Genetics of Breast and Gynecologic Cancers (PDQ®)–Health Professional Version

Low-Penetrance Genes and Loci

Polymorphisms underlying polygenic susceptibility to breast and gynecologic cancers are considered low penetrance, a term often applied to sequence variants associated with a minimal to moderate risk. This is in contrast to high-penetrance variants or alleles that are typically associated with more severe phenotypes, for example BRCA1/BRCA2 pathogenic variants leading to an autosomal dominant inheritance pattern in a family, and moderate-penetrance variants such as BRIP1, CHEK2, and RAD51C. (Refer to the High-Penetrance Breast and/or Gynecologic Cancer Susceptibility Genes and the Moderate-Penetrance Genes Associated With Breast and/or Gynecologic Cancer sections of this summary for more information.) Because these types of sequence variants (also called low-penetrance genes, alleles, variants, and polymorphisms) are relatively common in the general population, their overall contribution to cancer risk is estimated to be much greater than the attributable risk in the population from pathogenic variants in BRCA1 and BRCA2. For example, it is estimated by segregation analysis that half of all breast cancer occurs in 12% of the population that is deemed most susceptible.[1] There are no known low-penetrance variants in BRCA1/BRCA2. The N372H variation in BRCA2, initially thought to be a low-penetrance allele, was not verified in a large combined analysis.[2]

Two strategies have attempted to identify low-penetrance polymorphisms leading to breast cancer susceptibility: candidate gene and genome-wide searches. Both involve the epidemiologic case-control study design. The candidate gene approach involves selecting genes based on their known or presumed biological function, relevance to carcinogenesis or organ physiology, and then searching for or testing known genetic variants for an association with cancer risk. This strategy relies on imperfect and incomplete biological knowledge, and, despite some confirmed associations (described below), has been relatively disappointing.[2,3] The candidate gene approach has largely been replaced by genome-wide association studies (GWAS) in which a very large number of single nucleotide polymorphisms (SNPs) (approximately 1 million to 5 million) are chosen within the genome and tested, mostly without regard to their possible biological function, but instead to more uniformly capture all genetic variation throughout the genome.

Genome-Wide Searches

In contrast to assessing candidate genes and/or alleles, GWAS involve comparing a very large set of genetic variants spread throughout the genome. The current paradigm uses sets of as many as 5 million SNPs that are chosen to capture a large portion of common variation within the genome based on the HapMap and the 1000 Genomes Project.[4,5] By comparing allele frequencies between a large number of cases and controls, typically 1,000 or more of each, and validating promising signals in replication sets of subjects, very robust statistical signals of association have been obtained.[6-8] The strong correlation between many SNPs that are physically close to each other on the chromosome (linkage disequilibrium) allows one to “scan” the genome for susceptibility alleles even if the biologically relevant variant is not within the tested set of SNPs. Although this between-SNP correlation allows one to interrogate the majority of the genome without having to assay every SNP, when a validated association is obtained, it is not usually obvious which of the many correlated variants is causal.

Genome-wide searches are showing great promise in identifying common, low-penetrance susceptibility alleles for many complex diseases,[9] including breast cancer.[10-13] The first study involved an initial scan in familial breast cancer cases followed by replication in two large sample sets of sporadic breast cancer, the final being a collection of over 20,000 cases and 20,000 controls from the Breast Cancer Association Consortium.[10] Five distinct genomic regions were identified that were within or near the FGFR2, TNRC9, MAP3K1, and LSP1 genes or at the chromosome 8q region. The 8q region and others may harbor multiple independent loci associated with risk. Subsequent genome-wide studies have replicated these loci and identified additional ones.[11,12,14,14-19] Numerous SNPs identified through large studies of sporadic breast cancer appear to be associated more strongly with estrogen receptor–positive disease;[20] however, some are associated primarily or exclusively with other subtypes, including triple-negative disease.[21,22] An online catalog is available of SNP-trait associations from published GWAS for use in investigating genomic characteristics of trait/disease-associated SNPs.

Although the statistical evidence for an association between genetic variation at these loci and breast and ovarian cancer risk is overwhelming, the biologically relevant variants and the mechanism by which they lead to increased risk are unknown and will require further genetic and functional characterization. Additionally, these loci are associated with very modest risk (typically, an odds ratio [OR] <1.5), with more risk variants likely to be identified. No interaction between the SNPs and epidemiologic risk factors for breast cancer have been identified.[23,24] Furthermore, theoretical models have suggested that common moderate-risk SNPs have limited potential to improve models for individualized risk assessment.[25-27] These models used receiver operating characteristic (ROC) curve analysis to calculate the area under the curve (AUC) as a measure of discriminatory accuracy. A subsequent study used ROC curve analysis to examine the utility of SNPs in a clinical dataset of more than 5,500 breast cancer cases and nearly 6,000 controls, using a model with traditional risk factors compared with a model using both standard risk factors and ten previously identified SNPs. The addition of genetic information modestly changed the AUC from 58% to 61.8%, a result that was not felt to be clinically significant. Despite this, 32.5% of patients were in a higher quintile of breast cancer risk when genetic information was included, and 20.4% were in a lower quintile of risk. Whether such information has clinical utility is unclear.[25,28]

More limited data are available regarding ovarian cancer risk. Three GWAS involving staged analysis of more than 10,000 cases and 13,000 controls have been carried out for ovarian cancer.[29-31] As in other GWAS, the ORs are modest, generally about 1.2 or weaker but implicate a number of genes with plausible biological ties to ovarian cancer, such as BABAM1, whose protein complexes with and may regulate BRCA1, and TIRAPR, which codes for a poly (ADP-ribose) polymerase, molecules that may be important inBRCA1/BRCA2-deficient cells.

Because the individual and collective influences of these SNPs on cancer risk have not been evaluated prospectively, they are not considered clinically relevant.

In addition to genome-wide studies interrogating common genetic variants, sequencing-based studies involving whole-genome or whole-exome sequencing [32] are also identifying genes associated with breast cancer, such as XRCC2, a rare, moderate-penetrance, breast cancer susceptibility gene.[33] (Refer to the Clinical Sequencing section in the Cancer Genetics Overview PDQ summary for more information about whole-exome sequencing.)

References

Pharoah PD, Antoniou A, Bobrow M, et al.: Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet 31 (1): 33-6, 2002. [PUBMED Abstract]
Breast Cancer Association Consortium: Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium. J Natl Cancer Inst 98 (19): 1382-96, 2006. [PUBMED Abstract]
Dunning AM, Healey CS, Pharoah PD, et al.: A systematic review of genetic polymorphisms and breast cancer risk. Cancer Epidemiol Biomarkers Prev 8 (10): 843-54, 1999. [PUBMED Abstract]
Thorisson GA, Smith AV, Krishnan L, et al.: The International HapMap Project Web site. Genome Res 15 (11): 1592-3, 2005. [PUBMED Abstract]
Clarke L, Zheng-Bradley X, Smith R, et al.: The 1000 Genomes Project: data management and community access. Nat Methods 9 (5): 459-62, 2012. [PUBMED Abstract]
Evans DM, Cardon LR: Genome-wide association: a promising start to a long race. Trends Genet 22 (7): 350-4, 2006. [PUBMED Abstract]
Cardon LR: Genetics. Delivering new disease genes. Science 314 (5804): 1403-5, 2006. [PUBMED Abstract]
Chanock SJ, Manolio T, Boehnke M, et al.: Replicating genotype-phenotype associations. Nature 447 (7145): 655-60, 2007. [PUBMED Abstract]
Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447 (7145): 661-78, 2007. [PUBMED Abstract]
Easton DF, Pooley KA, Dunning AM, et al.: Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447 (7148): 1087-93, 2007. [PUBMED Abstract]
Stacey SN, Manolescu A, Sulem P, et al.: Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 39 (7): 865-9, 2007. [PUBMED Abstract]
Hunter DJ, Kraft P, Jacobs KB, et al.: A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 39 (7): 870-4, 2007. [PUBMED Abstract]
Turnbull C, Ahmed S, Morrison J, et al.: Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet 42 (6): 504-7, 2010. [PUBMED Abstract]
Gold B, Kirchhoff T, Stefanov S, et al.: Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci U S A 105 (11): 4340-5, 2008. [PUBMED Abstract]
Zheng W, Long J, Gao YT, et al.: Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet 41 (3): 324-8, 2009. [PUBMED Abstract]
Kibriya MG, Jasmine F, Argos M, et al.: A pilot genome-wide association study of early-onset breast cancer. Breast Cancer Res Treat 114 (3): 463-77, 2009. [PUBMED Abstract]
Murabito JM, Rosenberg CL, Finger D, et al.: A genome-wide association study of breast and prostate cancer in the NHLBI's Framingham Heart Study. BMC Med Genet 8 (Suppl 1): S6, 2007. [PUBMED Abstract]
Stacey SN, Manolescu A, Sulem P, et al.: Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 40 (6): 703-6, 2008. [PUBMED Abstract]
Ahmed S, Thomas G, Ghoussaini M, et al.: Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet 41 (5): 585-90, 2009. [PUBMED Abstract]
Reeves GK, Travis RC, Green J, et al.: Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci. JAMA 304 (4): 426-34, 2010. [PUBMED Abstract]
Haiman CA, Chen GK, Vachon CM, et al.: A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat Genet 43 (12): 1210-4, 2011. [PUBMED Abstract]
Stevens KN, Fredericksen Z, Vachon CM, et al.: 19p13.1 is a triple-negative-specific breast cancer susceptibility locus. Cancer Res 72 (7): 1795-803, 2012. [PUBMED Abstract]
Campa D, Kaaks R, Le Marchand L, et al.: Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium. J Natl Cancer Inst 103 (16): 1252-63, 2011. [PUBMED Abstract]
Milne RL, Gaudet MM, Spurdle AB, 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. Breast Cancer Res 12 (6): R110, 2010. [PUBMED Abstract]
Pharoah PD, Antoniou AC, Easton DF, et al.: Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med 358 (26): 2796-803, 2008. [PUBMED Abstract]
Gail MH: Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst 100 (14): 1037-41, 2008. [PUBMED Abstract]
Gail MH: Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst 101 (13): 959-63, 2009. [PUBMED Abstract]
Wacholder S, Hartge P, Prentice R, et al.: Performance of common genetic variants in breast-cancer risk models. N Engl J Med 362 (11): 986-93, 2010. [PUBMED Abstract]
Song H, Ramus SJ, Tyrer J, et al.: A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2. Nat Genet 41 (9): 996-1000, 2009. [PUBMED Abstract]
Goode EL, Chenevix-Trench G, Song H, et al.: A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat Genet 42 (10): 874-9, 2010. [PUBMED Abstract]
Bolton KL, Tyrer J, Song H, et al.: Common variants at 19p13 are associated with susceptibility to ovarian cancer. Nat Genet 42 (10): 880-4, 2010. [PUBMED Abstract]
Shendure J: Next-generation human genetics. Genome Biol 12 (9): 408, 2011. [PUBMED Abstract]
Park DJ, Lesueur F, Nguyen-Dumont T, et al.: Rare mutations in XRCC2 increase the risk of breast cancer. Am J Hum Genet 90 (4): 734-9, 2012. [PUBMED Abstract]