AzTrials
AzTrials

B-Cell Non-Hodgkin Lymphoma (NHL)BIO-FIL_MABNCT07767799

Biological Analysis of MABs in NHL in a Translational Prospective Observational Study Within Italian Clinical Practice

An observational study of B-Cell Non-Hodgkin Lymphoma (NHL), run by Fondazione Italiana Linfomi - ETS at 20 sites in Italy.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Sites and contacts
Official record

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Trial 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. Read more about this study

Who is it for?
Ages 18 years and older
Conditions the study is for
B-Cell Non-Hodgkin Lymphoma (NHL)
What is being tested?
WP1 - Task 1 - Liquid analyses, WP1 - Task 2 - Immunological analyses and WP1 - Task 3 - Tumor tissue analyses
How many people
1,000 participants
Where
20 sitesItaly
Study dates
Oct 2026to Oct 2041
Phase
Observational

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

Study population: 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.

Who may be able to join

  • 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.

Who cannot join

  • 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.

Trial locations and contacts

20 sites in 1 country. Contact the study team or a site near you to ask about taking part.

Uffici Studi FIL contact

+390131033169

startup@filinf.it

Uffici Studi FIL contact

+390599769910

gestionestudi@filinf.it

20 sites in 1 country

Italy20 sites

  • SCDU Ematologia -AOU SS. Antonio e Biagio e Cesare Arrigo di Alessandria

    Alessandria, 15121

    Marco Ladetto, Prof., marco.ladetto@uniupo.it

  • Divisione di Oncologia e dei Tumori immuno-correlati - IRCCS Centro di Riferimento Oncologico di Aviano

    Aviano, 33081

    Michele Spina, MD, mspina@cro.it

  • U.O.C Ematologia - IRCCS Istituto Tumori Giovanni Paolo II - Bari

    Bari, 70124

    Giacomo Loseto, MD, g.loseto@oncologico.bari.it

  • SC Ematologia - Azienda Ospedaliera Papa Giovanni XXIII - Bergamo

    Bergamo, 24127

    Giuseppe Gritti, MD, g.gritti@asst-pg23.it

  • Istituto di Ematologia "Seragnoli" - Policlinico S.Orsola-Malpighi

    Bologna, 40138

    Beatrice Casadei, MD, beatrice.casadei10@unibo.it

  • Ematologia - ASST Spedali Civili di Brescia

    Brescia, 25123

    Alessandra Tucci, MD, alessandra.tucci@asst-spedalicivili.it

  • Ematologia - Fondazione del Piemonte per l'Oncologia - IRCCS

    Candiolo, Torino, 10060

    Francesca Bonello, MD, francesca.bonello@ircc.it

  • Oncoematologia IOV - Ospedale di Castelfranco Veneto

    Castelfranco Veneto, Treviso, 31033

    Mariella Lo Schirico, MD, mariella.loschirico@iov.veneto.it

  • S.C. Ematologia - A.O. S. Croce e Carle

    Cuneo, 12100

    Claudia Castellino, MD, castellino.c@ospedale.cuneo.it

  • Unità funzionale di Ematologia -Azienda Ospedaliera Universitaria Careggi

    Florence, 50141

    Luca Nassi, MD, nassil@aou-careggi.toscana.it

  • Ematologia - Fondazione IRCCS Istituto Nazionale dei Tumori di Milano

    Milan, 20133

    Paolo Corradini, Prof., paolo.corradini@unimi.it

  • SC Ematologia - ASST Grande Ospedale Metropolitano Niguarda

    Milan, 20162

    Vittorio Ruggero Zilioli, MD, vittorioruggero.zilioli@ospedaleniguarda.it

  • SCDU Ematologia - AOU Maggiore della Carità di Novara

    Novara, 28100

    Gianluca Gaidano, Prof., gianluca.gaidano@med.uniupo.it

  • UOC Ematologia Dipartimento Oncologico Ematologico - P.O. Spirito Santo di Pescara - ASL Pescara

    Pescara, 65124

    Elsa Pennese, MD, elsa.pennese@asl.pe.it

  • Ematologia - Arcispedale Santa Maria Nuova - IRCCS -Azienda Unità Sanitaria Locale

    Reggio Emilia, 42123

    Angela Ferrari, MD, ferrari.angela@ausl.re.it

  • Dipartimento di Medicina Traslazionale e di Precisione - Istituto Ematologia - Policlinico Umberto I - Università "La Sapienza" - Roma

    Roma, 00161

    Alice Di Rocco, Prof., dirocco@bce.uniroma1.it

  • Ematologia Universitaria - A.O.U. Città della Salute e della Scienza di Torino

    Torino, 10126

    Simone Ferrero, Prof., simone.ferrero@unito.it

  • S.C. Ematologia - A.O.U. Città della Salute e della Scienza di Torino

    Torino, 10126

    Mattia Novo, MD, mnovo@cittadellasalute.to.it

  • U.O. Ematologia - AOU Integrata di Verona

    Verona, 37134

  • Ematologia - Ospedale S. Bortolo - ULSS 8 Berica

    Vicenza, 36100

    Maria Chiara Tisi, MD, mariachiara.tisi@aulss8.veneto.it

Other trials in Italy

About this study

Official title: Multilayer Biological Analysis of Novel Monoclonal Antibodies (MABs) in B-Cell Non-Hodgkin Lymphoma (NHL): A Translational and Prospective Observational Study Within Italian Clinical Practice (BIO-FIL_MAB Trial)

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.

Read the 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.

Being tested
WP1 - Task 1 - Liquid analyses, WP1 - Task 2 - Immunological analyses, WP1 - Task 3 - Tumor tissue analyses, WP1 - Task 4 - Imaging analyses, WP1 - Task 5 - Microbiome and metabolomics analyses
Keywords
NHL, NMAB, translational, biological, samples, observational, Liquid analyses, Immunological analyses, Tumor tissue analyses
Medical subject headings
Lymphoma, B-Cell

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • WP1 - Task 1 - Liquid analysesOther

    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.

  • WP1 - Task 2 - Immunological analysesOther

    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.

  • WP1 - Task 3 - Tumor tissue analysesOther

    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.

  • WP1 - Task 4 - Imaging analysesOther

    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.

  • WP1 - Task 5 - Microbiome and metabolomics analysesOther

    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.

  • WP2 - Task 1 - Liquid analysesOther

    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.

  • WP2 - Task 2 -Immunological analysesOther

    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.

  • WP2 - Task 3 - Tumor tissue analysesOther

    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.

  • WP2 - Task 4 - Imaging analysesOther

    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.

  • WP2 - Task 5 - Microbiome and metabolomics analysesOther

    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.

  • WP3 - Task 1 - Liquid analysesOther

    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.

  • WP3 - Task 2 - Immunological analysesOther

    Objective

    1. Evaluate the expansion of immunological cells along with their markers of activation, exhaustion, maturation, and chemotaxis.

  • WP3 - Task 3 - Tumor tissue analysesOther

    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.

  • WP3 - Task 4 - Imaging analysesOther

    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.

  • WP3 - Task 5 - Microbiome and metabolomics analysesOther

    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.
Study groups in NCT07767799
Study groupRoleWhat they receive
T-cell engager antibodies - Work package 1 (WP 1)WP1 includes all the FIL-MAB-approved cohorts related to novel T-cell engager therapies (e.g. bi-specifics and others).
Receives: 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
Immunoconjugates antibodies - Work package 2 (WP2)WP2 includes all the FIL-MAB-approved cohorts related to novel immunoconjugate therapies (e.g. Antibody-Drug Conjugates (ADCs)).
Receives: 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
Naked antibodies - Work package 3 (WP3)WP3 includes all the FIL-MAB-approved cohorts related to novel naked antibodies- based therapies.
Receives: 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

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Association between MRD status and Progression Free Survival (PFS).

    Measured: from enrollment start to final analyses (15 years)

  2. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Association between MRD status and Overall Survival (OS).

    Measured: from enrollment start to final analyses (15 years)

  3. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Association between between MRD status and clinical response.

    Measured: from enrollment start to final analyses (15 years)

  4. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Comparison between MRD negativity rates obtained by different BsAbs time to obtain MRD negativity by different BsAbs.

    Measured: from enrollment start to final analyses (15 years)

  5. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Correlation between baseline ctDNA levels and outcome (response, PFS, OS).

    Measured: from enrollment start to final analyses (15 years)

  6. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).

    Measured: from enrollment start to final analyses (15 years)

  7. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. 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).

    Measured: from enrollment start to final analyses (15 years)

  8. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Association of CH with PFS, OS and therapy-related toxicities.

    Measured: from enrollment start to final analyses (15 years)

  9. T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses

    1. Association of SNPs with PFS, OS and therapy-related toxicities.

    Measured: from enrollment start to final analyses (15 years)

  10. 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).

    Measured: from enrollment start to final analyses (15 years)

  11. T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses

    1. Measuring NK cells count at baseline and M3 and correlation with outcome.

    Measured: from enrollment start to final analyses (15 years)

  12. T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses

    1. Association between CD4+ Treg, CD4+, and CD8+ T lymphocyte counts at M3 and Complete Metabolic Response (CMR)/MRD-.

    Measured: from enrollment start to final analyses (15 years)

  13. T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses

    1. Association between CD4+ Treg, CD4+, and CD8+ T Lymphocyte counts at M3 and 2-Y PFS.

    Measured: from enrollment start to final analyses (15 years)

  14. T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses

    1. 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.

    Measured: from enrollment start to final analyses (15 years)

  15. T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses

    1. Correlation between T cell exhaustion and treatment failure.

    Measured: from enrollment start to final analyses (15 years)

  16. T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses

    1. 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).

    Measured: from enrollment start to final analyses (15 years)

  17. 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.

    Measured: from enrollment start to final analyses (15 years)

  18. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Association between MRD status and PFS.

    Measured: from enrollment start to final analyses (15 years)

  19. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Association between MRD status and OS.

    Measured: from enrollment start to final analyses (15 years)

  20. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Association between MRD status and clinical response.

    Measured: from enrollment start to final analyses (15 years)

  21. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Comparison between MRD negativity rates obtained by different BsAbs time to obtain MRD negativity by different BsAbs.

    Measured: from enrollment start to final analyses (15 years)

  22. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Correlation between baseline ctDNA levels and outcome (response, PFS, OS).

    Measured: from enrollment start to final analyses (15 years)

  23. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).

    Measured: from enrollment start to final analyses (15 years)

  24. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Association between baseline ctDNA and MRD with imaging biomarkers (TMTV, Dmax, SUVmax, AI features etc).

    Measured: from enrollment start to final analyses (15 years)

  25. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Association of CH with PFS, OS and therapy-related toxicities.

    Measured: from enrollment start to final analyses (15 years)

  26. Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses

    1. Association of SNPs with PFS, OS and therapy-related toxicities.

    Measured: from enrollment start to final analyses (15 years)

  27. 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).

    Measured: from enrollment start to final analyses (15 years)

  28. Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses

    1. Measuring NK cells count at baseline and M3 and correlation with outcome.

    Measured: from enrollment start to final analyses (15 years)

  29. Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses

    1. Association between CD4+ Treg, CD4+, and CD8+ T lymphocyte counts at M3 and CMR/MRD-.

    Measured: from enrollment start to final analyses (15 years)

  30. Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses

    1. Association between CD4+ Treg, CD4+, and CD8+ T Lymphocyte counts at M3 and 2-Y PFS.

    Measured: from enrollment start to final analyses (15 years)

  31. Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses

    1. 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.

    Measured: from enrollment start to final analyses (15 years)

  32. Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses

    1. Correlation between T cell exhaustion and treatment failure.

    Measured: from enrollment start to final analyses (15 years)

  33. Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses

    1. Correlation of T lymphocyte clusters and soluble mediators of inflammaging with safety (e.g. infections).

    Measured: from enrollment start to final analyses (15 years)

  34. 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.

    Measured: from enrollment start to final analyses (15 years)

  35. Immunoconjugates antibodies - Work package 2 (WP2) Task 3 - Tumor tissue analyses

    1. 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.

    Measured: from enrollment start to final analyses (15 years)

  36. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Association between MRD status and PFS.

    Measured: from enrollment start to final analyses (15 years)

  37. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Association between MRD status and OS.

    Measured: from enrollment start to final analyses (15 years)

  38. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Association between MRD status and clinical response.

    Measured: from enrollment start to final analyses (15 years)

  39. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Comparison between MRD negativity rates obtained by different naked antibodies time to obtain MRD negativity by different naked antibodies.

    Measured: from enrollment start to final analyses (15 years)

  40. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Correlation between baseline ctDNA levels and outcome (response, PFS, OS).

    Measured: from enrollment start to final analyses (15 years)

  41. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).

    Measured: from enrollment start to final analyses (15 years)

  42. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Association between baseline ctDNA and MRD with imaging biomarkers (TMTV, Dmax, SUVmax, AI features etc).

    Measured: from enrollment start to final analyses (15 years)

  43. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Association of CH with PFS, OS and therapy-related toxicities.

    Measured: from enrollment start to final analyses (15 years)

  44. Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses

    1. Association of SNPs with PFS, OS and therapy-related toxicities.

    Measured: from enrollment start to final analyses (15 years)

  45. 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.

    Measured: from enrollment start to final analyses (15 years)

  46. Naked antibodies - Work package 3 (WP3) Task 3 - Tumor tissue analyses

    1. 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.

    Measured: from enrollment start to final analyses (15 years)

  47. 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.

    Measured: from enrollment start to final analyses (15 years)

  48. All Work packages

    1. Association between plasma and lymph nodes microbiome and outcomes (ORR, Complete Response Rate (CRR), PFS, OS) in NMAB-approved treatments.

    Measured: from enrollment start to final analyses (15 years)

  49. All Work packages

    1. Evaluation of correlations between immune cell subsets (T-cell subsets, NK cells), immunological clusters, soluble mediators, and clinical efficacy.

    Measured: from enrollment start to final analyses (15 years)

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted17 Aug 2026
  2. Study startOct 2026 (estimated)
  3. Primary completionOct 2041 (estimated)
  4. Study completionOct 2041 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
1,000 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07767799
Sponsor's protocol ID
BIO-FIL_MAB
Acronym
BIO-FIL_MAB
Status last verified
Aug 2026
First submitted
5 Aug 2026
Last updated
17 Aug 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
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).

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