Application of machine learning to large in-vitro databases to identify cancer cell characteristics: telomerase reverse transcriptase (TERT) expression.

Advances in biotechnology and machine learning have created an enhanced environment for unearthing and exploiting previously unrecognized relationships between genomic and epigenetic data with potential therapeutic implications. We applied advanced algorithms to data from the Cancer Dependency Map to uncover increasingly complex relationships. Specifically, we investigate characteristics of tumor cell lines with varying levels of telomerase reverse transcriptase (TERT) expression in liver cancer. The findings indicate that the effect of CRISPR knockout of Histone Deacetylase 1 (HDAC1) and numerous individual respiratory complex I genes is strongly related to the level of TERT expression, with knockout being particularly efficacious at killing or inhibiting growth of tumor cells with low levels of TERT expression for HDAC1 and high levels for Complex I genes. These findings suggest key biomarkers for therapeutic efficacy and yield novel potential pathways for drug development and provide further proof of principle for the potential of artificial intelligence in oncology.

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

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.