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Chronos: a CRISPR cell population dynamics model

View ORCID ProfileJoshua M. Dempster, Isabella Boyle, Francisca Vazquez, David Root, View ORCID ProfileJesse S. Boehm, William C. Hahn, View ORCID ProfileAviad Tsherniak, View ORCID ProfileJames M. McFarland
doi: https://doi.org/10.1101/2021.02.25.432728
Joshua M. Dempster
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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Isabella Boyle
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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Francisca Vazquez
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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David Root
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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Jesse S. Boehm
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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  • ORCID record for Jesse S. Boehm
William C. Hahn
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
2Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02215, USA
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Aviad Tsherniak
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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James M. McFarland
1Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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  • For correspondence: jmmcfarl@broadinstitute.org
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Abstract

CRISPR loss of function screens are a powerful tool to interrogate cancer biology but are known to exhibit a number of biases and artifacts that can confound the results, such as DNA cutting toxicity, incomplete phenotype penetrance and screen quality bias. Computational methods that more faithfully model the CRISPR biological experiment could more effectively extract the biology of interest than typical current methods. Here we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of the dynamics of cell proliferation after CRISPR gene knockout. Chronos is able to exploit longitudinal CRISPR data for improved inference. Additionally, it accounts for multiple sources of bias and can effectively share information across screens when jointly analyzing large datasets such as Project Achilles and Score. We show that Chronos outperforms competing methods across a range of performance metrics in multiple types of experiments.

Competing Interest Statement

F.V. receives research support from Novo Ventures. D.E.R. receives research funding from members of the Functional Genomics Consortium (Abbvie, Bristol-Myers Squibb, Jannsen, Merck, Vir), and is a director of Addgene, Inc. A.T. is a consultant for Tango Therapeutics, Cedilla Therapeutics, and Foghorn Therapeutics. W.C.H. is a consultant for ThermoFisher, Solasta, MPM Capital, iTeos, Frontier Medicines, Tyra Biosciences, RAPPTA Therapeutics, KSQ Therapeutics, Jubilant Therapeutics and Paraxel.

Footnotes

  • https://doi.org/10.6084/m9.figshare.14067047

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 25, 2021.
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Chronos: a CRISPR cell population dynamics model
Joshua M. Dempster, Isabella Boyle, Francisca Vazquez, David Root, Jesse S. Boehm, William C. Hahn, Aviad Tsherniak, James M. McFarland
bioRxiv 2021.02.25.432728; doi: https://doi.org/10.1101/2021.02.25.432728
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Chronos: a CRISPR cell population dynamics model
Joshua M. Dempster, Isabella Boyle, Francisca Vazquez, David Root, Jesse S. Boehm, William C. Hahn, Aviad Tsherniak, James M. McFarland
bioRxiv 2021.02.25.432728; doi: https://doi.org/10.1101/2021.02.25.432728

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