Clinical Research Directory

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.