Introduction Multiple Myeloma (MM) develops from well-defined precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), where patients may remain stable or unknowingly rapidly progress to MM. Bone marrow (BM) biopsies are not routine for precursor disease management, and precursor patients are limited to monitoring a few biomarkers within peripheral blood (PB) for signs of progressive disease. Deep proteome profiling of PB plasma may help sensitively track disease, however, the complexity and dynamic range of plasma proteins has caused substantial limits of detection for MS-based proteomics. Technological advancements in multiplex assays with low cross-reactivity and minimal off-target events have enabled the use of plasma profiling for early disease detection, disease stage classification, defining high-risk disease features, and novel therapeutic target discovery. Here, we perform the first comprehensive plasma proteomic profiling study on patients across the MM disease continuum and longitudinal sequential samples from MM progressors and non-progressors.

Methods We performed high-throughput plasma proteomic profiling for approximately 3000 proteins simultaneously using the Olink® Explore 3072 library and Proximity Extension Assay (PEA) technology. Briefly, targeted proteins are recognized with multiplex matched pairs of antibodies labelled with unique DNA oligonucleotides. Upon antibody binding, the oligonucleotides come into proximity, hybridize, and are extended to generate a unique sequence for protein identification using next generation sequencing (NGS) readout.

We profiled a cohort of 174 individuals, including patients with MGUS (n=18), SMM (n=66), MM (n=18), and healthy individuals (n=72). Two time-point sequential samples were also profiled from MGUS to SMM progressors (n=17), SMM to MM progressors (n=17), MGUS non-progressors (n=9) and SMM non-progressors (n=27) with matched clinical follow-up time. Multi-timepoint samples from progressors range 0.48-5.88 years between samples (median of 1.53 years), and patients had a median clinical follow-up of 7.05 years. T-tests, ANOVAs, and a linear mixed effect (LME) model were used to identify proteins that change across disease stages, progression status, time, or their interaction. Results were adjusted for multiple testing using the Benjamini-Hochberg Method.

Results We identified 1302 significantly dysregulated proteins with the majority upregulated in progressive disease. We discovered many proteins that were significantly differentially expressed in MGUS, SMM, and NDMM vs. healthy donors (n=169, 713 and 621), in MGUS vs. SMM (n=467), MGUS vs. NDMM (n=243), and SMM vs. NDMM (n=304). We validated the use of PEA to capture plasma cell soluble proteins consistent with previous findings to serve as positive controls, including BCMA, which was significantly elevated in SMM and MM, baseline samples from progressors, and SMM-MM sequential samples while non-progressors remained stable. Additional MM-relevant proteins were also significantly expressed in our dataset, some of which are current therapeutic targets, including SLAMF7, SDC1, IL-6, CD69, CXCL5, and PECAM1. Notably, MGUS and SMM samples expressed proteins involved in proteotoxic stress and osteolysis, while MM samples expressed regulators of plasma cell differentiation, calcium modulation, cytokine secretion, T cell activation, and lymphocyte migration. A total of 990 proteins were significant in LME analysis; 12 proteins were significantly elevated in baseline samples of progressors vs. non-progressors, and 23 proteins significantly increased in expression over SMM-MM progression. Notably, two novel proteins that are vital for calcium homeostasis and integrin-mediated cell adhesion overlapped between these comparisons. These may act as new biomarkers for disease at high-risk of progression to MM.

Conclusions We performed the most comprehensive plasma proteomics study to date on MM disease stages and identified candidate high-risk disease biomarkers in longitudinal samples with significant clinical follow-up. Results reported here are preliminary and additional studies are underway to validate candidates in a larger cohort of patients and determine how best to integrate proteins into risk stratification models.

Barbagallo:Olink Proteomics: Current Employment, Other: Employee at Olink Proteomics. Mills:Olink Proteomics: Current Employment, Other: Employee at Olink Proteomics. Rucevic:Olink Proteomics: Current Employment, Other: Employee at Olink Proteomics. Auclair:AstraZeneca: Current Employment, Other: No conflicts to declare during the conduct of this study. Now an employee of AstraZeneca. Carr:Kymera: Membership on an entity's Board of Directors or advisory committees; Seer, Inc.: Membership on an entity's Board of Directors or advisory committees; PTM BioLabs: Membership on an entity's Board of Directors or advisory committees. Ghobrial:Menarini Silicon Biosystems: Honoraria; Huron Consulting: Honoraria; GSK: Honoraria; Bristol Myers Squibb: Honoraria; Aptitude Health: Honoraria; Amgen: Honoraria; Adaptive: Honoraria; AbbVie: Honoraria; Novartis: Research Funding; Celgene: Research Funding; Janssen: Honoraria; Oncopeptides: Honoraria; Pfizer: Honoraria; Sanofi: Honoraria; Sognef: Honoraria; Takeda: Honoraria; The Binding Site: Honoraria; Vor Biopharma: Honoraria; Veeva Systems: Honoraria; Window Therapeutics: Other: Advisory board participation.

Author notes


Asterisk with author names denotes non-ASH members.

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