Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
Year: 2026
Type: article
Abstract: PURPOSE: POSEIDON (NCT03164616) was a randomized, open-label, multicenter phase III trial comparing first-line durvalumab with or without tremelimumab in combination with chemotherapy versus chemotherapy alone in patients with metastatic non-small cell lung cancer (NSCLC). Overall survival (OS) and progression-free survival were significantly increased in the tremelimumab plus durvalumab and chemotherapy arm. We conducted a post hoc analysis (TRIDENT) to identify patients who may receive greater OS benefit from the addition of tremelimumab to durvalumab and chemotherapy. EXPERIMENTAL DESIGN: This analysis included clinical, genomic, and radiomic data from the POSEIDON trial (data cutoff March 12, 2021). Machine learning models leveraging multimodal data were trained to identify subpopulations of patients who benefit from the addition of tremelimumab to first-line durvalumab and chemotherapy. RESULTS: Using clinical and genomic data, the model was able to predict treatment benefit from adding tremelimumab to first-line durvalumab and chemotherapy, with the top ranked 50% of patients with nonsquamous tumors achieving a hazard ratio of 0.56 (95% confidence interval, 0.33-0.97). EGFR wild type, FGFR3 wild type, CDKN2A wild type, KRAS mutations, and STK11 mutations were the factors most associated with higher OS benefit. CONCLUSIONS: By utilizing machine learning models to analyze POSEIDON data, we yielded genetic signatures identifying patients with nonsquamous metastatic NSCLC who may derive greater OS benefit from the addition of tremelimumab to first-line durvalumab and chemotherapy. Such approaches could be used in the future to enhance precision in tailoring therapies for individual patients.
Source: Clinical Cancer Research
Authors: 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
Institutions: The University of Texas MD Anderson Cancer Center, Rutgers Cancer Institute, University of California, Los Angeles, Memorial Sloan Kettering Cancer Center, University of Turin, Sophia Genetics (France), Sophia Genetics (Switzerland), AstraZeneca (United States), AstraZeneca (Spain), AstraZeneca (United Kingdom)
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funders: American Association for Cancer Research, Amgen, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, AstraZeneca, GlaxoSmithKline, Sanofi, Gilead Sciences, International Association for the Study of Lung Cancer, McGill University, Regeneron Pharmaceuticals, Novocure, Aimmune Therapeutics, Mirati Therapeutics, BeiGene, Incyte, Revolution Medicines, Daiichi-Sankyo, Astellas Pharma, Université de Montréal, European Society for Medical Oncology, Servier, Aptitude Health, Genentech, Samsung, EMD Serono
OpenAlex ID: W7170035115