Les essais plateformes☆

[Targeted therapies and immunotherapy: Positioning in the field of innovation and challenges for the therapeutic decision-making process]

The better understanding of the molecular, cellular, and immunological mechanisms of cancer has led to the development of targeted therapies, then immunotherapy, which have changed the approach to cancer treatment. While these treatments differ from chemotherapy in their mechanisms of action, they also allow for increased personalization of cancer care through the development of technologies that target patients more precisely. However, they are associated with several challenges: the management of uncertainty associated with their risk-benefit balance due to the lack of long-term data and sometimes scientific evidence on their effects; the complexity of integrating molecular and immunological data into the therapeutic decision; the challenge of inequalities in access to these treatments often considered revolutionary due to the required molecular characterization and/or inclusion criteria for early-phase trials; and the challenge of their appropriation and adoption by physicians and patients. This narrative review explores each of these challenges in the context of shared decision-making, the promotion of which is a guarantee of quality and safety of care for cancer patients.

Optimal Design of Clinical Trials Involving Persons with Disorders of Consciousness

BACKGROUND

Limited data exist regarding the optimal clinical trial design for studies involving persons with disorders of consciousness (DoC), and only a few therapies have been tested in high-quality clinical trials. To address this, the Curing Coma Campaign Clinical Trial Working Group performed a gap analysis on the current state of clinical trials in DoC to identify the optimal clinical design for studies involving persons with DoC.

METHODS

The Curing Coma Campaign Clinical Trial Working Group was divided into three subgroups to (1) review clinical trials involving persons with DoC, (2) identify unique challenges in the design of clinical trials involving persons with DoC, and (3) recommend optimal clinical trial designs for DoC.

RESULTS

There were 3055 studies screened, and 66 were included in this review. Several knowledge gaps and unique challenges were identified. There is a lack of high-quality clinical trials, and most data regarding patients with DoC are based on observational studies focusing on patients with traumatic brain injury and cardiac arrest. There is a lack of a structured long-term outcome assessment with significant heterogeneity in the methodology, definitions of outcomes, and conduct of studies, especially for long-term follow-up. Another major barrier to conducting clinical trials is the lack of resources, especially in low-income countries. Based on the available data, we recommend incorporating trial designs that use master protocols, sequential multiple assessment randomized trials, and comparative effectiveness research. Adaptive platform trials using a multiarm, multistage approach offer substantial advantages and should make use of biomarkers to assess treatment responses to increase trial efficiency. Finally, sound infrastructure and international collaboration are essential to facilitate the conduct of trials in patients with DoC.

CONCLUSIONS

Conduct of trials in patients with DoC should make use of master protocols and adaptive design and establish international registries incorporating standardized assessment tools. This will allow the establishment of evidence-based practice recommendations and decrease variations in care.

Statistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery

Translational oncology research strives to explore a new aspect: identifying subgroups that exhibit treatment response even during pre-clinical phases. In this study, we focus on PDX models and their implementation in mouse clinical trials (MCT). Our primary objective was to identify subgroups with different treatment responses using Latent Class Mixed Model (LCMM).We used a public dataset and focused on one treatment, encorafenib, and two indications, melanoma and colorectal cancer, for which efficacy depends on a specific mutation BRAF V600E. One LCMM per indication was implemented to classify treatment responses at the PDX level, analyzing the growth kinetics of treated tumors and matched controls within the PDX models. A simulation study was carried out to explore the performance of LCMM in this context. For both applications, LCMM identified classes for which the higher the proportion of mutated BRAF V600E PDX models the greater the treatment effect, which is aligned with encorafenib use recommendations. The simulation study showed that LCMM could identify classes with large differences in treatment effects. LCMM is a suitable tool for MCT to explore treatment response subgroups of PDX. Once these subgroups are defined, characterization of their phenotypes/genotypes could be performed to explore treatment response predictors.