{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Abstract B044: Distributed Transcriptomic Modeling for Biomarker Discovery in Immuno-Oncology","author":[{"@type":"Person","name":"F. Abbas-Aghababazadeh"},{"@type":"Person","name":"Kewei Ni"},{"@type":"Person","name":"Kevin (Xin) Wang"},{"@type":"Person","name":"S. Nair"},{"@type":"Person","name":"Nasim BondarSahebi"},{"@type":"Person","name":"John Stagg"},{"@type":"Person","name":"Benjamin Haibe-Kains"}],"datePublished":"2025","isPartOf":{"@type":"Periodical","name":"Clinical Cancer Research"},"identifier":"https://doi.org/10.1158/1557-3265.aimachine-b044","description":"Abstract\n \n Background:\n Immuno-oncology (IO) therapies, particularly immune checkpoint inhibitors, have transformed cancer care but benefit only a subset of patients. Existing biomarkers such as PD-L1 expression, tumor mutational burden, and microsatellite instability have limited predictive power and are often cancer-type specific. Efforts to identify more generalizable biomarkers are hampered by tumor heterogeneity, restricted access to high-quality datasets, and stringent data privacy regulations, making traditional centralized discovery approaches difficult to scale. To address these challenges, we developed a distributed pipeline for discovering predictive transcriptomic biomarkers across diverse clinical datasets without sharing sensitive patient data.\n \n \n Methods:\n We developed a scalable, cloud-based distributed pipeline for biomarker discovery that operates across institutions without transferring sensitive patient data. Using curated gene expression profiles, we assessed tumor microenvironment and IO-related signatures in pan-cancer, cancer-specific, and treatment-specific contexts. A multivariable XGBoost model was trained within this distributed framework while maintaining local data governance. Model performance was evaluated against univariable signature-based models and multivariable models trained locally on individual datasets.\n \n \n Results:\n We implemented a distributed meta-integration pipeline to 18 datasets comprising 967 patients across seven cancer types treated with PD-1/PD-L1, CTLA-4, or combination immunotherapies. This analysis identified biomarkers specific to lung cancer and CTLA-4 therapy that were not detected in pan-cancer analyses, highlighting the value of context-specific associations. Several gene expression signatures were consistently linked to progression-free survival and treatment response in both pan-cancer and melanoma datasets, with notable overlap between PD-1/PD-L1 and dual checkpoint blockade therapies. Distributed multivariable models outperformed locally trained models on average, indicating improved generalizability, though performance often depended on cancer type and dataset composition, with univariable signature-based models outperforming in some settings.\n \n \n Conclusion:\n This study demonstrates a distributed framework for IO biomarker discovery that preserves patient privacy and facilitates collaboration across institutions. Our findings highlight the value of multi-institutional, diverse datasets in refining context-specific biomarkers and advancing precision IO therapies.\n \n \n Citation Format:\n Farnoosh Abbas-Aghababazadeh, Kewei Ni, Kevin (Xin) Wang, Sisira Kadambat Nair, Nasim BondarSahebi, John Stagg, Benjamin Haibe-Kains. Distributed Transcriptomic Modeling for Biomarker Discovery in Immuno-Oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B044.\n ","url":"https://innscience.ca/en/community/literature/cf755c9c-20dd-494d-a200-c5f523918f94"}