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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.

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.

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.

This file contains details too lengthy to put in the primary manuscript file as well as methods for supplemental data

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Quantitative fluorescence microscopy is severely hindered by intrinsic autofluorescence (AF). Endogenous fluorescent molecules in tissue and cell samples emit fluorescence that often dominates signals from specific dyes. This makes AF removal critical to the development and practice of quantitative fluorescence microscopy. In this study, we showed that AF signal could be separated from specific signal using a customized filter set. The filter set used the same excitation and beam splitter as the standard filter set, but the emission filter was red-shifted 40-60 nm from the peak of the specific dye. This filter set configuration collected mostly AF with minimum contribution from the specific dye. A linear transformation of AF images was required to correct for the difference in exposure and filter configuration. The constants (slope and intercept) in linear transformation were obtained through a pixel to pixel comparison between AF images (no staining) obtained by the standard filter set and the customized AF filter set. After staining of specific dye, the standard filter collecting target dye spectra was used to capture both target signal and AF, whereas customized filter was used to capture only AF. AF removal was accomplished by subtracting the linear transformed AF image from the image obtained from the standard filter. To validate our approach, we examined weak staining of androgen receptor in an AF abundant prostate tissue sample. Our method revealed a similar but cleaner nuclear staining of androgen receptor in a specimen, when compared to a traditional autofluorescence removal method.