Identification of Novel Protein Biomarkers in Bone Marrow Interstitial Fluid Links Coagulation Cascade Proteins to Survival Outcomes in Multiple Myeloma

Introduction:

Multiple myeloma (MM), the second most prevalent blood cancer, carries high morbidity with variability in clinical progression among patients that necessitates accurate risk stratification for effective therapy and life planning. Proteomic characterization of MM has been limited, but recent advancements in plasma proteomics now enable large-scale deep-proteomic profiling. In this study, we profiled the bone marrow interstitial fluid (BMIF) of 120 patients, including 83 with newly diagnosed MM (NDMM), 17 with relapsed/refractory MM (RRMM), 7 with monoclonal gammopathy of undetermined significance (MGUS), 7 with smoldering MM (SMM), and 5 non-MM individuals.

Methods:

Sample Prep and Proteomics:

120 BMIF samples from the myeloma tumor bank at the QEII Hospital (Halifax, NS, Canada) were processed at Seer™ (Redwood, CA) using the Proteograph™ XT. Peptides were analyzed by liquid chromatography coupled mass spectrometry in data-independent acquisition mode, with mass spectra processed by DIA-NN (1.8.1) within the Proteograph Analysis Suit (PAS).

Data Analysis:

All p values were adjusted using Benjamini-Hochberg for multiple testing. Differential intensity analysis was performed on the PAS online platform. Hazard ratios (HR) for overall survival (OS) were determined via Cox proportional hazard modeling. Gene set enrichment employed the clusterProfiler R package. Patients were hierarchically clustered based on protein group (PG) intensity, and log-rank tests were used to assess survival differences between clusters. Prognostic independence was evaluated using multivariate Cox proportional hazard modeling. Wilcoxon and Chi-square tests were used to analyze associations with continuous and categorical clinical covariates, respectively.

Results:

On average, 8,903 PGs were identified per sample, with a total of 11,128 PGs identified across the entire cohort. Of these, 4,190 PGs were common to all samples.

We assessed differential PG intensity between NDMM and its precursor conditions (MGUS and SMM). Several plasma cell-associated proteins were significantly more intense in NDMM (p < 1x10^-5), including SDC1 (CD138), TNFRSF17 (BCMA), and IRF4 (MUM1). The intensity of these proteins correlated with bone marrow plasma cell burden (SDC1: R² = 0.23, p = 0.002; TNFRSF17: R² = 0.17, p = 0.011; IRF4: R² = 0.23, P = 0.001).

To identify prognostic PGs in the NDMM group, we analyzed (A) PG-wise HRs for OS and (B) differentially intense PGs between the 15 longest- and 15 shortest-surviving NDMM patients. In (A), we found 68 PGs associated with increased survival and 65 with reduced survival. In (B), 321 PGs were more intense in long-survivors and 572 in short-survivors. Coagulation-related PGs (GO:0007596) were significantly over-represented in PGs associated with favorable prognosis in both analyses (A: p = 6.67e-06; B: p = 1.06e-21).

Clustering NDMM samples based on coagulation cascade PGs identified three groups with significantly different survival outcomes (log-rank p = 0.00041). The group with the poorest outcome (N = 27) exhibited a median survival of 18.6 months and low intensity of all coagulation factors, while the group with the best outcome (N = 36) had a median survival of 66.3 months and high intensity of all factors. These differences remained significant (log-rank p = 0.003, HR 0.47) in a multivariate Cox model adjusting for RISS stage, age, bone marrow plasma cell burden, m-protein quantity, and platelet count. No significant associations were found between coagulation group and INR, PTT, albumin, beta-2 microglobulin, LDH, or EGFR, nor with a patient's use at diagnosis of anticoagulants or antiplatelets, antiglycemics, antihypertensives, or antilipemics. There were also no significant associations between coagulation groups and the chemotherapeutic classes used over the followup period. In RISS stage 2 patients, the coagulation profile also classified them into three groups with significantly different survival outcomes (log-rank p = 0.0001).

Conclusion:

Proteomic assessment of MM BMIF highlights coagulation-related proteins as significant independent biomarkers for risk stratification in multiple myeloma. These novel biomarkers offer critical insights beyond current prognostic markers, enhancing our capacity to predict patient outcomes and personalize treatment strategies.

Forward:Kite/Gilead: Consultancy; BMS: Consultancy; BeiGene: Consultancy, Honoraria; AstraZeneca: Consultancy; Pfizer: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Seagen: Consultancy; Janssen: Consultancy; Servier: Consultancy; Abbvie: Consultancy. White:Sanofi: Honoraria; BMS: Honoraria; Antengene: Honoraria; Apobiologix: Honoraria; Forus: Honoraria; Karyopharm: Honoraria; Amgen: Honoraria; GSK: Honoraria; Pfizer: Honoraria.