An algorithm that combines machine learning ensemble modeling and network analysis to predict self-tolerant tumor-associated antigens for anti-cancer immunotherapy

Vera Gonzalez, J.; Eberhardt, M.; Lischer, C.; Weich, A.

doi:10.1101/2023.02.09.527464

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bioRxiv · 10.1101/2023.02.09.527464

An algorithm that combines machine learning ensemble modeling and network analysis to predict self-tolerant tumor-associated antigens for anti-cancer immunotherapy

Vera Gonzalez, J.·Eberhardt, M.·Lischer, C.·Weich, A.

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Abstract

Tumor-associated antigens (TAAs) and their derived peptides constitute the chance to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. Here, we present a computational pipeline that selects and ranks candidate antigens in a multi-pronged approach and applied it to the case of uveal melanoma. In addition to antigen expression in the tumor target and in healthy tissues, we incorporated a network analysis-derived antigen indispensability index motivated by computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model on peptide physicochemical characteristics.

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Vera Gonzalez, J., Eberhardt, M., Lischer, C., Weich, A.. 2023-02-10. An algorithm that combines machine learning ensemble modeling and network analysis to predict self-tolerant tumor-associated antigens for anti-cancer immunotherapy. https://doi.org/10.1101/2023.02.09.527464

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