Clinical trial NCT04300101: Mixed Molecular Clinical Index (MMCI) in Diffuse Large B-cell Lymphoma (DLBCL)
Official title: Mixed Molecular Clinical Index (MMCI) in Diffuse Large B-cell Lymphoma (DLBCL)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Diffuse Large B Cell Lymphoma
Keywords: Diffuse Large B cell lymphoma;
cell of origin
gene expression
immunohistochemistry
Lead sponsor: Istituto Romagnolo per lo Studio dei Tumori Dino Amadori IRST S.r.l. IRCCS
Sponsor class: OTHER
First posted: 2020-03-09
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Prospective cohort: All patients with diagnosis of DLBCL afferring referred to IRST-IRCCS, Oncology-Hematology Units of AVR, S. Orsola Hospital (Bologna).

Retrospective cohort: Patients with diagnosis of DLBCL referred to IRST-IRCCS, from 2011 to 2017 for whom clinical data and FFPE samples are available.

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.

Eligibility criteria: Inclusion Criteria for the prospective cohort:

* New diagnosis of High grade Diffuse large B cell Lymphoma undergoing first line standard treatment;
* Signed written informed consent;
* Availability of FFPE sample.

Inclusion Criteria for the retrospective cohort:

* Diagnosis of High grade Diffuse large B cell Lymphoma from 2011 to 2017;
* Availability of FFPE sample and clinical data.

Exclusion Criteria:

* Patients included in clinical trials.

Primary outcomes:
measure: identification of new prognostic biomarkers
timeFrame: 3 years
Secondary outcomes:
measure: identification of molecular and clinical parameters
timeFrame: 3 years
measure: characterization of tissue and circulating immune microenvironment
timeFrame: 3 years
measure: immune checkpoint genes analysis
timeFrame: 3 years
measure: mutational status
timeFrame: 3 years
measure: Correlation of protein expression, mutational status and the mRNA signatures
timeFrame: 3 years
measure: 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. .
timeFrame: 3 years
measure: 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.
timeFrame: 3 years
Other outcomes:
Locations:
city: Meldola
country: Italy
facility: Irst Irccs
state: FC
city: Ravenna
country: Italy
facility: Ospedale S. Maria delle Croci RAVENNA
state: RA
city: Bologna
country: Italy
facility: L'Azienda Ospedaliero-Universitaria Di Bologna Policlinico S. Orsola-Malpighi
city: Rimini
country: Italy
facility: Ospedale Infermi
Study officials:
affiliation: IRST IRCCS
name: Gerardo Musuraca, MD
role: PRINCIPAL_INVESTIGATOR
References: