br While the increased risk of type I EC in
While the increased risk of type I EC in LS patients is well docu-mented [20,21], the rate of type II EC in patients with LS is poorly de-fined. Some studies have found a higher percentage of endometrioid histology in LS-associated EC compared to sporadic EC , while others indicate a higher rate of type II cancers among patients with LS com-pared to young patients with sporadic EC . Our study supports the use of LS screening in both type I and II EC, given similar rates of LS among patients with both subtypes. This is consistent with current So-ciety of Gynecologic Oncology (SGO) guidelines recommending system-atic clinical and/or molecular screening for LS in all EC patients regardless of histologic subtype.
Among women diagnosed with EC, the use of universal tumor test-ing to identify those at high risk for LS has led to increased detection compared to the use of clinical screening protocols such as the Amsterdam or Bethesda criteria . Universal screening in EC patients b70 years of age at diagnosis has been shown to be cost-effective due to prevention of colorectal cancer in EC patients and their relatives [25,26]. A similar model of universal or age-related tumor testing, with valida-tion of germline mutation status, could identify patients with cancer predisposition gene mutations, allowing for more rigorous breast and/ or ovarian cancer screening after an EC diagnosis as well as early identi-fication and prophylactic treatment in affected relatives. Shu et al. per-formed somatic testing in 3 patients with germline BRCA mutations who developed uterine serous or serous-like cancer after prophylactic oophorectomy. All serous/serous-like tumors demonstrated loss of BRCA protein Eltanexor by immunohistochemistry (IHC) analysis. One case of leiomyosarcoma retained BRCA1 protein expression . This suggests that IHC protocols used for LS testing could be modified
to detect cancer predisposition gene mutations in patients with EC, though larger IHC studies are needed to validate these findings.
Strengths of this study include a large cohort of patients with type I and II EC, including those with USC. We also utilized a comprehensive, 21-gene panel of clinically-relevant cancer susceptibility genes. The ability to compare patients with both type I and II EC allowed the study to confirm similar rates of LS mutations and low rates of BRCA mu-tations among patients with both subtypes. However, the study is lim-ited by a low frequency of cancer predisposition gene mutations and lack of matched controls, which restricted statistical comparisons be-tween patients with type I and type II EC and between specific muta-tions, and estimation of absolute risks of EC for individual genes. Our study is also limited by a homogeneous patient population, so it is un-clear whether these results would be generalizable to all U.S. or interna-tional EC patients. Further study is needed to resolve these issues, to confirm the utility of somatic testing for identification of EC patients with cancer predisposition gene mutations, and to determine the cost-effectiveness of universal or age-related testing.
Overall, our results suggest a clinically significant rate of actionable cancer predisposition gene mutations and LS gene mutations among EC patients. Further study is needed to determine whether these muta-tions could be detected with IHC analysis of endometrial tumors. These findings suggest that somatic testing for an expanded panel of cancer predisposition gene mutations could identify patients in which germline testing is warranted.
Beverly Long: study design, manuscript writing, data preparation, data interpretation, table and figure preparation.
Jenna Lilyquist: methods section writing, data preparation, data analysis and mutation calling, manuscript editing, data interpretation.
Amy Weaver: statistics calculation and analysis, editing of manu-script, table preparation.
Chunling Hu: sample preparation, DNA extraction.
Rohan Gnanaolivu: DNA sequencing and bioinformatic analysis.
Kun Y. Lee: sample preparation, DNA extraction.
Steven N. Hart: DNA sequencing and analysis, bioinformatics/ statistics.
Eric C. Polley: DNA sequencing and analysis, bioinformatics/ statistics.
Jamie N. Bakkum-Gamez: study design, data interpretation, editing of manuscript.
Fergus J. Couch: study design, data interpretation, supervision of DNA sequencing and analysis, editing of manuscript.
Sean C. Dowdy: study design, data interpretation, supervision of manuscript preparation/writing, editing of manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.