MM-253: Single Cell Multi-Omic Analysis And Immune Cell Type Profiling Of Multiple Myeloma With t(4;14)

Abstract

Context Multiple myeloma (MM) is an incurable plasma cell (PC) malignancy and high risk (HR) MM remains an unmet clinical need. Translocation 4;14 occurs in 15% of MM and is associated with an adverse prognosis. A deeper understanding of the biology and immune micro-environment of t(4;14) MM is necessary for the development of effective targeted therapies. Objective Here we utilized Proteona’s ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM with the goal of better understanding the biology underlying this high risk patient cohort. Design Diagnostic bone marrow (BM) samples from 13 patients with t(4;14) MM were analysed using the ESCAPE platform from Proteona which simultaneously measures gene and cell surface protein expression in single cells. Resulting data were analyzed with MapSuite tools from Proteona and Seurat. Results The patients had a median age of 63 years. All received novel agent based induction. Median progression free and overall survival (PFS and OS) were 22 and 34 months respectively. MMSET was overexpressed in all PCs while FGFR3 expression could be categorized into low expression ( 80% of cells expressing FGFR3). Variation in the immune microenvironment of the BM was seen across all patient samples with no correlation between cell types present in the BM and PFS or OS. Plasma cells from the combined samples formed clusters that could be differentiated by gene expression with each cluster having a unique combination of cells of varying PFS. Gene and protein expression differences were seen in PCs when samples were clustered by PFS suggesting a role for these molecules in disease progression. Finally, we identified gene expression changes in one patient between a diagnostic sample and relapse disease. Conclusions We present the first application of single cell multi-omics immune profiling in high-risk MM. Our results suggest that t(4;14) MM is a molecularly heterogeneous disease. That heterogeneity, coupled with our small sample size may explain the lack of correlation between gene or protein expression with clinical outcomes. Single cell analysis of larger cohorts is required to build on our findings.