Huether, Robert

2621 Background: TMB is an emerging predictor of survival with immunotherapy. TMB is determined by taking the difference between somatic and germline datasets when tumor-normal pairs are available. In the case of commonly utilized tumor-only sequencing, additional steps are needed to estimate the somatic alterations. Computational tools have been developed that determine germline contribution based on sample copy state, purity estimates and occurrence of the variant in population databases. Given the potential bias of population datasets, we hypothesized that tumor-only filtering approaches may overestimate the actual TMB. Methods: We assessed the TMB from 50 tumors in 10 diseases including all missense, indels, and frameshift variants with an allelic fraction (AF) ≥5% and Coverage ≥100X within the tumor. Tumor-only TMB was evaluated against the gold standard of matched germline subtracted TMB at three levels. Level 1 removed all the tumor-only variants with AF in the non-TCGA ExAC database ≥1%. Level 2 removed all variants observed in population databases simulating a naive approach of removing germline variation. Level 3 used an internal tumor-only pipeline for calculating TMB. Results: There were significantly higher estimates of TMB with Level 1, Level 2 and Level 3 tumor-only filtering approaches than that determined by germline subtraction, resulting in significant bias. Whereas there was no correlation between TMB estimates and tumor-germline TMB for Level 1 filtering, there were improvements in correlations for Level 2 and Level 3. Conclusions: The tumor-only approaches that filter variants in population databases overestimate TMB compared to that determined by germline subtraction. Despite improved correlations with more stringent filtering approaches, these falsely elevated estimates may result in the inappropriate categorization of tumor specimens and negatively impact clinical trial results and patient outcomes. [Table: see text]

Abstract Clinical genetic testing panels are broadly used to gather information about cancer predisposition in individuals with personal and/or family history of breast cancer. However, the involvement of several of the genes on clinical testing panels in predisposition to breast cancer, such as MRE11A and RAD50, has recently come into question. In addition, accurate risk estimates for breast and other cancer are not well defined for the majority of genes on testing panels. We studied 60,000 women diagnosed with breast cancer who were tested for germline cancer predisposing mutations using hereditary cancer gene panels. Information on personal and family cancer history, age of diagnosis, and ethnicity of patients was obtained from test requisition forms. Greater than 90% met National Comprehensive Cancer Network HBOC testing criteria. To estimate gene-specific risks for breast cancer, case-control analyses were performed comparing the frequencies of pathogenic mutations from Caucasian cancer cases with frequencies from Caucasian, non-Finnish, non-TCGA controls from the Exome Aggregation Consortium (ExAC) database. Mutations were detected in 9% of breast cancer patients. Twelve genes displayed a significant association (p<0.05) with breast cancer. Nine of these genes, including ATM, RAD51D, NF1, and MSH6, were associated with moderate risk (RR>2.0) of breast cancer and three genes (BRCA1, BRCA2, PALB2) were associated with high risk (RR>5.0) of breast cancer. Cumulative age-dependent risk models were developed for each gene. This large clinical testing dataset of 60,000 women with breast cancer provides useful data for many predisposition genes previously lacking risk estimates, and should prove useful for clinical risk management of patients with inherited mutations in these genes. Citation Format: Couch FJ, Hu C, Lilyquist J, Shimelis H, Akinhanmi M, Na J, Polley EC, Hart SN, McFarland R, LaDuca H, Huether R, Goldgar DE, Dolinsky JS. Breast cancer risks associated with mutations in cancer predisposition genes identified by clinical genetic testing of 60,000 breast cancer patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr S2-01.

Abstract Multigene panel testing (MGPT) for hereditary cancer is increasing in popularity in the USA. Many panels include genes identified as hereditary breast and/or ovarian cancer (HBOC) genes despite limited data regarding the precise cancer risks associated with mutations in these genes. Here we report on results from BreastNext and OvaNext panel testing of 20 genes (ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, MLH1, MRE11A, MSH2, MSH6, NBN, NF1, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, TP53) in a cohort of 15,083 individuals. The majority of individuals were from high-risk breast and/or ovarian (Br/Ov) cancer families, with 92.4% of all probands meeting National Comprehensive Cancer Network HBOC testing criteria. Pathogenic mutations were identified in 9.4% of the overall cohort. To estimate gene-specific breast and ovarian cancer risks, case-control analyses were performed comparing the frequencies of pathogenic mutations from Caucasian breast or ovarian cancer cases from BreastNext and OvaNext with frequencies from Caucasian, non-Finnish, non-TCGA controls from the Exome Aggregation Consortium (ExAC) database. Mutations in the well studied ATM and CHEK2 genes were associated with moderate risks (OR>2) of breast cancer and mutations in PALB2 were associated with high-risks (OR>5) of breast cancer, consistent with previous reports. In addition, the study suggested that pathogenic mutations in MSH6, RAD51D, CDH1, and NF1 are associated with moderate to high risks of breast cancer. In contrast, RAD51C, RAD51D, and BRIP1 mutations were associated with high risks of ovarian cancer, PALB2 mutations were associated with moderate risks, but ATM and CHEK2 mutations were not associated with increased ovarian cancer risk. In addition, modeling of missense mutations in the predisposition genes using in silico prediction algorithms suggested that missense mutations in CDH1, CHEK2, MSH2, and MSH6 are associated with moderate risks of breast cancer and missense mutations in RAD51C increase risks of ovarian cancer. This large breast and ovarian cancer case-control analysis provides useful data for many predisposition genes previously lacking risk estimates, and should prove useful for clinical risk management of patients after clinical panel testing. Citation Format: Fergus J. Couch, David E. Goldgar, Steven N. Hart, Emily Hallberg, Raymond Moore, Huong Meeks, Robert Huether, Holly LaDuca, Elizabeth Chao, Jill Dolinsky. Breast and ovarian cancer risks associated with cancer predisposition gene mutations identified by multigene panel testing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2597.