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Robust ML Approach for Screening MET Drug Candidates in Combination with Immune Checkpoint Inhibitors

Present study highlights the significance of dataset size in ICI microbiota models and presents a methodology to enhance the performances of a multi-cohort-based ML approach. Conditioned to the performances we obtained, the healthy-pooled-donors-derived DS harbor a considerable ratio (91%) of ‘ICI Responder-like’, significantly higher than the mono-donor stools (73%) suggesting that pooled ecosystems from healthy donors could better convert ICI-non responders into responders.

Emmanuel Prestat, Elsa Schalck, Antoine Bonnefoy, Antoine Sabourin, Cyrielle Gasc, Carole Schwintner and Nathalie Corvaia, Robust machine learning approach for screening microbiome ecosystem therapies (MET) drug candidates in combination with immune checkpoint inhibitors. Journal for ImmunoTherapy of Cancer 2023;11:doi: 10.1136/jitc-2023-SITC2023.1304

 Click here to access the paper.
 

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