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

Abstract

POSEIDON (NCT03164616) was a randomized, open-label, multicenter phase 3 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. This analysis included clinical, genomic, and radiomic data from the POSEIDON trial (data cut-off March 12, 2021). Machine learning models leveraging multimodal data were trained to identify subpopulations of patients that benefit from the addition of tremelimumab to first-line durvalumab and chemotherapy. 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 non-squamous tumors achieving a HR of 0.56 (95% CI: 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. By utilizing machine learning models to analyze POSEIDON data, we yielded genetic signatures identifying patients with non-squamous 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 future to enhance precision in tailoring therapies for individual patients.