A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

To probe drug mechanism of action (MOA) and interrogate the genetic architecture of human cells, we carried out isogenic genome-wide CRISPR/Cas9 knockout screens against 310 diverse drugs, bioactive compounds, and stress conditions. Stringent statistical correction for gene knockout fitness defects yielded a large-scale matrix of >12,000 high confidence chemical-genetic interactions (CGIs). This dataset revealed many previously unappreciated off-target effects for well-characterized compounds and novel MOAs for uncharacterized compounds. The CGI matrix uncovered dense genetic modules that yielded new biological insights into phospholipidosis, mitotic regulation, metabolism, the DNA damage response, and mTOR signaling. The dataset allowed identification of multi-drug sensitization and resistance mechanisms, inference of gene function, elaboration of cross-process connectivity, evaluation of the cell type specificity of CGIs, prediction of chemical synergism, and extensive annotation of understudied genes. This resource provides a map of the genetic landscape in human cells and a framework to help guide drug discovery.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-01-29
DOI
https://doi.org/10.64898/2026.01.28.701823
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

Damien D’Amours, John D. Aitchison, Shannon McLaughlan, Corinne St‐Denis et al.
bioRxiv (Cold Spring Harbor Laboratory)
Computational Drug Discovery Methods
article

A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

Damien D’Amours, John D. Aitchison, Shannon McLaughlan, Corinne St‐Denis, Hugo Lavoie, Pierre Thibault, Ivan Topisirović, G. Sauvageau, Karine Normandin, David Papadopoli, Vincent Archambault, Roger Palou, Anne Marinier, Lea Harrington, M. Therrien, Caroline Huard, Randall King, Thierry Bertomeu, Brian T. Wilhelm, Alain Verreault, Mike Tyers, Philippe P. Roux, Frank Sicheri, Henry Say, Sylvain Meloche, Andrew Chatr‐aryamontri, Li Zhang, Chris Stark, Mohammadjavad Paydar, E. Elizabeth Patton, Jasmin Coulombe-Huntington, Peter B. Dirks, María Sánchez-Osuna, Bobby-Joe Breitkreutz, Almer M. van der Sloot, Sylvie Mader, Sandhya Manohar, Trang Hoang, Daniel J. St-Cyr, Katherine L. B. Borden
article en

Abstract

To probe drug mechanism of action (MOA) and interrogate the genetic architecture of human cells, we carried out isogenic genome-wide CRISPR/Cas9 knockout screens against 310 diverse drugs, bioactive compounds, and stress conditions. Stringent statistical correction for gene knockout fitness defects yielded a large-scale matrix of >12,000 high confidence chemical-genetic interactions (CGIs). This dataset revealed many previously unappreciated off-target effects for well-characterized compounds and novel MOAs for uncharacterized compounds. The CGI matrix uncovered dense genetic modules that yielded new biological insights into phospholipidosis, mitotic regulation, metabolism, the DNA damage response, and mTOR signaling. The dataset allowed identification of multi-drug sensitization and resistance mechanisms, inference of gene function, elaboration of cross-process connectivity, evaluation of the cell type specificity of CGIs, prediction of chemical synergism, and extensive annotation of understudied genes. This resource provides a map of the genetic landscape in human cells and a framework to help guide drug discovery.

bioRxiv (Cold Spring Harbor Laboratory)
Mount Sinai Hospital (CA), University Health Network (CA), Infectious Disease Research Institute (US), Harvard University (US), University of Ottawa (CA), University of Toronto (CA), University of Zurich (CH), Lunenfeld-Tanenbaum Research Institute (CA), Hospital for Sick Children (CA), Princess Margaret Cancer Centre (CA), Institute for Research in Immunology and Cancer (CA), Institute of Genetics and Cancer (GB), Mila - Quebec Artificial Intelligence Institute (CA), Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Polytechnique Montréal (CA), McGill University (CA), University of Nebraska Medical Center (US), Université de Montréal (CA), University of Edinburgh (GB)
Openalex Percentile: Top 72%
Computational Drug Discovery Methods
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