List of dysregulated oncogenes, tumor suppressor genes, and fusion proteins in the presence or absence of gain(1q) based on the Cancer Gene Census

Multiple myeloma evolution is characterized by the accumulation of genomic drivers over time. To unravel this timeline and its impact on clinical outcomes, we analyzed 421 whole-genome sequences from 382 patients. Using clock-like mutational signatures, we estimated a time lag of two to four decades between the initiation of events and diagnosis. We demonstrate that odd-numbered chromosome trisomies in patients with hyperdiploidy can be acquired simultaneously with other chromosomal gains (for example, 1q gain). We show that hyperdiploidy is acquired after immunoglobulin heavy chain translocation when both events co-occur. Finally, patients with early 1q gain had adverse outcomes similar to those with 1q amplification (>1 extra copy), but fared worse than those with late 1q gain. This finding underscores that the 1q gain prognostic impact depends more on the timing of acquisition than on the number of copies gained. Overall, this study contributes to a better understanding of the life history of myeloma and may have prognostic implications.

Co-occurrence of regions of gain(1q) and loss(1p). A. The percentage of patients top and per patient (bottom) with specific region of gain B. A histogram summarising the number of gained regions per patient. C. The percentage of patients top and per patient (bottom) with specific regions of copy number loss D. Histogram summarizing the number of gained regions per patient.

Differentially methylated Illumina HM27 probes in MM vs PC

BACKGROUND:The bone marrow (BM) niche contains non-hematopoietic elements including mesenchymal stromal cells (MSC) and bone marrow endothelial cells (BMEC) which provide mechanical support, and control hematopoietic cell growth and differentiation. Although it is known that multiple myeloma (MM) cells interact closely with the BM microenvironment, little is known about the impact of MM on non-hematopoietic niche-forming cells. METHODS:To address the role of the niche in MM pathogenesis, we utilized the 5TGM1 murine model. During the asymptomatic precursor stage of the model, we isolated the rare non-hematopoietic cells and performed single cell RNA sequencing. Using in-silico methods we characterized the individual cellular components of the niche, their relative abundance and differentiation state before and after exposure to MM cells as well as their intercellular interactions. RESULTS:MM engraftment increased the abundance of MSC-lineage cells, BMECs and enhanced endothelial to mesenchymal transition. An inflammatory and oxidative stress signal was identified together with polarization of MSC differentiation away from osteocyte formation towards adipocytes which provide growth factors that are known to support MM expansion. BMEC differentiation was polarized towards sinusoidal endothelial cells with a pro-angiogenic/pro-inflammatory phenotype. CONCLUSIONS:MM cells impact the BM niche by generating a pro-inflammatory microenvironment with MSC differentiation being changed to generate cell subsets that favor MM growth and survival. In order to induce remission and improve long-term outcome for MM patients these inflammatory and oxidative stress signals need to be reduced and normal niche differentiation trajectories restored.

Introduction Waldenstrom's macroglobulinemia (WM) is a lymphoplasmacytic lymphoma which recent DNA methylation studies have shown to exhibit multiple phenotypes. To enhance disease classification and explore the features and potential mechanisms underlying these subtypes, we performed a single-cell (sc) multiomic analysis on a series of MYD88 mutated WM cases, complemented by bulk RNA-seq and whole genome sequencing (WGS). Methods Single-cell multiomic analysis was performed on flow sorted CD19+/CD3- mature B-cells from 13 MYD88-mutated WM patients and was analyzed alongside reference B-cell populations derived from healthy tonsils. Patient-matched scRNA-seq and scATAC-seq data were preprocessed in Seurat and ArchR prior to modality integration in ArchR. We employed a combined automated and manual cell type annotation approach using the CellTypist and TRUST4 packages to identify cell types and assess clonality. Enrichment of transcription factor motifs at the single-cell level was inferred using the chromVAR package and pseudotime analyses were performed in ArchR. A second series of WM patients (n = 36) underwent bulk RNA-seq and a subset of both sc and bulk patients (n = 32) also had concordant WGS data. WGS data were preprocessed and analyzed for somatic variants using our genomics pipeline, the MGP1000. Results We show that the dominant feature of each patient's tumor phenotype was that of a clonally expanded memory B-cell (MBC) population. The analysis revealed three disease subtypes-MBC-like, PC-like, and intermediate-based on the presence of plasma cells (PC) and gene expression patterns of PC and MBC markers. Comparing the clonal MBC to healthy MBC showed that each subtype differentially expressed a set of unique genes but shared the bulk of differentially enriched transcription factor motifs, which were mostly related to cell proliferation and aberrant B-cell transcriptional regulation. Broadly, the transcriptional landscape of the MBC-like points to an excess of BCR signaling, the PC-like exhibit enhanced canonical NF-kB signaling and unfolded protein response, and the intermediate shared features of both. Pseudotime trajectory analysis using ArchR shows that WM is characterized by clonally expanded MBC with variably blocked capacities for plasma cell differentiation, with the MBC-like unable to differentiate into PC and the PC-like unable to complete terminal differentiation into a normal mature PC. Genes most correlated with pseudotime include SPI1, SPIB, BCL11A on the MBC end and XBP1, POU2AF1 (OCT1), and NFKB1 on the PC end. We validated the existence of the three disease subtypes using hierarchical clustering of bulk RNA-seq data. A novel WGS analysis of patients with expression data identified mutations in MYD88 (97%), IGLL5 (52%), CXCR4 (24%), HIST1H1E (18%), ARID1A (15%), and MAP3K14 (NIK) (12%), among others. The MBC-like patients carried all of the NIK mutations in addition to the majority of CXCR4, HIST1H1E, and ARID1A mutations (7/8, 5/6, and 4/5, respectively). For somatic copy number abnormalities, we identified del 6q (27%), del 22p (27%), trisomy 4 (21%), and del 13p (21%), among others. The majority of del 6q were found in the PC-like and intermediate subtypes (7/9). Conclusions We show the existence of 3 subtypes of WM with distinct transcriptional and chromatin accessibility profiles, which demonstrates that WM is a disease characterized by a failure to complete normal differentiation from an MBC to a PC. These subtypes were validated in a second series of WM patients using hierarchical clustering of bulk RNA-seq data. Compared to healthy populations, all subtypes are enriched in genes and open transcription factor motifs pointing to aberrant B-cell transcriptional regulation, enhanced proliferative ability, and immunoglobulin production. The MBC-like subtype has mutated CXCR4, HIST1HE, ARID1A, and NIK, demonstrates a unique gene expression profile suggestive of chronic BCR signaling, and exhibits activity of transcription factor motifs reflecting blocked PC differentiation including SPI1, SPIB, and BCL11A. In contrast, the PC-like and intermediate subtypes are enriched in del 6q, have a transcriptional landscape pointing to upregulated canonical NF-kB and an unfolded protein response, and are able to differentiate into early PC but not normal mature PC via enrichment of the transcription factors XBP1, POU2AF1, and NFKB1.

Supplemental Figure S7. Circos Plots for KRd/R Patients.

Supplemental Figure S6: Genomic Clustering of NDMM and HR-SMM.

Hi-C interaction and TAD map of CT1, TI1, D7 regions. The chromatin confirmation of the region encompassing CT1, TI1, and D7 across the 3 HMCLs (U266, RPMI8226, and KMS11) and PC for comparison. The region shows significant variability in the TAD strucure at the TENT5C locus across all samples in comparision to PC which could explain its dysregulation

Introduction Fibroblasts play an important role in facilitating the development of multiple solid cancers, but their role in the multiple myeloma (MM) microenvironment is less well understood. Fibroblasts and platelets have complementary functions in normal wound healing: damage and mechanical stress trigger both cell types to secret cytokines and chemokines which facilitate tissue growth. In solid malignancies, mechanical stress also triggers cancer-associated fibroblasts to promote tumor growth through similar mechanisms. Characterization of fibroblasts in the MM microenvironment may provide important parallel insights into targetable pathways by which fibroblasts promote MM growth and development. Methods We used single-cell RNA sequencing to profile bone marrow stromal fibroblasts in healthy mice and in the 5TGM1 mouse MM model. Using Seurat, fibroblasts were divided into 10 subpopulations which were characterized based on gene expression and Gene Set Enrichment Analysis (GSEA). To validate the clinical relevance of these subpopulations, we analyzed a human bone marrow stromal data set from MM patients and controls, but the human data lacked identifiable fibroblasts. To uncover pathways by which MM cells might reprogram murine fibroblasts, we then used NicheNet to predict ligand-receptor interactions between fibroblasts and MM cells, identifying interactions which could account most fully for the differential gene expression in fibroblasts, across conditions. Results Among the stromal cells, fibroblasts were identified by their expression of canonical markers including Dcn, Col1a1, and Fn1. Fibroblasts were enriched in MM compared to controls (11.46% vs. 9.42% of stromal cells; p = 0.000018, post-hoc chi-square test with Bonferroni correction). The distributions among the 10 fibroblast subclusters were also different across the two conditions (p < 2.2 * 10-16, chi-square test). Two subclusters were very substantially enhanced in the MM mice: fibroblasts-9 and -5. Fibroblasts-9 was composed of 66 of the 1630 MM fibroblasts and 9 of the 1339 healthy fibroblasts (4.05% vs. 0.67%; p < 0.000001, post-hoc chi-square test with Bonferroni correction). All 123 cells in fibroblasts-5 were found in the MM marrows. To probe the role of fibroblasts in shaping the MM microenvironment, we next sought to characterize these two subpopulations strongly associated with MM. Compared with other fibroblasts, fibroblasts-9 highly expressed numerous platelet markers, including many genes coding for platelet cell-surface receptors. Among these were Mpl (encoding the thrombopoietin receptor); Itga2b and Itgb3 (encoding CD41 and CD61, the two components of glycoprotein IIb/IIIa, a major platelet receptor for fibrin); Gp9, Gp5, Gp1ba, and Gp1bb (encoding all components of the glycoprotein Ib/IX/V complex, the main platelet receptor for von Willebrand factor); Treml1 (encoding TLT-1, a platelet fibrin receptor); Clec1b (encoding CLEC-2, a receptor for podoplanin expressed on platelets and immune cells); and P2ry12 (encoding P2Y12, the major platelet receptor for ADP). The most significantly overexpressed pathways by GSEA were HALLMARK_COAGULATION and HALLMARK_ANGIOGENESIS. Fibroblasts-9 may thus represent a novel fibroblast subtype with platelet-like markers, enriched in the MM bone marrow. Fibroblasts-5 was found exclusively in MM mice and is likely metabolically active, with significantly overexpressed HALLMARK_FATTY_ACID_METABOLISM, HALLMARK_OXIDATIVE_PHOSPHORYLATION, HALLMARK_MYC_TARGETS_V1, and HALLMARK_GLYCOLYSIS in GSEA analysis. Its role in the MM microenvironment, however, is not clear. We used NicheNet to predict ligand-receptor interactions between MM cells and fibroblasts. Col1a1 on MM cells was predicted to interact with Sdc1 and several integrins expressed by fibroblasts and to regulate Serpina3n. Serpina3n was most highly expressed by fibroblasts-5 and fibroblasts-9, and its human ortholog SERPINA3 has numerous protumor effects in other malignancies, such as suppression of apoptosis. Conclusions We found substantial differences between murine bone marrow fibroblast subpopulations in health versus MM, identifying potentially therapeutically targetable constituents of the MM microenvironment. Fibroblasts and platelets mediate a progrowth response in wound healing, and platelet-like fibroblasts may influence MM development.

Supplemental Figure S5. Structural Variant Hotspots from WGS Cases.

Supplemental Figure S2. Mutational Signatures Landscape of NDMM.

Acquisition of a hyperdiploid (HY) karyotype or immunoglobulin heavy chain (IGH) translocations are considered key initiating events in multiple myeloma (MM). To explore if other genomic events can precede these events, we analyzed whole-genome sequencing (WGS) data from 1173 MM samples. Integrating molecular time and structural variants (SV) within early chromosomal duplications, we indeed identified pre-gain deletions in 9.4% of HY patients without IGH translocations, challenging HY as the earliest somatic event. Remarkably, these deletions affected tumor suppressor genes (TSG) and/or oncogenes in 2.4% of HY patients without IGH translocations, supporting their role in MM pathogenesis. Furthermore, our study points to post-gain deletions as novel driver mechanisms in MM. Using multi-omics approaches to investigate their biological impact, we found associations with poor clinical outcome in newly diagnosed patients and profound effects on both oncogene and TSG activity, despite the diploid gene status. Overall, this study provides novel insights into the temporal dynamics of genomic alterations in MM.

Supplemental Figure S1: Sample Overview.

Supplemental Figure S8: EPRISM Outcomes using Significant KRD/R Genomic Lesions.

AbstractPurpose: Whole-genome sequencing (WGS) of patients with newly diagnosed multiple myeloma (NDMM) has shown recurrent structural variant (SV) involvement in distinct regions of the genome (i.e., hotspots) and causing recurrent copy-number alterations. Together with canonical immunoglobulin translocations, these SVs are recognized as “recurrent SVs.” More than half of SVs were not involved in recurrent events. The significance of these “rare SVs” has not been previously examined. Experimental Design: In this study, we utilize 752 WGS and 591 RNA sequencing data from patients with NDMM to determine the role of rare SVs in myeloma pathogenesis. Results: Ninety-four percent of patients harbored at least one rare SV event. Rare SVs showed an SV class-specific enrichment within genes and superenhancers associated with outlier gene expression. Furthermore, known myeloma driver genes recurrently impacted by point mutations were dysregulated by rare SVs. Conclusions: Overall, we demonstrate the association of rare SVs with aberrant gene expression supporting a potential driver role in myeloma pathogenesis.

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers have been identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and has already been studied using RNA-seq. In this study a massive (325,025 cells and 49 patients) single cell multiomic dataset was generated with jointly quantified ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identified an association between one plasma cell subtype with myeloma progression that we have called relapsed/refractory plasma cells (RRPCs). These cells are associated with 1q alterations, TP53 mutations, and higher expression of PHF19. We also identified downstream regulation of cell cycle inhibitors in these cells, possible regulation of the transcription factor (TF) PBX1 on 1q, and determined that PHF19 may be acting primarily through this subset of cells.