Integrative Multi-omics Analysis to Predict Monoclonal Gammopathies Clinical Evolution

Overview

This prospective, multicenter, observational study aims to identify molecular and immunological markers associated with disease progression in patients with monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). By integrating genomic, transcriptomic, immunophenotypic, and oral microbiome analyses, the study seeks to characterize the biological mechanisms underlying the transition to symptomatic multiple myeloma (MM). The study also includes in vitro modeling to investigate bone damage and immune dysfunction. Healthy volunteers (HV) undergoing joint replacement surgery for osteoarthritis will serve as controls. The ultimate goal is to improve early risk stratification and support future preventive strategies through a multi-omics approach. There is a pressing need for new strategies to identify high-risk individuals based on biological rather than purely clinical parameters. This study proposes an integrative, multi-omics approach to investigate the transition from MGUS/SMM to MM. By analyzing the immunome and oral microbiome alongside molecular profiling, the goal is to identify reliable biomarkers of progression. The resulting insights could be enable more accurate risk stratification and guide the design of future preventive clinical trials aimed at delaying or halting disease evolution.

Detailed description

Multiple myeloma (MM) is a hematologic malignancy characterized by the clonal proliferation of antibody-secreting plasma cells in the bone marrow. It accounts for approximately 10% of all blood cancers, with an incidence of 3-5per 100.000 individuals in Westen countries. MM is an incurable disease that leads to severe bone destruction and fractures due to the abnormal interaction between malignant plasma cells and the bone marrow microenvironment. Although new therapies have improved survival, MM remains a complex and genetically heterogeneous disease. Genomic instability is a hallmark of MM and includes both chromosomal abnormalities and gene mutations. Tumors may presenta s hyperdiploid - with multiple trisomies - or non-hyperdiploid, often involving translocations at the immunoglobulin heavy chain locus (IGH). These genetic differences impact prognosis. Additional recurrent alterations, such as deletions (13q, 17q), gains (1q), and mutations in genes like KRAS, NRAS, TP53, and BRAF, further illustrate the disease's biological diversity. Molecular profiling techniques, such as RNA sequencing and gene expression arrays, have identified gene expression patterns that correlate with prognosis, though only a few are currently used in clinical practice. MM is consistently preceded by two asymptomatic precursor conditions: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). These conditions are prevalent in older adults and share many molecular features with symptomatic MM, yet only a small fraction of cases progress annually - about 1% for MGUS and 10% for SMM. Disease evolution appears to depend not only on intrinsic genetic changes but also on interactions with the bone marrow microenvironment, which includes stromal cells, dendritic cells, T cells (especially Th17), NK cells and myeloid-derived suppressors cells. Immune dysfunction, antigen presentation defects, expansion of immunosuppressive cells, and high levels of inhibitory cytokines contribute to the emergence of an immunosuppressive niche that enables myeloma cells to escape immune surveillance and progress. Immunomodulatory drugs (ImiDs) and monoclonal antibodies, which can reactivate immune responses, are therefore central to treatment strategies. Recent evidence also suggests a link between the microbiota and disease progression. In experimental models, alterations in gut microbiota have been shown to affect immune responses, influencing disease onset. Currently available prognostic tools mainly reflect tumor burden rather than underlying biology. As such, they fail to accurately predict disease progression.

Interventions

• Other Collection of biological material
  For MGUS, SMM and MM patients, biological material (bone marrow aspirate, bone marrow biopsy, peripheral blood) consists exclusively of left-over samples obtained during routine diagnostic procedures and clinical practice management of their disease. For healthy volunteers, biological material includes waste bone tissue obtained during orthopedic surgery (endo- or arthro-prosthesis) and peripheral blood collected for research purposes. Both cohort of patient will be asked to donate gingival cr

Primary outcome measures

• Bone marrow and peripheral blood immunophenotypic characterization [Time frame: up to 24 months]
• Genomic and transcriptomic profiling of plasma cell [Time frame: Up to 24 months]
• Single-cell and spatial transcriptomic analyses [Time frame: up to 24 months]

Secondary outcome measures (2)

• Evaluation of oral microbiome composition [Time frame: up to 24, months]
• Functional validation of genetic profiles in osteolytic disease [Time frame: 24 months]

Eligibility criteria

Inclusion criteria

• Age >18 years
• Male or female patients
• Histologically confirmed diagnosis of MGUS, SMM, or MM according to ESMO 2021 guidelines
• Willing and able to provide written informed consent

HEALTHY VOLUNTEERS (HV)

• Age >60 years
• Diagnosis of osteoarthritis (OA)
• Scheduled for hospitalization for surgical treatment of OA (endoprosthesis or arthroplasty)
• Willing and able to provide written informed consent

Exclusion criteria

• Patients:

• Active current infection
• Autoimmune disease
• Women of childbearing potential unable to exclude pregnancy
• Use of high-dose corticosteroids within the past 7 days, potentially affecting immunome composition

Healthy Volunteers:

• Prior joint surgery or severe joint deformity
• Recent trauma, osteonecrosis, or OA caused by prior/current joint infection
• Metabolic disorders
• Previous or current cancer diagnosis
• Autoimmune diseases (e.g., rheumatoid arthritis)

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Cohort

Study locations

Italy · 4 centers

• Istituto Ortopedico Rizzoli IRCCS — Bologna
• UO Ematologia Azienda Ospedaliero-Universitaria "Policlinico Rodolico San Marco" — Catania
• UOC di Ematologia, Dipartimento di Oncologia, AOU Policlinico "Paolo Giaccone" — Palermo
• S.C Ematologia - Azienda USL IRCCS di Reggio Emilia — Reggio Emilia

Publications

• Rossi M, Altomare E, Botta C, Gallo Cantafio ME, Sarvide S, Caracciolo D, Riillo C, Gaspari M, Taverna D, Conforti F, Critelli P, Bertucci B, Iannone M, Polera N, Scumaci D, Arbitrio M, Amodio N, Di Martino MT, Paiva B, Tagliaferri P, Tassone P. miR-21 antagonism abrogates Th17 tumor promoting functions in multiple myeloma. Leukemia. 2021 Mar;35(3):823-834. doi: 10.1038/s41375-020-0947-1. Epub 202 PMID 32632096
• Leone P, Solimando AG, Malerba E, Fasano R, Buonavoglia A, Pappagallo F, De Re V, Argentiero A, Silvestris N, Vacca A, Racanelli V. Actors on the Scene: Immune Cells in the Myeloma Niche. Front Oncol. 2020 Oct 29;10:599098. doi: 10.3389/fonc.2020.599098. eCollection 2020. PMID 33194767
• Bolli N, Maura F, Minvielle S, Gloznik D, Szalat R, Fullam A, Martincorena I, Dawson KJ, Samur MK, Zamora J, Tarpey P, Davies H, Fulciniti M, Shammas MA, Tai YT, Magrangeas F, Moreau P, Corradini P, Anderson K, Alexandrov L, Wedge DC, Avet-Loiseau H, Campbell P, Munshi N. Genomic patterns of progression in smoldering multiple myeloma. Nat Commun. 2018 Aug 22;9(1):3363. doi: 10.1038/s41467-018-0505 PMID 30135448
• Ziccheddu B, Da Via MC, Lionetti M, Maeda A, Morlupi S, Dugo M, Todoerti K, Oliva S, D'Agostino M, Corradini P, Landgren O, Iorio F, Pettine L, Pompa A, Manzoni M, Baldini L, Neri A, Maura F, Bolli N. Functional Impact of Genomic Complexity on the Transcriptome of Multiple Myeloma. Clin Cancer Res. 2021 Dec 1;27(23):6479-6490. doi: 10.1158/1078-0432.CCR-20-4366. Epub 2021 Sep 15. PMID 34526359
• Robiou du Pont S, Cleynen A, Fontan C, Attal M, Munshi N, Corre J, Avet-Loiseau H. Genomics of Multiple Myeloma. J Clin Oncol. 2017 Mar 20;35(9):963-967. doi: 10.1200/JCO.2016.70.6705. Epub 2017 Feb 13. PMID 28297630
• Morgan GJ, Walker BA, Davies FE. The genetic architecture of multiple myeloma. Nat Rev Cancer. 2012 Apr 12;12(5):335-48. doi: 10.1038/nrc3257. PMID 22495321
• Palumbo A, Anderson K. Multiple myeloma. N Engl J Med. 2011 Mar 17;364(11):1046-60. doi: 10.1056/NEJMra1011442. No abstract available. PMID 21410373

Identifiers

NCT: NCT07214324 · 849/2022/TESS/IRCCSRE

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗