This a prospective, multicenter, observational pharmacological translational study designed to investigate the biological and imaging correlates of treatment with novel monoclonal antibodies (NMABs) in patients with B-cell non-Hodgkin lymphoma (NHL), enrolled in the observationa FIL\_MAB study. Patients enrolled in BIO FIL-MAB are concurrently participating in the FIL-MAB clinical cohort, ensuring that all clinical data-including treatment details, outcomes, and safety-are captured within the main observational study.
Patients will undergo systematic collection of biological specimens including tumor tissue, peripheral blood integrated with advanced imaging data. Biological analyses will encompass molecular, cellular, and immunological assessments, while imaging evaluations will include standardized functional and metabolic imaging techniques. All biological and imaging assessments will be performed as routine clinical visits, without requiring modifications to treatment or additional procedures beyond standard-of-care.
Adults (≥18 years old) are diagnosed with B-cell Non-Hodgkin Lymphoma;
Patients enrolled in the FIL_MAB trial (provided by Informed Consent Form (ICF) signature) who are scheduled to receive treatment with novel monoclonal antibodies (NMABs), either as monotherapy or in combination with other therapies;
Written informed consent to participate in this study.
Patients not enrolled in the FIL_MAB study.
Uncontrolled and/or active systemic infection (viral, bacterial or fungal), including active ongoing infection from SARSCoV-2;
Chronic or acute hepatitis B (HBV) or hepatitis C (HCV) requiring treatment. Note: subjects with serologic evidence of prior vaccination to HBV (i.e., HBsAg negative, HBsAb positive and HBcAb negative) or positive HBcAb from previous infection or intravenous immunoglobulins (IVIG) may participate; inactive carriers (HBsAg positive with undetectable HBV- DNA) are eligible. Patients with presence of HCV antibody are eligible only if PCR negative for HCV-RNA;
HIV seropositivity;
Being an observational translational study primarily aimed at descriptive and exploratory analyses, no formal statistical hypothesis testing is planned for BIO-FIL\_MAB. The sample size is therefore determined based on feasibility and the expected availability of patients participating in the parent FIL-MAB clinical cohort. Overall, it is anticipated that at least 1000 patients will be enrolled and followed, providing a robust population for integrated biology, immunology, and imaging analyses alongside clinical outcomes.
Cohort
Prospective
WP1 - Task 1 - Liquid analyses
Other interventionObjectives 1. To investigate the value of circulating tumor DNA (ctDNA)/ Minimal Residual Disease (MRD) status as prognostic biomarker for B-NHL patients treated with commercial bi-specifics antibodies (bsAbs). 2. To evaluate the potential role of clonal hematopoiesis (CH) in terms of therapy-related toxicities and treatment response. 3. To evaluate the potential prognostic role of germline single-nucleotide polymorphisms (SNPs) involved in drug metabolic pathways and cell-to-cell interactions.
WP1 - Task 2 - Immunological analyses
Other interventionObjectives 1. evaluate association between levels and subtypes of T cell in PB before and after bsAbs with COs. 2. Analyze expression of PD1, CD25, 41BB/CD137, CTLA4, CD28, and other T cell co-stimulatory molecules, and correlate with COs. 3. Evaluate expansion of NK cells along with their markers of activation, exhaustion, maturation, chemotaxis. 4. evaluate association between T cell exhaustion with treatment failure. 5. evaluate association between T cell exhaustion with previous lines of treatment or other clinical factors such as relapsed time. 6. evaluate association between T cell clusters with the development of cytopenia during treatment. 7. investigate whether immunosenescence (composition and activation status of PBMCs) and inflammaging (soluble mediators) can predict response and clinical outcomes in elderly patients (≧70) undergoing treatment with bsAbs. 8. Immunological characterization of T cell subset by bulk RNAseq before and after bsAbs with COs.
WP1 - Task 3 - Tumor tissue analyses
Other interventionObjectives 1. Association between specific mutational (Whole Genome Sequencing, WGS) and transcriptomic (Whole Transcriptome Sequencing, WTS) patterns with disease response to bsAbs therapy. 2. To investigate TP53 mutation and del17p as predictive factor of response to bsAbs. 3. Identifying specific relapse patterns, with the hypothesis that alterations in tumor genes facilitating immune evasion are enriched in clones emerging at relapse (i.e., secondary resistance). 4. To characterize intratumoral immune effector cell distribution and to assess T-cell functional fitness and exhaustion states within tumor-draining lymph nodes using Digital Spatial Profiling (DSP). 5. To investigate the association between bsAbs surface target antigens (e.g. CD20) expression level and response to bsAbs.
WP1 - Task 4 - Imaging analyses
Other interventionObjectives 1. explore how tumor metabolic activity signature predict prognosis and treatment response during bsAbs-approved treatments. 2. explore how tumor heterogeneity activity predicts prognosis and treatment response during bsAbs -approved treatments. 3. explore how PET findings are integrated with other biomarkers, we refine predictions of prognosis and treatment efficacy during bsAbs-approved treatments. 4. explore novel prognostic markers of progression in CT scans and PET scans. 5. apply advanced artificial intelligence methods (radiomics and deep learning) for automated extraction of complex imaging features from PET/CT scans, aiming to enhance prediction of prognosis and treatment response in patients undergoing bsAbs-approved treatments. 6. develop and validate AI-driven multimodal integration frameworks that combine imaging data with clinical and molecular biomarkers, refining risk stratification and enabling early detection of progression under bsAbs therapy.
WP1 - Task 5 - Microbiome and metabolomics analyses
Other interventionObjectives 1. To describe plasma and tissue microbiome composition and metabolomics during bsAbs -approved treatments. 2. To investigate whether microbiome/metabolomics predicts outcomes during bsAbs -approved treatments. 3. To investigate whether microbiome/metabolomics predicts treatment toxicity during bsAbs -approved treatments.
WP2 - Task 1 - Liquid analyses
Other interventionObjectives 1. To identify and validate biological and molecular biomarkers (i.e. ctDNA/MRD) that predict patient outcomes in patients treated with novel immunoconjugate therapies. 2. To evaluate the potential role of clonal hematopoiesis (CH) in terms of therapy-related toxicities and treatment response. 3. To evaluate the potential prognostic role of germline single-nucleotide polymorphisms (SNPs) involved in drug metabolic pathways and cell-to-cell interactions.
WP2 - Task 2 -Immunological analyses
Other interventionObjectives 1. evaluate the association between levels and subtypes of T cells in PB before and after ADCs with COs. 2. Analyze the expression of PD1, CD25, 41BB/CD137, CTLA4, CD28, and other T cell co-stimulatory molecules, and correlate them with COs. 3. Evaluate the expansion of NK cells along with their markers of activation, exhaustion, maturation, chemotaxis. 4. evaluate the association between T cell exhaustion with treatment failure. 5. evaluate the association between T cell exhaustion with previous lines of treatment or other clinical factors such as relapsed time. 6. evaluate the association between T cell clusters with the development of cytopenia during treatment. 7. investigate whether immunosenescence and inflammaging can predict response and clinical outcomes in elderly patients (≧ 70) undergoing treatment with ADCs. 8. Immunological characterization of T cell subset by bulk RNAseq before and after ADCs with clinical outcomes.
WP2 - Task 3 - Tumor tissue analyses
Other interventionObjectives 1. characterize intratumoral immune effector cell distribution and assess T-cell functional fitness and exhaustion states within tumor-draining lymphnodes using DSP. 2. investigate correlation between ADCs surface target antigens expression level and response to ADCs treatment 3. investigate MYC translocation alone or in association with BCL2 and or BCL6 translocation or other MYC chromosomal aberrations as predictive factors of response to ADCs assessed by FISH on diagnostic biopsy and last biopsy preADCs treatment. 4. investigate TP53 mutation and del17p as predictive factor of response to ADCs. 5. investigate mutations and CNVs as predictive factors of response to ADCs treatment. 6. investigate ADCs target antigens RNA expression level and correlation with response to ADCs treatment. 7. characterize transcriptomic and sRNA landscapes to identify gene expression signatures and microRNA profiles associated with response to ADCs treatment.
WP2 - Task 4 - Imaging analyses
Other interventionObjectives 1. explore how tumor metabolic activity signature predict prognosis and treatment response during ADCs-approved treatments. 2. explore how tumor heterogeneity activity predicts prognosis and treatment response during ADCs-approved treatments. 3. explore how PET findings are integrated with other biomarkers, we refine predictions of prognosis and treatment efficacy during ADCs-approved treatments. 4. explore novel prognostic markers of progression in CT scans and PET scans. 5. apply advanced artificial intelligence methods (radiomics and deep learning) for automated extraction of complex imaging features from PET/CT scans, aiming to enhance prediction of prognosis and treatment response in patients undergoing ADCs-approved treatments. 6. develop and validate AI-driven multimodal integration frameworks that combine imaging data (PET/CT) with clinical and molecular biomarkers, refining risk stratification and enabling early detection of progression under ADCs therapy.
WP2 - Task 5 - Microbiome and metabolomics analyses
Other interventionObjectives 1. To describe plasma and tissue microbiome and metabolomics composition during ADCs-treatments. 2. To investigate whether microbiome/metabolomics predicts outcomes during ADCs-approved treatments. 3. To investigate whether microbiome/metabolomics predicts treatment toxicity during ADCs-approved treatments.
WP3 - Task 1 - Liquid analyses
Other interventionObjectives 1. To identify and validate biological and molecular biomarkers (i.e. ctDNA/MRD) that predict patient outcomes in patients treated with novel naked antibodies. 2. To evaluate the potential role of clonal hematopoiesis (CH) in terms of therapy-related toxicities and treatment response. 3. To evaluate the potential prognostic role of germline single-nucleotide polymorphisms (SNPs) involved in drug metabolic pathways and cell-to-cell interactions.
WP3 - Task 2 - Immunological analyses
Other interventionObjective 1\) Evaluate the expansion of immunological cells along with their markers of activation, exhaustion, maturation, and chemotaxis.
WP3 - Task 3 - Tumor tissue analyses
Other interventionObjectives 1. To investigate the correlation between naked antibodies surface target antigens (e.g. CD19) expression level and response to naked antibodies. 2. To investigate MYC translocation alone or in association with BCL2 and or BCL6 translocation, or other MYC chromosomal aberrations as predictive factors of response to naked antibodies (assessed by FISH on diagnostic biopsy and last biopsy pre- naked antibodies). 3. To investigate TP53 mutation and del17p as predictive factor of response to naked antibodies. 4. To investigate mutations and copy number variations (CNVs) (either studied by targeted sequencing or by WES) as predictive factors of response to treatment.
WP3 - Task 4 - Imaging analyses
Other interventionObjectives 1. explore how tumor metabolic activity signature predict prognosis and treatment response during naked Abs-approved treatments. 2. explore how tumor heterogeneity activity predicts prognosis and treatment response during naked Abs-approved treatments. 3. explore how PET findings are integrated with other biomarkers, we refine predictions of prognosis and treatment efficacy during naked Abs-approved treatments. 4. explore novel prognostic markers of PD in CT and PET scans. 5. apply advanced artificial intelligence methods (radiomics and deep learning) for automated extraction of complex imaging features from PET/CT, aiming to enhance prediction of prognosis and treatment response in patients undergoing naked Abs-approved treatments. 6. develop and validate AI-driven multimodal integration frameworks that combine imaging data with clinical and molecular biomarkers, refining risk stratification and enabling early detection of progression under naked Abs therapy.
WP3 - Task 5 - Microbiome and metabolomics analyses
Other interventionObjectives 1. To describe plasma and tissue microbiome and metabolomics composition during naked antibodies -approved treatments. 2. To investigate whether microbiome/metabolomics predicts outcomes during naked antibodies -approved treatments. 3. To investigate whether microbiome/metabolomics predicts treatment toxicity during naked antibodies -approved treatment.
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
1\) Association between MRD status and Progression Free Survival (PFS).
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
2\) Association between MRD status and Overall Survival (OS).
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
3\) Association between between MRD status and clinical response.
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
4\) Comparison between MRD negativity rates obtained by different BsAbs time to obtain MRD negativity by different BsAbs.
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