Abstract 3645: Pathway-informed deep learning enhances cancer dependency prediction using multi-source CRISPR screening data

Abstract Precision cancer therapy relies on identifying tumor-specific genetic dependencies, which are genes critical for cancer cell survival and proliferation that can be leveraged as therapeutic vulnerabilities. The Cancer Dependency Map (DepMap) datasets from the Broad and Sanger Institutes provide a comprehensive resource for systematically uncovering these dependencies through genome-wide loss-of-function CRISPR screens. These datasets have enabled the development of predictive models for cancer cell lines or tumors that cannot be directly screened. Recent machine and deep learning approaches have utilized gene expression data, with or without the integration of other omics profiles, to predict such dependencies. However, existing methods often fall short in capturing pathway-level interactions among genes and in effectively integrating insights from both Broad and Sanger DepMap datasets due to limitations in model design and training strategies. To address these challenges, we present a novel deep learning model that integrates pathway-level information and both DepMap datasets to predict the dependency of individual genes in cancer cell lines. In our approach, each cancer cell line is represented by activity scores across thousands of biological pathways and functions, while each gene is modeled based on its involvement in these pathways. This design enables the model to comprehensively capture functional interactions and dependencies within the cellular context, rather than treating genes in isolation. To further enhance the representation of genes targeted for knockout, we implemented a contrastive learning framework. This framework was pretrained on the Sanger CRISPR screens to classify whether two genes exhibit similar dependency profiles and was subsequently fine-tuned on the Broad CRISPR screens to predict the dependency score of each gene in a specific cell line. The final deep learning model demonstrated strong predictive performance, achieving a median correlation coefficient of 0.86 between actual and predicted dependency scores across genes in individual cell lines. The model significantly outperformed baseline configurations that excluded pathway integration or contrastive learning. Notably, cell lines from lung, head and neck, esophageal, and gastric cancers exhibited particularly high prediction accuracy, potentially due to shared biological characteristics within these tumor types. Further investigation is warranted to identify factors contributing to this performance. In summary, our novel deep learning model improves cancer dependency prediction by leveraging pathway-level insights and integrating multiple DepMap datasets. Ongoing efforts include further model refinement, expanded validation experiments, and systematic model interpretation to uncover the underlying mechanisms captured by the model. Citation Format: Chien-Hung Shih, Yu-Chiao Chiu. Pathway-informed deep learning enhances cancer dependency prediction using multi-source CRISPR screening data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3645.