Impact of molecular testing in advanced melanoma on outcomes in a tertiary cancer center and as reported in a publicly available database. Issue 4 (9th June 2021)

• Record Type: Journal Article
• Authors: Dimitrova, Maya; Kim, Min Jae; Osman, Iman; Jour, George
• Is Part Of: Cancer reports. Volume 4:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-09
• Subjects: cancer genetics -- melanoma -- mutations -- targeted therapy; Cancer -- Periodicals; 616.994005
• DOI: 10.1002/cnr2.1380
• Languages: English
• ISSNs: 2573-8348

Abstract

Abstract: Background: In patients with advanced melanoma (MM), genomic profiling may guide treatment decisions in the frontline setting and beyond as specific tumor mutations can be treated with targeted therapy (TT). The range of panel sizes used to identify targetable mutations (TM) can range from a few dozen to whole exome sequencing (WES). Aim: We investigated the impact of panel size and mutation status on first‐line treatment selection and outcomes in MM. Methods and Results: We analyzed data for 1109 MM patients from three cohorts: 169 patients at NYULH and profiled with the 50 gene Ion Torrent panel (IT), 195 patients at MSKCC, profiled with the 400‐gene MSK‐IMPACT panel (MSK‐I) and 745 patients at seven different sites profiled with WES. Data for cohorts 2 and 3 were extrapolated from the publicly available cBioPortal. Treatment information was available for 100%, 25%, and 0% of patients in cohort 1, 2, and 3, respectively. BRAF and NRAS were among the top five most commonly mutated genes in the IT and MSK‐I, whereas for WES only BRAF was a top five mutation. There was no significant difference in OS for BRAF MUT patients treated with immune checkpoint inhibitors (ICI) vs TT in cohort 1 ( P = .19), nor for BRAF MUT patients from cohort 1 treated with ICI vs those from cohort 2 treated with TT ( P = .762). Conclusion: Public datasets provide population‐level data; however, the heterogeneity of reported clinical information limits their value and calls for data standardization. Without evidence of clear clinical benefit of a larger panel size, there is a rationale for adopting smaller, more cost effective panels in MM.