Actionable gene-based classification toward precision medicine in gastric cancer

Background: Intertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC). To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed.& para;& para;Methods: A total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE) tum or tissues were obtained from surgical or biopsy specimens and were subjected, to DMA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired, with US Food and Drug Administration-approved targeted therapies, and the evaluation o f Epstein-Barr virus (EBV) infection and microsatellite instability (MSI) status.& para;& para;Results: Comprehensive genomic sequencing detected at least one alteration o f 435 cancer-related genes in 194 GCs (93.7%) and o f 69 actionable genes in 141 GCs (68.1%). We classified the 207 GCs into four The Cancer Genome Atlas (TCGA) subtypes using the genomic profiling data; EBV (N = 9), MSI (N = 17), chromosomal instability (N = 119), and genomicaliy stable subtype (N = 62). Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated. phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32), while the others (N = 175) were sub-divided into six dusters including five with actionable gene alterations: ERBB2 (N = 25), CDKN2A, and CDKN2B (N = 10), KRAS (N = 10), BRCA2 (N = 9), and ATM duster (N = 12). The clinical utility of this classification was demonstrated by a case of unresectable GC. with a remarkable response to anti-HER2 therapy in the ERBB2 duster.& para;& para;Conclusions: This actionable gene-based classification creates a framework for further studies for realizing precision medicine in GC.