A novel visual analysis interface enables integrative analyses of cancer transcriptomics data and iden-tifies potential markers of immunotherapy response via machine learning.

Abstract

The Cancer Genome Atlas (TCGA) initiative has been essential for revealing key mechanisms in human cancer leading to the development of novel therapeutics. Analysis of the cancer transcriptomics data in the TCGA and other public repositories require coding skills that are associated with a steep learning curve for most non-specialists. To enable a wider utilization of these data, we introduce The Cancer Genome Explorer (TCGEx), a web-based visual data analysis interface that can perform a number of sophisticated analyses ranging from survival modeling and gene set enrichment analysis to unsupervised clustering and linear regression-based machine learning. In addition to providing access to preprocessed data from TCGA and immune checkpoint inhibition studies on cBioportal and CRI-iAtlas, the TCGEx platform allows users to upload and investigate their own data. Using this tool, we investigated the molecular subsets of human melanoma and identified microRNAs associated with intratumoral interferon signaling. We validated these findings using independent data from clinical trials involving immune check-point inhibitors for melanoma and other cancers. Moreover, our analyses unveiled a subset of cytokines predictive of positive responses to diverse immune checkpoint inhibitors prior to treatment initiation. Built on the R/Shiny framework, TCGEx modules offer customizable features to tailor the analysis to different study contexts and help generate publication-ready plots. TCGEx is freely available at https://tcgex.iyte.edu.tr, and it provides an interactive solution to extract meaningful insights from cancer transcriptomics data and guide scientific inquiry.