Population-based estimates of the risk of breast cancer associated with germline pathogenic variants in cancer-predisposition genes are critically needed for risk assessment and management in women with inherited pathogenic variants. In a population-based case–control study, we performed sequencing using a custom multigene amplicon-based panel to identify germline pathogenic variants in 28 cancer-predisposition genes among 32,247 women with breast cancer (case patients) and 32,544 unaffected women (controls) from population-based studies in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Associations between pathogenic variants in each gene and the risk of breast cancer were assessed. Pathogenic variants in 12 established breast cancer–predisposition genes were detected in 5.03% of case patients and in 1.63% of controls. Pathogenic variants in BRCA1 and BRCA2 were associated with a high risk of breast cancer, with odds ratios of 7.62 (95% confidence interval [CI], 5.33 to 11.27) and 5.23 (95% CI, 4.09 to 6.77), respectively. Pathogenic variants in PALB2 were associated with a moderate risk (odds ratio, 3.83; 95% CI, 2.68 to 5.63). Pathogenic variants in BARD1, RAD51C, and RAD51D were associated with increased risks of estrogen receptor–negative breast cancer and triple-negative breast cancer, whereas pathogenic variants in ATM, CDH1, and CHEK2 were associated with an increased risk of estrogen receptor–positive breast cancer. Pathogenic variants in 16 candidate breast cancer–predisposition genes, including the c.657_661del5 founder pathogenic variant in NBN, were not associated with an increased risk of breast cancer. This study provides estimates of the prevalence and risk of breast cancer associated with pathogenic variants in known breast cancer–predisposition genes in the U.S. population. These estimates can inform cancer testing and screening and improve clinical management strategies for women in the general population with inherited pathogenic variants in these genes. (Funded by the National Institutes of Health and the Breast Cancer Research Foundation.)

Purpose: Recent advances in DNA sequencing have led to the development of breast cancer susceptibility gene panels for germline genetic testing of patients. We assessed the frequency of mutations in 17 predisposition genes, including BRCA1 and BRCA2, in a large cohort of patients with triple-negative breast cancer (TNBC) unselected for family history of breast or ovarian cancer to determine the utility of germline genetic testing for those with TNBC. Patients and Methods: Patients with TNBC (N = 1,824) unselected for family history of breast or ovarian cancer were recruited through 12 studies, and germline DNA was sequenced to identify mutations. Results: Deleterious mutations were identified in 14.6% of all patients. Of these, 11.2% had mutations in the BRCA1 (8.5%) and BRCA2 (2.7%) genes. Deleterious mutations in 15 other predisposition genes were detected in 3.7% of patients, with the majority observed in genes involved in homologous recombination, including PALB2 (1.2%) and BARD1, RAD51D, RAD51C, and BRIP1 (0.3% to 0.5%). Patients with TNBC with mutations were diagnosed at an earlier age (P < .001) and had higher-grade tumors (P = .01) than those without mutations. Conclusion: Deleterious mutations in predisposition genes are present at high frequency in patients with TNBC unselected for family history of cancer. Mutation prevalence estimates suggest that patients with TNBC, regardless of age at diagnosis or family history of cancer, should be considered for germline genetic testing of BRCA1 and BRCA2. Although mutations in other predisposition genes are observed among patients with TNBC, better cancer risk estimates are needed before these mutations are used for clinical risk assessment in relatives.

Recent improvements in DNA sequencing technology have led to the development of multigene panels for clinical genetic testing of several conditions. In particular, panels targeting genes implicated in cancer susceptibility have increased the likelihood of detecting cancer-predisposing variants and offer advantages in time and cost compared with single gene testing.^1-3 A broad range of cancer susceptibility panels are available from genetic testing laboratories.^1,4-6 These include high-penetrance BRCA1 and BRCA2 breast and ovarian cancer genes; mismatch repair genes; high-penetrance CDH1, PTEN, STK11, and TP53 genes that are associated with hereditary diffuse gastric cancer as well as Cowden disease, Peutz-Jeghers syndrome, and Li-Fraumeni syndrome, respectively; and genes associated with moderate risks of breast cancer (2-fold to 5-fold), such as CHEK2 and ATM.^7,8 Patients with pathogenic variants in any of these genes are eligible for increased surveillance for cancer or other preventive measures. Cancer gene testing panels identify variants in substantial proportions of patients.^1,4,6 However, the frequency of variants in each of the panel genes among individuals qualifying for clinical genetic testing remains to be defined, and the risks of breast and other cancers associated with variants in many panel genes are not established. In this study, we report on the risks of breast cancer associated with inactivating variants in these genes identified by clinical genetic testing of patients with breast cancer by 1 laboratory.

Background: Given the high technical reproducibility and orders of magnitude greater resolution than microarrays, next-generation sequencing of mRNA (RNA-Seq) is quickly becoming the de facto standard for measuring levels of gene expression in biological experiments. Two important questions must be taken into consideration when designing a particular experiment, namely, 1) how deep does one need to sequence? and, 2) how many biological replicates are necessary to observe a significant change in expression? Results: Based on the gene expression distributions from 127 RNA-Seq experiments, we find evidence that 91% ± 4% of all annotated genes are sequenced at a frequency of 0.1 times per million bases mapped, regardless of sample source. Based on this observation, and combining this information with other parameters such as biological variation and technical variation that we empirically estimate from our large datasets, we developed a model to estimate the statistical power needed to identify differentially expressed genes from RNA-Seq experiments. Conclusions: Our results provide a needed reference for ensuring RNA-Seq gene expression studies are conducted with the optimally sample size, power, and sequencing depth. We also make available both R code and an Excel worksheet for investigators to calculate for their own experiments.

HepaRG cells, derived from a female hepatocarcinoma patient, are capable of differentiating into biliary epithelial cells and hepatocytes. More importantly, differentiated HepaRG cells are able to maintain activities of many xenobiotic-metabolizing enzymes, and expression of the metabolizing enzyme genes can be induced by xenobiotics. The ability of these cells to express and induce xenobiotic-metabolizing enzymes is in stark contrast to the frequently used HepG2 cells. The previous studies have mainly focused on a set of selected genes; therefore, it is of significant interest to know the extent of similarity of gene expression at whole genome levels in HepaRG cells and HepG2 cells compared with primary human hepatocytes and human liver tissues. To accomplish this objective, we used Affymetrix (Santa Clara, CA) U133 Plus 2.0 arrays to characterize the whole genome gene expression profiles in triplicate biological samples from HepG2 cells, HepaRG cells (undifferentiated and differentiated cells), freshly isolated primary human hepatocytes, and frozen liver tissues. After using similarity matrix, principal components, and hierarchical clustering methods, we found that HepaRG cells globally transcribe genes at levels more similar to human primary hepatocytes and human liver tissues than HepG2 cells. In particular, many genes encoding drug-processing proteins are transcribed at a more similar level in HepaRG cells than in HepG2 cells compared with primary human hepatocytes and liver samples. The transcriptomic similarity of HepaRG with primary human hepatocytes is encouraging for use of HepaRG cells in the study of xenobiotic metabolism, hepatotoxicology, and hepatocyte differentiation.