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Biological Analysis of MABs in NHL in a Translational Prospective Observational Study Within Italian Clinical Practice (BIO-FIL_MAB)

11. august 2026 oppdatert av: Fondazione Italiana Linfomi - ETS

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.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Intervensjon / Behandling

Detaljert beskrivelse

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.

Studietype

Observasjonsmessig

Registrering (Antatt)

1000

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Studer Kontakt Backup

Studiesteder

  • Italia
      • Alessandria, Italia, 15121
        • SCDU Ematologia -AOU SS. Antonio e Biagio e Cesare Arrigo di Alessandria
        • Ta kontakt med:
      • Aviano, Italia, 33081
        • Divisione di Oncologia e dei Tumori immuno-correlati - IRCCS Centro di Riferimento Oncologico di Aviano
        • Ta kontakt med:
      • Bari, Italia, 70124
        • U.O.C Ematologia - IRCCS Istituto Tumori Giovanni Paolo II - Bari
        • Ta kontakt med:
      • Bergamo, Italia, 24127
        • SC Ematologia - Azienda Ospedaliera Papa Giovanni XXIII - Bergamo
        • Ta kontakt med:
      • Bologna, Italia, 40138
        • Istituto di Ematologia "Seragnoli" - Policlinico S.Orsola-Malpighi
        • Ta kontakt med:
      • Brescia, Italia, 25123
      • Cuneo, Italia, 12100
      • Florence, Italia, 50141
        • Unità funzionale di Ematologia -Azienda Ospedaliera Universitaria Careggi
        • Ta kontakt med:
      • Milan, Italia, 20133
        • Ematologia - Fondazione IRCCS Istituto Nazionale dei Tumori di Milano
        • Ta kontakt med:
      • Milan, Italia, 20162
      • Novara, Italia, 28100
      • Pescara, Italia, 65124
        • UOC Ematologia Dipartimento Oncologico Ematologico - P.O. Spirito Santo di Pescara - ASL Pescara
        • Ta kontakt med:
      • Reggio Emilia, Italia, 42123
        • Ematologia - Arcispedale Santa Maria Nuova - IRCCS -Azienda Unità Sanitaria Locale
        • Ta kontakt med:
      • Roma, Italia, 00161
        • Dipartimento di Medicina Traslazionale e di Precisione - Istituto Ematologia - Policlinico Umberto I - Università "La Sapienza" - Roma
        • Ta kontakt med:
      • Torino, Italia, 10126
        • Ematologia Universitaria - A.O.U. Città della Salute e della Scienza di Torino
        • Ta kontakt med:
      • Torino, Italia, 10126
        • S.C. Ematologia - A.O.U. Città della Salute e della Scienza di Torino
        • Ta kontakt med:
      • Verona, Italia, 37134
        • U.O. Ematologia - AOU Integrata di Verona
      • Vicenza, Italia, 36100
    • Torino
      • Candiolo, Torino, Italia, 10060
        • Ematologia - Fondazione del Piemonte per l'Oncologia - IRCCS
        • Ta kontakt med:
    • Treviso
      • Castelfranco Veneto, Treviso, Italia, 31033

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Nei

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

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.

Beskrivelse

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.

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Kohorter og intervensjoner

Gruppe / Kohort
Intervensjon / Behandling
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).
Annen: WP1 - 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.
Annen: WP1 - 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.
Annen: WP1 - 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.
Annen: WP1 - 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.
Annen: WP1 - 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.
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)).
Annen: WP2 - 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.
Annen: WP2 - 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.
Annen: WP2 - 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.
Annen: WP2 - 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.
Annen: WP2 - 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.
Naked antibodies - Work package 3 (WP3)
WP3 includes all the FIL-MAB-approved cohorts related to novel naked antibodies- based therapies.
Annen: WP3 - 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.
Annen: WP3 - Task 2 - Immunological analyses

Objective

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

Annen: WP3 - 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.
Annen: WP3 - 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.
Annen: WP3 - 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.

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
1) Association between MRD status and Progression Free Survival (PFS).
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
2) Association between MRD status and Overall Survival (OS).
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
3) Association between between MRD status and clinical response.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
4) Comparison between MRD negativity rates obtained by different BsAbs time to obtain MRD negativity by different BsAbs.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
5) Correlation between baseline ctDNA levels and outcome (response, PFS, OS).
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
6) Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
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).
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
8) Association of CH with PFS, OS and therapy-related toxicities.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) -Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
9) Association of SNPs with PFS, OS and therapy-related toxicities.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
Tidsramme: 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).
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
2) Measuring NK cells count at baseline and M3 and correlation with outcome.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
3) Association between CD4+ Treg, CD4+, and CD8+ T lymphocyte counts at M3 and Complete Metabolic Response (CMR)/MRD-.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
4) Association between CD4+ Treg, CD4+, and CD8+ T Lymphocyte counts at M3 and 2-Y PFS.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
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.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
6) Correlation between T cell exhaustion and treatment failure.
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 2 -Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
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).
from enrollment start to final analyses (15 years)
T-cell engager antibodies - Work package (WP 1) - Task 3 - Tumor tissue analyses
Tidsramme: 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.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
1) Association between MRD status and PFS.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
2) Association between MRD status and OS.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
3) Association between MRD status and clinical response.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
4) Comparison between MRD negativity rates obtained by different BsAbs time to obtain MRD negativity by different BsAbs.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
5) Correlation between baseline ctDNA levels and outcome (response, PFS, OS).
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
6) Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
7) Association between baseline ctDNA and MRD with imaging biomarkers (TMTV, Dmax, SUVmax, AI features etc).
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
8) Association of CH with PFS, OS and therapy-related toxicities.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
9) Association of SNPs with PFS, OS and therapy-related toxicities.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
Tidsramme: 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).
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
2) Measuring NK cells count at baseline and M3 and correlation with outcome.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
3) Association between CD4+ Treg, CD4+, and CD8+ T lymphocyte counts at M3 and CMR/MRD-.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
4) Association between CD4+ Treg, CD4+, and CD8+ T Lymphocyte counts at M3 and 2-Y PFS.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
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.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
6) Correlation between T cell exhaustion and treatment failure.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 2 - Immunological analyses
Tidsramme: from enrollment start to final analyses (15 years)
7) Correlation of T lymphocyte clusters and soluble mediators of inflammaging with safety (e.g. infections).
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 3 - Tumor tissue analyses
Tidsramme: 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.
from enrollment start to final analyses (15 years)
Immunoconjugates antibodies - Work package 2 (WP2) Task 3 - Tumor tissue analyses
Tidsramme: from enrollment start to final analyses (15 years)
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.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
1) Association between MRD status and PFS.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
2) Association between MRD status and OS.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
3) Association between MRD status and clinical response.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
4) Comparison between MRD negativity rates obtained by different naked antibodies time to obtain MRD negativity by different naked antibodies.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
5) Correlation between baseline ctDNA levels and outcome (response, PFS, OS).
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
6) Association between MRD status and other clinical and biological prognostic markers (e.g. mutational patterns, T-cell phenotypes).
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
7) Association between baseline ctDNA and MRD with imaging biomarkers (TMTV, Dmax, SUVmax, AI features etc).
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
8) Association of CH with PFS, OS and therapy-related toxicities.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 1 - Liquid analyses
Tidsramme: from enrollment start to final analyses (15 years)
9) Association of SNPs with PFS, OS and therapy-related toxicities.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 3 - Tumor tissue analyses
Tidsramme: 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.
from enrollment start to final analyses (15 years)
Naked antibodies - Work package 3 (WP3) Task 3 - Tumor tissue analyses
Tidsramme: from enrollment start to final analyses (15 years)
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.
from enrollment start to final analyses (15 years)
All Work packages
Tidsramme: 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.
from enrollment start to final analyses (15 years)
All Work packages
Tidsramme: from enrollment start to final analyses (15 years)
2) Association between plasma and lymph nodes microbiome and outcomes (ORR, Complete Response Rate (CRR), PFS, OS) in NMAB-approved treatments.
from enrollment start to final analyses (15 years)
All Work packages
Tidsramme: from enrollment start to final analyses (15 years)
3) Evaluation of correlations between immune cell subsets (T-cell subsets, NK cells), immunological clusters, soluble mediators, and clinical efficacy.
from enrollment start to final analyses (15 years)

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Fondazione Italiana Linfomi - ETS

Etterforskere

  • Studiestol: Riccardo Moia, MD, Divisione di Ematologia, Dipartimento di Medicina Traslazionale Università del Piemonte Orientale, AOU Maggiore della Carità, Novara (Italy)
  • Studiestol: Simone Ferrero, Prof., Ematologia Universitaria, A.O.U. Città della Salute e della Scienza di Torino, Torino (Italy)
  • Studiestol: Rita Tavarozzi, MD, SCDU Ematologia, Azienda Ospedaliera SS Antonio e Biagio e C. Arrigo, Alessandria, Italy

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Antatt)

1. oktober 2026

Primær fullføring (Antatt)

1. oktober 2041

Studiet fullført (Antatt)

1. oktober 2041

Datoer for studieregistrering

Først innsendt

5. august 2026

Først innsendt som oppfylte QC-kriteriene

11. august 2026

Først lagt ut (Faktiske)

17. august 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

17. august 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

11. august 2026

Sist bekreftet

1. august 2026

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • BIO-FIL_MAB

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

JA

IPD-planbeskrivelse

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).

IPD-delingstidsramme

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.

Tilgangskriterier for IPD-deling

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.

IPD-deling Støtteinformasjonstype

  • STUDY_PROTOCOL
  • SEVJE
  • ANALYTIC_CODE

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .

Abonnere