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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The authors would like to thank Eoin McDonnell, Kris Wood, and Jonathan Mizrahi for relevant discussions.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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