PI: Stefan Hohaus – IRCCS Fondazione Policlinico Universitario A. Gemelli
IRE Principal Collaborators Involved:Maria Rizzo, Francesco Marchesi, Gianluca Bossi, Giulia Regazzo, Rossella Loria

Project duration: 24 months

Project code: PNRR-MAD-2022-12376707

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, accounting for 30-40% of cases. While the standard R-CHOP therapy cures 55-60% of patients, about 15% are primary refractory and 25-30% relapse, usually within 24 months. Relapsed/refractory DLBCL (R/R DLBCL) remains an unmet medical need, with limited success from conventional salvage therapies. However, emerging immunotherapies, including CAR-T cells and bispecific antibodies, offer promising results.

DLBCL is biologically heterogeneous, comprising subtypes with distinct molecular and immunological characteristics, influencing treatment sensitivity and resistance patterns. This project aims to decipher the biology of R/R DLBCL subtypes by identifying predictive biomarkers, including miRNA-based tumor signatures, to optimize sequential treatment decisions.

The study will employ a comprehensive approach, integrating miRNA expression profiling, genomic and epigenetic analyses, and immunotherapy-actionable tumor antigen identification through flow cytometry. It aims to correlate molecular profiles with clinical outcomes, enabling the development of predictive models for treatment response and survival.

The primary purpose of this project is to identify and validate predictive biomarkers for R/R DLBCL to guide sequential treatment decisions. The goal is to enhance therapeutic strategies, tailoring treatments based on individual tumor biology and improving long-term patient survival. This personalized approach aims to maximize treatment efficacy while minimizing resistance and adverse effects.

Identification of Biomarkers: Discovery and validation of miRNA-based tumor signatures and other predictive biomarkers to classify R/R DLBCL subtypes.

Predictive Models: Development of predictive models for treatment response and patient survival, integrating molecular profiles with clinical data.

Personalized Treatment Strategies: Optimization of sequential treatment decisions, enabling personalized therapeutic approaches for R/R DLBCL patients.

Clinical Impact: Improvement in long-term survival rates, enhanced quality of life, and reduced healthcare burdens for DLBCL patients.

Scientific Advancement: Increased understanding of the molecular mechanisms driving DLBCL relapse and resistance, paving the way for future biomarker discoveries and innovative therapeutic strategies.