Identification Predictive Markers of Immunochemotherapy Response to the Primary Cutaneous Diffuse Large B Cell Lymphoma by Transcriptomic Proteomics and Mutational Analysis

The objective is to identify predictive biomarkers of response to R-chemotherapy and understand what genomic markers, transcriptomic or proteomic would identify those patients ahead. This would ultimately help to identify whether dysregulated biological pathway could be targeted specifically among patients with personalized treatment.

Like its counterpart systemic Lymphoma Diffuse large B cell, the sequential analysis of the same patient at diagnosis and relapse can also identify candidate genes and biological pathways involved in recurrence and help to design new therapeutic strategies.

Another objective is ultimately the development of an integrative bioinformatics tool for anticipating the therapeutic response. The tumor model used is certainly a rare cancer but prototypical ABC lymphomas allowing access to relapse hardware and relative homogeneity that will allow a study of a smaller number of cases for the development of correlations models genomics / proteomics. This is a pivotal project integrating proteomic data and Molecular Biology (mutation, expression) based on the pooling of skills of clinical teams, biological and bioinformatics to validate a predictive model for treatment response able to integrate prognostic markers known as TNM the dual expression MYC / BCL2 and forward to predict the therapeutic response using this modeling by CBIB. We would then validate the model established in this series of training on a range of national validation as part of an observational study with GFELC (Groupe Français d'Etude des Lymphomes Cutanés).