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Supplemental Figures from Stromal-Based Signatures for the Classification of Gastric Cancer

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posted on 2023-03-31, 00:27 authored by Mark T. Uhlik, Jiangang Liu, Beverly L. Falcon, Seema Iyer, Julie Stewart, Hilal Celikkaya, Marguerita O'Mahony, Christopher Sevinsky, Christina Lowes, Larry Douglass, Cynthia Jeffries, Diane Bodenmiller, Sudhakar Chintharlapalli, Anthony Fischl, Damien Gerald, Qi Xue, Jee-yun Lee, Alberto Santamaria-Pang, Yousef Al-Kofahi, Yunxia Sui, Keyur Desai, Thompson Doman, Amit Aggarwal, Julia H. Carter, Bronislaw Pytowski, Shou-ching Jaminet, Fiona Ginty, Aejaz Nasir, Janice A. Nagy, Harold F. Dvorak, Laura E. Benjamin

These figures contain extensions of data that would not fit in the primary manuscript but are important for a complete understanding of the work. These include analysis of other data sets, correlations between genomic profiling our stromal subtyping and information on bioinformatics and animal models.

History

ARTICLE ABSTRACT

Treatment of metastatic gastric cancer typically involves chemotherapy and monoclonal antibodies targeting HER2 (ERBB2) and VEGFR2 (KDR). However, reliable methods to identify patients who would benefit most from a combination of treatment modalities targeting the tumor stroma, including new immunotherapy approaches, are still lacking. Therefore, we integrated a mouse model of stromal activation and gastric cancer genomic information to identify gene expression signatures that may inform treatment strategies. We generated a mouse model in which VEGF-A is expressed via adenovirus, enabling a stromal response marked by immune infiltration and angiogenesis at the injection site, and identified distinct stromal gene expression signatures. With these data, we designed multiplexed IHC assays that were applied to human primary gastric tumors and classified each tumor to a dominant stromal phenotype representative of the vascular and immune diversity found in gastric cancer. We also refined the stromal gene signatures and explored their relation to the dominant patient phenotypes identified by recent large-scale studies of gastric cancer genomics (The Cancer Genome Atlas and Asian Cancer Research Group), revealing four distinct stromal phenotypes. Collectively, these findings suggest that a genomics-based systems approach focused on the tumor stroma can be used to discover putative predictive biomarkers of treatment response, especially to antiangiogenesis agents and immunotherapy, thus offering an opportunity to improve patient stratification. Cancer Res; 76(9); 2573–86. ©2016 AACR.