Comprehensive genomic meta-analysis identifies intra-tumoural stroma as a predictor of survival in patients with gastric cancer.
Journal Article (Journal Article)
OBJECTIVE: Gastric adenocarcinoma (gastric cancer, GC) is a major cause of global cancer mortality. Identifying molecular programmes contributing to GC patient survival may improve our understanding of GC pathogenesis, highlight new prognostic factors and reveal novel therapeutic targets. The authors aimed to produce a comprehensive inventory of gene expression programmes expressed in primary GCs, and to identify those expression programmes significantly associated with patient survival. DESIGN: Using a network-modelling approach, the authors performed a large-scale meta-analysis of GC transcriptome data integrating 940 gastric transcriptomes from multiple independent patient cohorts. The authors analysed a training set of 428 GCs and 163 non-malignant gastric samples, and a validation set of 288 GCs and 61 non-malignant gastric samples. RESULTS: The authors identified 178 gene expression programmes ('modules') expressed in primary GCs, which were associated with distinct biological processes, chromosomal location patterns, cis-regulatory motifs and clinicopathological parameters. Expression of a transforming growth factor β (TGF-β) signalling associated 'super-module' of stroma-related genes consistently predicted patient survival in multiple GC validation cohorts. The proportion of intra-tumoural stroma, quantified by morphometry in tissue sections from gastrectomy specimens, was also significantly associated with stromal super-module expression and GC patient survival. CONCLUSION: Stromal gene expression predicts GC patient survival in multiple independent cohorts, and may be closely related to the intra-tumoural stroma proportion, a specific morphological GC phenotype. These findings suggest that therapeutic approaches targeting the GC stroma may merit evaluation.
- Wu, Y; Grabsch, H; Ivanova, T; Tan, IB; Murray, J; Ooi, CH; Wright, AI; West, NP; Hutchins, GGA; Wu, J; Lee, M; Lee, J; Koo, JH; Yeoh, KG; van Grieken, N; Ylstra, B; Rha, SY; Ajani, JA; Cheong, JH; Noh, SH; Lim, KH; Boussioutas, A; Lee, J-S; Tan, P
- August 2013
Volume / Issue
- 62 / 8
Start / End Page
- 1100 - 1111
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)