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Application of machine learning to large in vitro databases to identify drug–cancer cell interactions: azithromycin and KLK6 mutation status

Oncogene volume 40, pages 3766–3770 (2021)Cite this article

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Abstract

Recent advances in machine learning promise to yield novel insights by interrogation of large datasets ranging from gene expression and mutation data to CRISPR knockouts and drug screens. We combined existing and new algorithms with available experimental data to identify potentially clinically relevant relationships to provide a proof of principle for the promise of machine learning in oncological drug discovery. Specifically, we screened cell line data from the Cancer Dependency Map for the effects of azithromycin, which has been shown to kill cancer cells in vitro. Our findings demonstrate a strong relationship between Kallikrein Related Peptidase 6 (KLK6) mutation status and the ability of azithromycin to kill cancer cells in vitro. While the application of azithromycin showed no meaningful average effect in KLK6 wild-type cell lines, statistically significant enhancements of cell death are seen in multiple independent KLK6-mutated cancer cell lines. These findings suggest a potentially valuable clinical strategy in patients with KLK6-mutated malignancies.

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Fig. 1: R2 (https://en.wikipedia.org/wiki/Coefficient_of_determination) for predicting CRISPR knockout effects on holdout datasets for the 730 genes selected to be both efficacious and selective through the application of the arbitrarily selected thresholds efficacy = −0.56, selectivity = 1 as defined by Shimada et al. [28].
Fig. 2: The effect of azithromycin on cancer cell lines.

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Acknowledgements

The authors would like to thank Eoin McDonnell, Kris Wood, and Jonathan Mizrahi for relevant discussions.

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Authors and Affiliations

  1. Zephyr AI, Washington, DC, USA

    Jeff Sherman, Grant Verstandig & Yisroel Brumer

  2. Red Cell Partners, Washington, DC, USA

    Jeff Sherman, Grant Verstandig & Yisroel Brumer

  3. Columbia University, New York City, NY, USA

    John W. Rowe

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  1. Jeff Sherman
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  2. Grant Verstandig
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Correspondence to Jeff Sherman.

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Conflict of interest

JS, GV, and YB have roles at Red Cell Partners and Zephyr AI that involve the application of AI to cancer drug discovery. The authors all report no known financial interest in azithromycin.

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Sherman, J., Verstandig, G., Rowe, J.W. et al. Application of machine learning to large in vitro databases to identify drug–cancer cell interactions: azithromycin and KLK6 mutation status. Oncogene 40, 3766–3770 (2021). https://doi.org/10.1038/s41388-021-01807-4

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