Novel proteomic characterization of multiple myeloma bone marrow interstitial fluid links prognosis to coagulation pathways

Abstract

Background: Multiple myeloma (MM), the second most prevalent hematological malignancy, carries high morbidity with variability in clinical progression among patients. This necessitates accurate risk stratification for effective therapy and life planning. While extensively genomically and transcriptomically characterized, MM remains modestly studied from a proteomic perspective. As proteomics is a closer measure of phenotype than genomic and transcriptomic assessments, addressing this gap in the literature may yield new insights into disease biology and novel biomarkers.

Methods: Herein, we applied a new sample preparation approach for mass-spectrometry based proteomics to bone marrow interstitial fluid (BMIF) from patients with MM or its precursors.

Results: We achieved deep coverage of the proteome, identifying > 11,000 protein groups (PGs) across our cohort, with an average of ~ 8900 PGs per sample. Of these, 194 PGs were significantly associated with overall survival (OS). These survival-associated PGs were enriched for those involved in coagulation, and clustering newly diagnosed MM (NDMM) based on coagulation-related proteins revealed three distinct groups characterised by globally high, medium, and low intensity of coagulation-related proteins. The group with low intensity of coagulation-related PGs had significantly reduced OS (log-rank p = 0.00078). Clustering was independent of measured clinical covariates, including chemotherapeutic regimens used, Revised International Staging System (R-ISS stage), International Normalised Ratio (INR), and age, among others.

Conclusion: Our findings support the value of fluid-based proteomic assessment of MM and suggest that coagulation-related PGs could serve as valuable novel biomarkers for risk stratification in multiple myeloma, warranting further investigation into this area.

Keywords: Coagulation Proteins; Multiple Myeloma; Prognosis; Proteomic.

Fig. 1

Plasma cell associated proteins are more intense in MM BMIF than MGUS or SMM BMIF: (A and B) Differential intensity analysis of PGs between MM and MGUS (A), and MM and SMM (B). The analysis was conducted on proteins with less than 25% missingness, PG intensities were median normalized within samples, and t-tests were used to compare mean PG intensity between groups. Correction for multiple testing was performed using FDR

Fig. 2

BMIF PGs define high-, medium-, and low-risk MM groups: (A and B) Cox hazard ratios for OS were calculated on each PG without missingness (A) and on each PG with less than 25% missingness (B). Correction for multiple testing was by FDR. C) Heatmap of PGs without missingness that were significantly associated with OS. Intensities were Z-scored, and clustering was performed on Euclidian distance using the Ward’s method. (D and E) Kaplan Meier for both OS (D) and PFS (E). Trace colours correspond to the clusters in (C)

Fig. 3

Overall survival-associated PGs in BMIF are enriched for coagulation: Gene set enrichment (A) and Wikipathway analysis (B) on the PGs with hazard ratios < 0.5 and p < 0.05 after correction for multiple testing

Fig. 4

Low-intensity of coagulation cascade PGs in BMIF identified high-risk MM: (A) Heatmap of PGs within the coagulation cascade. Intensities were Z-scored, and clustering was performed on Euclidian distance using the Ward’s method. (B and C) Kaplan Meier for both OS (B) and PFS (C). Trace colors correspond to the clusters in (A)

Fig. 5

Coagulation groups in BMIF prognostically independent of current clinical workup: (A) Forrest plot of the hazard ratio, confidence interval, and significance of coagulation clusters assessed in a multivariate Cox hazard model. (B) Box plots of continuous clinical covariates showing their distribution in each coagulation group. Wilcoxon was used to compare means between groups, and p values were corrected using FDR. (C) Kaplan Meier for OS with coagulation clustering applied to each RISS group