DOI: 10.5061/dryad.v41ns1s11
title: Conserved angio-immune subtypes of the cancer microenvironment predict response to immune checkpoint blockade therapy

description: Immune checkpoint blockade (ICB) therapy has revolutionized cancer
 treatment. However, only a fraction of the patients respond to ICB
 therapy. Accurate prediction of patients to likely respond to ICB would
 maximize the efficacy of ICB therapy. The tumor microenvironment (TME)
 dictates tumor progression and therapy outcome. Here, we classify the TME
 by analyzing the transcriptome from 11,069 cancer patients based on
 angiogenesis and T-cell activity. We find three distinct angio-immune TME
 subtypes conserved across 30 non-hematological cancers. There is a clear
 inverse relationship between angiogenesis and anti-tumor immunity in TME.
 Remarkably, patients displaying TME with low angiogenesis with strong
 anti-tumor immunity show the most significant responses to ICB therapy in
 four cancers. Re-evaluation of the renal cell carcinoma clinical trials
 provides compelling evidence that the baseline angio-immune state is
 robustly predictive of ICB responses. This study offers a rationale for
 incorporating baseline angio-immune scores for future ICB treatment
 strategies.
description: Data files are derived from processing as derived from
 manuscript. Code to generate angio-immune clusters is provided.
description: # Conserved angio-immune subtypes of the cancer microenvironment predict
 response to immune checkpoint blockade therapy Data files are derived from
 processing as derived from manuscript. Code to generate angio-immune
 clusters is provided. All data analyzed is from previously published
 datasets. Enrichment results files contain GSVA enrichment results used to
 derive angio-immune clusters. Clinical files contain information on
 response/survival status. R code to generate clusters  ## Description of
 the data and file structure **Data hosted on Dryad**: *
 angio-immune-clusters.xlsx - provides the cluster information for all TCGA
 patients analyzed * cluster = Pan cancer angio-immune cluster label  * x =
 TCGA sample names **Data hosted on Zenodo**: *
 Bladder-Cancer-Enrichment-Results.txt - provides enrichment results for
 anti-PDL1 pre-treatment patients with bladder cancer. Enrichment results
 range from -1 to 1 * Column names are sample IDs  * Row names are
 individual pathways for which enrichment is calculated *
 Bladder-Cancer-Clinical.txt - provides clinical information for anti-PDL1
 pre-treatment patients with bladder cancer * Best Confirmed Overall
 Response: Response label for each sample * Binary response: Binary version
 of Best Confirmed Overall Response * FMOne mutation burden per MB:
 mutational burden per MB * Tissue: Tissue of origin of sample * os:
 overall survival time * cencos: censoring info for overall survival * TCGA
 subtype: TCGA molecular subtype defined by transcriptomic data * Cluster:
 angio-immune cluster * CXCL9 and PD1: expression level of CXCL9 and PD1 *
 Ifngsigentrez Mhc1sigentrez and Mhc2sigentrez: Enrichment score (-1 to 1)
 of published signatures associated with clinical response to immune
 checkpoint blockade  * Gastric-Cancer-Enrichment-Results.txt - provides
 enrichment results for anti-PD1 pre-treatment patients with gastric
 cancer. Enrichment results range from -1 to 1 * Column names are sample
 IDs  * Row names are individual pathways for which enrichment is
 calculated * Gastric-Cancer-Angio-Immune-Clusters-Response.txt - provides
 treatment response information for anti-PD1 pre-treatment patients with
 gastric cancer * Angio-immune cluster for each sample * Binary response
 for each sample * Melanoma-Enrichment-Results.txt - provides enrichment
 results for anti-PD1 pre-treatment patients with melanoma. Enrichment
 results range from -1 to 1 * For each patient sample, enrichment results
 for each pathway queried * Column names are sample IDs  * Row names are
 individual pathways for which enrichment is calculated *
 Melanoma-Angio-Immune-Cluster-and-Clinical.txt - provides clinical
 information for anti-PD1 pre-treatment patients with melanoma * ID: Sample
 ID * pfs * pfs.event * OS * OS.Event * Response: best classified response
 * Angio-Immune Cluster: label for angio-immune cluster * CXCL9, PDL1 &
 PD1: TCGA expression of genes * IMPRES mhc2sig mhc1sig ifngsig:
 Calculation of previously published signatures as described in the
 manuscript * Javelin-Renal-Enrichment-and-Cluster-Results.txt - provides
 clinical information for pre-treatment patients with renal cancer. 
 Enrichment results range from -1 to 1 * For each patient sample,
 enrichment results for each pathway queried, and the angio-immune cluster
 label * Column names are sample IDs  * Row names are individual pathways
 for which enrichment is calculated * Genesets_File.txt - provides all
 genesets used to derive angio-immune clusters *
 pan-cancer-xcell-results.txt - provides xCell enrichment results for all
 pan-cancer patients. Enrichment values range from -1 to 1 * Provides the
 enrichment of deconvoluted cell types for each sample in the TCGA that was
 analyzed * Angio-Immune_Cluster_Identification.R - R code to derive
 angio-immune clusters * Pan-Cancer-Enrichment-Results.txt - Pan cancer
 enrichment results for all TCGA patients.  * Column names are sample IDs 
 * Row names are individual pathways for which enrichment is calculated
 **Structure of files** * Enrichment results data files contain the GSVA
 enrichment of 91 gene sets as defined in the anchoring manuscript for each
 sample. * Clinical data files minimally contain response, survival
 information for samples included in analysis ## Sharing/Access information
 Data was derived from the following sources: * Data hosted on Dryad:
 [https://gdc.cancer.gov/about-data/publications/pancanatlas](https://gdc.cancer.gov/about-data/publications/pancanatlas) * Data hosted on Zenodo: [http://tide.dfci.harvard.edu/](http://tide.dfci.harvard.edu/) ## Code/Software R script to identify angio-immune clusters is included in this dataset. Code uses GSVA package in R to calculate enrichment of genesets presented in Genesets_File.txt.