Tobacco-specific carcinogens induce ...

Author Manuscript Published OnlineFirst on August 29, 2017; DOI: 10.1158/1940-6207.CAPR-17-0198 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

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Tobacco-specific carcinogens induce hypermethylation, DNA adducts and

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DNA damage in Bladder Cancer

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Feng Jin1#, Jose Thaiparambil2#, Sri Ramya Donepudi1, Venkatrao Vantaku3, Danthasinghe Waduge

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Badrajee Piyarathna3, Suman Maity1, Rashmi Krishnapuram3, Vasanta Putluri1, Franklin Gu4, Preeti

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Purwaha3, Salil Kumar Bhowmik3, Chandrashekar R Ambati1, Friedrich-Carl von Rundstedt

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Roghmann7, Sebastian Berg7, Joachim Noldus7, Kimal Rajapakshe3, Daniel Gödde8, Stephan Roth9,

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Stephan

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Balasubramanyam Karanam12, Martha K. Terris13, Shyam M Kavuri14, Seth P. Lerner4, Farrah

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Kheradmand15, Cristian Coarfa1,3, Arun Sreekumar1,3,4, Yair Lotan16, Randa El-Zein2, Nagireddy

Störkel8,

Stephan

Degener9,

Michailidis10,

George

Benny

Abraham

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, Florian

Kaipparettu11,

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Putluri1,3*

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1

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Baylor College of Medicine, Houston, TX, USA; 2Department of Radiology, Houston Methodist Research

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Institute, Houston, TX, USA; 3Department of Molecular and Cell Biology, 4Verna and Marrs McLean

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Department of Biochemistry, Baylor College of Medicine, Houston, TX, USA; 5Scott Department of

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Urology, Baylor College of Medicine, Houston, TX, USA; 6Department of Urology, Jena University

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Hospital, Friedrich-Schiller-University, Jena, Germany;

Dan L. Duncan Cancer Center, Advanced Technology Core, Alkek Center for Molecular Discovery,

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Department of Urology, Marien Hospital,

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Ruhr-University Bochum, Herne, Germany; Department of Pathology, 9Department of Urology Helios

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Klinikum, Witten-Herdecke University, Wuppertal, Germany;

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Florida, Gainesville, FL, USA;

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Medicine, TX, USA;

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Tuskegee, AL, USA; 13Augusta University, Augusta, GA, USA;

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Department of Statistics, University of

Department of Molecular and Human Genetics, Baylor College of

Department of Biology and Center for Cancer Research, Tuskegee University, 14

Lester and Sue Smith Breast Center,

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Baylor College of Medicine, Houston, TX, USA;

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Research in Inflammatory Diseases, Michael E. DeBakey VA, Baylor College of Medicine, Houston, TX,

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USA;

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Department of Medicine & Center for Translational

Department of Urology, University of Texas Southwestern, Dallas, TX, USA. # Authors

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contributed equally to the generation of the manuscript.

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*

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Nagireddy Putluri, Ph.D.

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Department of Molecular and Cellular Biology

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Baylor College of Medicine, Houston, TX 77030, Tel.: (713) 798 3139: Email: [email protected]

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Conflict of interest statement: Authors do not have any conflict of interest

Corresponding author:

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Downloaded from cancerpreventionresearch.aacrjournals.org on September 5, 2017. © 2017 American Association for Cancer Research.


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Abstract

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Smoking is a major risk factor for the development of Bladder Cancer (BLCA); however, the

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functional consequences of the carcinogens in tobacco smoke and BLCA-associated metabolic

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alterations remains poorly defined. We assessed the metabolic profiles in BLCA smokers and

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non-smokers, and identified the key alterations in their metabolism. Liquid Chromatography –

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Mass Spectrometry (LC-MS), and bioinformatic analysis were performed to determine the

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metabolome associated with BLCA smokers and were further validated in cell line models.

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Smokers with BLCA were found to have elevated levels of methylated metabolites, polycyclic

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aromatic hydrocarbons (PAHs), DNA adducts and DNA damage. DNA methyltransferase 1

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(DNMT1) expression was significantly higher in smokers than non-smokers with BLCA. An

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integromics approach, using multiple patient cohorts, revealed strong associations between

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smokers and high-grade BLCA. In vitro exposure to the tobacco smoke carcinogens, 4-

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(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and benzo[a]pyrene (BaP) led to increase

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in levels of methylated metabolites, DNA adducts, and extensive DNA damage in BLCA cells.

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Co-treatment of BLCA cells with these carcinogens and the methylation inhibitor 5-aza-2'-

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deoxycytidine (AZA) rewired the methylated metabolites, DNA adducts, DNA damage. These

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findings were confirmed through the isotopic labeled metabolic flux analysis. Screens using

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smoke associated metabolites and DNA adducts could provide robust biomarkers and improve

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individual risk prediction in BLCA smokers. Non-invasive predictive biomarkers that can stratify

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the risk of developing BLCA in smokers could aid in early detection and treatment.

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Introduction

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Bladder Cancer (BLCA) is a leading cause of cancer-related deaths globally [1-4] and

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approximately 50% of BLCA patients are cigarette smokers [5-7]. In addition, patients with a 2



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current or past history of smoking have a threefold higher chance of developing BLCA [8-11].

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Moreover, the intensity and duration of smoking has been shown to affect the grade and stage of

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the BLCA and high-dose smokers have more aggressive BLCA phenotypes [8, 12]. Smokers

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with BLCA are also more likely to have resistance to chemotherapy [13]. At least 70 tobacco

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smoke compounds, including 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and

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benzo[a]pyrene (BaP) are carcinogens [14, 15]. NNK has been identified as a potent carcinogen

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which induces DNA adducts, mutations and promotes tumor growth through receptor-mediated

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effects [14, 16]. Thus, prognostic and predictive biomarkers that can stratify the risk of

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developing BLCA based on smoking habits are urgently needed. Recently, we found that hyper

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methylation of xenobiotic enzymes was associated with BLCA progression [17]. However, the

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metabolic signature of smoke-induced BLCA and its downstream effects remain to be

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elucidated.

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Using a metabolomics approach, we compared the metabolism in smoking and non-

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smoking patients to identify metabolic signatures and determined the underlying mechanism

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associated with smoking-induced BLCA. Additionally, we detected methylated metabolites in

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the urine of patients, which could be used to develop a non-invasive, urine-based assay to detect

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BLCA in smokers.

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Methods

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A total number of 119 pathologically-verified BLCA tissues (78 smokers and 41 non-

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smokers), normal bladder (n= 10 smokers) or benign tissues (n= 14 smokers), and 108 BLCA

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urine samples (71 smokers and 37 non-smokers) were obtained from different tumor banks

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(Supplementary Table 1). We procured sample cohorts of smokers and non-smokers, which 3



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include early to late stages (ta, t1, t2, t3 and t4), distant lymph nodes (N0, N1, N2, N3 and Nx)

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and Lympho Vascular Invasion (LVI) status of BLCA (Supplementary Table 1). Metabolites

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and DNA adducts were examined using a 6490 QQQ equipped with a 1290 LC-MS (Agilent

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Technologies, USA). Receiver Operator Characteristic (ROC) curves and logistic regression

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modelling were used to assess BLCA in urine samples. An integrative approach was used to

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identify transcriptomic signatures differentiating high-grade from low-grade BLCA in public

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data sets and evaluated the correlation between the BLCA grade and smoking status of the

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patients. In vitro models were established to study the tobacco smoke compounds effects.

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Comprehensive information of sample preparation protocols, metabolites, DNA adducts, quality

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control, data acquisition and processing, and statistical analysis is described in the

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supplementary methods.

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Results

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Smoker-associated BLCA Metabolic Profiles

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Metabolic profiling by LC-MS was used to characterize metabolites. A summary of the

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identified metabolites, DNA adducts, their experimental masses and retention times are shown in

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Supplementary Tables 2 and 3. The experimental strategy used for profiling is shown in

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Figure 1A.

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(Supplementary methods).

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metabolites out of 300 identified metabolites (Figure 1B), and the former are different from the

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66 altered metabolites found in smokers of normal bladder and BLCA tissues (Supplementary

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Figure 1B). Specifically, smokers with BLCA had elevated levels of methylated metabolites,

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hexosamine biosynthetic pathway intermediates, acetylated metabolites, Poly-cyclic aromatic

We measured the metabolites using different chromatographic methods In comparison to non-smokers, BLCA smokers have 90 altered

4



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hydrocarbons (PAH)/ their aromatic counterparts and hydroxylated derivatives compared to non-

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smokers with BLCA (Figure 1B).

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Furthermore, expression of intermediates of the methionine cycle was altered, in which

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S-(5'-adenosyl)-L-methionine (SAM) and S-adenosyl-L-homocysteine (SAH) were decreased

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and increased, respectively in smokers compared to non-smokers with BLCA. In addition to

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changes in the primary metabolites, aniline a xenobiotic compound is known to be involved in

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bladder carcinogenesis [18-20], was high in smokers compared to non-smokers with BLCA.

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Pathway Analysis

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To address the deregulated metabolic pathways in smokers with BLCA, we used the

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online enrichment analysis platform ConsensusPathPDB (CPDB) [21] and mapped the

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differential metabolites between smokers and non-smokers using KEGG, Reactome, and

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GOBP(Gene Ontology Biological Process) database pathway analysis. We observed alterations

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in the levels of metabolites associated with methionine metabolism, DNA methylation, nicotine

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metabolism, glutathione metabolism, nucleotides and methionine salvage pathways in smokers

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with BLCA (Figure 1C).

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Evaluation of Tissue-derived PAHs and Methylated Metabolites in Urine

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Given the ability to discriminate between smokers and non-smokers based on the expression of

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BLCA-associated metabolites, we investigated the potential metabolites which can be used as

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urine biomarkers. We used urine specimens from BLCA patients (smokers and non-smokers) to

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measure tissue-derived BLCA-associated metabolites using a single reaction monitoring (SRM)-

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based approach (Supplementary Tables 1, 2).

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signatures in smokers with BLCA, 52 were detected in the urine samples (Supplementary

Out of the 90 tissue-specific metabolic

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Figure 1A), out of which, 40 were differentially expressed in smokers and non-smokers and

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were further used to run individually as ROC classifiers. Briefly, to run a ROC curve, a logistic

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regression model was used to build a classifier which was trained on a randomly selected subset

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of two-thirds of the urine specimens (n = 72, training set) and its predictive performance was

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assessed on the remaining one-third of the samples (n = 36, test set) (Figure 1D). Logistic

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regression based classification model using all 52 metabolites was used to derive the activity

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score [which has an Area Under the Curve (AUC) of 0.70 with a p-value of 0.03]

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(Supplementary Figure 1C). Based on the performance of the individual metabolites, 23 (out of

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the 40 differential compounds) were found to have significant ROC (p-value