Evidence of Gene–Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors
Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene–environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4×10−6) and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1×10−4). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01–1.16) in nulliparous women and ranged from 1.03 (0.96–1.10) in parous women with one birth to 1.26 (1.16–1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85–0.98) in those with an alcohol intake of <20 g/day and 1.45 (1.14–1.85) in those who drank ≥20 g/day. Additionally, interaction was found between 1p11.2-rs11249433 and ever being parous (Pinteraction = 5.3×10−5), with a per-allele OR of 1.14 (1.11–1.17) in parous women and 0.98 (0.92–1.05) in nulliparous women. These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors.
Vyšlo v časopise:
Evidence of Gene–Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors. PLoS Genet 9(3): e32767. doi:10.1371/journal.pgen.1003284
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1003284
Souhrn
Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene–environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4×10−6) and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1×10−4). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01–1.16) in nulliparous women and ranged from 1.03 (0.96–1.10) in parous women with one birth to 1.26 (1.16–1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85–0.98) in those with an alcohol intake of <20 g/day and 1.45 (1.14–1.85) in those who drank ≥20 g/day. Additionally, interaction was found between 1p11.2-rs11249433 and ever being parous (Pinteraction = 5.3×10−5), with a per-allele OR of 1.14 (1.11–1.17) in parous women and 0.98 (0.92–1.05) in nulliparous women. These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors.
Zdroje
1. MavaddatN, AntoniouAC, EastonDF, Garcia-ClosasM (2010) Genetic susceptibility to breast cancer. Mol Oncol 4: 174–191.
2. CampaD, KaaksR, Le MarchandL, HaimanCA, TravisRC, et al. (2011) Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium. J Natl Cancer Inst 103: 1252–1263.
3. KawaseT, MatsuoK, SuzukiT, HirakiA, WatanabeM, et al. (2009) FGFR2 intronic polymorphisms interact with reproductive risk factors of breast cancer: results of a case control study in Japan. Int J Cancer 125: 1946–1952.
4. PrenticeRL, HuangY, HindsDA, PetersU, PettingerM, et al. (2009) Variation in the FGFR2 gene and the effects of postmenopausal hormone therapy on invasive breast cancer. Cancer Epidemiol Biomarkers Prev 18: 3079–3085.
5. RebbeckTR, DeMicheleA, TranTV, PanossianS, BuninGR, et al. (2009) 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. Carcinogenesis 30: 269–274.
6. TravisRC, ReevesGK, GreenJ, BullD, TipperSJ, et al. (2010) Gene-environment interactions in 7610 women with breast cancer: prospective evidence from the Million Women Study. Lancet %19 375: 2143–2151.
7. MilneRL, GaudetMM, SpurdleAB, FaschingPA, CouchFJ, et al. (2010) 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. Breast Cancer Res 12: R110.
8. Breast Cancer Association Consortium (2006) Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium. J Natl Cancer Inst 98: 1382–1396.
9. YangXR, Chang-ClaudeJ, GoodeEL, CouchFJ, NevanlinnaH, et al. (2011) Associations of breast cancer risk factors with tumor subtypes: a pooled analysis from the Breast Cancer Association Consortium studies. J Natl Cancer Inst 103: 250–263.
10. BroeksA, SchmidtMK, ShermanME, CouchFJ, HopperJL, et al. (2011) Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium. Hum Mol Genet 20: 3289–3303.
11. KongA, SteinthorsdottirV, MassonG, ThorleifssonG, SulemP, et al. (2009) Parental origin of sequence variants associated with complex diseases. Nature 462: 868–874.
12. VachonCM, ScottCG, FaschingPA, HallP, TamimiRM, et al. (2012) Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk. Cancer Epidemiol Biomarkers Prev
13. ButlerLM, GoldEB, GreendaleGA, CrandallCJ, ModugnoF, et al. (2008) Menstrual and reproductive factors in relation to mammographic density: the Study of Women's Health Across the Nation (SWAN). Breast Cancer Res Treat 112: 165–174.
14. LoehbergCR, HeusingerK, JudSM, HaeberleL, HeinA, et al. (2010) Assessment of mammographic density before and after first full-term pregnancy. Eur J Cancer Prev 19: 405–412.
15. AllenNE, BeralV, CasabonneD, KanSW, ReevesGK, et al. (2009) Moderate alcohol intake and cancer incidence in women. J Natl Cancer Inst 101: 296–305.
16. CampNJ, ParryM, KnightS, AboR, ElliottG, et al. (2012) Fine-mapping CASP8 risk variants in breast cancer. Cancer Epidemiol Biomarkers Prev 21: 176–181.
17. CoxA, DunningAM, Garcia-ClosasM, BalasubramanianS, ReedMW, et al. (2007) A common coding variant in CASP8 is associated with breast cancer risk. Nat Genet 39: 352–358.
18. DumitrescuRG, ShieldsPG (2005) The etiology of alcohol-induced breast cancer. Alcohol 35: 213–225.
19. FigueroaJD, Garcia-ClosasM, HumphreysM, PlatteR, HopperJL, et al. (2011) Associations of common variants at 1p11.2 and 14q24.1 (RAD51L1) with breast cancer risk and heterogeneity by tumor subtype: findings from the Breast Cancer Association Consortium. Hum Mol Genet 20: 4693–4706.
20. FuYP, EdvardsenH, KaushivaA, ArhancetJP, HoweTM, et al. (2010) NOTCH2 in breast cancer: association of SNP rs11249433 with gene expression in ER-positive breast tumors without TP53 mutations. Mol Cancer 9: 113.
21. BoydNF, MelnichoukO, MartinLJ, HislopG, ChiarelliAM, et al. (2011) Mammographic density, response to hormones, and breast cancer risk. J Clin Oncol 29: 2985–2992.
22. LioYC, MazinAV, KowalczykowskiSC, ChenDJ (2003) Complex formation by the human Rad51B and Rad51C DNA repair proteins and their activities in vitro. J Biol Chem 278: 2469–2478.
23. MeindlA, HellebrandH, WiekC, ErvenV, WappenschmidtB, et al. (2010) Germline mutations in breast and ovarian cancer pedigrees establish RAD51C as a human cancer susceptibility gene. Nat Genet 42: 410–414.
24. ChlebowskiRT, PrenticeRL (2008) Menopausal hormone therapy in BRCA1 mutation carriers: uncertainty and caution. J Natl Cancer Inst 100: 1341–1343.
25. GhoussainiM, FletcherO, MichailidouK, TurnbullC, SchmidtMK, et al. (2012) Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet 44: 312–318.
26. StaceySN, ManolescuA, SulemP, RafnarT, GudmundssonJ, et al. (2007) Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 39: 865–869.
27. BeralV (2003) Breast cancer and hormone-replacement therapy in the Million Women Study. Lancet 362: 419–427.
28. Flesch-JanysD, SlangerT, MutschelknaussE, KroppS, ObiN, et al. (2008) Risk of different histological types of postmenopausal breast cancer by type and regimen of menopausal hormone therapy. Int J Cancer 123: 933–941.
29. TurnbullC, AhmedS, MorrisonJ, PernetD, RenwickA, et al. (2010) Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet 42: 504–507.
30. HeinR, BeckmannL, Chang-ClaudeJ (2008) Sample size requirements for indirect association studies of gene-environment interactions (G x E). Genet Epidemiol 32: 235–245.
31. MorimotoLM, WhiteE, NewcombPA (2003) Selection bias in the assessment of gene-environment interaction in case-control studies. Am J Epidemiol 158: 259–263.
32. Garcia-ClosasM, ThompsonWD, RobinsJM (1998) Differential misclassification and the assessment of gene-environment interactions in case-control studies. Am J Epidemiol 147: 426–433.
33. ZondervanKT, CardonLR (2004) The complex interplay among factors that influence allelic association. Nat Rev Genet 5: 89–100.
34. FletcherO, JohnsonN, OrrN, HoskingFJ, GibsonLJ, et al. (2011) Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study. J Natl Cancer Inst 103: 425–435.
35. ThomasG, JacobsKB, KraftP, YeagerM, WacholderS, et al. (2009) A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet 41: 579–584.
36. MilneRL, BenitezJ, NevanlinnaH, HeikkinenT, AittomakiK, et al. (2009) Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042. J Natl Cancer Inst 101: 1012–1018.
37. MilneRL, GoodeEL, Garcia-ClosasM, CouchFJ, SeveriG, et al. (2011) Confirmation of 5p12 as a susceptibility locus for progesterone-receptor-positive, lower grade breast cancer. Cancer Epidemiol Biomarkers Prev 20: 2222–2231.
38. van den BrandtPA, SpiegelmanD, YaunSS, AdamiHO, BeesonL, et al. (2000) Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol 152: 514–527.
39. WacholderS, ChanockS, Garcia-ClosasM, El GhormliL, RothmanN (2004) Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 96: 434–442.
40. R Development Core Team (2011) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2013 Číslo 3
- Je „freeze-all“ pro všechny? Odborníci na fertilitu diskutovali na virtuálním summitu
- Gynekologové a odborníci na reprodukční medicínu se sejdou na prvním virtuálním summitu
Najčítanejšie v tomto čísle
- Fine Characterisation of a Recombination Hotspot at the Locus and Resolution of the Paradoxical Excess of Duplications over Deletions in the General Population
- Molecular Networks of Human Muscle Adaptation to Exercise and Age
- Genome-Wide Association Study and Gene Expression Analysis Identifies as a Predictor of Response to Etanercept Therapy in Rheumatoid Arthritis
- Recurrent Rearrangement during Adaptive Evolution in an Interspecific Yeast Hybrid Suggests a Model for Rapid Introgression