Quantifying MM disease burden by BCMA?

In this issue of Blood, Ikeda and colleagues successfully demonstrated the utility of soluble B-cell maturation antigen (sBCMA) as a quantitative metric for tumor burden in oligosecretory (O-S) and nonsecretory (Non-S) multiple myeloma (MM).¹ Alone, this is an important confirmation of sBCMA’s role as a putative biomarker, but it also highlights the lack of a ground truth or a tool(s) to directly assess disease burden for all patients with MM (and throughout disease course). Although the International Myeloma Working Group and others have painstakingly developed guidelines for the measurement of disease burden and response,² we remain dependent upon semiquantitative measurement of dynamic serologic markers (serum protein electrophoresis and immunofixation [IFE], serum free light chains [SFLCs]), urine markers (urine protein electrophoresis and IFE), limited access to contemporary imaging, as well as the sampling of systemic disease with an invasive and localized bone marrow biopsy (BMBx). Moreover, and the focus of the article by Ikeda and colleagues, disease burden assessment is made more challenging in patients with de novo “unmeasurable” secretion of monoclonal paraproteins (M-proteins) as well as late in disease when secretory activity decreases and disease becomes more tumorous and less well reflected by BMBx sampling. Accordingly, sBCMA or alternative techniques (or combination of techniques) need to be integrated into disease assessment.

BCMA is a member of the tumor necrosis factor superfamily that promotes the survival and proliferation of plasma cells via binding of the B-cell activating factor/B-lymphocytes stimulator and a proliferation-inducing ligand (APRIL).³ It is expressed at higher levels on MM cells as well as plasma cells in precursor states (monoclonal gammopathy of underdetermined significance/smoldering multiple myeloma).³ This selective expression of BCMA on MM cells has made it an ideal for targeting chimeric antigen receptor T-cell therapies, T-cell engager, and antibody drug conjugates.³ Importantly, this cell surface receptor is actively cleaved by the cytosolic gamma secretase complex releasing sBCMA into the milieu.³ The soluble peptide has been shown to act as a decoy receptor, neutralizing the activity of APRIL under physiologic conditions. Like M-proteins, disease-specific levels of sBCMA found in MM also make it ideal for tracking disease.⁴ Additionally, sBCMA is not affected by renal function and has a relatively short half-life (24-36 hours) as opposed to SFLC and serum M-proteins, respectively.⁴ In turn, these factors suggest that sBCMA may be superior to M-protein to assess for disease burden and response in the right context.

To further investigate the role of sBCMA as a barometer of disease, Ikeda et al investigated peripheral blood sBCMA levels in O-S and Non-S newly diagnosed MM. The authors examined 115 patients with newly diagnosed multiple myeloma, comparing sBCMA with several metrics of tumor volume: (1) M-protein levels, (2) total diffusion volume (tDV, whole-body diffusion-weighted magnetic resonance imaging), (3) percent BM plasma cells (BMPC, high-resolution digital imaging), and (4) circulating tumor cells (CTCs, multicolor flow cytometry). At baseline, a strong correlation was observed with percent BMPCs and CTCs, and a moderate correlation was noted with tDV and M-proteins (better for SFLC than immunoglobulin G [IgG] or IgA). Importantly, sBCMA was shown to correlate with percent BMPCs better than M-proteins. Additionally, using a combined model of sBCMA, percent BMPC, tDV, and percent CTCs provided a more reliable predictor than the individual elements. These data supported their hypothesis that sBCMA correlated with MM disease burden and could aid in disease monitoring in O-S/Non-S MM. Ikeda and colleagues also affirmed that increased sBCMA levels at baseline correlated with international staging system and gain/amp1q21. Despite this, sBCMA levels did not correlate with other high-risk cytogenic abnormalities.

Having demonstrated that sBCMA was an accurate surrogate for disease burden in secretory MM, the authors next examined the utility in monitoring responses in 17 patients with O-S/Non-S MM (3 de novo and 14 relapsed patients), as well as those with secretory disease. The longitudinal sBCMA dynamics were consistent across both the O-S/Non-S and secretory groups, and increased sBCMA preceded M-protein relapse by approximately 6 months in the O-S/Non-S subgroup. Collectively, the authors demonstrate that sBCMA is a viable tool to assess and monitor disease burden in O-S/Non-S MM, thus providing an important path for quantitative measure of systemic disease burden for this underserved patient subgroup.

This article also indirectly raises a critical question, or maybe challenge, that we need to address as our technology, treatment, and patient outcomes advance. Can we develop and then use better tools to quantify MM tumor burden and response? The relatively poor concordance between these “standard” tools for assessing response² highlights the fact that a “ground truth” for monitoring of MM remains elusive. We are dependent on imperfect metrics for the assessment of MM and, in fact, exclude groups of patients from trials as their disease is not quantifiable. M-protein values have long been given us very good measures of response dynamics. We are moving toward adopting quantitative techniques with higher sensitivity than those used routinely.⁵ Protein sequencing and monitoring with mass spectrometry⁶ are very attractive techniques. Next-generation sequencing of CTCs or cell-free DNA represent alternative methodologies meeting these criteria.⁷ Additionally, these tools may provide critical molecular characteristics of systemic MM even late in disease when the BMBx becomes less representative of the clonal dynamics.

In conclusion, Ikeda and colleagues provide an excellent foundation for the employment of sBCMA as a quantitative metric for tumor burden in O-S and Non-S MM. Their work also highlights our need to continue to develop more quantitative and representative markers of disease burden. Lastly, we need to continue evaluate the utility of sBCMA as both a prognostic and predictive biomarker in the context of BCMA-targeting therapies (C. L. Freeman, J. Noble, M. Menges, R. Villanueva, J. Y. Nakashima, N. B. Figura, R. P. Tonseth, D. W. Idiaquez, L. Skelson, E. Smith, J. Abraham-Miranda, S. Corallo, G. de Avila, O. A. Castaneda Puglianini, H. Liu, M. Alsina, T. Nishihori, K. H. Shain, R. Baz, B. Blue, A. Grajales Cruz, J. M. Koomen, R. M. Atkins, D. K. Hansen, A. Silva, J. Kim, Y. Balagurunathan, and F. L. Locke, unpublished data, October 2024).^8,9 

Conflict-of-interest disclosure: K.H.S. reports honoraria from Bristol Myers Squibb, Janssen (Johnson & Johnson), Amgen, Adaptive, Sanofi, GlaxoSmithKline, Regeneron, and Takeda and research funding to the institution from AbbVie and Karyopharm, outside the submitted work.