Abstract: Introduction: Treatment strategies incorporating proteasome inhibitors, immunomodulators, and autologous transplantation induce durable remissions in most newly diagnosed multiple myeloma (NDMM) patients. However, for 20% of patients even the most intensive therapies have not resulted in satisfactory outcomes. Currently available risk scores do not fully appreciate the complex biology of MM and have limited sensitivity and/or specificity for identification of high risk (HR) disease. We therefore aimed to characterize the mutational landscape of transplant-eligible NDMM patients who relapsed within 2 years after treatment initiation, thereby defining true clinical HRMM. To elucidate the clonal structure and evolution in these patients, we performed deep whole genome sequencing (WGS, ~80x) and RNAseq of samples collected at baseline and first relapse. Methods: We included 34 transplanted NDMM patients who experienced early relapse during maintenance within 2 years after treatment initiation. Tumor samples were collected from 20 and 31 patients at baseline and first relapse, respectively. Paired samples taken at both time points were available from 17 patients. WGS and RNAseq data were pre-processed using in-house pipelines. Single nucleotide variants (SNVs), indels, translocations, and copy number variants (CNVs) were called using Platypus, SOPHIA and ACESeq. Subclones were identified using SciClone. RNAseq data was aligned using STAR. Fusion genes were called by Arriba. Differential gene expression was assessed using DESeq2. Results: At baseline, only 12/20 patients would have been classified as HR according to conventional markers, including presence of t(4;14), t(14;16), amp(1q), clonal del(17p) or ISS3. In 5 patients del(17p) was solely observed in a minor sublone, which was selected during treatment and became dominant at relapse in 3 of them. Selection of amp(1q)-positive subclones was seen in 2 patients, illustrating that subclonal amp(1q) or del(17p) are frequent events in HR patients, and - in contrast to recent results - could contribute to early relapse. Translocations involving MYC have also been reported to be of prognostic impact. At baseline 9 of 20 patients were positive for this event, with BMP6 and the lambda locus being the translocation partner in 2 patients each. At relapse we found an additional MYC-lambda, and two MYC-kappa translocations, supporting recent observations that MYC-light chain translocations are associated with aggressive disease. We identified a median of 40 (range: 17-233) nonsilent somatic SNVs per patient at baseline and 61 (range: 14-322) at relapse. Yet, comparing paired samples there was no significant increase in SNVs. In our HRMM set, 21 of 64 recently identified driver genes were mutated at baseline with KRAS (n = 6), TP53 (n = 6), NRAS (n = 2), and DIS3 (n = 2) being the most frequently affected genes. 6 of them - ACTG1, DIS3, FAM46C, NFKB2, RB1, and TRAF3 - were involved in fusion genes. At relapse the number of mutated driver genes increased to 29, and 10 of 31 patients presented with a clonal TP53 mutation. All patients with a TP53 mutation also showed deletion of the second allele or LOH. Including other tumor suppressor genes, such as RB1, CDKN2C, or TRAF3, 12/20 NDMM and 20/31 relapsed patients had at least one bi-allelic aberration, doubling the number of HR patients with such events compared to considering TP53 alone. Longitudinally, we observed all patterns of clonal evolution that were recently described for unselected patients. Stable evolution was primarily seen in patients achieving partial remission, supporting a model where some tumor cells survive in a protective microenvironment (ME). In deep responders, however, branching evolution was the dominant patterns. This observation rather supports strong cell-intrinsic mechanisms and rapid selection of aggressive minor subclones in clinically defined HRMM. Conclusions: Understanding the mutational landscape in HRMM and drivers of early relapse is crucial in order to improve treatment options. Our study highlights the importance of bi-allelic events in HR and suggests that focusing on TP53 is not sufficient, if all HR cases are to be identified. Of note, 3 patients in the entire cohort would have been classified as low risk by conventional risk scores and even at relapse did not carry any bi-allelic event, indicating the existence of unknown somatic HR aberrations or a protective ME. Disclosures Müller-Tidow: MSD: Membership on an entity's Board of Directors or advisory committees. Goldschmidt:MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Chugai: Honoraria, Research Funding; Mundipharma: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding.

Abstract: Introduction: Treatments incorporating autologous transplantation (ASCT), proteasome inhibitors (PIs), and immunomodulatory drugs (IMIDs) induce deep and durable remissions in most multiple myeloma (MM) patients, resulting in prolonged survival. Yet, patients who suffer from early relapse within 2 years of treatment initiation or become refractory to PIs and IMIDs still have a dismal outcome. The mutational landscape in early relapsed MM (ERMM) and relapsed/refractory MM (RRMM) has been comprehensively described. Several aberrations are associated with these two types of high-risk disease but little is known about the biological difference between them. To this end, we have comparatively and sequentially analyzed whole genome and RNA sequencing data from ERMM and RRMM patients. Methods: We included 32 patients who had relapsed within 2 years of first-line therapy (ERMM group, 30 after ASCT). Samples were collected at first relapse. Paired baseline samples were available from 17 patients. For the RRMM group, we included 43 patients with a median of 5 prior lines of therapy (range 2 - 13; 88% with ASCT), who had relapsed after PIs and IMiDs. For 22 of them consecutive tumor samples were available. Sequencing data were pre-processed using in-house pipelines. Mutations, indels, translocations, and copy number variants were called using Platypus, SOPHIA and ACESeq. Mutational signatures were identified with MMSig and subclonal structures with SciClone. Differential gene expression was assessed using DESeq, gene set enrichment analysis was performed with hypeR, and gene fusions were detected with Arriba. Results: Nonsynonymous mutations occurred more frequently in RRMM (median=180) compared to ERMM at first relapse (median=62, p & lt;0.001). While TP53 mutations were more often seen in ERMM (31% of cases vs. 21% in RRMM), NRAS mutations were enriched in RRMM (37% vs. 22%). Bi-allelic inactivation of TP53, RB1, or CDKN2C was more frequent in ERMM (44% vs. 30% in RRMM). In 11/14 ERMM patients these events were already present at baseline. Genes associated with sensitivity to PARP inhibition, homologous recombination deficiency, HECT, Pi3K and NOTCH signaling were more often mutated in RRMM (p & lt;0.05). While mutations associated with PI-resistance were equally common in both groups (~20%), IMID-resistance mutations were more common in RRMM (23% vs 9%). We observed a median number of 60 and 48 fusion genes in ERMM and RRMM, respectively. Fusions involving B2M, TXNDC5, PVT1 and MYC were more frequent in ERMM, while SPINK family and MAGEC1 fusions were more common in RRMM. Analysis of mutational signatures revealed a major impact of signature MM1 (associated with melphalan-exposure), in 66% of RRMM patients. In contrast, only 22% of ERMM samples showed this signature (p & lt;0.001). Signature 3 (defective homologous recombination-based DNA damage repair) was rarely detectable in ERMM (4/32) but one of the major signatures in RRMM (16/43). Analyzing expression profiles, we found upregulated genes in ERMM that were enriched for epithelial-mesenchymal transition, hypoxia, glycolysis and KRAS/IL6-JAK-STAT3 signaling. For RRMM we found no significantly enriched gene set. Yet, 50 upregulated genes were ribosomal protein pseudogenes. Longitudinally, we mainly observed branching evolution in ERMM and RRMM. Major changes in the clonal substructure with new dominant clones were seen in 65% and 55% of ERMM and RRMM, respectively. No changes ("stable" evolution) were rare in both ERMM (3/17), and RRMM (4/22). Conclusions: According to our results ERMM and RRMM are biologically distinct entities of MM. While ERMM is characterized by inactivation of tumor suppressors and upregulation of gene sets associated with hypoxia and glycolysis, RRMM shows mutations in multiple gene networks, upregulation of ribosomal protein pseudogenes with unknown function and a signature linked to defective DNA repair, suggesting multifactorial mechanisms that lead from first relapse to end-stage relapsed refractory disease. Comparing paired samples, we did not observe major difference in evolution patterns between ERMM and RRMM. Yet, the low prevalence of the melphalan MM1 signature in ERMM suggests selection of pre-existing clones in this entity. In contrast, single tumor cells exposed to melphalan are often the precursors of clones dominating at the RRMM stage, indicating that first-line ASCT has a long-term effect on MM evolution. Disclosures John: Proteona: Research Funding. Mueller-Tidow:Janssen-Cilag Gmbh: Membership on an entity's Board of Directors or advisory committees; Deutsche Forschungsgemeinschaft: Research Funding; Deutsche Krebshilfe: Research Funding; Daiichi Sankyo: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; BiolineRx: Research Funding; Bayer AG: Research Funding; Jose-Carreras-Siftung: Research Funding; Wilhelm-Sander-Stiftung: Research Funding; BMBF: Research Funding. Goldschmidt:Johns Hopkins University: Other: Grants and/or provision of Investigational Medicinal Product; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Incyte: Research Funding; Molecular Partners: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Novartis: Honoraria, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma GmbH: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; University Hospital Heidelberg, Internal Medicine V and National Center for Tumor Diseases (NCT), Heidelberg, Germany: Current Employment; GlaxoSmithKline (GSK): Honoraria; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; Chugai: Honoraria, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Dietmar-Hopp-Foundation: Other: Grants and/or provision of Investigational Medicinal Product:; Merck Sharp and Dohme (MSD): Research Funding. Raab:Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Heidelberg Pharma: Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees.

Abstract: Intratumor heterogeneity becomes most evident after several treatment lines when multi-drug resistant subclones accumulate. To address this clinical challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. Here, we integrate whole genome sequencing, single-cell transcriptomics (scRNA-seq) and chromatin accessibility (scATAC-seq) together with mitochondrial DNA (mtDNA) mutations to define subclonal architecture and evolution for longitudinal samples from 15 relapsed/refractory multiple myeloma (RRMM) patients. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (i) Pre-existing epigenetic profiles of subclones associated with survival advantages, (ii) converging phenotypic adaptation of genetically distinct subclones, and (iii) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multi-omics analysis can be applied to track and characterize distinct multi-drug resistant subclones over time for the identification of novel molecular targets against them.

Abstract: Introduction: Despite significant improvements in therapy during the last decade, most multiple myeloma (MM) patients develop refractory disease over time. Treatment of refractory MM is a major challenge, likely due to the still poorly characterized inter- and intratumor heterogeneity at this stage of the disease, and the complex interplay of MM cells with the microenvironment (ME). In particular, there is an urgent need to unravel how these features of MM are linked to molecular mechanisms of drug resistance. Methods: We resolved the cellular composition, underlying transcriptional inter- and intra-patient heterogeneity and molecular treatment response of relapsed/refractory MM by single cell RNA sequencing (scRNA-seq). Using droplet-based microfluidics, ~230,000 single cell gene expression profiles from bone marrow (BM) aspirates of 21 patients sorted into CD138+and CD138- fractions were acquired, allowing for a comprehensive analysis of both MM cells as well as their ME. Patients had a median of 4 prior lines of therapy including both a proteasome inhibitor and an immunomodulator and were refractory to their immediate prior line of therapy at time of sampling. In addition, paired samples before either pomalidomide- or carfilzomib-based therapies were analyzed for 16/21 patients. Genomic aberrations in individual patients were mapped by interphase fluorescence in situ hybridization. Cells were clustered and CD138+ MM subtypes as well as immune cell-types of the ME were identified from their single cell transcriptomes and a copy number variation (CNV) analysis. As a reference for non-malignant cells and to construct a developmental B-cell trajectory the Human Cell Atlas BM scRNA-seq reference dataset was used. To characterize interactions of MM cells with their ME, the correlated expression of ligand-receptor pairs was exploited. Results: The analysis of inter- and intra-tumor heterogeneity of molecular MM subgroups revealed distinct transcriptome signatures with contributions that could be assigned to differences in heavy and light chain immunoglobulin expression as well as known genomic alterations, including t(11;14), t(4;14) and hyperdiploidy. MM cells from individual patients largely maintained a plasma cell specific gene expression profile but a partial loss of plasma cell identity was detected based on mapping to a developmental B-cell trajectory. It was characterized by the upregulation of subgroup transcriptome signatures associated with earlier stages of B-cell development in almost 50% of patients, such as a pre-B or mature B cell-like phenotype. Within individual samples, subclonal MM cell populations with specific gene expression programs were resolved based on the CNV analysis and included those characterized by expression of the immune-activator CD27 and the modulator of WNT signaling FRZB. The analysis of longitudinally collected samples revealed both changes in the cell subtype cluster structure as well as drug-specific adaptation of gene expression programs in distinct subpopulations persisting or emerging at relapse. These profile changes were characterized by e.g. downregulation of Myc target genes upon pomalidomide treatment or induction of heat shock proteins under carfilzomib. Within the ME of refractory MM patients, we observed that the fraction of B cells and CD4+ T cells was strongly reduced while CD14+ and CD16+ monocytes as well as dendritic cells expanded. Notably, the immune checkpoint protein PD-1H (aka VISTA) that inhibits T cell activation was highly expressed in cell types from the myeloid compartment in contrast to healthy donors. Further, a ligand-receptor analysis revealed that MM cells displayed the strongest interactions with monocytes, which were mediated by MIF, BAFF and other cytokines. Conclusions: Our study demonstrates the value of scRNA-seq analysis for identifying crucial transcriptome features that classify refractory MM subtypes and their evolution in response to treatment including regulation of drug resistance associated signaling pathways. Our data suggest that refractory MM cells shape the myeloid compartment in the BM to generate an immune suppressive ME. Understanding the evolution of MM cell heterogeneity and the bone marrow milieu in refractory disease will lead to novel treatment approaches and eventually improve patient outcome. Disclosures Müller-Tidow: MSD: Membership on an entity's Board of Directors or advisory committees. Goldschmidt:John-Hopkins University: Research Funding; Molecular Partners: Research Funding; Amgen: Consultancy, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding.

Abstract: Introduction: Multiple myeloma (MM) is a heterogeneous malignancy of clonal plasma cells that accumulate in the bone marrow (BM). Despite new treatment approaches, in most patients resistant subclones are selected by therapy, resulting in the development of refractory disease. While the subclonal architecture in newly diagnosed patients has been investigated in great detail, intra-tumor heterogeneity in relapsed/refractory (RR) MM is poorly characterized. Recent technological and computational advances provide the opportunity to systematically analyze tumor samples at single-cell (sc) level with high accuracy and througput. Here, we present a pilot study for an integrative analysis of sc Assay for Transposase-Accessible Chromatin with high-throughput sequencing (scATAC-seq) and scRNA-seq with the aim to comprehensively study the regulatory landscape, gene expression, and evolution of individual subclones in RRMM patients. Methods: We have included 20 RRMM patients with longitudinally collected paired BM samples. scATAC- and scRNA-seq data were generated using the 10X Genomics platform. Pre-processing of the sc-seq data was performed with the CellRanger software (reference genome GRCh38). For downstream analyses the R-packages Seurat and Signac (Satija Lab) as well as Cicero (Trapnell Lab) were used. For all patients bulk whole genome sequencing (WGS) data was available, which we used for confirmatory studies of intra-tumor heterogeneity. Results: A comprehensive study at the sc level requires extensive quality controls (QC). All scATAC-seq files passed the QC, including the detected number of cells, number of fragments in peaks or the ratio of mononucleosomal to nucleosome-free fragments. Yet, unsupervised clustering of the differentially accessible regions resulted in two main clusters, strongly associated with sample processing time. Delay of sample processing by 1-2 days, e.g. due to shipment from participating centers, resulted in global change of chromatin accessibility with more than 10,000 regions showing differences compared to directly processed samples. The corresponding scRNA-seq files also consistently failed QC, including detectable genes per cell and the percentage of mitochondrial RNA. We excluded these samples from the study. Analysing scATAC-seq data, we observed distinct clusters before and after treatment of RRMM, indicating clonal adaptation or selection in all samples. Treatment with carfilzomib resulted in highly increased co-accessibility and & gt;100 genes were differentially accessible upon treatment. These genes are related to the activation of immune cells (including T-, and B-cells), cell-cell adhesion, apoptosis and signaling pathways (e.g. NFκB) and include several chaperone proteins (e.g. HSPH1) which were upregulated in the scRNA-seq data upon proteasome inhibition. The power of our comprehensive approach for detection of individual subclones and their evolution is exemplarily illustrated in a patient who was treated with a MEK inhibitor and achieved complete remission. This patient showed two main clusters in the scATAC-seq data before treatment, suggesting presence of two subclones. Using copy number profiles based on WGS and scRNA-seq data and performing a trajectory analysis based on scATAC-seq data, we could confirm two different subclones. At relapse, a seemingly independent dominant clone emerged. Upon comprehensive integration of the datasets, one of the initial subclones could be identified as the precursor of this dominant clone. We observed increased accessibility for 108 regions (e.g. JUND, HSPA5, EGR1, FOSB, ETS1, FOXP2) upon MEK inhibition. The most significant differentially accessible region in this clone and its precursor included the gene coding for krüppel-like factor 2 (KLF2). scRNA-seq data showed overexpression of KLF2 in the MEK-inhibitor resistant clone, confirming KLF2 scATAC-seq data. KLF2 has been reported to play an essential role together with KDM3A and IRF1 for MM cell survival and adhesion to stromal cells in the BM. Conclusions: Our data strongly suggest to use only immediately processed samples for single cell technologies. Integrating scATAC- and scRNA-seq together with bulk WGS data showed that detection of individual clones and longitudinal changes in the activity of cis-regulatory regions and gene expression is feasible and informative in RRMM. Disclosures Goldschmidt: John-Hopkins University: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Molecular Partners: Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees.