Clonal Heterogeneity in Multiple Myeloma: Personalized Insights from Bone Marrow Analysis

Introduction: Multiple Myeloma (MM), a clonal plasma cell malignancy, remains incurable despite advances in therapy. The disease`s heterogeneity, marked by sub-clonal diversity, contributes to therapy resistance and relapse. Current flow cytometry protocols for MM are limited to a few markers, constraining their diagnostic and therapeutic utility. This study aims to establish a robust, multi-parameter flow cytometry (FACS) protocol to enhance clonal and sub-clonal identification in MM, providing actionable insights for personalized treatments.

Materials and Methods: Bone marrow aspirates from MM patients at diagnosis were processed using a 14-color FACS panel, incorporating markers that are significant for MM diagnosis, prognosis or treatment (CD38, CD138, BCMA, CD56, CD81, CD319, CD24, CD33, CD19, CD20, CD117, CD45, CD27). Samples were stained with pre-optimized antibody mixes and analyzed on a CytoFLEX LX cytometer. Data were processed using classical gating and t-SNE-based next-generation analysis to map clonal compositions. A clinical database was developed to correlate FACS profiles with therapeutic outcomes.

Results and Discussion: The 14-color FACS protocol demonstrated enhanced resolution in distinguishing MM clones and sub-clones, revealing significant heterogeneity even at diagnosis. Clonal analysis identified dominant and minor sub-clones with distinct phenotypes, such as BCMA-high or CD56-positive clones. t-SNE visualization provided an intuitive map of clonal diversity, aiding in recognizing patterns associated with treatment resistance.

Conclusions: The optimized 14-color FACS protocol offers a powerful tool for detailed clonal and sub-clonal analysis in MM. By enabling precise profiling of MM at diagnosis and during treatment, this method holds potential to guide personalized therapeutic strategies, improve response rates, and delay relapse. Future work includes integrating longitudinal data to track clonal evolution across treatments, and correlating FACS results with clinical outcomes.