Predicting Reactions and Effects of Drugs Immunotherapy and Complications Through Oncosafety (PREDICTO Clinical Study)

Immune Checkpoint Inhibitors (ICI) have revolutionized cancer therapy, providing unprecedented responses in a wide range of malignancies. However, they induced various immune-related adverse events (iRAE) that can be life-threatening. About 20% of patients treated with an ICI monotherapy, and up to 60% of patients treated with a combination of ICIs, experienced a severe iRAE. Most side effects are reversible if managed early, but can affect survival and quality of life, leading to treatment interruptions or hospitalization. Some of these irAEs, particularly those affecting hormonal functions, may be irreversible and persist even after treatment discontinuation.

The development of predictive biomarkers of such toxicities is an unmet medical need. The variety of mechanisms involved in iRAE, and the lack of effective animal models, could probably explain why the topic remains largely unexplored. To date, some biomarkers predictive of the occurrence of iRAE, irrespective of the type of organ affected, have been identified by state-of-the-art techniques on small cohorts prior to treatment initiation, but none is individually robust enough to be used in daily practice.

We hypothesize that a signature derived from the integrative analysis of various biological parameters (immunomonitoring, auto-immunity features, viral monitoring, microbiota monitoring, fragmentome analysis, pharmacokinetics, radiomics and genetics), available in routine hospital practice, could answer this question, and thus enable the development of specific prevention strategies

The objectives are :

Primary objective:

Identify a baseline predictive signature for severe iRAE, irrespective of the type of organ affected.

Secondary objectives:

* Identify a predictive signature for severe iRAE including baseline and T1 data, irrespective of the type of organ affected. * Identify a baseline predictive signature for organ-specific severe iRAE. * Identify a predictive signature for organ-specific severe iRAE including baseline and T1 data. * Identify a baseline predictive signature for severe iRAE, irrespective of the type of organ affected, for patient receiving an anti-PD(L)1 in monotherapy. * Identify a baseline predictive signature for severe iRAE, irrespective of the type of organ affected, for patient receiving an anti-PD(L)1 in combination. * Identify a baseline predictive signature for severe iRAE, irrespective of the type of organ affected, for each specific immunotherapy received. * Compare the predictive signatures between responders and non-responders according to RECIST 1.1 in order not to overlook the influence of clinical response on the variability observed. * Describe the results obtained for each biological parameter between severe irAEs and non-severe irAEs patients. * Describe patient-reported outcomes and quality of life parameters.