Abstract 1249: cBioPortal for Cancer Genomics

Abstract cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale clinico-genomic data. cBioPortal provides a suite of user-friendly visualizations and analyses, including OncoPrints, mutation lollipop plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. The public site (https: //www. cbioportal. org) is accessed by 35, 000 unique visitors each month and hosts data from 390 studies spanning individual labs and large consortia. All data is available in the cBioPortal Datahub: https: //github. com/cBioPortal/datahub; in 2023 we added 35 studies (~16, 000 samples). In addition, 86 instances of cBioPortal are installed at academic institutions and companies worldwide. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of 197, 000 clinically sequenced samples from 19 institutions (https: //genie. cbioportal. org). The GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations including response, outcome, and treatment history, which can all be visualized in cBioPortal. BPC cohorts for non-small cell lung cancer (~2, 000 samples) and colorectal cancer (~1, 500 samples) are available, with more cancer types to come. This past year, we significantly enhanced existing features. Group comparison now includes a Mutations tab showing a mirrored lollipop plot and outcomes analysis now supports hazard ratios and landmark analysis. Arm-level copy number can now be compared across groups, as can any data in the generic assay format. We also enhanced the study view. Users can now add charts to summarize the copy number of a specific gene across the cohort. The option to add custom data charts now supports numerical data as well as categorical data. Filters in study view can now be manually submitted, which provides a performance improvement when applying multiple filters in a large cohort. Larger-scale performance improvements are currently underway. We continue to improve support for multimodal datasets incorporating derived data elements, including cell type counts and fractions per sample from imaging or single cell data. The MSK-SPECTRUM ovarian cancer study has samples profiled with bulk sequencing, scRNASeq, H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 1249.