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

Oncogene volume 40, pages 5038–5041 (2021)Cite this article

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Abstract

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.

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Fig. 1: Correlation between cell survival and TERT expression.
Fig. 2: Dependency of respiratory protein complex 1 related genes on TERT expression.
Fig. 3: NDUF gene example 1.
Fig. 4: NDUF gene example 2.

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Acknowledgements

The authors would like to thank Eoin McDonnell, Kris Wood, and Jack Rowe for relevant conversations.

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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

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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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Competing interests

The authors have roles at Red Cell Partners and Zephyr AI that involve the application of AI to cancer drug discovery. The authors report no known financial interest in HDAC1 or complex I inhibitors.

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Sherman, J., Verstandig, G. & Brumer, Y. Application of machine learning to large in-vitro databases to identify cancer cell characteristics: telomerase reverse transcriptase (TERT) expression. Oncogene 40, 5038–5041 (2021). https://doi.org/10.1038/s41388-021-01894-3

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