A multiomics approach to identify signatures of response and resistance to immunotherapy in R/R Diffuse Large B-cell Lymphoma

The therapeutic armamentarium for relapsed and refractory (R/R) Diffuse Large B-cell Lymphoma (DLBCL) has been greatly enriched in recent years by a variety of cellular and antibody-based immunotherapies, including chimeric antigen receptor (CAR) T-cell, bispecific antibodies and antibody-drug conjugates. On average, 50% of R/R DLBCL patients fail these novel 

forms of immunotherapies, thus representing a challenging unmet medical need. 

To address these issues, we designed a translational research project based on a personalized multi-omics approach to identify biomarkers of disease response and potential resistance mechanisms. Patients will be analyzed before immunotherapy and longitudinally after that. In this scenario, genetic tools represent a promising addition to clinical criteria, histological features, and imaging techniques. Specifically, the analysis of circulating tumor DNA (ctDNA) from peripheral blood may non-invasively reveal disease heterogeneity, monitor disease eradication, and identify genetic biomarkers predictive of response and resistance to bispecific antibodies or CR T-cells. Similarly, transcriptional analysis of peripheral blood mononuclear cells (PBMC) at the single-cell level might enhance our understanding of the immune subsets signatures and T-cell receptor (TCR) repertoires that mediate response/resistance to novel forms of immunotherapy. In addition, we plan to monitor CAR T-cell expansion kinetics by flow cytometry and correlate this parameter with disease outcome. Finally, we plan to use innovative deconvolution algorithms (CIBERSORTx, Ecotyper) to investigate the composition of tumor microenvironment in biopsy tissue of refractory patients before and after immunotherapy. Transcriptomic results will be further validated by digital gene expression measurement (NanoString technology) and in situ analyses (Digital Spatial Profiling) to dissect cell-to-cell 

interactions and functions. 

Information obtained by ctDNA and single-cell analysis will be subsequently integrated with data from advanced imaging techniques through computational models to produce high-risk signatures correlated with response to therapy. This will be the basis for designing innovative, mutational-guided, individualized therapeutic programs.