MM-019: Circulating cell-free DNA as a minimally invasive tool for tracking genomic aberrations and treatment response in multiple myeloma: A proof-of-concept study in Indian patients

Abstract

Introduction: Multiple myeloma (MM) is a hematological malignancy marked by uncontrolled plasma cell expansion in the bone marrow (BM). Traditional monitoring via BM aspirate is invasive and may miss tumor heterogeneity. Cell-free DNA (cfDNA) analysis offers a minimally invasive alternative, enabling assessment of comprehensive tumor burden and genomic profiling to improve disease surveillance and treatment evaluation. Aims : To optimize cfDNA-based technology for detecting and monitoring genomic aberrations inMMusing targeted next-generation sequencing (NGS) and droplet digital PCR (ddPCR). To identify genomic aberrations influencing therapy response and disease progression, and evaluate cfDNA for monitoring treatment response in MM. Methods: Targeted NGS was performed on bone marrow plasma cells (BMPC) from 175 newly diagnosed multiple myeloma (NDMM) samples, with data analyzed using ANNOVAR and the COSMIC database to identify pathogenic mutations. NGS identified trackable baseline mutations in KRAS, NRAS, BRAF, DNAH5, and FAT4 genes across 70 patients. Flow cytometry-based minimal residual disease (MRD) data from 84 follow-up cfDNA samples were analyzed and categorized into four groups according to the total abnormal plasma cells (TAPC) detected. The correlation between cfDNA mutations and MRD positivity or negativity was examined across these four groups. Results: Pathogenic mutations were detected in 54% of patients (94/175), with 34% of genes (16/47) harboring mutations. Key mutations: KRAS (20%), NRAS (13%), BRAF (7%), FAT4 (6%), FAT3 (6%), DNAH5 (5%), TP53 (4%), DIS3 (3%), and RB1 (2%). BM mutations were detected in cfDNA for 95% of patients (50/52). The mutation profiles in cfDNA, particularly for KRAS, NRAS, BRAF, DNAH5, and FAT4, correlated with disease kinetics and demonstrated a high concordance (91%) with BM flow cytometry-based MRD detection. We also observed that cfDNA analysis might be more sensitive in detecting MRD in extramedullary disease (EMD) cases, where abnormal plasma cells were absent inBM but mutations were detectable in cfDNA. Conclusion: The strong concordance between cfDNA ddPCR analysis and BM flow cytometry-based MRD detection suggests that cfDNA has potential as a complementary tool for disease monitoring. The high prevalence of MAPK pathway mutations highlights keyMMtargets, and cfDNA genomic profiling correlates with clinical outcomes, aiding therapy response prediction.