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Consensus Molecular Subtypes Efficiently Classify Gastric Adenocarcinomas and Predict the Response to Anti-PD-1 Immunotherapy

SIMPLE SUMMARY: Gastric adenocarcinoma (GAC) is most commonly classified based on a system developed by the Cancer Genome Atlas in 2014. However, this subtyping system cannot efficiently identify suitable candidates for immunotherapy. Because GAC is highly heterogeneous and closely related to CRC at...

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Detalles Bibliográficos
Autores principales: Wu, Xiangyan, Ye, Yuhan, Vega, Kenneth J., Yao, Jiannan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367605/
https://www.ncbi.nlm.nih.gov/pubmed/35954402
http://dx.doi.org/10.3390/cancers14153740
Descripción
Sumario:SIMPLE SUMMARY: Gastric adenocarcinoma (GAC) is most commonly classified based on a system developed by the Cancer Genome Atlas in 2014. However, this subtyping system cannot efficiently identify suitable candidates for immunotherapy. Because GAC is highly heterogeneous and closely related to CRC at the molecular and functional levels, we explored the clinical utility of CMS classification originally developed for CRC and found that the CMS subtyping system can efficiently classify GAC. CMS1-4 classifications in GAC recapitulated their corresponding CRC subtype characteristics. Notably, CMS1 predicted a favorable response to anti-PD-1 therapy, and CMS4 outperformed the classical TCGA subtyping prognostic prediction and identified patients with an unfavorable anti-PD-1 response. Strikingly, partitioning the CMS4 subtype by EMT activation identified an additional anti-PD-1-susceptible patient subgroup. These results provide new insights that may help to improve clinical outcomes in immunotherapy candidates. ABSTRACT: Background: Gastric adenocarcinoma (GAC) is highly heterogeneous and closely related to colorectal cancer (CRC) both molecularly and functionally. GAC is currently subtyped using a system developed by TCGA. However, with the emergence of immunotherapies, this system has failed to identify suitable treatment candidates. Methods: Consensus molecular subtypes (CMSs) developed for CRC were used for molecular subtyping in GAC based on public expression cohorts, including TCGA, ACRG, and a cohort of GAC patients treated with the programmed cell death 1 (PD-1) inhibitor pembrolizumab. All aspects of each subtype, including clinical outcome, molecular characteristics, oncogenic pathway activity, and the response to immunotherapy, were fully explored. Results: CMS classification was efficiently applied to GAC. CMS4, characterized by EMT activation, stromal invasion, angiogenesis, and the worst clinical outcomes (median OS 24.2 months), was the predominant subtype (38.8%~44.3%) and an independent prognostic indicator that outperformed classical TCGA subtyping. CMS1 (20.9%~21.5%) displayed hypermutation, low SCNV, immune activation, and best clinical outcomes (median OS > 120 months). CMS3 (17.95%~25.7%) was characterized by overactive metabolism, KRAS mutation, and intermediate outcomes (median OS 85.6 months). CMS2 (14.6%~16.3%) was enriched for WNT and MYC activation, differentiated epithelial characteristics, APC mutation, lack of ARID1A, and intermediate outcomes (median OS 48.7 months). Notably, CMS1 was strongly correlated with immunotherapy biomarkers and favorable for the anti-PD-1 drug pembrolizumab, whereas CMS4 was poorly responsive but became more sensitive after EMT-based stratification. Conclusions: Our study reveals the practical utility of CMS classification for GAC to improve clinical outcomes and identify candidates who will respond to immunotherapy.