An observational study in B-Cell Non-Hodgkin Lymphoma (NHL), sponsored by Fondazione Italiana Linfomi - ETS. Not yet recruiting at 20 sites in Italy. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-10-27.
Sponsored by Fondazione Italiana Linfomi - ETS · Observational
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.
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:
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.
885 studies on the registry are indexed under Lymphoma, B-Cell; 286 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 150 across 69 observational studies indexed under Lymphoma, B-Cell.
Browse Lymphoma, B-Cell studies →Fondazione Italiana Linfomi - ETS is the lead sponsor of 91 studies on the registry; 23 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Evidence of other uncontrolled condition(s) including, but not limited to:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Judged by the study doctor to matter for your health or for the study. It is a doctor’s decision, so ask the study team if you are unsure whether something counts.
Hepatitis B is a virus that infects the liver. These are separate tests: HBsAg positive usually means a current infection (a recent vaccination can briefly cause it too); total anti-HBc (HBcAb) positive means an infection now or in the past; HBV DNA measures how much virus is in the blood.
Some studies accept a past infection if HBV DNA is undetectable, sometimes with antiviral medicine during the study. Check what this study allows.
Hepatitis C is a virus that infects the liver. An HCV antibody test can stay positive after an infection has cleared (and is occasionally positive without one); an HCV RNA test shows whether the virus is there now.
After treatment, hepatitis C counts as cured when HCV RNA is still undetectable 12 weeks or more after the treatment ended. Studies often accept people cured this way.
The human immunodeficiency virus, which weakens the immune system.
Many studies now accept people whose HIV is controlled on treatment, judged by the viral load and the CD4 count. Check the limits this study sets.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Clinically significant
Judged by the study doctor to matter for your health or for the study. It is a doctor’s decision, so ask the study team if you are unsure whether something counts.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Hepatitis B tests
Hepatitis B is a virus that infects the liver. These are separate tests: HBsAg positive usually means a current infection (a recent vaccination can briefly cause it too); total anti-HBc (HBcAb) positive means an infection now or in the past; HBV DNA measures how much virus is in the blood.
Some studies accept a past infection if HBV DNA is undetectable, sometimes with antiviral medicine during the study. Check what this study allows.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Hepatitis C tests
Hepatitis C is a virus that infects the liver. An HCV antibody test can stay positive after an infection has cleared (and is occasionally positive without one); an HCV RNA test shows whether the virus is there now.
After treatment, hepatitis C counts as cured when HCV RNA is still undetectable 12 weeks or more after the treatment ended. Studies often accept people cured this way.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
HIV
The human immunodeficiency virus, which weakens the immune system.
Many studies now accept people whose HIV is controlled on treatment, judged by the viral load and the CD4 count. Check the limits this study sets.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
WP1 includes all the FIL-MAB-approved cohorts related to novel T-cell engager therapies (e.g. bi-specifics and others).
Other: WP1 - Task 1 - Liquid analyses · Other: WP1 - Task 2 - Immunological analyses · Other: WP1 - Task 3 - Tumor tissue analyses · Other: WP1 - Task 4 - Imaging analyses · Other: WP1 - Task 5 - Microbiome and metabolomics analyses
WP2 includes all the FIL-MAB-approved cohorts related to novel immunoconjugate therapies (e.g. Antibody-Drug Conjugates (ADCs)).
Other: WP2 - Task 1 - Liquid analyses · Other: WP2 - Task 2 -Immunological analyses · Other: WP2 - Task 3 - Tumor tissue analyses · Other: WP2 - Task 4 - Imaging analyses · Other: WP2 - Task 5 - Microbiome and metabolomics analyses
WP3 includes all the FIL-MAB-approved cohorts related to novel naked antibodies- based therapies.
Other: WP3 - Task 1 - Liquid analyses · Other: WP3 - Task 2 - Immunological analyses · Other: WP3 - Task 3 - Tumor tissue analyses · Other: WP3 - Task 4 - Imaging analyses · Other: WP3 - Task 5 - Microbiome and metabolomics 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Objective 1\) Evaluate the expansion of immunological cells along with their markers of activation, exhaustion, maturation, and chemotaxis.
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.
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.
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.
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
1\) Association between MRD status and Progression Free Survival (PFS).
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
2\) Association between MRD status and Overall Survival (OS).
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
3\) Association between between MRD status and clinical response.
Time frame: from enrollment start to final analyses (15 years)
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.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
5\) Correlation between baseline ctDNA levels and outcome (response, PFS, OS).
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
6\) Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
7\) Association between baseline ctDNA and MRD with imaging biomarkers (Total Metabolic Tumor Value (TMTV), Maximum Tumor Dissemination (Dmax), Standardized Uptake Value maximum (SUVmax), Artificial Intelligence (AI) features etc).
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
8\) Association of CH with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
9\) Association of SNPs with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
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).
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
2\) Measuring NK cells count at baseline and M3 and correlation with outcome.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
3\) Association between CD4+ Treg, CD4+, and CD8+ T lymphocyte counts at M3 and Complete Metabolic Response (CMR)/MRD-.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
4\) Association between CD4+ Treg, CD4+, and CD8+ T Lymphocyte counts at M3 and 2-Y PFS.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
5\) Expression of co-stimulatory molecules such as PD1, CD25, 41BB/CD137, CTLA4, and CD28 on T cells at M3 and their correlation with achieving a CMR.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
6\) Correlation between T cell exhaustion and treatment failure.
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
7\) Correlation of T lymphocyte clusters and soluble mediators of inflammaging with safety (e.g. Cytokine Release Syndrome (CRS), Immune Effector Cell-Associated Neurotoxicity Syndrome (ICANS), infections).
Time frame: from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 3 - Tumor tissue analyses
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.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
1\) Association between MRD status and PFS.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
2\) Association between MRD status and OS.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
3\) Association between MRD status and clinical response.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
4\) Comparison between MRD negativity rates obtained by different BsAbs time to obtain MRD negativity by different BsAbs.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
5\) Correlation between baseline ctDNA levels and outcome (response, PFS, OS).
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
6\) Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
7\) Association between baseline ctDNA and MRD with imaging biomarkers (TMTV, Dmax, SUVmax, AI features etc).
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
8\) Association of CH with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
9\) Association of SNPs with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
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).
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
2\) Measuring NK cells count at baseline and M3 and correlation with outcome.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
3\) Association between CD4+ Treg, CD4+, and CD8+ T lymphocyte counts at M3 and CMR/MRD-.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
4\) Association between CD4+ Treg, CD4+, and CD8+ T Lymphocyte counts at M3 and 2-Y PFS.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
5\) Expression of co-stimulatory molecules such as PD1, CD25, 41BB/CD137, CTLA4, and CD28 on T cells at M3 and their correlation with achieving a CMR.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
6\) Correlation between T cell exhaustion and treatment failure.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
7\) Correlation of T lymphocyte clusters and soluble mediators of inflammaging with safety (e.g. infections).
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 3 - Tumor tissue analyses
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.
Time frame: from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 3 - Tumor tissue analyses
2\) Association between target antigen surface level and OS, PFS and ORR with ADCs treatment, assessed in immunohistochemistry (IHC) on diagnosis or last relapse biopsy before ADCs treatment and correlation with biological and imaging predictors. Correlation of CH with PFS, OS and therapy-related toxicities and correlation of SNPs with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
1\) Association between MRD status and PFS.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
2\) Association between MRD status and OS.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
3\) Association between MRD status and clinical response.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
4\) Comparison between MRD negativity rates obtained by different naked antibodies time to obtain MRD negativity by different naked antibodies.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
5\) Correlation between baseline ctDNA levels and outcome (response, PFS, OS).
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
6\) Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
7\) Association between baseline ctDNA and MRD with imaging biomarkers (TMTV, Dmax, SUVmax, AI features etc).
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
8\) Association of CH with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
9\) Association of SNPs with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 3 - Tumor tissue analyses
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.
Time frame: from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 3 - Tumor tissue analyses
2\) Association between target antigen surface level and OS, PFS and ORR with naked antibodies based-treatment, assessed in immunohistochemistry (IHC) on diagnosis or last relapse biopsy before naked antibodies based-treatment and correlation with biological and imaging predictors. Correlation of CH with PFS, OS and therapy-related toxicities and correlation of SNPs with PFS, OS and therapy-related toxicities.
Time frame: from enrollment start to final analyses (15 years)
All Work packages
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.
Time frame: from enrollment start to final analyses (15 years)
All Work packages
2\) Association between plasma and lymph nodes microbiome and outcomes (ORR, Complete Response Rate (CRR), PFS, OS) in NMAB-approved treatments.
Time frame: from enrollment start to final analyses (15 years)
All Work packages
3\) Evaluation of correlations between immune cell subsets (T-cell subsets, NK cells), immunological clusters, soluble mediators, and clinical efficacy.
Time frame: from enrollment start to final analyses (15 years)
Plan to share: Yes — Qualified researchers may contact the FIL board at segreteriadirezione@filinf.it to share invidual-level patients' clinical data analysed for this manuscript (for the avoidance of doubt, no identifiable data, such as name, address, hospital name, date of birth, or any other identifying data, will be shared and should not be requested).
Supporting information: Study protocol, Statistical analysis plan, Analytic code
Time frame: In compliance with the domestic ethics guideline and applicable legislation, invidual deindentified patients' data underlying the results reported in the publication article (including study protocol, statistical analysis plan and data coding) can be shared until 5 years after the publication of the article.
Access criteria: For each data sharing request, it is essential that a proforma (available on request) is completed that describes the general purpose, specific aims, data items requested, analysis plan and acknowledgment of the trial management team. Requests will be reviewed based on scientific merit and ethical principles. Requestors who are granted access to the data will be required to complete a data sharing agreement that will be signed by the requester and FIL.
No publications or documents are linked to this record.
Start is estimated for Oct 2026; primary completion for Oct 2041, 180 months later.
How these figures are worked out, and which studies are left out of them.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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Fondazione Italiana Linfomi - ETS