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were eliminated by use of Tukey’s method, which defines outliers as three interquartile ranges below the 25th percentile or above the 75th percentile (8 ). Statistical analyses were carried out with SAS for Windows software (SAS Institute) and SUDAAN software (Research Triangle Institute). The CVw and CVg exceeded the CVi for laboratory analytes (Table 1). For most laboratory analytes, the mean BP and WP analyte values were similar despite some demographic differences between the two groups. The analytical quality specifications for imprecision and bias can be judged by use of the CVw and CVg. For example, the total cholesterol imprecision should be less than one half of CVw (8.2%), or 4.1%. The method imprecision was 2.3%. The bias for total cholesterol should be ⬍0.25[(CVw)2 ⫹ (CVg)2]1/2, or 0.25[(0.079)2 ⫹ (0.226)2]1/2, or ⬃6.0%. The quality specification for total error is estimated as B ⫹ 1.65(I), or 6.0% ⫹ 1.65(4.1%), or 12.8%. This compares with the National Cholesterol Education Program (NCEP) performance criteria of ⬍3% for imprecision and bias and 9% for total error (9 ). Serum sodium had the lowest CVg (1.6%) and lowest CVw (1.3%). This reflects the narrow homeostatic range for sodium that the body maintains. High CVw and CVg values were seen for several analytes. High CV values could result from natural population or individual variations, diurnal variations, disease, outlying analyte values, and relatively lower analyte values. The ratio of CVw to CVg, also known as the index of individuality, is important in determining the use of population-based reference (normal) intervals in detecting changes of disease status in individuals (10, 11 ). When the index of individuality is low (⬍0.6), the individual results stay within a narrow range compared with the population reference interval. Hence, a low index suggests the utility of evaluating serial changes in analyte values in an individual, and population-based reference intervals would be of limited use. A high index (⬎1.4) suggests that the reference interval is appropriate. The index of individuality ranged from 0.13 for serum alkaline phosphatase to 0.83 for serum bicarbonate (Table 1). In this study, BP and WP estimates of CV were obtained for some selective environmental and nutritional analytes. NHANES III provides a better estimate of CVg than do other short-term local studies because the NHANES III sample was nationally representative (1, 12, 13 ). The CVw estimate was limited by the nonrandom, self-selected design and reflected a mean of 17 days between two measurements. The estimate of CVw could be improved by use of a stratified, multistage probability design over different time periods. Differences for CVw and CVg among subpopulations (gender, age, race, and ethnicity) can be further investigated by use of NHANES III data. References 1. Ricos C, Alvarez V, Cava F, Garcia-Lario JV, Hernandez A, Jimenez CV, et al. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest 1999;59:491–500. 2. Fraser CG. Biological variation: from principles to practice. Washington DC: AACC Press, 2001:151pp.
3. Fraser CG, Harris EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409 –37. 4. National Center for Health Statistics. Plan and operation of the third National Health and Nutrition Examination Survey, 1988 –94. Vital Health Stat 1994;1(32):415pp. 5. US Department of Health and Human Services. Third National Health and Nutrition Examination Survey (NHANES III), 1988 –94. NHANES III second examination file documentation. Series 11, No. 3A. Hyattsville, MD: National Center for Health Statistics, 1999. http://www.cdc.gov/nchs/about/major/ nhanes/nh3data.htm#NHANES%20III%20Series%2011,%20No.%203a (accessed September 2004). 6. National Center for Health Statistics. Laboratory procedures used for the Third National Health and Nutrition Examination Survey (NHANES III), 1988 – 94. http://www.cdc.gov/nchs/data/nhanes/nhanes3/cdrom/nchs/manuals/ labman.pdf (accessed September 2004). 7. Wolter KM. Introduction to variance estimation. New York: Springer-Verlag, 1985:221– 47. 8. Tukey JW. Exploratory data analysis. Reading, MA: Addison-Wesley Publishing Co., 1977:44. 9. Current status of blood cholesterol measurement in clinical laboratories in the United States: a report from the laboratory standardization panel of the National Cholesterol Education Program. Clin Chem 1988;34:193–201. 10. Fraser CG. Inherent biological variation and reference values. Clin Chem Lab Med 2004;42:758 – 64. 11. Harris EK. Effects of intra- and interindividual variation on the appropriate use of normal intervals. Clin Chem 1974;20:1535– 42. 12. Fraser CG. Biological variation in clinical chemistry, an update: collated data, 1988 –1991. Arch Pathol Lab Med 1992;116:916 –23. 13. Ricos C, Garcia-Lario JV, Alvarez V, Caval F, Domenech M, Hernandez A, et al. Biological variation database, and quality specifications for imprecision, bias and total error (desirable and minimum). The 2004 update. http:// www.westgard.com/guest26.htm (accessed September 2004). Previously published online at DOI: 10.1373/clinchem.2004.039354
Breast Cancer Progression and Host Polymorphisms in the Chemokine System: Role of the Macrophage Chemoattractant Protein-1 (MCP-1) ⴚ2518 G Allele, Giorgio Ghilardi,1* Maria Luisa Biondi,2 Anna La Torre,2 Lodovica Battaglioli,2 and Roberto Scorza1 (1 Dipartimento MCO, Clinica Chirurgica Generale, Universita` degli Studi di Milano–Polo S. Paolo, Milan, Italy; 2 Laboratorio di Chimica Clinica e Microbiologia, Ospedale S. Paolo–Polo Universitario, Milan, Italy; * address correspondence to this author at: Dipartimento MCO, Clinica Chirurgica Generale, Universita` degli Studi di Milano–Polo S. Paolo, Via A. Di Rudinı`, 8, I-20142 Milan, Italy; e-mail
[email protected]) Interaction between tumor cells and stroma is essential for tumor growth. Tumor cells stimulate the formation of stromal tissue, which excretes a variety of growth factors, cytokines, and proteases. Tumor-associated macrophages (TAMs) are one of the major components of tumor stromal tissue and are capable of eliciting diverse aspects of tumor growth as either a positive or negative regulator (1 ). In breast carcinoma, large numbers of infiltrating T cells and TAMs are often observed. The leukocyte infiltrate is found within the tumor stromal areas as well as in the epithelial areas that constitute the tumor mass (2–13 ). Recent reports suggest that the inflammatory reaction at the breast tumor site affects tumor growth and progression. Whereas lymphocytes have been shown to have divergent effects on development of breast cancer (2, 10 –
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13 ), it is widely accepted that high concentrations of TAMs are correlated with poor prognosis (2–10 ). Macrophage infiltration into tumors is regulated by several cytokines and chemokines, in particular macrophage chemoattractant protein-1 (MCP-1). MCP-1 is a member of the C-C chemokine family and possesses chemotactic activity for monocytes and T lymphocytes (14 –17 ). MCP-1 is produced not only by tumor cells, but also by stromal cells such fibroblasts, endothelial cells, and monocytes. MCP gene transfer enhances the metastatic potential of cancer cells with increased neovascularization, whereas MCP-1 itself activates monocyte cytostatic function against tumor cells (18, 19 ). Recently, several studies have focused on other chemokines and chemokine receptors in the susceptibility and progression of cancer (20 ), in particular “regulated on activation normal T cell expressed and secreted” (RANTES), a molecule that attracts T cells and monocytes, and its receptor CCR5 (21, 22 ). A third chemokine, stromal cell-derived factor-1 (SDF1), seems to be important in breast cancer progression and was demonstrated to be overproduced in breast cancer tissue (23–27 ). Genetic variations commonly occur in the regulatory regions of chemokine genes, and such polymorphisms affect chemokine gene transcription in response to inflammatory stimuli. Consistent with this, polymorphisms have been described in the genes encoding for MCP-1, RANTES, CCR5, and SDF-1 (28 –31 ). The aim of this study was to investigate possible correlations between polymorphisms in the genes encoding for MCP-1, RANTES, SDF-1, and CCR5 and breast cancer clinical phenotypes, specifically the ability of genetic analysis to identify a subgroup of breast cancer patients with a disease that appears more aggressive or prone to metastasize. We determined the MCP-1 ⫺2518, RANTES ⫺403, CCR5 delta32, and SDF-1 ⫺801 genotypes in DNA isolated from peripheral blood samples of a consecutive unselected series of 83 white women from northern Italy with breast cancer of different stages who underwent surgery, and 141 age-matched healthy white women (control group). The median age of the breast cancer patients was 62 years (range, 24 –91 years), and the median age of the controls was 63 years (range, 29 – 83 years). We followed all patients in this study for 6 –30 months (median follow-up, 21 months). Our Institutional Ethical Committee approved this study, and consent was obtained from patients and controls. The presence (M⫹) or absence (M⫺) of metastasis at the time of operation and during the follow-up, in addition to axillary lymph node invasion at the time of pathologic staging, were considered as well and matched with the distribution of allelic variants (Table 1 in the Data Supplement that accompanies the online version of this Technical Brief at http://www.clinchem.org/content/ vol51/issue2/). Vascular invasion was defined as the presence of cancer
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cells within endothelium-lined spaces in the hematoxylin/eosin-stained specimens. Whole blood (3 mL) from patients and controls was collected into potassium EDTA. DNA was prepared with Istagene Matrix extraction reagents (Bio-Rad Laboratories). The PCR reactions for MCP-1, RANTES, CCR5, and SDF-1 were carried out in a total volume of 25 L with 5 L of extracted genomic DNA; 100 M each of dATP, dGTP, dTTP, and dCTP; 1.5 mM MgCl2; 1 U of Taq polymerase; and the two primers, forward and reverse, each at a concentration of 80 nM. The CCR5 delta32 deletion was identified by electrophoresis on a 2% agarose gel; the other three genotypes were determined with the PCR-restriction fragment length polymorphism assay described by Szalai et al. (32 ). We used the Fisher exact test to check for differences in allele distributions among the groups. Odds ratios (ORs; approximate relative risk) were calculated as an index of the association of chemokine genotypes with each phenotype. For each OR, two-tailed probability values and 95% confidence intervals (CIs) were calculated. All statistical analyses were two-sided and were performed with Stata Statistical Software (Stata Corporation). We used P ⬍0.05 as the cutoff point for statistical significance. Allele frequencies in both control and patient populations were within Hardy–Weinberg equilibrium for the four genotypes. In breast cancer patients, we found no differences in the variant distributions for MCP-1 ⫺2518A/G, RANTES ⫺403G/A, SDF-1 ⫺801G/A, and CCR5 delta32 compared with controls. The relevant values are summarized in Table 2 of the online Data Supplement. SDF-1, RANTES, and CCR5 variant distributions showed no statistically significant differences between subgroup M⫹ (presence of metastases) vs controls or between subgroups M⫹ vs M⫺ (absence of metastases) or M⫺ vs controls. We observed no differences in relation to vascular and lymph node invasion. As for the MCP-1 ⫺2518A/G promoter polymorphism, we observed a strong correlation between the presence of at least one G allele and the M⫹ subgroup at the end of the follow-up period: for A/A vs A/G ⫹ G/G, the OR was 2.83 (95% CI, 1.06 –7.64; P ⫽ 0.020) for M⫹ vs M⫺ patients; and for M⫹ patients vs controls, the OR was 2.09 (95% CI, 1.15–7.52; P ⫽ 0.012; Table 1). At the end of the follow-up, 26 patients with stage I to II disease developed metastases, whereas 40 remained metastasis free. The genotype distribution was as follows: A/A, 8 M⫹ and 25 M⫺ patients; A/G ⫹ G/G; 18 M⫹ and 15 M⫺ patients (P ⫽ 0.023). This study is the first to implicate host chemokine gene variants in the progression of breast cancer. Analysis of polymorphisms in the chemokine system indicated that breast cancer patients carrying at least one G allele for the MCP-1 gene regulatory region were at increased risk of developing metastases independently of the initial stage. We observed no correlation with RANTES, SDF-1, and CCR5 polymorphisms. Macrophage infiltration is a cornerstone of inflammation and neoangiogenesis, which negatively affect prognosis of invasive breast cancer (8 ). Therefore, any genetic
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Table 1. MCP-1 genotypes in breast cancer patients with (Mⴙ) or without (Mⴚ) metastases at the end of follow-up.a Mⴙ patients (n ⴝ 39)
Mⴚ patients (n ⴝ 44)
n
n
MCP-1 genotype A/A A/G G/G
14 22 3
G allele frequency
28
a
%
36 56 8 0.36
%
27 16 1
61 36 3
18
0.21
OR (95% CI)
P
2.83 (1.06–7.64)
0.020
2.17 (1.03–4.64)
0.026
Genotype data are expressed as the genotype frequency. The OR for each genotype was calculated as A/A vs A/G ⫹ G/G.
variation accelerating transcriptional activity in genes encoding for proteins involved in macrophage infiltration could be suspected to enhance tumor progression and metastases. Although several studies showed the importance of signaling factors, i.e., RANTES, CCR5, and SDF-1, in the susceptibility and progression of breast cancer, our data failed to demonstrate a possible relationship to the genetic patterns of the patients (21–27 ). MCP-1 has been shown to inhibit the generation of T lymphocytes in response to tumor aggression (33 ), and therefore it is likely to be involved in immune response to breast cancer (34 ). The presence of a common functional single-nucleotide polymorphism (SNP) in the MCP-1 gene was identified by Rovin et al. (28 ), who found that a biallelic G/A polymorphism at position ⫺2518 of the MCP-1 gene 5⬘-flanking region influenced the transcriptional activity of the putative distal regulatory segment of the gene. Furthermore, this polymorphism correlated with individual differences in monocyte MCP-1 production. Monocytes from individuals carrying a G allele at ⫺2518 produced more MCP-1 after treatment with interleukin-1 than monocytes from A/A homozygous individuals. It was suggested that MCP-1 plays key roles in macrophage recruitment, expression of angiogenic factors, and activation of matrix metalloproteinases in patients with breast cancer (35 ). These conclusions are substantially confirmed by our study. In the present series, patients classified at stages I and II at the time of the diagnosis developed distant metastases significantly more frequently when carrying at least one G allele compared with A/A homozygotes. Our study moves upward to a higher level of control of MCP-1 production. The presence of at least one G allele in the MCP-1 gene promoter of patients with stage I or II disease at the time of diagnosis enhances their risk of metastasis by a factor of 2.67 compared with patients diagnosed in the same stage but homozygous for the allele A. This finding coincides with the known overexpression of MCP-1 in breast cancer tissue (35 ). In apparent contrast to the findings of Saji et al. (35 ), who found a significant association between MCP-1 expression in tumor stromal cells and vascular invasion (lymphatic as well as venous vessels) and a tendency of lymph node involvement, we could not confirm any associations between the presence of a MCP-1 G allele in the host and vascular or lymph node invasion.
In a previous study, we reported a correlation between a functional matrix metalloproteinase-3 (MMP-3) gene polymorphism, the more active 5A variant, and breast cancer susceptibility and found that 5A homozygosity is an independent factor of poorer prognosis (36 ). The results reported here seem to run in the same direction because protease production (including MMP-3) is one of the protumor biological functions of TAMs (20 ) that are recruited and activated by MCP-1. Although suggestive and consistent with our hypothesis, the present results must be considered cautiously. Further studies are needed to confirm the role of a functional MCP-1 gene SNP regarding the relationships between breast cancer and host. This is a very complex matter involving an incredibly high number of variables, each of which may influence in some degree this relationship. Functional MCP-1 gene SNPs represent only one of many factors involved in determining the prognosis of breast cancer. Should our data be confirmed, in the future MCP-1 could be a reliable candidate for inclusion in a panel of genetic risk factors conditioning the course of the disease. References 1. Mantovani A, Bottazzi B, Colotta F, Sozzani S, Ruco L. The origin and function of tumor-associated macrophages. Immunol Today 1992;13:265–70. 2. Stewart THM, Heppner GH. Immunological enhancement of breast cancer. Parasitology 1997;115:S141–53. 3. Lee AHS, Happerfield LC, Bobrow LG, Millis RR. Angiogenesis and inflammation in invasive carcinoma of breast. J Clin Pathol 1997;50:669 –73. 4. van Netten JP, George EJ, Ashmead BJ, Fletcher C, Thorton IG, Coy P. Macrophage-tumor cell associations in breast cancer. Lancet 1993;342: 872–3. 5. van Netten JP, Ashmead BJ, Parker RL, Thornton IG, Fletcher C, Cavers D, et al. Macrophage-tumor cell associations: a factor in metastasis of breast cancer? J Leukoc Biol 1993;54:360 –2. 6. Visscher DW, Tabaczka P, Long D, Crissman JD. Clinicopathologic analysis of macrophage infiltrates in breast carcinoma. Pathol Res Pract 1995;191: 1133–9. 7. Lewis CE, Leek R, Harris A, McGee JOD. Cytokine regulation of angiogenesis in breast cancer: the role of tumor-associated macrophages. J Leukoc Biol 1995;57:747–51. 8. Leek R, Lewis CE, Whitehouse R, Greenall M, Clarke J, Harris AL. Association of macrophage infiltration with angiogenesis and prognosis in invasive breast carcinoma. Cancer Res 1996;56:4625–9. 9. van Netten JP, Ashmed BJ, Cavers D, Fletcher C, Thorton IG, Antonsen BL, et al. “Macrophages” and their putative significance in human breast cancer [Letter]. Br J Cancer 1992;66:220 –1. 10. O’Sullivan C, Lewis CE. Tumour-associated leukocytes: friends or foes in breast carcinoma [Review]. J Pathol 1994;172:229 –35. 11. Lewin KY, Zuccarini O, Sloane JP, Beverley PCL. An immunohistological study of leukocyte localization in benign and malignant breast tissue. Int J Cancer 1985;36:433– 8. 12. Gottlinger HG, Rieber P, Gokel JM, Lohe KJ, Riethmuller G. Infiltrating
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A single nucleotide polymorphism in the matrix metalloproteinase 3 promoter enhances breast cancer susceptibility. Clin Cancer Res 2002;8: 3820 –3. DOI: 10.1373/clinchem.2004.041657
Biological Variation of Coenzyme Q10, Sarah L. Molyneux,1,2* Christopher M. Florkowski,1 Michael Lever,1,2 and Peter M. George1 (1 Canterbury Health Laboratories, Christchurch, New Zealand; 2Department of Chemistry, University of Canterbury, Christchurch, New Zealand; * address correspondence to this author at: PO Box 151, Christchurch, 8000 New Zealand; fax 64-3-3640889, e-mail
[email protected]) Coenzyme Q10 (CoQ10) is an essential cofactor in the mitochondrial electron transport chain and is found in all cell membranes. It is present in the body in both the reduced and oxidized forms, with the reduced form (CoQ10H2) having antioxidant properties. CoQ10 has been implicated in disease processes, including Parkinson disease (1 ), diabetes (2 ), and Alzheimer disease (3 ), as well as in aging (4 ), oxidative stress (4, 5 ), and hydroxymethylglutaryl-CoA reductase inhibitor (statin) therapy (6 ). In particular, changes in CoQ10 may be relevant to statininduced myalgia (7 ). Generally the bioavailability of dietary CoQ10 is low, and the formulations of CoQ10 supplements affect bioavailability (8, 9 ). There is also a significant difference between individuals in absorption of CoQ10 from supplements (9 ). Knowledge of the biological variation of CoQ10 in healthy individuals enables interpretation of whether a significant change has occurred in response to a disease state or supplementation. We investigated the inter- and intraindividual variation of plasma total CoQ10, the ratio of total CoQ10 to LDLcholesterol (LDL-C), and the ratio of total CoQ10 to total cholesterol (TC). Reference intervals for these variables in healthy New Zealand adults were also determined. For determination of the inter- and intraindividual variation of CoQ10, 10 healthy adult male volunteers were enrolled. Exclusion criteria included taking CoQ10 or other vitamin supplements, smoking, and use of any medications within 4 weeks before initiation of the study. All participants were self-reportedly healthy and disease free throughout the study. The median age of the participants was 23.5 (range, 21–28) years, the median weight was 69 (60 –100) kg, and the median body mass index (BMI) was 21.4 (18.5–28.6) kg/m2. Blood samples were taken in the morning after a 10-h fast. Samples were taken at least 1 week apart, over a period of 2 months, with seven samples being taken in total. Blood samples were collected into evacuated glass tubes containing lithium heparin. Blood was immediately placed on ice and centrifuged within 1 h of collection; the resulting plasma was stored at ⫺80 °C until analysis.
