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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...

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Detalles Bibliográficos
Autores principales: Ichikawa, Hiroshi, Nagahashi, Masayuki, Shimada, Yoshifumi, Hanyu, Takaaki, Ishikawa, Takashi, Kameyama, Hitoshi, Kobayashi, Takashi, Sakata, Jun, Yabusaki, Hiroshi, Nakagawa, Satoru, Sato, Nobuaki, Hirata, Yuki, Kitagawa, Yuko, Tanahashi, Toshiyuki, Yoshida, Kazuhiro, Nakanishi, Ryota, Oki, Eiji, Vuzman, Dana, Lyle, Stephen, Takabe, Kazuaki, Ling, Yiwei, Okuda, Shujiro, Akazawa, Kohei, Wakai, Toshifumi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664811/
https://www.ncbi.nlm.nih.gov/pubmed/29089060
http://dx.doi.org/10.1186/s13073-017-0484-3
Descripción
Sumario: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. METHODS: A total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE) tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA 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 of Epstein-Barr virus (EBV) infection and microsatellite instability (MSI) status. RESULTS: Comprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7%) and of 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 genomically 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 clusters including five with actionable gene alterations: ERBB2 (N = 25), CDKN2A, and CDKN2B (N = 10), KRAS (N = 10), BRCA2 (N = 9), and ATM cluster (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 cluster. CONCLUSIONS: This actionable gene-based classification creates a framework for further studies for realizing precision medicine in GC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-017-0484-3) contains supplementary material, which is available to authorized users.