Refining patient selection for next-generation immunotherapeutic early-phase clinical trials with a novel and externally validated prognostic nomogram - Losurdo, Agnese; Dipasquale, Angelo; Giordano, Laura; Persico, Pasquale; Lorenzi, Elena; Di Muzio, Antonio; Barigazzi, Chiara; Korolewicz, James; Mehan, Aman; Mohammed, Oreoluwa; Scheiner, Benhard; Pinato, David J.; et al. (Frontiers in Immunology, 2024)
Introduction: Identifying which patient may benefit from immunotherapeutic early-phase clinical trials is an unmet need in drug development. Among several proposed prognostic scores, none has been validated in patients receiving immunomodulating agents (IMAs)-based combinations. Patients and methods: We retrospectively collected data of 208 patients enrolled in early-phase clinical trials investigating IMAs at our Institution, correlating clinical and blood-based variables with overall survival (OS). A retrospective cohort of 50 patients treated with IMAs at Imperial College (Hammersmith Hospital, London, UK) was used for validation. Results: A total of 173 subjects were selected for analyses. Most frequent cancers included nonsmall cell lung cancer (26%), hepatocellular carcinoma (21.5%) and glioblastoma (13%). Multivariate analysis (MVA) revealed 3 factors to be independently associated with OS: line of treatment (second and third vs subsequent, HR 0.61, 95% CI 0.40-0.93, p 0.02), serum albumin as continuous variable (HR 0.57, 95% CI 0.36-0.91, p 0.02) and number of metastatic sites (<3 vs ≥3, HR 0.68, 95% CI 0.48-0.98, p 0.04). After splitting albumin value at the median (3.84 g/dL), a score system was capable of stratifying patients in 3 groups with significantly different OS (p<0.0001). Relationship with OS reproduced in the external cohort (p=0.008). Then, from these factors we built a nomogram. Conclusions: Prior treatment, serum albumin and number of metastatic sites are readily available prognostic traits in patients with advanced malignancies participating into immunotherapy earlyphase trials. Combination of these factors can optimize patient selection at study enrollment, maximizing therapeutic intent.