Abstract B051: Facilitating translational research with interactive tools for immuno-oncology data

Abstract

Abstract With the explosive growth in data and results from immuno-oncology (IO) studies, improved ways to easily share, integrate and explore available data and results are needed. The Cancer Research Institute (CRI) iAtlas (www.cri-iatlas.org) is an interactive web-based platform and set of analytic tools for studying interactions between tumors and the immune microenvironment. These tools allow researchers to explore associations among a variety of immune characterizations as well as with genomic and clinical phenotypes. The initial version of CRI iAtlas is based on an analysis performed by The Cancer Genome Atlas (TCGA) Research Network on the TCGA data set comprising over 10,000 tumor samples and 33 tumor types (Thorsson et al., 2018). The platform will be expanded to include other immunogenomic data sets and workflows. In the TCGA analysis, each tumor sample was scored for a variety of computationally estimated immune-based readouts, including immune cell composition, adaptive cell receptor repertoire, neoantigen load, and expression of genes coding for immunomodulatory proteins. Immune-based subtypes, spanning multiple tumor types, were identified. The web tool allows researchers to explore the data readouts as well as the relation between them in individual TCGA tumor types and across the global immune subtypes identified in the study. CRI iAtlas is made possible through a collaboration between CRI, Sage Bionetworks, and the Institute for Systems Biology. The main feature of the iAtlas web tool is the iAtlas Explorer, which provides several Analysis Modules to explore and visualize results. Each module presents information organized by theme, with multiple views and interactive controls to enhance and extend the information included in the original manuscript figures. Sample Group Overview: summaries of selected groups (including six immune subtypes that span cancer tissue types and molecular subtypes); Tumor Microenvironment: overall immune infiltrate and relative immune cell proportions in selected sample groups; Immune Feature Trends: distributions of immune readouts across selected groups, and associations between readouts within groups; Clinical Outcomes: trends of and associations with survival outcomes across sample groups based on immune characteristics; Immunomodulators: expression trends of genes that code for immunomodulating proteins, including checkpoint proteins. In response to community feedback, we are extending the iAtlas portal with two modules, one allowing researchers to classify their own RNAseq samples into immune subtypes, and the other allowing researchers to upload their own sample categories for analysis with the tool. As the resource evolves, we expect that the CRI iAtlas will help to accelerate discovery and improve patient outcomes by providing researchers greater access to immunogenomics data to better understand the immunologic characteristics of the tumor microenvironment and its potential impact on patient responses to immunotherapy. Reference: Thorsson et al. The immune landscape of cancer. Immunity 2018;48(4):812-30.e14. doi: 10.1016/j.immuni.2018.03.023. Citation Format: Vesteinn Thorsson, James A. Eddy, Andrew Lamb, David L. Gibbs, Ilya Shmulevich, Justin Guinney. Facilitating translational research with interactive tools for immuno-oncology data [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B051.