Stromal-Based Signatures for the Classification of Gastric Cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Jiangang Liu
PROVIDER: E-GEOD-76630 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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2016-01-08 | E-GEOD-76628 | biostudies-arrayexpress
Project description:Increasing success is being achieved in the treatment of malignancies with stromal-targeted therapies, predominantly in anti-angiogenesis and immunotherapy, predominantly checkpoint inhibitors. Despite 15 years of clinical trials with anti-VEGF pathway inhibitors for cancer, we still find ourselves lacking reliable predictive biomarkers to select patients for anti-angiogenesis therapy. For the more recent immunotherapy agents, there are many approaches for patient selection under investigation. Notably, the predictive power of an Ad-VEGF-A164 mouse model to drive a stromal response with similarities to a wound healing response shows relevance for human cancer and was used to generate stromal signatures. We have developed gene signatures for 3 stromal states and leveraged the data from multiple large cohort bioinformatics studies of gastric cancer (TCGA, ACRG) to further understand how these relate to the dominant patient phenotypes identified by previous bioinformatics efforts. We have also designed multiplexed IHC assays that robustly represent the vascular and immune diversity in gastric cancer. Finally, we have used this methodology to arrive at a hypothesis of how angiogenesis and immunotherapy may fit into the experimental approaches for gastric cancer treatments. The Ad-VEGF-A164 flank model was performed as described in Flank tissue from harvest day 0, day 5, day 20, and day 60 was taken for RNA generation. Samples for messenger RNA (mRNA) profiling studies were processed by Asuragen, Inc. (Austin, TX, USA) using GeneChip® Mouse Genome 430 2.0 Array (Affymetrix, Santa Clara, CA) according to the company's standard operating procedures as described previously in detail [45]. A summary of the image signal data, detection calls and gene annotations for every gene interrogated on the arrays was generated using the Affymetrix Statistical Algorithm MAS 5.0 (GCOS v1.3) algorithm (scaling factor = 1500).
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