Transcriptomic Dataset for Network based identification of key Master Regulators for Immunologic Constant of Rejection

The goal of this study was to identify molecular alterations governing mechanisms for the immune exclusion phenotype. We developed a network-based approach to identify key transcription factors (TF) associated with poor immunologic anti-tumor activity. Based on the Immunologic Constant of Rejection (ICR) signature, tumors may be classified as immune active (ICR High) or immune silent (ICR Low). We used The Cancer Genome Atlas (TCGA) RNA-seq data of 12 specific cancer types (2,307 samples, 3,674 TFs, and 23,216 target genes) to build gene regulatory networks, determine each TF’s regulon, followed by determination of activity matrix of TFs for all tumor samples, and finally run a fast gene-set enrichment analysis to identify the most important TFs, named Master Regulators (MR), that are unique to ICR Low and ICR High tumors respectively.