Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women

IntroductionRecently, several genome-wide association studies (GWAS) have identified novel single nucleotide polymorphisms (SNPs) associated with breast cancer risk. However, most of the studies were conducted among Caucasians and only one from Chinese.MethodsIn the current study, we first tested whether 15 SNPs identified by previous GWAS were also breast cancer marker SNPs in this Chinese population. Then, we grouped the marker SNPs, and modeled them with clinical risk factors, to see the usage of these factors in breast cancer risk assessment. Two methods (risk factors counting and odds ratio (OR) weighted risk scoring) were used to evaluate the cumulative effects of the five significant SNPs and two clinical risk factors (age at menarche and age at first live birth).ResultsFive SNPs located at 2q35, 3p24, 6q22, 6q25 and 10q26 were consistently associated with breast cancer risk in both testing set (878 cases and 900 controls) and validation set (914 cases and 967 controls) samples. Overall, all of the five SNPs contributed to breast cancer susceptibility in a dominant genetic model (2q35, rs13387042: adjusted OR = 1.26, P = 0.006; 3q24.1, rs2307032: adjusted OR = 1.24, P = 0.005; 6q22.33, rs2180341: adjusted OR = 1.22, P = 0.006; 6q25.1, rs2046210: adjusted OR = 1.51, P = 2.40 × 10-8; 10q26.13, rs2981582: adjusted OR = 1.31, P = 1.96 × 10-4). Risk score analyses (area under the curve (AUC): 0.649, 95% confidence interval (CI): 0.631 to 0.667; sensitivity = 62.60%, specificity = 57.05%) presented better discrimination than that by risk factors counting (AUC: 0.637, 95% CI: 0.619 to 0.655; sensitivity = 62.16%, specificity = 60.03%) (P < 0.0001). Absolute risk was then calculated by the modified Gail model and an AUC of 0.658 (95% CI = 0.640 to 0.676) (sensitivity = 61.98%, specificity = 60.26%) was obtained for the combination of five marker SNPs, age at menarche and age at first live birth.ConclusionsThis study shows that five GWAS identified variants were also consistently validated in this Chinese population and combining these genetic variants with other risk factors can improve the risk predictive ability of breast cancer. However, more breast cancer associated risk variants should be incorporated to optimize the risk assessment.

[1]  V. Beral,et al.  Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci. , 2010, JAMA.

[2]  W. Willett,et al.  A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1) , 2009, Nature Genetics.

[3]  G. Abecasis,et al.  A note on exact tests of Hardy-Weinberg equilibrium. , 2005, American journal of human genetics.

[4]  Patrick Neven,et al.  Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium. , 2011, Human molecular genetics.

[5]  P. Gregersen,et al.  Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33 , 2008, Proceedings of the National Academy of Sciences.

[6]  Deborah Hughes,et al.  Genome-wide association study identifies five new breast cancer susceptibility loci , 2010, Nature Genetics.

[7]  R. Wilkins Polygenes, risk prediction, and targeted prevention of breast cancer. , 2008, The New England journal of medicine.

[8]  Hongbing Shen,et al.  EGF promoter SNPs, plasma EGF levels and risk of breast cancer in Chinese women , 2008, Breast Cancer Research and Treatment.

[9]  D. Pang,et al.  Risk of genome-wide association study newly identified genetic variants for breast cancer in Chinese women of Heilongjiang Province , 2010, Breast Cancer Research and Treatment.

[10]  Lester L. Peters,et al.  Genome-wide association study identifies novel breast cancer susceptibility loci , 2007, Nature.

[11]  Y. Aulchenko,et al.  Association of FGFR2 gene polymorphisms with the risk of breast cancer in population of West Siberia , 2009, European Journal of Human Genetics.

[12]  T. Walsh,et al.  Spectrum of mutations in BRCA1, BRCA2, CHEK2, and TP53 in families at high risk of breast cancer. , 2006, JAMA.

[13]  Hongbing Shen,et al.  Genetic variants in fibroblast growth factor receptor 2 (FGFR2) contribute to susceptibility of breast cancer in Chinese women. , 2008, Carcinogenesis.

[14]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

[15]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[16]  K. Shiraki,et al.  Breast cancer genetics: what we know and what we need , 2001, Nature Medicine.

[17]  T. Rebbeck,et al.  Hormone-dependent effects of FGFR2 and MAP3K1 in breast cancer susceptibility in a population-based sample of post-menopausal African-American and European-American women. , 2008, Carcinogenesis.

[18]  J. Ferlay,et al.  Global Cancer Statistics, 2002 , 2005, CA: a cancer journal for clinicians.

[19]  Mitchell H Gail,et al.  Comparing breast cancer risk assessment models. , 2010, Journal of the National Cancer Institute.

[20]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[21]  J. Long,et al.  Evaluation of 11 Breast Cancer Susceptibility Loci in African-American Women , 2009, Cancer Epidemiology, Biomarkers & Prevention.

[22]  Mitchell H Gail,et al.  Personalized estimates of breast cancer risk in clinical practice and public health , 2011, Statistics in medicine.

[23]  P. Gregersen,et al.  The 6q22.33 Locus and Breast Cancer Susceptibility , 2009, Cancer Epidemiology, Biomarkers & Prevention.

[24]  B. Arun,et al.  Clinical application of breast cancer risk assessment models. , 2010, Future oncology.

[25]  V. Beral,et al.  Gene–environment interactions in 7610 women with breast cancer: prospective evidence from the Million Women Study , 2010, The Lancet.

[26]  Keda Yu,et al.  Combining accurate genetic and clinical information in breast cancer risk model , 2011, Breast Cancer Research and Treatment.

[27]  J. Long,et al.  Evaluation of Breast Cancer Susceptibility Loci in Chinese Women , 2010, Cancer Epidemiology, Biomarkers and Prevention.

[28]  D. Easton,et al.  Models of genetic susceptibility to breast cancer , 2006, Oncogene.

[29]  D. Gudbjartsson,et al.  Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor–positive breast cancer , 2007, Nature Genetics.

[30]  Y. Teo,et al.  Ability to predict breast cancer in Asian women using a polygenic susceptibility model , 2011, Breast Cancer Research and Treatment.

[31]  Wei Lu,et al.  Genetic and clinical predictors for breast cancer risk assessment and stratification among Chinese women. , 2010, Journal of the National Cancer Institute.

[32]  M. Thun,et al.  Performance of Common Genetic Variants in Breast-cancer Risk Models , 2022 .

[33]  D. Easton,et al.  The BOADICEA model of genetic susceptibility to breast and ovarian cancer , 2004, British Journal of Cancer.

[34]  J. Gray,et al.  The genetics and genomics of cancer , 2003, Nature Genetics.

[35]  Peter Kraft,et al.  Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium. , 2011, Journal of the National Cancer Institute.

[36]  Jinbo Chen,et al.  Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. , 2006, Journal of the National Cancer Institute.

[37]  F. Collins,et al.  Implications of the Human Genome Project for medical science. , 2001, JAMA.

[38]  Ellen Warner,et al.  Clinical practice. Breast-cancer screening. , 2011, The New England journal of medicine.

[39]  Peter Kraft,et al.  Heterogeneity of Breast Cancer Associations with Five Susceptibility Loci by Clinical and Pathological Characteristics , 2008, PLoS genetics.

[40]  Jane E. Carpenter,et al.  Common breast cancer susceptibility loci are associated with triple-negative breast cancer. , 2011, Cancer research.

[41]  Hongbing Shen,et al.  Replication and functional genomic analyses of the breast cancer susceptibility locus at 6q25.1 generalize its importance in women of chinese, Japanese, and European ancestry. , 2011, Cancer research.

[42]  A. Whittemore,et al.  Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case-control study , 2010, Breast Cancer Research.

[43]  W. Willett,et al.  A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer , 2007, Nature Genetics.

[44]  Clement Adebamowo,et al.  Ancestry-Shift Refinement Mapping of the C6orf97-ESR1 Breast Cancer Susceptibility Locus , 2010, PLoS genetics.

[45]  Hongbing Shen,et al.  Genetic variants in trinucleotide repeat-containing 9 (TNRC9) are associated with risk of estrogen receptor positive breast cancer in a Chinese population , 2010, Breast Cancer Research and Treatment.

[46]  Hongbing Shen,et al.  Genetic variants of 6q25 and breast cancer susceptibility: a two-stage fine mapping study in a Chinese population , 2011, Breast Cancer Research and Treatment.

[47]  Wonshik Han,et al.  Common Genetic Variants Associated with Breast Cancer in Korean Women and Differential Susceptibility According to Intrinsic Subtype , 2011, Cancer Epidemiology, Biomarkers & Prevention.

[48]  N E Day,et al.  A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes , 2002, British Journal of Cancer.

[49]  M. Beckmann,et al.  Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042. , 2009, Journal of the National Cancer Institute.

[50]  J. Haines,et al.  Genome-wide association study identifies a novel breast cancer susceptibility locus at 6q25.1 , 2009, Nature Genetics.

[51]  M. Thun,et al.  Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2 , 2009, Nature Genetics.