Mixed Molecular Clinical Index (MMCI) in Diffuse Large B-cell Lymphoma (DLBCL)
- Conditions
- Diffuse Large B Cell Lymphoma
- Registration Number
- NCT04300101
- Brief Summary
This is a prospective and retrospective observational study. The primary objective is to identify new prognostic biomarkers for DLBCL patients in terms of progression-free survival (PFS) and able to add predictive capacity to recognized important clinical factors.
The secondary objectives are:
* to identify new biomarkers associated with overall survival (OS) and objective response rate (ORR)
* to characterize tissue and circulating immune microenvironment of DLBCL patients by bulk and single cell transcriptomics;
* to assess the correlation between the expression of immune checkpoint genes and mRNA signature;
* to describe the mutational status of a panel of genes relevant to DLBCL pathogenesis;.
* to assess the correlation between protein expression, mutational status and the messenger RNA (mRNA) signature.
* to investigate the association between radiomic features obtained from PET images and patient and tumour characteristics and clinical outcomes (PFS, OS, ORR).
For each enrolled patient, immunohistochemical determinations will be performed: Cell of origin (COO) (Germinal Cell -GC- or activated B-cell - ABC- type according with Hans algorithm ), evaluation of cluster of differentiation antigen 20 (CD20), cluster of differentiation antigen 5 (CD5), cluster of differentiation antigen 10 (CD10), Bcl6, Bcl2 (cut off\>50%), Multiple Myeloma 1 / Interferon Regulatory Factor 4 protein (MUM1/IRF4), c-myc (cut off\>40%) and Ki67, fluorescence in situ hybridization (FISH) for c-myc and if rearranged, for Bcl2 e Bcl6 ). Moreover, paraffin embedded (FFPE) tumor specimens will be collected for RNA extraction and mRNA expression mutational and proteomics analysis, centralized at IRST-IRCCS.
- Detailed Description
Diffuse large B-cell lymphoma (DLBCL) is an heterogeneous group of cancers classified together on the basis of morphology, immunophenotype, genetic alterations and clinical behavior. The distinction of DLBCL into cell-of origin (COO) categories, based on patterns of gene expression reminiscent ( germinal center B-cell- the GC group and activated B-cell- the ABC group-), as defined and characterized by the Lymphoma \& Leukemia Molecular Profiling Project (LLMPP), has profound biological, prognostic and potential therapeutic implications and in addiction, the negative prognostic effect of myelocytomatosis oncogene (MYC), B-cell lymphoma 2 (BCL2) and B-cell lymphoma-6 (BCL6) alterations in DLBCL has been showed largely dependent on COO subtypes . Furthermore, the combination of BCL2, MYC and BCL6 alterations with IPI (International Prognostic Index), identifies markedly worse prognostic groups within individual COO subtypes. The original methods used to define these entities, performed gene expression profiling (GEP) using microarrays on RNA derived from frozen tissue (FT). Subsequently, in an attempt to determine COO in standard practice using commonly available formalin-fixed paraffin-embedded tissue (FFPE) less precise but relatively inexpensive binary immunohistochemical (IHC) methods has been used . However in particular in non GC, the rate of concordance was unsatisfactory. A high degree of agreement has been demonstrated instead in COO determining, with a signature of 20 genes from formalin-fixed paraffin embedded (FFPE) tumor specimens, with Lymph2Cx kit (nCounter® Technology, NanoString Technologies), becoming the gold standard suggested in World Health Organization (WHO) classification . However recently, was demonstrated that the COO and BCL2, MYC, BCL6 status are not enough to describe the molecular risk of these patients, suggesting a genetic substructure that still to be discovered . Moreover, the tumor microenvironment and in particular the ratio of immune effectors and checkpoint molecules also have a prognostic role in DLBCL. Besides, elevated frequency of myofibroblasts, dendritic cells, and cluster of differentiation 4 (CD4) positive T cells correlated with better outcomes.
In conclusion, a comprehensive genomic analysis of these patients and a deep characterization of the immune compartment and immune checkpoints (Nanostring, immunohistochemistry for BCL2, MYC, BCL6, mutation analysis, proteomic analysis etc.) joined with IPI score, will allow the creation of a mixed, molecular, clinical, index (MMCI) to identify extremely poor prognostic groups, within each COO subtype, to consider a risk-adapted treatments in future.
It is a prospective and retrospective observational study with a total duration of 36 months.
The primary objective is the identification of new prognostic biomarkers for DLBCL patients in terms of progression-free survival (PFS) and able to add predictive capacity to recognized important clinical factors.
The secondary objectives are:
* to identify new biomarkers associated with overall survival (OS) and objective response rate (ORR);
* to characterize tissue and circulating immune microenvironment of DLBCL patients by bulk and single cell transcriptomics;
* to assess the correlation between the expression of immune - checkpoint genes and mRNA signature;
* to describe the mutational status of a panel of genes relevant to DLBCL pathogenesis;.
* to assess the correlation between protein expression, mutational status and the mRNA signature.
* to investigate the association between radiomic features obtained from PET images and patient and tumour characteristics and clinical outcomes (PFS, OS, ORR).
For each enrolled patient, immunohistochemical determinations will be performed by each Pathology Unit: COO (GC o ABC type according with Hans algorithm ), evaluation of CD20, CD5, CD10, Bcl6, Bcl2 (cut off\>50%), MUM1/IRF4, c-myc (cut off\>40%) and Ki67, FISH for c-myc and if rearranged, for Bcl2 e Bcl6). Moreover, paraffin embedded (FFPE) tumor specimens will be collected for mRNA expression mutational and proteomics analysis, centralized at IRST-IRCCS.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 300
Not provided
- Patients included in clinical trials.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method identification of new prognostic biomarkers 3 years To identify new prognostic biomarkers for DLBCL patients that combined to clinical factors (IPI) are able to create a MMCI, predictive in terms of progression-free survival (PFS) of DLBCL patients.
- Secondary Outcome Measures
Name Time Method identification of molecular and clinical parameters 3 years to identify molecular and clinical parameters associated with overall survival (OS) and objective response rate (ORR)
immune checkpoint genes analysis 3 years to assess the correlation between the expression of immune checkpoint genes and mRNA signatures
mutational status 3 years describe the mutational status of a panel of genes relevant to DLBCL pathogenesis
Correlation of protein expression, mutational status and the mRNA signatures 3 years assess the correlation between protein expression, mutational status and the mRNA signatures.
To analyze the correlation between radiomic features from PET images, tumor characteristics and clinical outcomes, through a multi-omics artificial intelligence approach using ROC algorithm. . 3 years Investigate the correlation between radiomic features obtained from PET images, tumour characteristics and clinical outcomes (PFS, OS, ORR). ROC (Receiver operating characteristic) curve will be computed and compared between the models. Depending on the outcome (time-to-event or binary) different regression models (Cox model for PFS and OS and logistic model for ORR) will be used.
To analyze the correlation between radiomic features from PET images, tumor characteristics and clinical outcomes, through a multi-omics artificial intelligence approach using c-index algorithm. 3 years Investigate the correlation between radiomic features obtained from PET images, tumour characteristics and clinical outcomes (PFS, OS, ORR). C-index curve will be computed and compared between the models. Depending on the outcome (time-to-event or binary) different regression models (Cox model for PFS and OS and logistic model for ORR) will be used.
characterization of tissue and circulating immune microenvironment 3 years to characterize tissue and circulating immune microenvironment by bulk and single cell transcriptomics
Trial Locations
- Locations (4)
Irst Irccs
🇮🇹Meldola, FC, Italy
Ospedale S. Maria delle Croci RAVENNA
🇮🇹Ravenna, RA, Italy
L'Azienda Ospedaliero-Universitaria Di Bologna Policlinico S. Orsola-Malpighi
🇮🇹Bologna, Italy
Ospedale Infermi
🇮🇹Rimini, Italy
Irst Irccs🇮🇹Meldola, FC, ItalyGerardo Musuraca, MDContact+39 0543739100gerardo.musuraca@irst.emr.it