Genetics of Breast and Gynecologic Cancers (PDQ®)–Health Professional Version - National Cancer Institute

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associations (described below), has been relatively disappointing.[2,3] The candidate gene approach has largely been replaced by genome-wide association studies (GWAS) in which a very large number of single nucleotide polymorphisms (SNPs) (approximately 1 million to 5 million) are chosen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 (ER)–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 in BRCA1/BRCA2-deficient cells.

Polygenic risk scores for breast and ovarian cancer

The collective influence of many genetic variants has more recently been evaluated using an aggregate score. In 2015, a polygenic risk score (PRS) comprising all of the known breast cancer risk genetic variants or SNPs was estimated in women of European ancestry using 41 studies in the Breast Cancer Association Consortium (BCAC), including more than 33,000 breast cancer cases and 33,000 controls.[32] This early attempt at estimating a PRS for breast cancer included 77 SNPs, which collectively conferred lifetime risks of developing breast cancer by age 80 years of 3.5% and 29% for women in the lowest and highest 1% of the PRS, respectively.[32] Since then, PRSs incorporating additional genetic variants and examining other breast cancer–related outcomes including tumor and pathological characteristics, mode of detection, and contralateral breast cancer (CBC) have been estimated.[33-40] In 2019, the PRS with the highest discriminatory ability to date was developed and prospectively validated in the largest GWAS datasets available (79 studies in BCAC and more than 190,000 women in the U.K. Biobank), which incorporates information on 313 genetic variants and is optimized for ER-positive and ER-negative breast cancer.[39] Compared with women in the middle quintile, those in the highest 1% of PRS313 had 4.04-, 4.37-, and 2.78-fold risks of developing breast cancer overall, ER-positive disease, and ER-negative disease, respectively.[39] Lifetime absolute risk of breast cancer by age 80 years for women in the lowest and highest 1% of PRS313 ranged from 2% to 31% for ER-positive breast cancer, while for ER-negative disease, the absolute risks ranged from 0.55% to 4%.[39]

Common genomic variants associated with the development of a first primary breast cancer are also associated with the development of CBC.[40] Women in the highest quartile of the PRS had a 1.6-fold increased risk of developing CBC compared with the lowest quartile.[40] Moreover, PRSs of breast and ovarian cancers have been assessed in women who are carriers of BRCA1 and BRCA2 pathogenic variants, and have been found to be predictive of cancer risk in these women, supporting the hypothesis of a shared polygenic component of cancer risk between the general population and variant carriers.[36] The PRS for ER-negative disease had the strongest association with breast cancer risk in BRCA1 variant carriers, while the strongest association in BRCA2 variant carriers was seen for the overall breast cancer PRS. BRCA1 variant carriers had cumulative lifetime risks of 56% and 75% of developing breast cancer at the 10th and 90th percentile of the PRS, respectively. The ovarian cancer PRS was strongly associated with risk for both BRCA1 and BRCA2 variant carriers. For BRCA2 variant carriers, the ovarian cancer risk was 6% and 19% by age 80 years for those at the 10th and 90th percentile of PRS, respectively. The authors noted that the incorporation of the PRS into risk prediction models may better inform decisions on cancer risk management for this population.[36]

Several studies have examined the extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs, and reporting improved discriminatory accuracy after inclusion of the PRS.[41-46] For example, in a study combining PRS77 with clinical models, the AUC for predicting breast cancer before age 50 years improved by more than 20%.[42] Clinical trials, including WISDOM and MyPeBs, are in progress to study the potential clinical utility of the PRS for making screening decisions and understanding outcomes.[47] Because PRSs have been largely developed and validated in populations of European ancestry, the utility and prediction accuracy of these PRSs in non-European populations is unknown.

Whole-Genome and Whole-Exome Sequencing

In addition to GWAS interrogating common genetic variants, sequencing-based studies involving whole-genome or whole-exome sequencing [48] are also identifying genes associated with breast cancer, such as XRCC2, a rare, moderate-penetrance breast cancer susceptibility gene.[49] (Refer to the Clinical Sequencing section in the PDQ summary on Cancer Genetics Overview for more information about whole-exome sequencing.)

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