PharmacoDB 2.0 : Improving scalability and transparency of in vitro pharmacogenomics analysis

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present: (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug response analysis such as tissue distribution of dose-response metrics and biomarker analysis; (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug response phenotypes of cancer models. HIGHLIGHTSO_LIPharmacoDB 2.0 includes new and updated large pharmacogenomic datasets. The data processing for PharmacoDB is made fully reproducible through the use of the ORCESTRA platform and automated data ingestion pipelines C_LIO_LIThe new release contains enriched annotations for drugs and cell lines via connectivity to external databases, as well as new analytical methods for tissue-specific and pan-cancer biomarker discovery C_LIO_LIThe new version of PharmacoDB incorporates a scalable and reproducible framework that can accelerate the implementation of analytical pipelines including machine learning/AI for biomarker discovery in the future C_LI