Abstract A024: DeepVul: A multi-task transformer model for joint prediction of gene essentiality and drug response

Abstract Introduction Precision oncology, which matches patients with optimal treatments, currently benefits only a minority of patients. This limited impact stems from two challenges: (i) only a small set of genomic alterations are actionable, and (ii) oncogenic mutations do not reliably predict tumor dependency. In heterogeneous tumors, drugs may eliminate only a subset of cells, while resistant populations persist and drive recurrence. To address these gaps, we present DeepVul, a multi-task transformer that jointly predicts gene essentiality and drug response from transcriptomes. By aligning gene expression with genetic and pharmacologic perturbations in a shared latent space, DeepVul predicts cancer-cell vulnerabilities to many genes and drugs from a single molecular readout, rendering whole-genome transcriptomes clinically actionable. Benchmarking showed that DeepVul matches or complements mutation-based approaches and, through interpretability, identifies mechanisms of response and resistance. DeepVul is publicly available at https://github.com/alaaj27/DeepVul.git. Methods DeepVul learns a latent representation linking gene expression to essentiality and perturbation data via a multi-task objective; a second objective fine-tunes drug response by minimizing per-drug MSE. The architecture comprises feature transformation, encoder-based feature extraction, and optional fine-tuning (frozen vs. tunable encoder). We evaluate on held-out cohorts with ablations against baselines. Results DeepVul matched or outperformed baselines in per-gene correlation and correctly predicted essentiality for 46% of genes with correlation >0.3, with the largest gains on actionable oncogenes. Trained on DepMap and applied without retraining, it generalized to an independent Sanger cohort, whereas the baseline model failed to generalize. For drug response, transfer from essentiality with a frozen encoder outperformed fine-tuning. Compared with mutation-based rules, DeepVul identified populations sensitive to targeted therapies that showed significantly lower experimental essentiality across 32 actionable genes (p<0.05); the BRAF-only analysis showed the same trend but was not significant due to limited sample size. SHAP/LISA recovered known resistance biology, including STAT3-mediated resistance to BRAF inhibitors. Conclusions DeepVul predicts gene essentiality and drug response from expression profiles, matches or exceeds baselines, and generalizes across cohorts without retraining—an important requirement for clinical translation. Using BRAF and 32 actionable genes as use cases, it identifies treatment-sensitive cell lines with accuracy comparable to mutation-based precision oncology while extending coverage beyond the few currently actionable genes. DeepVul complements mutation-based strategies by providing scalable, transcriptome-driven predictions of therapeutic vulnerabilities and may inform transcriptome-based clinical decision support. Citation Format: My Bach Nguyen, Ala Jararweh, David Arredondo, Oladimeji Macaulay, Luis Tafoya, Yue Hu, Avinash Sahu, Genevieve Boland, Keith Flaherty, Mikaela Dicome. DeepVul: A multi-task transformer model for joint prediction of gene essentiality and drug response [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr A024 .