Utilizing machine learning to identify multimodal signatures for patients who would benefit from the addition of tremelimumab to durvalumab and chemotherapy (TRIDENT)

Clinical Cancer Research · Published 2026-07-22 · DOI 10.1158/1078-0432.ccr-25-3729

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Authors (22)

Ferdinandos Skoulidis, Salma K. Jabbour, Edward B. Garon, Puneeth Iyengar, Giorgio Scagliotti, Loïc Ferrer, Guillaume Etchepare, Olivier Gallinato, Jérôme Faure, Paul Bernard, Thierry Colin, Philippe Menu, Yian Lin, Ling Cai, Ammar Ahmed Chaudhry, Amanda Remorino, Ross Stewart, Luisa Luciani-Silverman, Katy Miller, David Dellamonica, Jolyon Faria, Yiduo Zhang

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Year

2026

Citation

Skoulidis, F., Jabbour, S., Garon, E., et al. (2026). Utilizing machine learning to identify multimodal signatures for patients who would benefit from the addition of tremelimumab to durvalumab and chemotherapy (TRIDENT). Clinical Cancer Research. https://doi.org/10.1158/1078-0432.ccr-25-3729

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