B-Cell Non-Hodgkin Lymphoma (NHL)
Conditions
Keywords
NHL, NMAB, translational, biological, samples, observational, Liquid analyses, Immunological analyses, Tumor tissue analyses
Brief summary
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.
Detailed description
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 observational FIL\_MAB study. All clinical observations, including baseline characteristics, treatment exposure, and follow-up, are collected through the FIL-MAB study database, with a minimum follow-up of 60 months (5 years) from enrollment and correlated with biological findings for translational analysis performed in BIO-FIL\_MAB study. The BIO-FIL\_MAB study will employ a structured schedule of biological and imaging assessments to monitor treatment outcomes and gather translational data. The timeline will be aligned with routine clinical practice: * Prior to NMAB Treatment * During NMAB Therapy (3 months after start of therapy, 9 months after start of therapy, progression/relapse). This structured schedule ensures a comprehensive evaluation of both clinical and biological treatment effects, aligning with the study's translational objectives. As an observational translational study primarily intended for descriptive and exploratory analyses, no formal statistical hypothesis testing is planned. Therefore, the sample size has been determined based on feasibility considerations and the expected availability of patients participating in the parent FIL-MAB clinical cohort, thereby ensuring a robust population for integrated biological, immunological, and imaging analyses in association with clinical outcomes. Overall, it is anticipated that at least 1000 patients will be consecutively enrolled and followed longitudinally in BIO-FIL\_MAB study.
Interventions
OTHERWP1 - Task 1 - Liquid analyses
Objectives 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.
OTHERWP1 - Task 2 - Immunological analyses
Objectives 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.
OTHERWP1 - Task 3 - Tumor tissue analyses
Objectives 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.
OTHERWP1 - Task 4 - Imaging analyses
Objectives 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.
OTHERWP1 - Task 5 - Microbiome and metabolomics analyses
Objectives 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.
OTHERWP2 - Task 1 - Liquid analyses
Objectives 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.
OTHERWP2 - Task 2 -Immunological analyses
Objectives 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.
OTHERWP2 - Task 3 - Tumor tissue analyses
Objectives 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.
OTHERWP2 - Task 4 - Imaging analyses
Objectives 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.
OTHERWP2 - Task 5 - Microbiome and metabolomics analyses
Objectives 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.
OTHERWP3 - Task 1 - Liquid analyses
Objectives 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.
OTHERWP3 - Task 2 - Immunological analyses
Objective 1\) Evaluate the expansion of immunological cells along with their markers of activation, exhaustion, maturation, and chemotaxis.
OTHERWP3 - Task 3 - Tumor tissue analyses
Objectives 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.
OTHERWP3 - Task 4 - Imaging analyses
Objectives 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.
OTHERWP3 - Task 5 - Microbiome and metabolomics analyses
Objectives 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.
Sponsors
Fondazione Italiana Linfomi - ETS
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* Patients not enrolled in the FIL\_MAB study. * Evidence of other clinically significant uncontrolled condition(s) including, but not limited to: * 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; * Refusal or inability to provide informed consent. * Refusal or inability to provide biological specimens.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| All Work packages | from enrollment start to final analyses (15 years) | 1\) Prognostic quantitative PET indices: Metabolic Tumor Volume (MTV), Total Glycolytic Volumes (TLG), SUVmax and SUVpeak, other index of tumor dissemination (maximum distance between the lesion, product of distance and MTV, etc.…) and radiomics index. |
| T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses | from enrollment start to final analyses (15 years) | 1\) Association between MRD status and Progression Free Survival (PFS). |
| T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses | from enrollment start to final analyses (15 years) | 1\) Quantification of CD4+ and CD8+T lymphocyte clusters and soluble mediators of inflammagin, at baseline, month +3 (M3) and End Of Treatment (EOT), and correlation with clinical outcome (PFS, OS). |
| T-cell engager antibodies - Work package (WP 1) - Task 3 - Tumor tissue analyses | from enrollment start to final analyses (15 years) | 1. Correlation of specific mutational profiles with Overall Response Rate (ORR) rates, 2-Y PFS and 2-Y OS. 2. Correlation of specific transcriptomic signatures with ORR rates, 2-Y PFS and 2-Y OS. 3. Correlation of intra-tumoral T-cell populations and non-T-cell populations with ORR rates, 2-Y PFS and 2-Y OS. 4. 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). 5. Correlation between target antigen surface level (i.e. CD20) with ORR rates, 2-Y PFS and 2-Y OS. |
| Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses | from enrollment start to final analyses (15 years) | 1\) Association between MRD status and PFS. |
| Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses | from enrollment start to final analyses (15 years) | 1\) Quantification of CD4+ and CD8+T lymphocyte clusters and soluble mediators of inflammagin, at baseline, month +3 (M3) and EOT, and correlation with clinical outcome (PFS, OS). |
| Immunoconjugates antibodies - Work package 2 (WP2) Task 3 - Tumor tissue analyses | from enrollment start to final analyses (15 years) | 1\) Association between target antigen surface level and CRR with ADCs treatment, assessed in immunohistochemistry (IHC) on diagnosis or last relapse biopsy before ADCs treatment. |
| Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses | from enrollment start to final analyses (15 years) | 1\) Association between MRD status and PFS. |
| Naked antibodies - Work package 3 (WP3) Task 3 - Tumor tissue analyses | from enrollment start to final analyses (15 years) | 1\) Association between target antigen surface level and CRR with naked antibodies-based treatment, assessed in immunohistochemistry (IHC) on diagnosis or last relapse biopsy before naked antibodies-based therapies. |
Countries
Italy
Contacts
CONTACTUffici Studi FIL
STUDY_CHAIRRiccardo Moia, MD
Divisione di Ematologia, Dipartimento di Medicina Traslazionale Università del Piemonte Orientale, AOU Maggiore della Carità, Novara (Italy)
STUDY_CHAIRSimone Ferrero, Prof.
Ematologia Universitaria, A.O.U. Città della Salute e della Scienza di Torino, Torino (Italy)
STUDY_CHAIRRita Tavarozzi, MD
SCDU Ematologia, Azienda Ospedaliera SS Antonio e Biagio e C. Arrigo, Alessandria, Italy
Outcome results
None listed