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Pan J, Kwon JJ, Talamas JA, Borah AA, Vazquez F, Boehm JS, Tsherniak A, Zitnik M, McFarland JM, Hahn WC. Sparse dictionary learning recovers pleiotropy from human cell fitness screens. Cell Syst 2022; 13:286-303.e10. [PMID: 35085500 PMCID: PMC9035054 DOI: 10.1016/j.cels.2021.12.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/30/2021] [Accepted: 12/21/2021] [Indexed: 12/28/2022]
Abstract
In high-throughput functional genomic screens, each gene product is commonly assumed to exhibit a singular biological function within a defined protein complex or pathway. In practice, a single gene perturbation may induce multiple cascading functional outcomes, a genetic principle known as pleiotropy. Here, we model pleiotropy in fitness screen collections by representing each gene perturbation as the sum of multiple perturbations of biological functions, each harboring independent fitness effects inferred empirically from the data. Our approach (Webster) recovered pleiotropic functions for DNA damage proteins from genotoxic fitness screens, untangled distinct signaling pathways upstream of shared effector proteins from cancer cell fitness screens, and predicted the stoichiometry of an unknown protein complex subunit from fitness data alone. Modeling compound sensitivity profiles in terms of genetic functions recovered compound mechanisms of action. Our approach establishes a sparse approximation mechanism for unraveling complex genetic architectures underlying high-dimensional gene perturbation readouts.
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Affiliation(s)
- Joshua Pan
- Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Jason J Kwon
- Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Jessica A Talamas
- Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Ashir A Borah
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Jesse S Boehm
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aviad Tsherniak
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Marinka Zitnik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Department of Biomedical Informatics, Boston, MA 02215, USA; Harvard University, Data Science Initiative, Cambridge, MA 02138, USA
| | | | - William C Hahn
- Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02215, USA; Brigham and Women's Hospital and Harvard Medical School, Department of Medicine, Boston, MA 02215, USA.
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