Abstract 3205: classifieRc: An interactive web interface for the molecular classification of colorectal cancer from RNA-sequencing data

Abstract

Abstract Background: Transcriptional predictors are increasingly important for sub-typing cancer patients and understanding disease aetiology, predicting patient outcomes and response to treatment. Colorectal cancers (CRC) can be classified by transcriptionally inferred consensus (CMS) and intrinsic (CRIS) sub-types, annotating transcriptional profiles derived from colorectal cancer samples with these sub-types and other transcriptionally inferred predictors requires an experienced bioinformatician and is time-consuming. Methods: Publically available R packages CMSclassifier, CRISclassifier, MCP-counter and DoRothEA have been integrated into a web interface using the R/Shiny framework which allows users to upload gene expression data which can be additionally normalized with DESeq2. Visualization of this data can be interacted with through using plotly. Results: To address this bottleneck, we developed the classifieRC Shiny app, which enables rapid analysis, annotation of colorectal cancer transcriptional profiles with state-of-the-art transcriptional CRC sub-typing (CMS and CRIS) as well as estimation of cellular composition (MCP-Counter) and transcription factor activity predictions (DoRothEA). classifieRC can be accessed through a web-based interface, a locally deployable R-script or executable software, with capability of publishing datasets and their resulting analysis to Shiny.IO. Conclusions: classifieRc enables researchers to rapidly annotate colorectal transcriptomic datasets with molecular sub-types and of functional predictions without the need for a dedicated bioinformatician, expediting insights related patient cohort analyses and novel discoveries. classifieRc provides an easy to use flexible framework for functional annotation transcriptomic datasets and a platform for development of other disease specific apps. Citation Format: Gerard Quinn, Tamas Sessler, Wendy Allen, Sarah Maguire, Philip Dunne, Darragh McArt, Harper VanSteenhouse, Peter Gallagher, Andrea Lees, Dan Longley, Bruce Seligmann, Mark Wappett, Simon McDade. classifieRc: An interactive web interface for the molecular classification of colorectal cancer from RNA-sequencing data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3205.