Assessing a New Paradigm in Defining High Risk Precursor Disease State for Patients with Plasma Cell Dyscrasia: Fluctuating Monoclonal Protein

Multiple Myeloma (MM) is a malignancy originating from mature plasma cells in the bone marrow, typically preceded by clinically asymptomatic precursor conditions. Early identification of the risk of progression from these precursor states, like Monoclonal Gammopathy of Undetermined Significance (MGUS) or Smoldering Myeloma, to full-blown MM is critical for improving patient outcomes. The current risk models for MM rely on limited clinical and laboratory parameters, unable to fully encompass the complex nature of the disease continuum. To overcome these challenges, developing and validating a more comprehensive risk model is imperative, incorporating a wider array of clinical, genetic, and immune-related factors. This approach will enhance predictive accuracy and clinical applicability significantly. Notably, changes in serum monoclonal protein spike (M-spike) levels can reflect dynamic fluctuations in the net clonal mass, representing the ongoing battle between the plasma cell clone and the anti-myeloma immune response within patients. However, the impact of this highly dynamic state on the risk of progression to MM remains largely unexplored. Our study aims to bridge this knowledge gap by investigating the association between the dynamic M-spike pattern and the risk of progression from a precursor clone to MM, offering valuable insights into a potential novel prognostic marker for disease advancement.

Methods: This study aimed to redefine high-risk precursor states in patients with plasma cell dyscrasias, focusing on positive serum electrophoresis (SPEP) results recorded between January 2011 and January 2021. We categorized precursor states as patients with positive SPEP, excluding those who had received active multiple myeloma (MM) treatment within 90 days of their first positive SPEP, showed M-spike values exceeding 3g/l, or displayed evident signs of disease progression. To assess M-spike patterns, we classified them as stable or fluctuating, using a 20% difference criterion to differentiate between the two (i.e., fluctuations less than 20% from baseline were labeled as stable). The time-to-treatment (TTT) was calculated from the first positive SPEP to the initiation of treatment, with death considered a competing risk.

Results: The study included 565 patients, among whom 372 showed a stable M-spike pattern, while 193 exhibited a fluctuating pattern. The racial composition of the study population was predominantly white (75.58%), followed by black (21.77%) and other races (2.65%). The distribution of races was virtually identical in both the fluctuation and stable groups (p=0.977). The fluctuation group had a slightly lower proportion of females (49.7%) than the stable group (53.2%), but this difference was not statistically significant (p=0.432). Univariate analysis revealed that patients in the fluctuating group had a significantly higher odds ratio (OR) of requiring treatment (OR: 4.5, 95% CI: 1.37 - 14.18, p = 0.013) than those in the stable group. Interestingly, the prevalence of autoimmune conditions differed significantly between the groups. While 11.86% of the whole group had an autoimmune disorder, the proportion was lower in the fluctuation group (9.8%) than in the stable group (16.7%) (p=0.028).Confirming these findings, the multivariate time-to-event analysis further demonstrated that patients with fluctuating M-spike patterns had a greater likelihood of needing therapy (Hazard Ratio: 3.73, 95% CI: 1.19 - 11.66, p = 0.024) compared to those with stable patterns.

Conclusion: Our study introduces a novel approach to defining high-risk precursor states in patients with plasma cell dyscrasias, emphasizing the significance of undulating monoclonal protein patterns. The dynamic nature of these fluctuations, representing the delicate interplay between pro-growth myeloma clones and anti-tumor immunity, emerges as a strong predictor of progression to MM. Further investigation and validation of these findings in more extensive studies hold promise for advancing personalized risk assessment and the development of targeted therapeutic strategies to prevent or delay the onset of multiple myeloma.