Abstract

Category Multiple Myeloma and Plasma Cell Dyscrasias: Basic and Translational Introduction Multiple myeloma (MM) is a hematological cancer resulting from abnormal proliferation of plasma cells (PC) in bone marrow. The progression of the disease from Monoclonal Gammopathy of Undetermined Significance (MGUS), Smoldering Multiple Myeloma (SMM), to MM involves multiple drivers and dynamic interactions of premalignant/malignant cells. In the advancement of single-cell technologies, the same-cell readouts of nucleus RNA sequencing (snRNA-seq) and Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) provide a less biased measurement of gene expressions and chromatin accessible regions. Profiling and analyzing both omics data at different stages of MM may reveal essential molecular signatures and regulatory elements for microenvironmental modification and evolution. These insights could benefit MM patients as novel targets for early therapeutic intervention at different stages of progression. Methods Bone marrow aspirates were collected from 33 individuals: Healthy donors (n=3), patients with MGUS (n=2), SMM (n=5), MM (n=21), and Plasma Cell Leukemia (PCL; n=2). Viable mononuclear cells underwent CD138+ sorting and nuclei isolation prior to library preparation. Libraries were prepared with the Chromium Multiome kit (10X Genomics) and sequenced on NovaSeq S4 flow cells at UAMS. The clonal V(D)J arrangements of each sample and expression level from HD samples were used to distinguish malignant (MM) from normal plasma cells (PC). The MM data from snATAC-seq and snRNA-seq were integrated using Signac with the weighted nearest neighbor methods from Seurat. All inter- and intra-tumor clusters were evaluated for markers of high-risk disease, differential gene expression, differentially accessible regions, and transcription factor activities. Results We performed snRNA-seq and snATAC-seq sequencing to generate multi-stage, molecular classified-specific chromatin accessibility and transcriptional profiles of plasma cell neoplasms comprising 439,134 cells across 33 samples. The malignant plasma cells for each sample were extracted by the unique clonotypic signature of the immunoglobulin gene rearrangement. Of these, we detected the highest clonality in MGUS samples, suggesting clonal competition at the premalignant stage. Based on our primary microarray data, we detected the overexpression of molecular signatures for most cells in each sample, confirming the intratumor cell heterogeneity. Furthermore, we used the ratio of 70-gene signature of aggressive disease and proliferation signatures to investigate the clonal composition of MM cells corresponding to the risk of relapse. Surprisingly, the proportion of high-risk cells increased respectively from low to high-risk samples. The expansion of high-risk clonality might exhibit competition among malignant cell subpopulations regarding microenvironmental adaptation. The multiomics integration generated less distinct clusters between MGUS and SMM, indicating the gradual complex alterations of subclonal gene expression and chromatin accessibility in malignant transformation. Interestingly, most accessible regions were found in intronic/distal intergenic regions implying the long-range gene regulation from cis-regulatory elements. Based on the positive/negative correlation of binding motifs within differentially accessible regions, we identified unique enriched transcription factors for each disease stage, suggesting the mechanism patterns in the evolutionary process of malignant cells. Conclusion In the largest multiomics study to date of CD138+ cells, we demonstrate the malignant clonal evolution using gene expression and chromatin accessibility profiling from patients with early-stage disease to highly aggressive form. Throughout the time of disease progression, we found an increase in the proportion of malignant cells with high-risk signatures. Also, we uncover unique signatures of chromatin accessibility for each molecular classification that can further refine our understanding of functional heterogeneity in multiple myeloma. Collectively, our joint single nuclei profiling reveals additional insights into the malignant clonal evolution and investigates novel regulatory elements involved in disease progression.

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