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A machine learning approach predicts essential genes and pharmacological targets in cancer

Coryandar Gilvary, Neel S. Madhukar, Kaitlyn Gayvert, Miguel Foronda, Alexendar Perez, Christina S. Leslie, Lukas Dow, Gaurav Pandey, Olivier Elemento
doi: https://doi.org/10.1101/692277
Coryandar Gilvary
HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USACaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USASandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USATri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
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Neel S. Madhukar
HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USACaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USASandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USATri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USAOneThree Biotech, New York, NY 10021, USA
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Kaitlyn Gayvert
HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USACaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USASandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USATri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
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Miguel Foronda
Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA
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Alexendar Perez
Department of Anesthesia and Perioperative Care, UCSF, San Francisco, CA 94143, USA
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Christina S. Leslie
Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USACancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Lukas Dow
Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USADepartment of Biochemistry, Weill Cornell Medicine, New York, NY 10021, USA
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Gaurav Pandey
Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
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Olivier Elemento
HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USACaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USASandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USATri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USAOneThree Biotech, New York, NY 10021, USAWorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10065, USA
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  • For correspondence: ole2001@med.cornell.edu
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ABSTRACT

Loss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.

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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 July 04, 2019.
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A machine learning approach predicts essential genes and pharmacological targets in cancer
Coryandar Gilvary, Neel S. Madhukar, Kaitlyn Gayvert, Miguel Foronda, Alexendar Perez, Christina S. Leslie, Lukas Dow, Gaurav Pandey, Olivier Elemento
bioRxiv 692277; doi: https://doi.org/10.1101/692277
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A machine learning approach predicts essential genes and pharmacological targets in cancer
Coryandar Gilvary, Neel S. Madhukar, Kaitlyn Gayvert, Miguel Foronda, Alexendar Perez, Christina S. Leslie, Lukas Dow, Gaurav Pandey, Olivier Elemento
bioRxiv 692277; doi: https://doi.org/10.1101/692277

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