Harmonization and Integration of Pharmacogenomics Screens

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ID: 317125
2026
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Abstract
Abstract Motivation Large pharmacogenomics screens have generated a wealth of information cataloguing the responses of more than a thousand tumor cell-line models to FDA-approved and exploratory drugs. Although centralized repositories have consolidated data access, the diversity of experimental platforms and response metrics used in these screens have made it challenging to integrate and compare their measured drug responses. Towards better pharmacogenomic data harmonization, we surveyed a range of data analysis protocols based on different curve-fitting functions (sigmoid, piecewise linear), different response metrics (IC50, EC50, integrated AUC), and different drug concentration windows (full range or truncated). Results We found that an AUC derived from a sigmoidal curve fitted to a truncated dose range yields the strongest agreement between screening platforms, significantly bettering other protocols surveyed. This harmonization procedure also best aligns drug responses across successive iterations of the same platform. These findings broadly inform efforts to integrate drug response data in large-scale analyses. Availability The source code to generate drug response profiles and correlations are available at https://github.com/digitaltumors/Pharmacogenomics_Screens_Harmonization.git. Supplementary information Supplemental figures are available at Bioinformatics online. Supplemental tables are found on Zenodo: https://doi.org/10.5281/zenodo.17196025, https://doi.org/10.5281/zenodo.17981848, and https://zenodo.org/records/20620409.
Reference Key
openalex_W7164530502 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aleysha T. Chen, Marcus R. Kelly, Trey Ideker, Nicole M. Mattson
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag382
URL
https://doi.org/10.1093/bioinformatics/btag382
Keywords Keywords not found

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