Introduction:

Serum B-cell maturation antigen (sBCMA) is an emerging non-invasive biomarker for assessing tumor burden and treatment response in multiple myeloma (MM). While several Western studies have established its diagnostic and prognostic utility, data from South Asian populations is limited. Differences in patient demographics, disease biology, and access to diagnostics may influence biomarker utility, making the validation of sBCMA in this population essential. This prospective study focuses on a South Indian cohort, evaluating sBCMA levels across the spectrum of plasma cell dyscrasias and healthy controls. It also explores correlations between sBCMA and established prognostic markers in newly diagnosed MM (NDMM).

Methods:

This single-center prospective study was conducted between July 2023 and February 2025, enrolling 219 individuals categorized into six subgroups: NDMM (n=60), smoldering MM (SMM, n=17), monoclonal gammopathy of undetermined significance (MGUS, n=32), relapsed MM (n=17), MM in remission (n=17), and age- and gender-matched controls (n=76). Serum samples were collected and stored at –80°C until analysis. sBCMA levels were quantified using ELISA (RayBio® Human TNFRSF17, Catalogue No: ELH-TNFRSF-17) with all samples run in triplicate and their mean values were taken to ensure analytical precision.

Comparisons of median sBCMA levels across subgroups and ISS/RISS stages (I–III) were done using the Kruskal-Walli's test, followed by post hoc Dunn's test for pairwise comparisons. In the NDMM subgroup, Spearman's rank correlation was used to assess the relationship between sBCMA and established markers of tumor burden and disease severity. Risk stratification based on mSMART 4.0 was analysed using the Mann–Whitney U test.

Results:

The median age of the cohort was 62 years (range:58-69 years), with 54.8% males. A significant difference in sBCMA levels were observed across subgroups (p < 0.001). The highest median sBCMA levels were noted in NDMM (255 ng/mL) and relapsed MM (240 ng/mL), while markedly lower levels were observed in patients in remission (80 ng/mL, p<0.001) and those with MGUS (81 ng/mL, p<0.001), both comparable to controls (74 ng/mL). SMM exhibited significantly elevated levels (200 ng/mL, p <0.001) compared to controls, indicating that sBCMA may detect early disease activity.

In NDMM, sBCMA showed strong positive correlations with bone marrow plasma cell percentage by morphology (ρ = 0.759, p < 0.001) and flow cytometry findings (ρ = 0.537, p = 0.001). Moderate correlations were observed with M-protein (ρ = 0.287, p = 0.026) and β₂-microglobulin (ρ = 0.396, p = 0.005). Significant negative correlations were seen with hemoglobin (ρ = –0.508, p < 0.001) and serum albumin (ρ = –0.408, p = 0.001). No significant association was found with age, serum creatinine, or involved free light chains. These findings suggest that sBCMA reflects tumor burden independently of renal function, age, and secretory output, making it particularly useful in assessing oligo-/non-secretory myeloma, especially in resource-limited settings.Patients with high-risk disease, by mSMART 4.0, had significantly higher median sBCMA (278 ng/mL) than standard-risk disease (245 ng/mL; p = 0.03). sBCMA levels also progressively increased in both ISS and R-ISS, with significant differences between R-ISS I and III (p = 0.032), II and III (p = 0.016), and ISS I and III (p = 0.015).

Conclusions:

This prospective study offers important real-world insights into sBCMA levels across the spectrum of plasma cell dyscrasias in a South Indian population, utilizing age- and gender-matched healthy controls. The findings validate sBCMA as a reliable, non-invasive biomarker that correlates strongly with tumor burden and established prognostic markers in multiple myeloma. Importantly, its utility appears independent of renal function and secretory phenotype, highlighting its relevance in routine clinical settings where access to advanced diagnostics may be constrained. These results support the potential integration of sBCMA into diagnostic and monitoring algorithms for plasma cell disorders, particularly in resource-limited or diverse healthcare environments. Ongoing longitudinal follow-up will further clarify its role in predicting treatment response and detecting relapse.

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