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bioRxiv · 10.1101/2024.10.17.618944

DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response

Jararweh, A.·Arredondo, D.·Macaulay, O.·Dicome, M.·Tafoya, L.·Hu, Y.·Virupakshappa, K.·Boland, G.·Flaherty, K.·Sahu, A.
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Abstract

Despite their potential, current precision oncology approaches benefit only a small fraction of patients due to their limited focus on actionable genomic alterations. To expand its applicability, we propose DeepVul, a multi-task transformer-based model designed to predict gene essentiality and drug response from cancer transcriptome data. DeepVul aligns gene expressions, gene perturbations, and drug perturbations into a latent space, enabling simultaneous and accurate prediction of cancer cell vulnerabilities to numerous genes and drugs. Benchmarking against existing precision oncology approaches revealed that Deep-Vul not only matches but also complements oncogene-defined precision methods. Through interpretability analyses, DeepVul identifies underlying mechanisms of treatment response and resistance, as demonstrated with BRAF vulnerability prediction. By leveraging whole-genome transcriptome data, DeepVul enhances the clinical actionability of precision oncology, aiding in the identification of optimal treatments across a broader range of cancer patients. DeepVul is publicly available at https://github.com/alaaj27/DeepVul.git.

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Jararweh, A., Arredondo, D., Macaulay, O., Dicome, M., Tafoya, L., Hu, Y., Virupakshappa, K., Boland, G., Flaherty, K., Sahu, A.. 2024-10-21. DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response. https://doi.org/10.1101/2024.10.17.618944

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