Robust Identification of Drug Mechanism of Action Associated Genes Across Heterogeneous Drug Treated Datasets
- 1National Institute of Health
Abstract
Identifying genes associated with the mechanism of action (MOA) of anticancer therapies is essential for understanding drug response. Functional genomics approaches, including genome-wide CRISPR knock out screens, enable systematic gene prioritization following treatment; however, findings from individual datasets are typically interpreted after identifying overlapping genes across datasets to confirm biological hypotheses. Integrating such heterogeneous datasets remains challenging due to variability across experimental conditions. Existing approaches can miss biologically relevant genes due to dataset-specific noise, can fail to capture genes exhibiting consistent but moderate effects across studies. Here, we present ConcuRx, a statistical framework for cross-study integration and gene prioritization across heterogeneous CRISPR drug-response screens. This approach produces more stable gene rankings than single-dataset analyses, improving prioritization of known MOA-associated genes and recovering biologically relevant genes that are overlooked by average- and overlap-based approaches. Applied to CRISPR drug-response screens for the synergistic drug pair of ATR and PARP inhibitors, ConcuRx strengthened support for 15 experimentally validated hypotheses collected from published studies, including mechanisms supported by independent studies not included in the ConcuRx analysis. These findings demonstrate that modelling between-study variability improves identification and prioritization of MOA-associated genes and provides a robust strategy for integrating heterogeneous functional genomics data in precision oncology.
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BibTeX
@misc{sharmin2026robust,
title = {{Robust Identification of Drug Mechanism of Action Associated Genes Across Heterogeneous Drug Treated Datasets}},
author = {M. Sharmin and M. Aziz and E. Mathe},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.15.759469},
url = {https://www.biorxiv.org/content/10.64898/2026.10.15.759469v1},
note = {CC0},
}
RIS
TY - UNPB
TI - Robust Identification of Drug Mechanism of Action Associated Genes Across Heterogeneous Drug Treated Datasets
AU - M. Sharmin
AU - M. Aziz
AU - E. Mathe
PY - 2026
DA - 2026/10/15
PB - bioRxiv
DO - 10.64898/2026.10.15.759469
UR - https://www.biorxiv.org/content/10.64898/2026.10.15.759469v1
AB - Identifying genes associated with the mechanism of action (MOA) of anticancer therapies is essential for understanding drug response. Functional genomics approaches, including genome-wide CRISPR knock out screens, enable systematic gene prioritization following treatment; however, findings from individual datasets are typically interpreted after identifying overlapping genes across datasets to confirm biological hypotheses. Integrating such heterogeneous datasets remains challenging due to variability across experimental conditions. Existing approaches can miss biologically relevant genes due to dataset-specific noise, can fail to capture genes exhibiting consistent but moderate effects across studies. Here, we present ConcuRx, a statistical framework for cross-study integration and gene prioritization across heterogeneous CRISPR drug-response screens. This approach produces more stable gene rankings than single-dataset analyses, improving prioritization of known MOA-associated genes and recovering biologically relevant genes that are overlooked by average- and overlap-based approaches. Applied to CRISPR drug-response screens for the synergistic drug pair of ATR and PARP inhibitors, ConcuRx strengthened support for 15 experimentally validated hypotheses collected from published studies, including mechanisms supported by independent studies not included in the ConcuRx analysis. These findings demonstrate that modelling between-study variability improves identification and prioritization of MOA-associated genes and provides a robust strategy for integrating heterogeneous functional genomics data in precision oncology.
ER -