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Gastroenterology. 2013 Sep;145(3):554-65. doi: 10.1053/j.gastro.2013.05.010. Epub 2013 May 14.

Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil.

Author information

1
Centre for Computational Biology, Duke-National University of Singapore Graduate Medical School, Singapore.

Abstract

BACKGROUND & AIMS:

Almost all gastric cancers are adenocarcinomas, which have considerable heterogeneity among patients. We sought to identify subtypes of gastric adenocarcinomas with particular biological properties and responses to chemotherapy and targeted agents.

METHODS:

We compared gene expression patterns among 248 gastric tumors; using a robust method of unsupervised clustering, consensus hierarchical clustering with iterative feature selection, we identified 3 major subtypes. We developed a classifier for these subtypes and validated it in 70 tumors from a different population. We identified distinct genomic and epigenomic properties of the subtypes. We determined drug sensitivities of the subtypes in primary tumors using clinical survival data, and in cell lines through high-throughput drug screening.

RESULTS:

We identified 3 subtypes of gastric adenocarcinoma: proliferative, metabolic, and mesenchymal. Tumors of the proliferative subtype had high levels of genomic instability, TP53 mutations, and DNA hypomethylation. Cancer cells of the metabolic subtype were more sensitive to 5-fluorouracil than the other subtypes. Furthermore, in 2 independent groups of patients, those with tumors of the metabolic subtype appeared to have greater benefits with 5-fluorouracil treatment. Tumors of the mesenchymal subtype contain cells with features of cancer stem cells, and cell lines of this subtype are particularly sensitive to phosphatidylinositol 3-kinase-AKT-mTOR inhibitors in vitro.

CONCLUSIONS:

Based on gene expression patterns, we classified gastric cancers into 3 subtypes, and validated these in an independent set of tumors. The subgroups have differences in molecular and genetic features and response to therapy; this information might be used to select specific treatment approaches for patients with gastric cancer.

KEYWORDS:

5-FU; 5-fluorouracil; BFRM; Bayesian Factor Regression Modeling; CHC; CHC_IFS; CNA; CSC; Cancer Classification; DPD; FDR; GC-Class; GO; Gene Ontology; IFS; KEGG; Kyoto Encyclopedia of Genes and Genomes; PI3K; Personalized Cancer Treatment; SPEM; Stomach Cancer; TS; cancer stem cell; consensus hierarchical clustering; consensus hierarchical clustering with iterative feature selection; copy number alteration; dihydropyrimidine dehydrogenase; false discovery rate; gastric cancer classifier; iterative feature selection; mRNA; mTOR; mammalian target of rapamycin; messenger RNA; phosphatidylinositol 3-kinase; spasmolytic-polypeptide-expressing metaplasia; thymidylate synthase

PMID:
23684942
DOI:
10.1053/j.gastro.2013.05.010
[Indexed for MEDLINE]

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