OA-35: High-throughput plasma proteomics to define the precursor multiple myeloma proteome and identify candidate high-risk disease biomarkers of progression

Introduction: Multiple Myeloma (MM) develops from precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM). Bone marrow (BM) biopsies are not routine in precursor disease management and only few proteins within peripheral blood (PB) suggest progressive disease. PB proteome profiling may better help track disease; however, the dynamic range of the plasma proteome has limited detection depth for MS-based proteomics without depleting abundant proteins and fractionating the samples after digestion to peptides. We perform the first comprehensive plasma proteomic profiling study using advanced multiplex immunoassays on patients across the MM disease continuum and sequential samples from progressive and stable disease.

Methods: We performed plasma profiling for 3000 proteins using the Olink® Explore 3072 library and Proximity Extension Assay (PEA) technology. Proteins were recognized by multiplexed antibodies labelled with DNA oligonucleotides that upon binding come into proximity, hybridize, and are extended to generate a unique sequence for protein identification with sequencing. We profiled 423 plasma samples from 348 individuals, including healthy controls (n=58), MGUS (n=67), SMM (n=179), MM (n=44), as well as sequential samples from patients with progressive (n=27) and stable disease with matched clinical follow-up time. Precursor defined samples from progressors range 1.03-5.88yrs prior to diagnosis, with a median clinical follow-up time of 7.05yrs. T-tests, ANOVAs, and a linear mixed effect model identified proteins across disease stages, progression status, and time with results adjusted for multiple testing using the Benjamini-Hochberg Method.

Results: We captured circulating levels of proteins highly expressed on the surface of plasma cells, including CD38, SDC1, BCMA and SLAMF7, highlighting the ability of PEA technology to detect clinically relevant therapeutic targets. We identified proteins that significantly discriminated MGUS, SMM, and NDMM from healthy donors (n=222, 423, 494). Top classifiers included proinflammatory cytokines IL1, IL5, IL6, IL16, and IL18 which are known to create a BM environment that promotes malignant cell development by suppressing the microenvironment, promoting cellular adhesion, or increasing angiogenesis. Consistent with previous findings, baseline BCMA levels were significantly elevated in progressors vs. non-progressors, further supporting the potential utility of BCMA measurements for disease monitoring. Four novel proteins vital for calcium homeostasis and integrin-mediated cell adhesion significantly increased from healthy to MM and in progressors vs. non-progressors, nominating these proteins as candidate biomarkers of high-risk disease.

Conclusions: We performed the most comprehensive plasma proteomics study to date on MM disease stages and identified candidate high-risk disease biomarkers. Additional studies are underway to validate candidates and integrate proteins into risk stratification models.