Comparison of AYA versus non-AYA ovarian cancer genomic landscape in NCI-MATCH trial.

Abstract

e17617 Background: NCI-MATCH, a signal-finding precision medicine trial, centrally screened tumors from ̃6000 patients age > 18 with refractory/relapsed cancer using Oncomine Comprehensive Assay (OCA) targeted gene panel. Screened cohort included a subset of patients with ovarian cancer age 18-39 yrs, overlapping adolescent and young adult (AYA) range (NCI consensus definition: 15-39 yrs). Objective of this study was to compare tumor genomic features of AYA to non-AYA ovarian cancers. Methods: Patient clinicopathologic, demographic, and tumor mutation (SNVs, Indels, CNVs by central OCA) data from NCI-MATCH were available. Analyses were restricted to mutation profiles generated by OCA version 2 (OCA v2), which assessed 143 genes and was used for most samples. Proportions of cases with mutations in each gene were compared for AYA and non-AYA groups by 2-sided Fisher’s exact tests. For each gene, association between age (continuous independent variable) and presence of mutation (binary dependent variable) was assessed using logistic regression. Benjamini-Hochberg adjusted p-values were computed; false discovery rate (FDR) was controlled at 10%. Results: Data from 455 ovarian cancers (437 epithelial, 18 stromal), including 21 AYA and 434 non-AYA cases, were included in this analysis. Among the 28 genes most frequently (in > 6 patient tumors) mutated and altered, CTNNB1 was mutated in 9.5% of AYA patients compared to 0.9% in non-AYA (unadj. p=0.027) but failed to meet 10% FDR criterion (FDR-adj. p=0.7). KRAS mutation was more frequent in AYA than non-AYA but not significantly after adjustment (FDR-adj. p=0.7). Logistic regression results showed TP53 mutation was significantly associated with older age (FDR-adj. p<0.0001), and ATM mutation was borderline associated with younger age (FDR-adj. p=0.052). No other differences, including in clinically actionable mutations ( BRCA1/2, MSH2), were observed. Table displays selected results. Conclusions: This preliminary study shows that no genes were mutated in significantly different proportion between AYA and non-AYA groups, but modeling age as a continuous variable highlighted known association of TP53 mutation with older age and a trend towards association of ATM mutation with younger age. More comprehensive tumor mutation profiling and analyses of additional tumor types may reveal further insights into rare AYA cancers. [Table: see text]