The study integrates pan-cancer proteogenomic data from 1,043 patients across 10 cancer types, genetic screen data from cell lines, and tumor antigen predictions to identify a comprehensive landscape of protein and peptide targets for drug repurposing and therapy development. Key findings include: 1. **Pan-cancer Analysis of Druggable Proteins**: The analysis reveals a wide range of druggable proteins, with a significant number of targets in tiers 1-5, which are either already targeted by FDA-approved drugs or have potential for new therapeutic applications. 2. **Proteomic and Genetic Screen Integration**: By combining proteomic data from tumors with genetic screen data from cell lines, the study identifies protein overexpression- or hyperactivation-driven dependencies, facilitating the identification of effective drug targets. 3. **Synthetic Lethality and Tumor Suppressor Gene Loss**: The study highlights the importance of synthetic lethality in targeting tumor suppressor gene loss, providing a strategy to address these critical vulnerabilities in cancer therapy. 4. **Neoantigen and Tumor-Associated Antigen Identification**: Computational workflows and experimental validation are used to prioritize mutant peptides as neoantigens and tumor-associated antigens, which are promising targets for immunotherapy. 5. **Web Portal for Data Access**: A web portal is provided to access the identified targets and supporting data, facilitating further research and clinical applications. The study's comprehensive approach to data integration and systematic identification of therapeutic targets has the potential to expand the landscape of available treatments for various cancers.The study integrates pan-cancer proteogenomic data from 1,043 patients across 10 cancer types, genetic screen data from cell lines, and tumor antigen predictions to identify a comprehensive landscape of protein and peptide targets for drug repurposing and therapy development. Key findings include: 1. **Pan-cancer Analysis of Druggable Proteins**: The analysis reveals a wide range of druggable proteins, with a significant number of targets in tiers 1-5, which are either already targeted by FDA-approved drugs or have potential for new therapeutic applications. 2. **Proteomic and Genetic Screen Integration**: By combining proteomic data from tumors with genetic screen data from cell lines, the study identifies protein overexpression- or hyperactivation-driven dependencies, facilitating the identification of effective drug targets. 3. **Synthetic Lethality and Tumor Suppressor Gene Loss**: The study highlights the importance of synthetic lethality in targeting tumor suppressor gene loss, providing a strategy to address these critical vulnerabilities in cancer therapy. 4. **Neoantigen and Tumor-Associated Antigen Identification**: Computational workflows and experimental validation are used to prioritize mutant peptides as neoantigens and tumor-associated antigens, which are promising targets for immunotherapy. 5. **Web Portal for Data Access**: A web portal is provided to access the identified targets and supporting data, facilitating further research and clinical applications. The study's comprehensive approach to data integration and systematic identification of therapeutic targets has the potential to expand the landscape of available treatments for various cancers.