Characterizing Genomic Variants Defining a Plasma Cell Disorder Patient Cohort Using a Broad Next Generation Multi-Gene DNA Sequencing Panel
Authors: Ezell Kristin , Hooper Katharine , Kirk Kaylyn , Vankina Ritika , Kumar Swarup 
Published: December 2024

Plasma cell disorders exist on a spectrum ranging from premalignant states such as monoclonal gammopathy of undetermined significance (MGUS) through malignancies such as multiple myeloma (MM), Waldenstrom's macroglobulinemia (WM), light-chain amyloidosis and POEMS syndrome amongst others. Whilst current diagnostic criteria for MM relies on plasma cell percentage and clonality in bone marrow sampling, as well as imaging and a limited laboratory profile, this criteria does not take into account recurrent genetic mutations associated with plasma cell disorders. Currently, the molecular changes that define progression from premalignant to malignant plasma cell disorders are not well characterized and most molecular studies are limited to MM cases. As recent advances have allowed access to analyze and characterize genomic profiles via commercially available next-generation sequencing (NGS) platforms, we aimed to examine the genomic landscape across the spectrum of plasma cell disorders as an avenue for further study of genetic markers for progression.We have studied genomic variants identified by Tempus xT® 648-gene-panel sequencing in 158 DNA samples derived from bone marrow biopsy (144), tissue biopsy (3) or peripheral blood (11) samples in patients diagnosed with a plasma cell disorder between January 2020 and June 2024. 48 cases of MGUS, 12 cases of smoldering MM, 72 cases of MM, 9 cases of light-chain amyloidosis and 17 other cases, including solitary plasmacytoma, POEMS syndrome and WM, defined the cohort. NGS reports were digitized as sample-specific CSV files then compiled into datasets via the use of Python (ver 3.10.7). Each NGS report was verified against patient medical records for accuracy and diagnosis was confirmed by chart review. Data was analyzed using Excel (ver 16.77.1) and open-source Jupyter (ver 6.4.12). Data was further analyzed via the use of the web-based Enrichr® platform (accessed July 2024) to identify pathways that these gene mutations may affect. In the 158 sample cohort, 372 genes were identified as mutated, and, in total, 1,007 variants of these 372 genes were identified. Variants of unknown significance (VUS) were the most common type of gene mutation identified, affecting 271 genes whilst oncogenic gene variants i.e. biologically relevant (BR) and potentially actionable (PA) variants affected 59 genes. Oncogenic genes recurrently identified in the entire cohort were DNMT3A (29 variants), TP53 (13 variants) and TET2 (8 variants). In those identified as VUS, the most common genes were KMT2C (MLL3) (24 variants), ARID1B (17 variants) and NOTCH1 (14 variants).Overall, the premalignant cohort (MGUS and smoldering MM) had significantly lower mutation numbers than malignant samples (MM, light-chain amyloidosis and others), both inclusive and exclusive of oncogenic mutations (one-way ANOVA p&lt;.00001). Across the top 50 genes identified as VUS in premalignant samples, the mean number of mutations was 2.12 per sample whilst in the malignant cohort, this was 4.96 per sample (Mann-Whitney U p&lt;.00001). Across the entire cohort, pathway determination to the MSigDB Hallmark 202 database via Enrichr®, showed 271 genes designated VUS were most strongly aligned with Wnt-beta Catenin Signaling, UV Response Dn and Apoptosis pathways (Odds Ratio 14.92, 8.21 and 7.86 respectively) whilst 59 oncogenic genes were most strongly aligned with E2F Targets, IL-6/JAK/STAT3 Signaling and PI3K/AKT/mTOR Signaling pathways (Odds Ratio 18.71, 19.94 and 16.38, respectively).Through genomic profiling, we aimed to identify and stratify mutations that may be recurrent across the spectrum of plasma cell disorders as well as discern unique mutation profiles and frequencies across premalignant and malignant states. In our dataset, we have demonstrated several recurrent genomic events which warrant investigation, highlighting the need for further analysis. We will continue to collect data with the aim of further characterizing the complex landscape of all plasma cell disorders.