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 angiogenesis model was performed as previously described. Animals were treated with various anti-VEGF Receptor antibodies via intraperitoneal injection at the doses (DC101 20 mpk or G6 10 mpk) and time points (day 0, day 5, day 20, day 60) to target all of the different populations of tumor-surrogate blood vessels, as they each develop at different time points. At least 5 animals, equally matched, were used per group. At the end of the experiment, angiogenic sites in flanks were photographed and tissues were taken for histology and RNA preparation.