Utilizing machine learning to identify multimodal signatures for patients who would benefit from the addition of tremelimumab to durvalumab and chemotherapy (TRIDENT)
In brief
Half of non-squamous NSCLC patients see about a 44% lower death risk when tremelimumab is added
In a post-hoc analysis of the POSEIDON trial, a machine-learning model using clinical and genomic data identified the top 50% of patients with non-squamous metastatic lung cancer who experienced a hazard ratio of 0.56 for overall survival with the tremelimumab-durvalumab-chemo regimen. Key genetic markers linked to this benefit included EGFR, FGFR3, CDKN2A wild-type and KRAS or STK11 mutations. Validation in prospective cohorts is needed before routine use.
- Journal
- Clinical cancer research : an official journal of the American Association for Cancer Research (Q1)
- Published
- 22 July 2026
- Study design
- Non-randomized / quasi-experimental trial
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Ferdinandos Skoulidis, Salma K Jabbour, Edward B Garon, Puneeth Iyengar, Giorgio Scagliotti, Loïc Ferrer, et al.
- PMID
- 42485106
- DOI
- 10.1158/1078-0432.CCR-25-3729
Why clinicians should know about it
- Picked for Health Informatics (paper of the day, 23 July 2026).
Abstract
PURPOSE: 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. EXPERIMENTAL DESIGN: 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. 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 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. CONCLUSIONS: 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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.