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A total of 40 Multiple Myeloma (MM) patients at clinical relapse who progressed during Proteasome Inhibitors (PIs) or Immunomodulating Drugs (IMiDs)-based therapies and who are assigned to antiCD38-based salvage treatments, will be enrolled. We will collect bone marrow (BM) and peripheral blood (PB) samples from patients at specific timepoints:
Aim 1: Evaluation of cell-intrinsic mechanisms on BM.
Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) will be performed on marker CD138+ purified cells to evaluate their genomic profile, and on buccal swab DNA to restrict the analysis to variants or structural abnormalities that have a clear somatic status, and are therefore specific to the tumor cells. In details:
1.1 WGS: libraries will be prepared with TruSeqâ„¢(Kit Illumina) DNA Polymerase Chain Reaction (PCR)-Free Library Preparation Kit (Illumina, San Diego, CA) from 500ng of genomic DNA, aiming for an average target insert of 300bp. Sequencing will be performed on a 150bp-paired end protocol, at a target depth of 40x for tumor samples and 30x for normal samples.
1.2 WES: libraries will be prepared with SureSelectXT Human All Exon V6 (Agilent technologies int., Santa Clara, CA) from 100ng of genomic DNA, aiming for an average target insert of 300bp. Sequencing will be performed on a 150bp-paired end protocol, aiming for a target depth of 200x for tumor samples and 100x for normal samples.
1.3 Data analysis: next generation sequencing scoring system output format files (*.FASTQ files) will be aligned to the reference genome using Burrows-Wheeler Alignment Tool (BWAmem), and deduplicated aligned Binary Alignment Map (BAM) files will be analyzed using the following published tools available at the Wellcome Trust Sanger Institute (WTSI):
The clonal composition of the sample and the genomic evolution of myeloma over time will be inferred from the adjusted cancer cell fraction of the variants identified, clustered and analyzed using a hierarchical bayesian Dirichlet process.
The mutational processes operative at various phases of MM will be analyzed using a Non-Negative Matrix Factorization (NNMF) approach to extract mutational signatures from the array of substitutions in their 5' and 3' context.
The possible driver mutation role of all extracted missense mutation will be evaluated by the recently published dN/dS algorithm.
RNA-seq on marker CD138+ purified cells to evaluate transcriptomic profile will be performed using TruSeq Stranded Messanger RiboNucleic Acid (mRNA) Library Prep Kit (Illumina, San Diego, CA) on 500 ng total RNA, followed by sequencing, aiming for 100x106 total reads per sample. DNA excision repair protein (ERCC) spike-in mix will be added to facilitate normalization of the expression levels between samples. Reads will be aligned with Tophat2 to call SNVs, indels, and detect gene fusions. Cufflinks2 will be used to profile gene expression and detect novel transcript isoforms. Overall gene transcript expression levels will be quantified using the Reads Per Kilobase Million (RPKM) metric based on uniquely mapping reads.
Flow cytometry analysis will be performed on BM samples to examine potential determinants of immunotherapy sensitivity/resistance and the expression of specific targets including marker Cluster of Differentiation 38 (CD38), B-cell maturation antigen (BCMA), marker Cluster of Differentiation 33 (CD33), Programmed death-ligand 1 (PDL1), and marker Cluster of Differentiation 19 (CD19) prior to treatment, at response and at relapse. We will evaluate MM percent positive cells and Mean Fluorescence Intensity (MFI) in order to monitor the antigen expression during the evolution of the disease. Receptor density will also be performed. Moreover the European cytoflow consortium of International Myeloma Foundation (EuroFlow-IMF) MM minimal residual disease (MRD) panel will be applied to monitor MRD in particular by using a multiepitope (ME) antiCD38 to detect possible determinants of resistance. Moreover this panel will allow us to monitor the phenotype evolution of the clonal population, looking in particular at the shift towards more immature cells, which has been suggested as a mechanism of resistance to bortezomib.
Storage of viable marker CD138-: we will evaluate distribution of marker CD38 also on marker CD138- cells and we will determine if genomic or immunophenotypic lesions responsible for resistance could be present also in the marker CD138- fraction.
Aim 2: Evaluation of cell-extrinsic mechanism on BM and PB.
Aim 3: After comprehensively characterizing the genomic, transcriptomic and immunophenotypic features of CD138+ cells, and having a clear picture of the effector/suppressive immune population in MM, we will then correlate these features with clinical data. In detail, we will create a database including the following columns:
Although the relatively small size of the cohort will limit statistical power and the possibility to perform subgroup analysis, this attempt to identify biomarkers could improve the clinical management of the patient, by prioritizing the vast array of salvage treatments in MM and thus decreasing costs.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Evaluation of patients resistance to immunotherapies in Multiple Myeloma | Diagnostic Test | There are only collections of the samples. |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity vs resistance to new immunotherapies | The patients' response to new drugs administration will be evaluated identifying genomic aberrations (e.g. TRAF3 deletion/mutation and Cereblon mutation) or impaired cellular surface molecules expression (eg CD38, CD55, CD59 expression). | 5 years |
| Cell extrinsic mechanisms of response | The analysis will include T-cells population (eg. CD38+, CD4+, CD8+, Tregs cells), regulatory and suppressive immune populations (MDSCs) and cytokines (eg Activin-A, IL-3, IL-6, RANKL, OPG, MIP-1α, MIP-3α and DKK-1) characterization. | 5 years |
| Biomarkers of response | Response measured through cytofluorimetric analysis. Results will be integrated by statistical models (e.g. univariate, multivariate analysis) and then correlated to mutational results and patients' available clinical data in order to predict outcome. | 5 years |
| Biomarkers of response | Response measured through mutational analysis. Results will be integrated by statistical models (e.g. univariate, multivariate analysis) and then correlated to cytofluorimetric results and patients' available clinical data in order to predict outcome. | 5 years |
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Inclusion Criteria:
Exclusion Criteria:
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MM patients at clinical relapse who progressed during PIs or IMiDs-based therapies and who are assigned to antiCD38-based salvage treatments
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Dipartimento di Biotecnologie Molecolari e Scienze per la Salute | Torino | TO | 10126 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39030183 | Derived | Ziccheddu B, Giannotta C, D'Agostino M, Bertuglia G, Saraci E, Oliva S, Genuardi E, Papadimitriou M, Diamond B, Corradini P, Coffey D, Landgren O, Bolli N, Bruno B, Boccadoro M, Massaia M, Maura F, Larocca A. Genomic and immune determinants of resistance to daratumumab-based therapy in relapsed refractory multiple myeloma. Blood Cancer J. 2024 Jul 19;14(1):117. doi: 10.1038/s41408-024-01096-6. |
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| ID | Term |
|---|---|
| D009101 | Multiple Myeloma |
| ID | Term |
|---|---|
| D054219 | Neoplasms, Plasma Cell |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D020141 | Hemostatic Disorders |
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Peripheral blood and bone marrow.
| D014652 |
| Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D010265 | Paraproteinemias |
| D001796 | Blood Protein Disorders |
| D006402 | Hematologic Diseases |
| D006425 | Hemic and Lymphatic Diseases |
| D006474 | Hemorrhagic Disorders |
| D008232 | Lymphoproliferative Disorders |
| D007160 | Immunoproliferative Disorders |
| D007154 | Immune System Diseases |