A SNaPshot of potentially personalized care: Molecular diagnostics in gynecologic cancer

Background. Genetic abnormalities underlie the development and progression of cancer, and represent potential opportunities for personalized cancer therapy in Gyn malignancies. Methods. We identified Gyn oncology patients at the MGH Cancer Center with tumors genotyped for a panel of mutations by SNaPshot, a CLIA approved assay, validated in lung cancer, that uses SNP genotyping in degraded DNA from FFPE tissue to identify 160 described mutations across 15 cancer genes (AKT1, APC, BRAF, CTNNB1, EGFR, ERBB2, IDH1, KIT, KRAS, MAP2KI, NOTCH1, NRAS, PIK3CA, PTEN, TP53). Results. Between 5/17/10 and 8/8/13, 249 pts consented to SNaPshot analysis. Median age 60 (29-84) yrs. Tumors were ovarian 123 (49%), uterine 74(30%), cervical 14(6%), fallopian 9(4%), primary peritoneal 13(5%), or rare 16(6%) with the incidence of testing high grade serous ovarian cancer (HGSOC) halving over time. SNaPshot was positive in 75 (30%), with 18 of these (24%) having 2 or 3 (n = 5) mutations identified. TP53 mutations are most common in high-grade serous cancers yet a low detection rate (17%) was likely related to the assay. However, 4 of the 7 purely endometrioid ovarian tumors (57%) harbored a p53 mutation. Of the 38 endometrioid uterine tumors, 18 mutations (47%) in the PI3Kinase pathway were identified. Only 9 of 122 purely serous (7%) tumors across all tumor types harbored a 'drugable' mutation, compared with 20 of 45 (44%) of endometrioid tumors (p < 0.0001). 17 pts subsequently enrolled on a clinical trial; all but 4 of whom had PIK3CA pathway mutations. Eight of 14 (47%) cervical tumors harbored a 'drugable' mutation. Conclusion. Although SNaPshot can identify potentially important therapeutic targets, the incidence of 'drugable' targets in ovarian cancer is low. In this cohort, only 7% of subjects eventually were treated on a relevant clinical trial. Geneotyping should be used judiciously and reflect histologic subtype and available platform. (C) 2016 Elsevier Inc. All rights reserved.