolases. Adducts may also be removed by DNA repair mechanisms. As such, this kinetic relationship alone cannot be used to explain the complex disease process of cancer. Attenuation of linearity may be due to incorrect assignment of individual exposure. Dose–response may be nonlinear due to depletion of more susceptible individuals [201]. Although the coefficient of variation of 32 P-postlabeling studies has not been firmly established, others have estimated it may be as high as 25% [202] or as low as 6% [203]. This imprecision may be attenuated if the error is evenly distributed. As such, measurement error would blur existing associations rather than reveal false associations.

259


Table 4 The 13 Occupational 32P-Postlabeling Studies Which Were Included in the Meta-Analysis of the Study of Peluso et al. [18] B(a)P ng/m3 DNA Adducts Mean Standard Deviation or Range N

Occupation

Smoking Habits

Coke oven workers

Smokers/ >2000–3550 nonsmokers

Mean Standard Deviation References

9

0.9 0.6

Coke oven workers

1000–2000

8

0.6 0.7

Coke oven workers

470–200 nonsmokers

2

2.7 3.7

Foundry workers

Smokers/ 50–200 nonsmokers

6

1.0 1.1

Foundry workers

Smokers/ 200 nonsmokers

5

2.4 0.2

Foundry workers


32

1.7 0.1

Foundry workers


17

2.5 1.2

Foundry workers


15

2.1 1.4

Foundry workers

Smokers/ 12–