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Analysis of the genetic basis for multiple myeloma (MM) has informed many of our current concepts of the biology that underlies disease initiation and progression. Studying these events in further detail is predicted to deliver important insights into its pathogenesis, prognosis and treatment. Information from whole genome sequencing of structural variation is revealing the role of these events as drivers of MM. In particular, we discuss how the insights we have gained from studying chromothripsis suggest that it can be used to provide information on disease initiation and that, as a consequence, it can be used for the clinical classification of myeloma precursor diseases allowing for early intervention and prognostic determination. For newly diagnosed MM, the integration of information on the presence of chromothripsis has the potential to significantly enhance current risk prediction strategies and to better characterize patients with high-risk disease biology. In this article we summarize the genetic basis for MM and the role played by chromothripsis as a critical pathogenic factor active at early disease phases.

Supplementary Data from Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma

Purpose:We designed a comprehensive multiple myeloma targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards.Experimental Design:The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNA). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical FISH (translocations), multiplex ligation probe analysis (MLPA; CNAs), whole-genome sequencing (WGS; CNAs, mutations, translocations), or droplet digital PCR (ddPCR) of known standards (mutations).Results:Canonical immunoglobulin heavy chain translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for 1 patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2 = 0.969. Variant allele frequency (VAF) for 74 mutations were compared between sequencing and ddPCR with concordance of R2 = 0.9849.Conclusions:In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost-effective, comprehensive, clinically actionable, and can be routinely deployed to assist risk stratification at diagnosis or posttreatment to guide sequencing of therapies.

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Purpose:Copy-number changes and translocations have been studied extensively in many datasets with long-term follow-up. The impact of mutations remains debated given the short time to follow-up of most datasets.Experimental Design:We performed targeted panel sequencing covering 125 myeloma-specific genes and the loci involved in translocations in 223 newly diagnosed myeloma samples recruited into one of the total therapy trials.Results:As expected, the most commonly mutated genes were NRAS, KRAS, and BRAF, making up 44% of patients. Double-Hit and BRAF and DIS3 mutations had an impact on outcome alongside classical risk factors in the context of an intensive treatment approach. We were able to identify both V600E and non-V600E BRAF mutations, 58% of which were predicted to be hypoactive or kinase dead. Interestingly, 44% of the hypoactive/kinase dead BRAF-mutated patients showed co-occurring alterations in KRAS, NRAS, or activating BRAF mutations, suggesting that they play a role in the oncogenesis of multiple myeloma by facilitating MAPK activation and may lead to chemoresistance.Conclusions:Overall, these data highlight the importance of mutational screening to better understand newly diagnosed multiple myeloma and may lead to patient-specific mutation-driven treatment approaches.

Supplementary Table from Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma

Supplementary Figure from Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma

Background Oncogenic overexpression of integrin-β7 (ITGB7) in cases of high-risk multiple myeloma (MM) was reported to promote enhanced interactions between neoplastic plasma-B cells and stromal cells to develop cell-adhesion mediated drug resistance. Methods Expression profiles of adhesion related genes were analyzed in a cohort of MM patients containing major IgH translocations or hyperdiploidies (HY), diagnosed at the premalignant monoclonal gammopathy of undetermined significance (MGUS; n = 103), smoldering multiple myeloma; (SMM; n = 190) or MM (MM; n = 53) stage. Differential expression was integrated with loci-specific alterations in DNA-methylation and chromatin marks in MM patients. A CRISPR-based targeted induction of DNA-methylation at the ITGB7 super-enhancer (SE) in MM.1S cells was employed to intersect the impact of cis-regulatory elements on ITGB7 expression. Results ITGB7 was significantly (p ITGB7 in primary-B cells that is maintained under bivalent chromatin, which undergoes a process of chromatin-state alterations and develops into an active enhancer in cases of the t(4;14) subgroup or SE in cases of the t(14;16) subgroup. We also demonstrate that while targeted induction of DNA-methylation at the ITGB7-SE further upregulated the gene, inhibition of ITGB7-SE-associated transcription factor bromodomain-4 downregulated expression of the gene. Conclusions Our findings suggest an epigenetic regulation of oncogenic overexpression of ITGB7 in MM cells, which could be critical in MM progression and an attractive therapeutic target.

Chromosomal translocations are important drivers of haematological malignancies whereby proto-oncogenes are activated by juxtaposition with enhancers, often called enhancer hijacking. We analysed the epigenomic consequences of rearrangements between the super-enhancers of the immunoglobulin heavy locus (IGH) and proto-oncogene CCND1 that are common in B-cell malignancies. By integrating BLUEPRINT epigenomic data with DNA breakpoint detection, we characterised the normal chromatin landscape of the human IGH locus and its dynamics after pathological genomic rearrangement. We detected an H3K4me3 broad domain (BD) within the IGH locus of healthy B cells that was absent in samples with IGH-CCND1 translocations. The appearance of H3K4me3-BD over CCND1 in the latter was associated with overexpression and extensive chromatin accessibility of its gene body. We observed similar cancer-specific H3K4me3-BDs associated with hijacking of super-enhancers of other common oncogenes in B-cell (MAF, MYC and FGFR3/NSD2) and T-cell malignancies (LMO2, TLX3 and TAL1). Our analysis suggests that H3K4me3-BDs can be created by super-enhancers and supports the new concept of epigenomic translocation, where the relocation of H3K4me3-BDs from cell identity genes to oncogenes accompanies the translocation of super-enhancers.

Evidence has accumulated regarding the association of some types of long noncoding RNA (lncRNAs) with severity and progression of multiple myeloma (MM). In this study, we explore the expression of novel lncRNA in different molecular subtypes of MM and examine their correlation with the prognosis of the patient. Whole transcriptome RNA sequencing of 643 newly diagnosed MM samples was performed. De novo and reference guided transcript assembly pipelines were used for RNA-seq data processing and discovery of novel lncRNAs in MM. We identified 8,556 potentially novel lncRNA transcripts expressed in patients with MM. Of these, 1,264 novel transcripts showed significant differential expression between the different molecular subtypes of MM. Through bioinformatic analysis, we identify their potential targets and roles in MM. Functional enrichment analysis of nearby coexpressed genes was used to predict involved pathways. The function was also inferred by comparing the k-mer content with known lncRNAs. Two of the novel lncRNAs had a significant association with progression free survival and/or overall survival. In conclusion, we identified many novel lncRNAs, describe their expression pattern among different genetic subtypes of MM and provide evidence of their potential role in the pathogenesis, progression, and prognosis of the disease.

Deciphering genomic architecture is key to identifying novel disease drivers and understanding the mechanisms underlying myeloma initiation and progression. In this work, using the CoMMpass dataset, we show that structural variants (SV) occur in a nonrandom fashion throughout the genome with an increased frequency in the t(4;14), RB1, or TP53 mutated cases and reduced frequency in t(11;14) cases. By mapping sites of chromosomal rearrangements to topologically associated domains and identifying significantly upregulated genes by RNAseq we identify both predicted and novel putative driver genes. These data highlight the heterogeneity of transcriptional dysregulation occurring as a consequence of both the canonical and novel structural variants. Further, it shows that the complex rearrangements chromoplexy, chromothripsis and templated insertions are common in MM with each variant having its own distinct frequency and impact on clinical outcome. Chromothripsis is associated with a significant independent negative impact on clinical outcome in newly diagnosed cases consistent with its use alongside other clinical and genetic risk factors to identify prognosis.

Circulating cell-free DNA (cfDNA) has the potential to capture spatial genetic heterogeneity in myeloma (MM) patients. We assessed whether cfDNA levels vary according to risk status defined by the 70 gene expression profile (GEP70). cfDNA levels in 77 patients were significantly higher in the GEP70 high-risk (HR) group compared to the low-risk (LR) group and correlated weakly with clinical markers including lactate dehydrogenase, β2 -microglobulin, and ISS. Patients with high cfDNA levels were associated with a worse PFS (hazard ratio 6.4; 95% CI of ratio 1.9-22) and OS (hazard ratio 4.4; 95% CI of ratio 1.2-15.7). Circulating tumor DNA (ctDNA) was elevated in the HR group and ctDNA correlated strongly with GEP70 risk score (Spearman r = .69, P = .0027). cfDNA concentrations were significantly elevated between days 3-5 after chemotherapy before falling back to baseline levels. ctDNA in two patients showed a similar spike in levels between days 3 and 5 after chemotherapy with a concomitant increase in allele fraction of KRAS mutations. We assessed cfDNA levels in 25 patients with smoldering myeloma with serial samples and showed increased allele fraction of mutated KRAS at progression in cfDNA. Our study shows that cfDNA is a dynamic tool to capture genetic events in myeloma.

Purpose: Copy-number changes and translocations have been studied extensively in many datasets with long-term follow-up. The impact of mutations remains debated given the short time to follow-up of most datasets. Experimental Design: We performed targeted panel sequencing covering 125 myeloma-specific genes and the loci involved in translocations in 223 newly diagnosed myeloma samples recruited into one of the total therapy trials. Results: As expected, the most commonly mutated genes were NRAS, KRAS, and BRAF, making up 44% of patients. Double-Hit and BRAF and DIS3 mutations had an impact on outcome alongside classical risk factors in the context of an intensive treatment approach. We were able to identify both V600E and non-V600E BRAF mutations, 58% of which were predicted to be hypoactive or kinase dead. Interestingly, 44% of the hypoactive/kinase dead BRAF-mutated patients showed co-occurring alterations in KRAS, NRAS, or activating BRAF mutations, suggesting that they play a role in the oncogenesis of multiple myeloma by facilitating MAPK activation and may lead to chemoresistance. Conclusions: Overall, these data highlight the importance of mutational screening to better understand newly diagnosed multiple myeloma and may lead to patient-specific mutation-driven treatment approaches.

Disruption of the normal splicing patterns of RNA is a major factor in the pathogenesis of a number of diseases. Increasingly research has shown the strong influence that splicing patterns can have on cancer progression. Multiple Myeloma is a molecularly heterogeneous disease classified by the presence of key translocations, gene expression profiles and mutations but the splicing patterns in MM remains largely unexplored. We take a multifaceted approach to define the extent and impact of alternative splicing in MM. We look at the spliceosome component, SF3B1, with hotspot mutations (K700E and K666T/Q) shown to result in an increase in alternative splicing in other cancers. We discovered a number of differentially spliced genes in comparison of the SF3B1 mutant and wild type samples that included, MZB1, DYNLL1, TMEM14C and splicing related genes DHX9, CLASRP, and SNRPE. We identified a broader role for abnormal splicing showing clear differences in the extent of novel splice variants in the different translocation groups. We show that a high number of novel splice loci is associated with adverse survival and an ultra-high risk group. The enumeration of patterns of alternative splicing has the potential to refine MM classification and to aid in the risk stratification of patients.

Osteolytic bone disease is a hallmark of multiple myeloma (MM). A significant fraction (~20%) of MM patients do not develop osteolytic lesions (OLs). The molecular basis for the absence of bone disease in MM is not understood. We combined PET-CT and gene expression profiling (GEP) of purified BM CD138+ MM cells from 512 newly diagnosed MM patients to reveal that elevated expression of cystatin M/E (CST6) was significantly associated with the absence of OL in MM. An enzyme-linked immunosorbent assay revealed a strong correlation between CST6 levels in BM serum/plasma and CST6 mRNA expression. Both recombinant CST6 protein and BM serum from patients with high CST6 significantly inhibited the activity of the osteoclast-specific protease cathepsin K and blocked osteoclast differentiation and function. Recombinant CST6 inhibited bone destruction in ex vivo and in vivo myeloma models. Single-cell RNA-Seq showed that CST6 attenuates polarization of monocytes to osteoclast precursors. Furthermore, CST6 protein blocks osteoclast differentiation by suppressing cathepsin-mediated cleavage of NF-κB/p100 and TRAF3 following RANKL stimulation. Secretion by MM cells of CST6, an inhibitor of osteoclast differentiation and function, suppresses osteolytic bone disease in MM and probably other diseases associated with osteoclast-mediated bone loss.

Deciphering the evolution of tumor cells in the whole bone marrow (BM) cavity is key to inform curative strategies in multiple myeloma. Here, we performed spatial-longitudinal sequencing, including 144 samples collected from 25 patients during up to 14 years. Applying imaging-guided sampling we observed three evolution patterns, including relapse driven by a single tumor cell, competing relapse sub-clones and a novel pattern characterized by unique treatment-resistant sub-clones at distinct BM locations. Our study highlights focal bone lesions (FL) as hotspots of tumor evolution and provides possible explanations for their negative prognostic impact. We show a close phylogenetic relationship between baseline FL and relapse sub-clones. In patients with ≥3 FDG-PET-positive FL at diagnosis, relapse was driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion was typically seen (p

Purpose: We designed a comprehensive multiple myeloma targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. Experimental Design: The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNA). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical FISH (translocations), multiplex ligation probe analysis (MLPA; CNAs), whole-genome sequencing (WGS; CNAs, mutations, translocations), or droplet digital PCR (ddPCR) of known standards (mutations). Results: Canonical immunoglobulin heavy chain translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for 1 patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2 = 0.969. Variant allele frequency (VAF) for 74 mutations were compared between sequencing and ddPCR with concordance of R2 = 0.9849. Conclusions: In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost-effective, comprehensive, clinically actionable, and can be routinely deployed to assist risk stratification at diagnosis or posttreatment to guide sequencing of therapies.

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.

Sequencing studies have shed some light on the pathogenesis of progression from smouldering multiple myeloma (SMM) and symptomatic multiple myeloma (MM). Given the scarcity of smouldering samples, little data are available to determine which translational programmes are dysregulated and whether the mechanisms of progression are uniform across the main molecular subgroups. In this work, we investigated 223 SMM and 1348 MM samples from the University of Arkansas for Medical Sciences (UAMS) for which we had gene expression profiling (GEP). Patients were analysed by TC-7 subgroup for gene expression changes between SMM and MM. Among the commonly dysregulated genes in each subgroup, PHF19 and EZH2 highlight the importance of the PRC2.1 complex. We show that subgroup specific differences exist even at the SMM stage of disease with different biological features driving progression within each TC molecular subgroup. These data suggest that MMSET SMM has already transformed, but that the other precursor diseases are distinct clinical entities from their symptomatic counterpart.

Background Somatic variant callers are used to find mutations in sequencing data from cancer samples. They are very sensitive and have high recall, but also may produce low precision data with a large proportion of false positives. Further ad hoc filtering is commonly performed after variant calling and before further analysis. Improving the filtering of somatic variants in a reproducible way represents an unmet need. We have developed Filters for Next Generation Sequencing (FiNGS), software written specifically to address these filtering issues. Results Developed and tested using publicly available sequencing data sets, we demonstrate that FiNGS reliably improves upon the precision of default variant caller outputs and performs better than other tools designed for the same task. Conclusions FiNGS provides researchers with a tool to reproducibly filter somatic variants that is simple to both deploy and use, with filters and thresholds that are fully configurable by the user. It ingests and emits standard variant call format (VCF) files and will slot into existing sequencing pipelines. It allows users to develop and implement their own filtering strategies and simple sharing of these with others.

In multiple myeloma (MM), Notch signaling, a pathway mediating cell-to-cell communication between cells in the tumor niche, promotes MM proliferation and bone destruction. We previously reported that osteocytes (Ots), the most abundant bone cells, provide a microenvironment conducive for MM progression and bone destruction. Ots activate Notch signaling, increase Notch3 expression, and stimulate proliferation in MM cells. We detected Notch3 in CD138+ cells from MM patients and human and murine MM cell lines. Yet, the role of Notch3 in MM is unknown. Here, we used a shRNA approach to knockdown Notch3 to study its role in MM cells and their communication with Ots. The protein levels of active Notch3 (NICD3) were decreased by 80% in 5TGM1 MM cells transduced with shRNA against Notch3 compared to control shRNA-control cells. In contrast, Notch1, 2, and 4 NICD protein levels remained unchanged compared to controls. Notch3 knockdown decreased Notch target gene and cyclinD1 expression, reduced proliferation by 35%, and modestly increased apoptosis in MM cells. Additionally, Notch3 knockdown decreased Rankl expression and the ability of MM cells to promote osteoclastogenesis in vitro. Consistent with these observations, bioinformatic analysis of the transcriptome of CD138+ cells from newly diagnosed patients revealed upregulated processes related to positive regulation of cell proliferation and osteoclastogenesis in MM patients with high vs. low Notch3 expression. Next, we examined the role of Notch3 in MM-Ots communication. Notch3 knockdown in MM cells partially prevented the upregulation of Notch target genes and cyclinD1 expression and proliferation induced by direct contact with Ots. Inhibition of all Notch receptors with GSI fully prevented osteocyte-induced proliferation and Notch activation, suggesting that in addition to Notch3, other Notch receptors mediate MM-osteocyte communication. Remarkably, shRNA-mediated Notch2 inhibition did not alter MM cell proliferation or communication with osteocytes. Lastly, we analyzed the effects of Notch3 knockdown in MM cells in ex vivo and in vivo models. Using ex vivo bone organ cultures, we found less MM proliferation and lower levels of the resorption marker CTX in conditioned media from bones cultured with shRNA-Notch3 MM cells compared to control bones cultured alone. We injected mice intratibially with shRNA-Notch3 or shRNA-control 5TGM1 MM cells. After 5 weeks, mice bearing shRNA-Notch3 cells had a 50% decrease in tumor burden, 50% reduction in osteolytic lesions, and exhibited 30% more cancellous bone compared to mice bearing control MM cells. Together, these preclinical and clinical findings support that Notch3 signaling is a crucial mediator of homotypic and heterotypic communication in the MM tumor niche. Future studies are needed to evaluate Notch3 in the tumor microenvironment as a therapeutic target for the treatment of MM. Citation Format: Hayley M. Sabol, Tania Amorim, Cody Ashby, David Halladay, Judith Anderson, Meloney Cregor, Megan Sweet, Intawat Nookaew, Noriyoshi Kurihara, G. David Roodman, Teresita Bellido, Jesus Delgado-Calle. Notch3 signaling between myeloma cells and osteocytes in the tumor niche promotes tumor growth and bone destruction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5672.

In multiple myeloma (MM), communication via Notch signaling in the tumor niche stimulates tumor progression and bone destruction. We previously showed that osteocytes activate Notch, increase Notch3 expression, and stimulate proliferation in MM cells. We show here that Notch3 inhibition in MM cells reduced MM proliferation, decreased Rankl expression, and abrogated the ability of MM cells to promote osteoclastogenesis. Further, Notch3 inhibition in MM cells partially prevented the Notch activation and increased proliferation induced by osteocytes, demonstrating that Notch3 mediates MM-osteocyte communication. Consistently, pro-proliferative and pro-osteoclastogenic pathways were upregulated in CD138

Multiple myeloma (MM), a terminally differentiated B cell malignancy, remains difficult to cure. Understanding the molecular mechanisms underlying the progression of MM may identify therapeutic targets and lead to a fundamental shift in treatment of the disease. Deubiquitination, like ubiquitination, is a highly regulated process, implicated in almost every cellular process. Multiple deubiquitinating enzymes (DUBs) have been identified, but their regulation is poorly defined. Here, we determined that TRIP13 increases cellular deubiquitination. Overexpression of TRIP13 in mice and cultured cells resulted in excess cellular deubiquitination by enhancing the association of the DUB USP7 with its substrates. We show that TRIP13 is an oncogenic protein because it accelerates B cell tumor development in transgenic mice. TRIP13-induced resistance to proteasome inhibition can be overcome by a USP7 inhibitor in vitro and in vivo. These findings suggest that TRIP13 expression plays a critical role in B cell lymphoma and MM by regulating deubiquitination of critical oncogenic (NEK2) and tumor suppressor (PTEN, p53) proteins. High TRIP13 identifies a high-risk patient group amenable to adjuvant anti-USP7 therapy.

Introduction The iliac crest is the usual sampling site for minimal residual disease (MRD) monitoring in Multiple Myeloma (MM). However, the disease distribution in the bone marrow (BM) is often heterogeneous. Functional imaging can be used to complement MRD detection at a single site, thereby accounting for asymmetrically distributed disease. Diffusion weighted MRI with background suppression (DWIBS) is a novel functional imaging method that can detect disease in a higher proportion of newly diagnosed MM (NDMM) patients than 18F-fluorodeoxyglucose positron emission tomography (PET), as it is independent of the tumor metabolism. Yet, its performance for monitoring of residual disease has not been described. The aims of this study were 1) to compare DWIBS to PET for the detection of residual disease in patients achieving complete remission (CR), and 2) to test whether DWIBS and PET could complement MRD flow cytometry with a sensitivity of 1x10-5. To address these aims, we investigated 168 NDMM and 33 relapsed patients for whom DWIBS, PET, and MRD were available at the onset of CR during first-line and salvage therapy, respectively. Methods All patients signed written consent in accordance with the Declaration of Helsinki. Residual focal lesions (FLs) were defined as well delineated focal intensities above the surrounding BM background. For DWIBS FLs were considered if restriction could be confirmed on ADC maps. 8-color MRD flow cytometry with a limit of detection of 1x10-5 was available for 83 NDMM and all 33 salvage therapy patients. The Kaplan-Meier method was used for survival analyses. PFS time was measured from onset of CR to relapse or death from any cause or censored at the date of last contact. Paired-end whole exome sequencing of CD138-enriched MM cells was performed on an Illumina HiSeq 2500. Mutations were called from BWA aligned sequencing reads using MuTect. Subclonal reconstruction was done using SciClone. Results Compared to PET, DWIBS detected more CR patients with residual FLs (21% vs. 6%), and the concordance between PET and DWIBS was low. Only 6 of the DWIBS-positive patients also presented with FLs in PET. Yet, 5 patients had PET+/DWIBS- FLs, suggesting that the two techniques are complementary. Both, DWIBS+ and PET+ FLs negatively impacted PFS (p For 83 patients MRD data were available. Combining MRD and imaging, residual disease was detectable in 53 patients (64%). The best outcome was seen for 30 double negative (MRD-/Imaging-) patients (3 events with a median follow-up of 3.6 years), the worst outcome was seen for 10 double positive (MRD+/Imaging+) patients (median PFS: 2.1 years). Only 4 of 86 patients were MRD-/Imaging+, indicating that residual FLs are rare in MRD-negative NDMM patients at a sensitivity of 1x10-5. A heterogeneous disease distribution is a common feature of late-stage patients. To test if this increased heterogeneity confounded MRD, we investigated a set of 33 heavily pretreated patients who achieved CR during salvage therapy. Combining MRD and imaging data, we detected residual disease in 25 patients (76%). Of note, the proportion of patients, who were MRD-negative but had residual FLs on functional imaging was significantly higher compared to NDMM (8/16 vs 4/34 patients, p=0.01). At the same time, 10 patients (30%) were MRD+ but Imaging-, supporting the idea that a combined MRD/Imaging approach can improve detection of residual disease and should be used in late-stage patients. To obtain insights in the underlying biology, we performed longitudinal multi-region sequencing of a subset of these CR patients. Our findings support the concept of persistence and progression of multiple spatially separated clones in the BM irrespective of being in an MRD-negative CR. Thereby, focal residual disease could be shown to contribute to relapse. Conclusion DWIBS is a promising tool for detection of residual disease and complements PET. The combination of MRD diagnostics and functional imaging improves prediction of outcome, with double-negativity and double positivity defining groups with excellent and dismal PFS, respectively. Prospective trials using this information to tailor therapy are warranted. From a biological perspective, this study highlights the confounding effects of spatial heterogeneity and limited dissemination of clones within the BM on MRD diagnostics. This may especially be true for patients achieving deep responses during salvage therapies. Disclosures Roy Choudhury: University of Arkansas for Medical Sciences: Employment, Research Funding. Epstein:University of Arkansas for Medical Sciences: Employment. Barlogie:International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; Multiple Myeloma Research Foundation: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend. Davies:Takeda: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Research Funding.

Introduction: Multiple myeloma (MM) is a genetically heterogeneous disease where risk stratification and outcomes are associated with translocations involving the immunoglobulin (Ig) loci and MYC, copy number abnormalities including gain(1q), del(1p), and del(17p), as well as mutations. Additionally, MM tumors may harbor rare mutations in genes that are targetable in other tumors, such as in IDH1 and IDH2. Therefore, we designed a comprehensive MM targeted sequencing panel to interrogate the common genomic abnormalities in MM and validated it against known standards. Methods: The targeted panel was designed to include the exons of 228 genes which are either frequently mutated, associated with prognosis or risk stratification, clinically actionable, or sites of important copy number abnormalities. Additional targets were added across the genome to identify hyperdiploidy. These targeted regions encompass the mutation detection part of the panel and involve approximately 990 kb. The Ig loci and region surrounding MYC were tiled to capture translocations and copy number changes. In total, this translocation part of the panel involves approximately 4.7 Mb. The mutation and translocation panels are manufactured separately and combined during the assay resulting in a 5:1 sequencing ratio, respectively, which prevents over-sequencing of the large translocation panel. 100 ng DNA extracted from CD138+ bone marrow cells (n=223) and from non-tumor tissue (peripheral blood or saliva) was processed using the HyperCap workflow (KAPA Biosystems). Of the 223, 48 samples were processed in a clinical diagnostic laboratory. Adapter ligated DNA was hybridized with a mixture of the mutation and translocation panel and purified, amplified libraries were sequenced using 75 bp paired end reads. Sequences were aligned to hg19 and mutations and translocations identified using Strelka and Manta. Copy number was determined using the ratio of non-tumor to tumor reads in each targeted region. Data were validated using clinical FISH (translocations, n=116), MLPA (copy number, n=101), known standards (mutations), ddPCR (mutations), and whole genome sequencing (WGS; translocations and copy number, n=122). Results: Canonical IgH translocations were detected in 43.2% of patients by the panel, and all agreed with WGS. FISH detected one additional "variant" t(4;14), but did not detect 4 translocations detected by both sequencing methods. In the remainder of the samples no canonical IgH translocation was detected, agreeing with FISH results. Non-canonical translocations were detected in 14.5% of samples, 43% of which were to the MYC locus. MYC translocations were detected in 37.3% of samples with copy number abnormalities occurring surrounding MYC in 32.7% of samples. Overall, MYC abnormalities were detected in 46.4% of samples. Copy number was determined by panel sequencing and MLPA for 22 regions that were directly comparable between the technologies in 101 patient samples and 13 myeloma cell lines. The copy number concordance between the technologies was 96.9% and 99.6% in patient samples and cell lines, respectively. For the important prognostic regions, the concordance was R 2=0.962 (CDKN2C), R 2=0.986 (CKS1B), and R 2=0.973 (TP53). Panel copy number data were also compared to WGS data and showed complete concordance across the three prognostic regions, which the exception of 2 samples. In these 2 samples a homozygous deletion was detected by the panel but not by WGS. The deletions were 6.2 and 8.0 kb in size, one encompassing the coding sequencing of TP53 and the other exons 1-4 of TP53. A larger homozygous deletion of 36.3 kb was detected by both sequencing methods. Mutation detection validation was performed using Horizon Discovery samples with known variant allele frequencies (VAF) for common mutations. We were able to determine the sequencing VAF for 74 mutations across 5 samples which had a concordance of R 2=0.9849 between the expected and observed frequencies. The minimum detected VAF was 1.3% at an average depth of 891x. We also performed ddPCR on 6 patient samples with the common KRAS, NRAS and BRAF mutations which resulted in a VAF concordance of R 2=0.9983. Conclusion: We have developed a targeted sequencing panel for MM patient samples that is as robust or better than both FISH and WGS. A full protocol for sample processing and analysis is available, and has been used in a clinical diagnostic laboratory. Disclosures Ahsan: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Abu Zaid: Pieris: Current equity holder in publicly-traded company; Incyte: Research Funding; Pharamcyclic: Research Funding; Syndax: Consultancy, Research Funding. Ramasamy: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Celgene (BMS): Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive biotech: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Yong: GSK: Honoraria; Amgen: Honoraria; BMS: Research Funding; Sanofi: Honoraria, Research Funding; Takeda: Honoraria; Autolus: Research Funding; Janssen: Honoraria, Research Funding. Morgan: Takeda: Honoraria. Abonour: Celgene-BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Research Funding; Jensen: Honoraria, Research Funding; GSK: Consultancy, Honoraria, Research Funding. Flynt: Bristol Myers Squibb: Current Employment. Ansari-Pour: Bristol Myers Squibb: Consultancy. Gooding: Bristol Myers Squibb: Research Funding. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau.

Introduction In Multiple Myeloma (MM) the emergence of treatment resistant clones is a characteristic feature of relapse and this is particularly so for high-risk cases. A key driver event mediating progression, risk status and relapse is gain(1q) (1q+). We report on the impact of 1q+ on the genetic profile seen at first relapse in a uniformly treated, newly diagnosed series of 56 patients enrolled to the NCRI Myeloma XI Trial. Methods We included 56 high risk patients, defined as relapse within 30 months of maintenance randomisation (median 19 months, range 8-51). Of the 56 patients, 30 received lenalidomide maintenance and 26 were observed. Whole exome sequencing was conducted at presentation and relapse to a median depth of 122x for tumour samples and 58x for controls. Libraries were prepared using the SureSelectQXT sample prep kit and SureSelect Clinical Research Exome kit. MuTect was used to determine gene variants and SciClone clustering was undertaken to map mutation variant allele frequencies. MANTA was used to determine translocations and Sequenza for copy number aberrations. Clonal structure and mechanisms of clonal evolution were assessed using kernel density estimation of the cancer clonal fraction for all mutations. Wilcoxon matched-pairs signed rank tests (2-sided) were used to determine the significance between paired data sets, including mutational load. Fishers exact test was used to determine the difference between two nominal variables. Results We looked at mutational, structural and clonal evolution events in all patients based on 1q+ status at relapse. At diagnosis, 34% (19/56) patients had evidence of 1q+, increasing to 46% (26/56) at relapse, with all patients harbouring 1q+ at presentation having the lesion at relapse. There was a significantly higher non-synonymous mutational load at relapse in patients with 1q+, 107 vs 126 (p=0.047), compared to those without 1q+, 36 vs 44 (p=0.140). Twenty two genes known to be significant in MM and mutations within the genes known to be important in IMiD mechanism of action were reviewed. Of the patients with 1q+, 92% (24/26) had at least one mutation during the course of the disease, compared to 77% in those without 1q+ (p=0.15). The impact on tumour suppressor gene regions including deletions of chromosome 1p, 13, 14 and 17p was analyzed. Of the patients with 1q+, 77% (20/26) of patients had a deletion of one of these regions during the disease course, compared to 57% (17/30) of patients without 1q+ (p=0.16). At relapse a change in the profile of these lesions was noted in 23% (6/26) patients with 1q+, compared to 20% (6/30) patients without 1q+ (p=1). Translocations involving MYC (t MYC) were also determined and found in 27% (7/26) of patients with 1q+ and 27% (8/30) of patients without (p=1). As with 1q+, t MYC was always preserved at relapse. Mechanisms of evolution leading to relapse were established for all patients. Branching and linear evolution predominated, noted to be the mechanism leading to relapse in 88% (23/26) patients with 1q+ and 83% (25/30) without (p0.71). Stable evolution was noted in the remaining patients. 1q+ occurring as a new event at relapse was associated with branching or linear evolution in all patients (n=7), consistent with a change in clonal structure. Conclusion These data reveal that 1q+ is conserved throughout the disease course, suggesting it imparts a survival advantage and treatment resistant phenotype to the clone(s) containing it. The presence of 1q+ is associated with a significant increase in mutational load at relapse and a greater incidence of tumour suppressor gene structural deletions, mechanisms that may contribute to clonal evolution and therapeutic escape. Disclosures Jones: BMS/Celgene: Other: Conference fees; Janssen: Honoraria. Pawlyn: Celgene / BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Weinhold: Sanofi: Honoraria. Walker: Sanofi: Speakers Bureau; Bristol Myers Squibb: Research Funding. Cairns: Merck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding; Celgene / BMS: Other: travel support, Research Funding. Kaiser: AbbVie: Consultancy; Seattle Genetics: Consultancy; BMS/Celgene: Consultancy, Other: Travel support, Research Funding; Amgen: Honoraria; Karyopharm: Consultancy, Research Funding; Pfizer: Consultancy; Janssen: Consultancy, Other: Educational support, Research Funding; GSK: Consultancy; Takeda: Consultancy, Other: Educational support. Cook: Pfizer: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria. Drayson: Abingdon Health: Current holder of individual stocks in a privately-held company. Jackson: oncopeptides: Consultancy; takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; GSK: Consultancy, Honoraria, Speakers Bureau; J and J: Consultancy, Honoraria, Speakers Bureau; celgene BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; amgen: Consultancy, Honoraria, Speakers Bureau; Sanofi: Honoraria, Speakers Bureau. Davies: BMS: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees.

Introduction While Multiple myeloma (MM) is a disease of the elderly diagnosed at a median age of 69 years with nearly a third being above the age 75, little is known about the impact of aging processes on either disease biology or clinical outcomes. Treatment decisions are complicated, and it is important to take account three interacting variables: tumor genetics, comorbidities and the efficacy and toxicity of the treatment selected. While frailty scores help stratify elderly MM patients by functional status, quantitative measures of aging could provide biological markers to enhance clinical staging systems, standardize decision making, and guide treatment choices in the elderly MM population. In this work, we characterized the genetics of older MM patients compared to younger patients, and determined the associations of age with clonal hematopoiesis and telomere length (TL), both of which have been shown to be impacted by aging. Methods Using the MMRF CoMMpass IA15 data, we analyzed 972 NDMM patients with whole genome long insert sequencing with matching whole exomes. Using paired samples, we determined mutations (Mutect2 and Strelka), copy number (Control-FREEC v. 11.4), translocations (Manta v. 1.4.0), complex rearrangements (ChainFinder and ShatterSeek), as previously described and TL using Telomerecat. Looking at the germline data, we quantified Clonal Hematopoiesis of Indeterminate Potential (CHIP) and quantified TL using the same approach. Results The overall survival of patients aged over sixty-five is significantly worse than patients younger than this age (HR 1.7 (CI 95% 1.3-2.3), p Using a Bayesian approach, we show that, that del(16p) and del(6q) were more frequent in older patients (Corr=0.10, BF=1.1 and Corr=0.13, BF=11). Similarly, mutational signatures did not substantially differ between age groups with the exception of the proportion of APOBEC (SBS2 and 5) which was higher in the group over age > 80 (χ2=11, p=0.02). We determined both simple and complex structural variants and found that the prevalence of chromothripsis increased with age (χ2=10.8, p=0.001). To determine whether this may be related to chromosomal instability occurring as a consequence of aging we examined the extent of telomere attrition. A significant negative correlation was identified between TL and age (F=9.5, p=0.002) but there were no correlations with complex rearrangements. We did, however, find that TL was significantly shorter in the TP53 (χ2=9, p=0.002) and ATM (χ2=7.2, p=0.007) mutated groups suggesting TL shortening may be associated with DNA instability. To further determine the association of short TL in malignant plasmacells with adverse outcomes we ranked patients based on TL quartile and determined the impact on outcome for the shortest TL. We show that 14%, 29%, 24%, 29%, and 21% of the >50, 50-60, 60-70-70-80, and >80 year old patients were within this short TL group. There was a significant correlation with adverse overall-survival both in the younger and older patients, Figure 1A. To understand and quantify the impact of aging of the normal hematopoietic system on outcomes in MM we quantified CHIP and TL on the germline samples. CHIP was seen in 156 patients (16%) and DNMT3A, ASXL1, and TET2 were the more frequent mutations. Patients with CHIP were significantly older (χ2=3.9, p=0.005), as it was seen in 22% of the over 80. The only signatures identified using a fitting approach for these CHIP mutations were the two age related mutational signatures (SBS1 and SBS5). Interestingly, patients with CHIP did not have significantly adverse clinical outcome. To understand the impact of genetics and markers of aging in the older population we performed a multivariate analysis on the subset of patients over age 65 (n=375). Like others, we found that the well described prognostic genetic risk factors (del(17p), TP53 mutations, t(4;14), t(14;16), and amp1q) did not appear to contribute to the independent assessment of risk when taking into account age, ISS, and performance status (ECOG≥2). We show that in this population of older myeloma that short TL was, however, an independent marker for negative outcome, Figure 1B. Conclusion: We highlight the importance of TL, a composite factor that takes into account both DNA instability, copy number losses, and aging as a potential novel biological marker to assess outcome and aid personalized treatment decisions in older patients with MM. Disclosures Bauer: Synthekine: Current Employment. Braunstein:Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Verastem: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees. Landgren:Amgen: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Karyopharma: Research Funding; BMS: Consultancy, Honoraria; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Glenmark: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy, Honoraria; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Merck: Other. Davies:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotech: Honoraria; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Morgan:Karyopharm: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding.

Background: Immunomodulatory drugs (IMiDs) are the current backbone of standard and experimental combination myeloma therapies at all stages of disease, but the majority of patients eventually relapse. The mechanisms driving IMiD resistance are poorly understood. Previous studies looking for genetic drivers of resistance have looked at core members of the CRL4CRBN E3-ubiquitin ligase complex (CUL4-RBX1-DDB1-CRBN) and identified infrequent mutations and deletions in cereblon (CRBN), but at a rate that cannot account for resistance in the majority of patients. More recently several in vitro studies have identified novel regulators of cereblon activity including the COP9 signalosome, E2 ubiquitin conjugating enzymes, neddylation modifiers and additional IMiD neosubstrates. In this study paired presentation/relapse samples from newly diagnosed patients recruited to a clinical trial (UK NCRI Myeloma XI trial: NCT01554852) of largely IMiD-based therapies were used to investigate the role of mutations and deletions in all genes implicated in IMiD activity. For comparison, cell line models of resistance were generated in vitro. Methods: 56 patients who received IMiD induction therapy followed by either lenalidomide maintenance (n=30) or observation (n=26), and subsequently relapsed, underwent whole exome sequencing (WES) of CD138+ cells, median depth 122x for tumour samples and 58x for paired germline controls. Non-synonymous mutations and deletions present in tumour but not germline controls were considered. Cell line models were generated using the IMiD sensitive MM1s cell line. Cells were cultured in 10xGI50 concentrations of lenalidomide/pomalidomide alongside a control exposed to the same %DMSO. WES was carried out and non-synonymous mutations identified. Mutations present in the lenalidomide resistant (Len-R) and pomalidomide resistant (Pom-R) but not their relevant DMSO exposed control were considered. From recent publications a list of 42 genes (Figure 1) involved in cereblon pathway regulation and IMiD response was curated, termed "CRBN/IMiD genes". Mutations in CRBN/IMiD genes in the patient dataset and cell line models were examined. Results: In the patient data set 12/42 (28.6%) of the CRBN/IMiD genes were found to be mutated, with a total of 17 mutations in 14/56 (25%) patients identified. 9/17 (53%) were identified in patients who had received lenalidomide maintenance and 8/17 (47%) in the observation group. Importantly, in the patients receiving lenalidomide maintenance, 6 of the 9 (66.7%) mutations had a higher cancer clonal fraction (CCF) at relapse, suggesting they may have been selected for by exposure to treatment. Comparatively, in mutations identified in patients undergoing observation, only 3 of the 8 (37.5%) mutations had a higher CCF at relapse compared with presentation. The only deletion in CRBN/IMiD genes was in SETX, in one patient at relapse. Only one mutation or deletion was identified in CRBN itself, a missense mutation at relapse at g.3:3195148A>C, encoding a Cys326Gly sequence modification at the protein level. Interestingly, Cys326 is one of 4 cysteines in CRBN coordinating a single zinc ion to form a Zn finger motif, which stabilises the Thalidomide Binding Domain (TBD) of the protein, suggesting this mutation may have had functional significance. In the cell line models full resistance up to 100xGI50 concentrations was established by 12 weeks. The resistant cell lines had cross-resistance to the other IMiDs and comparable morphology, growth rates and responses to non-IMiD drugs as their sensitive counterpart. Resistant cells had reduced levels of CRBN mRNA and protein expression. Functional assays demonstrated that well characterised downstream effects of IMiD treatment were abrogated: transcription factors Ikaros and Aiolos not degraded and no downregulation of IRF4 mRNA. The Pom-R cell line had a mutation affecting a CRBN splice site 5' of exon 8. No other mutations or deletions in the 42 IMiD pathway genes were identified in either the Len-R or Pom-R lines. Conclusions: CRBN and other genes in the IMiD response pathway were mutated or deleted in around 25% of patients suggesting other mechanisms, for example epigenetic alterations, underlie resistance acquisition in a significant proportion. Models for both CRBN/IMiD gene mutated and unmutated resistant states have been generated and will be used to study mechanisms of IMiD resistance. Figure Disclosures Jones: Celgene: Honoraria, Research Funding. Che:Monte Rosa Therapeutics: Research Funding. Le Bihan:Monte Rosa Therapeutics: Research Funding. Wang:Monte Rosa Therapeutics: Research Funding. Kaiser:Bristol-Myers Squibb, Chugai, Janssen, Amgen, Takeda, Celgene, AbbVie, Karyopharm, GlaxoSmithKline: Consultancy; Janssen, Amgen, Celgene, Bristol-Myers Squibb, Takeda: Honoraria; Bristol-Myers Squibb/Celgene, Janssen, Karyopharm: Research Funding; Bristol-Myers Squibb, Takeda: Other: Travel expenses. Jackson:Takeda: Honoraria, Research Funding, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Gsk: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding, Speakers Bureau. Davies:Celgene/BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotech: Honoraria; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Chopra:Apple Tree Life Sciences: Current Employment; Monte Rosa Therapeutics: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Research Funding. Morgan:GSK: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Pawlyn:Takeda: Consultancy, Other: Travel expenses; Celgene: Consultancy, Honoraria, Other: Travel expenses; Janssen: Honoraria, Other: Travel expenses; Amgen: Consultancy, Other: Travel expenses.

TO THE EDITOR: The key event in the pathogenesis of systemic light chain amyloidosis (AL) is an unstable misfolded secondary or tertiary structure of a monoclonal immunoglobulin (IG) light chain that precipitates in the extracellular compartments.[1][1] The plasma cell disorder underlying AL is

SummaryThe landscape of structural variants (SVs) in multiple myeloma remains poorly understood. Here, we performed comprehensive classification and analysis of SVs in multiple myeloma, interrogating a large cohort of 762 patients with whole genome and RNA sequencing. We identified 100 SV hotspots involving 31 new candidate driver genes, including drug targets BCMA (TNFRSF17) and SLAMF7. Complex SVs, including chromothripsis and templated insertions, were present in 61 % of patients and frequently resulted in the simultaneous acquisition of multiple drivers. After accounting for all recurrent events, 63 % of SVs remained unexplained. Intriguingly, these rare SVs were associated with up to 7-fold enrichment for outlier gene expression, indicating that many rare driver SVs remain unrecognized and are likely important in the biology of individual tumors.

MYC is a widely acting transcription factor and its deregulation is a crucial event in many human cancers. MYC is important biologically and clinically in multiple myeloma, but the mechanisms underlying its dysregulation are poorly understood. We show that MYC rearrangements are present in 36.0% of newly diagnosed myeloma patients, as detected in the largest set of next generation sequencing data to date (n=1,267). Rearrangements were complex and associated with increased expression of MYC and PVT1, but not other genes at 8q24. The highest effect on gene expression was detected in cases where the MYC locus is juxtaposed next to super-enhancers associated with genes such as IGH, IGK, IGL, TXNDC5/BMP6, FAM46C and FOXO3. We identified three hotspots of recombination at 8q24, one of which is enriched for IGH-MYC translocations. Breakpoint analysis indicates primary myeloma rearrangements involving the IGH locus occur through non-homologous end joining, whereas secondary MYC rearrangements occur through microhomology-mediated end joining. This mechanism is different to lymphomas, where non-homologous end joining generates MYC rearrangements. Rearrangements resulted in overexpression of key genes and chromatin immunoprecipitation-sequencing identified that HK2, a member of the glucose metabolism pathway, is directly over-expressed through binding of MYC at its promoter.

Background Iron is an essential element for cell growth, including cancer cells, and is present in the microenvironment. We have shown that multiple myeloma (MM) cells have abnormal iron metabolism and harbor increased intracellular iron. However, the mechanism by which MM cells retain iron has remained largely elusive. Methods Expression and clinical relevance of the transferrin receptor in MM samples were analyzed in publicly available microarray and RNA-sequencing databases. Macrophages were isolated from C57BL/6J mice and were induced to specific subtypes by cytokines or culturing with MM cells. The 5TGM1-KaLwRij MM mice were used to confirm whether MM cells induce macrophage polarization in vivo. Specific subtypes of macrophage and transferrin receptor expression in MM cells were assessed by flow cytometry. Expression of ferroportin (FPN1) and ferritin in MM cells and/or macrophages were analyzed by Western blots. Single-cell RNA-sequencing (scRNA-seq), RNA-seq, and gene expression profiles (GEPs) were employed to identify ferroportin-signaling pathways in both tumor cells and macrophages of primary human MM samples. Results MM cells induced polarization with a significant increase of CD38+CD206- M1 macrophages both in vitro and in vivo. We also confirmed that the tumor associated macrophages (TAMs) were increased in the 5TGM1-KaLwRij MM mice. MM cells upregulated ferroportin expression in macrophages to provide iron to MM cells in co-culture studies and in vivo models. The transferrin receptor antibody treatment prevented MM cells from taking up iron from macrophages. scRNA-seq identified a subset of FPN1+ TAMs in human bone marrow aspirates, which are assumed to provide iron to MM cells. Using RNA-seq and GEPs analyses in primary human samples, multiple signaling pathways were differentially modulated in FPN1+ versus FPN1- TAMs, including those related to inflammation and apoptosis Conclusions Increased expression of the transferrin receptor in MM cells strongly suggests that tumor cells take up iron from its environment. MM cells promote intracellular iron mobilization in macrophages, which provide iron to MM cells in a transferrin-dependent manner. Blockade of iron trafficking between MM cells and macrophages might be a promising approach to MM therapy. Disclosures van Rhee: EUSA: Consultancy; CDCN: Consultancy; Karyopharm: Consultancy; Adaptive Biotech: Consultancy; Takeda: Consultancy.

Hyperhaploid multiple myeloma is a rare numerical aberration group defined by a range of 24-34 chromosomes, which is associated with a poor prognosis with a 5-year survival rate of 23%. Hyperhaploid patient samples (n=8) were sequenced and copy number and mutations identified. Samples had a median of 13 monosomies (range 12-14), which in general were those not associated with trisomies in hyperdiploid samples. The chromosomes traditionally trisomic in hyperdiploid myeloma were disomic in hyperhaploid myeloma with retention of heterodisomy. We examined the hyperhaploid samples for frequently mutated genes and found that 8/8 (100%) hyperhaploid samples had a mutation in TP53, exceeding the overall rate of mutation in newly diagnosed patients (5.5%), indicating an oncogenic dependency in this group. All samples with TP53 mutation also had monosomy of chromosome 17, indicating bi-allelic inactivation of TP53. As such, this high risk group is part of double-hit myeloma.

The emergence of treatment resistant sub-clones is a key feature of relapse in multiple myeloma. Therapeutic attempts to extend remission and prevent relapse include maximizing response and the use of maintenance therapy. We used whole exome sequencing to study the genetics of paired samples taken at presentation and at relapse from 56 newly diagnosed patients, following induction therapy, randomized to receive either lenalidomide maintenance or observation as part of the Myeloma XI trial. Patients included were considered high risk, relapsing within 30 months of maintenance randomization. Patients achieving a complete response had predominantly branching evolutionary patterns leading to relapse, characterized by a greater mutational burden, an altered mutational profile, bi-allelic inactivation of tumor suppressor genes, and acquired structural aberrations. Conversely, in patients achieving a partial response, the evolutionary features were predominantly stable with a similar mutational and structural profile seen at both time points. There were no significant differences between patients relapsing after lenalidomide maintenance versus observation. This study shows that the depth of response is a key determinant of the evolutionary patterns seen at relapse. This trial is registered at clinicaltrials.gov identifier: 01554852.

Next-generation sequencing is empowering genetic disease research. However, it also brings significant challenges for efficient and effective sequencing data analysis. We built a pipeline, called DNAp, for analyzing whole exome sequencing (WES) and whole genome sequencing (WGS) data, to detect mutations from disease samples. The pipeline is containerized, convenient to use and can run under any system, since it is a fully automatic process in Docker container form. It is also open, and can be easily customized with user intervention points, such as for updating reference files and different software or versions. The pipeline has been tested with both human and mouse sequencing datasets, and it has generated mutations results, comparable to published results from these datasets, and reproducible across heterogeneous hardware platforms. The pipeline DNAp, funded by the US Food and Drug Administration (FDA), was developed for analyzing DNA sequencing data of FDA. Here we make DNAp an open source, with the software and documentation available to the public at http://bioinformatics.astate.edu/dna-pipeline/.

Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including IDH1, IDH2, HUWE1, KLHL6, and PTPN11. Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in FGFR3, DIS3, and PRKD2; t(11;14) with mutations in CCND1 and IRF4; t(14;16) with mutations in MAF, BRAF, DIS3, and ATM; and hyperdiploidy with gain 11q, mutations in FAM46C, and MYC rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

OBJECTIVES/SPECIFIC AIMS: 1) Determine the mutational landscape, including translocation, mutations and mutational signatures as well as copy number variations of pPCL and identify significant differences to non pPCL MM. 2) Determine whether genetic changes pertinent to pPCL could be explored as therapeutic targets to improve the dismal prognosis of this patient population. METHODS/STUDY POPULATION: Samples from overall 19 pPCL patients that presented to the Myeloma Center, UAMS between 2000-2018 were used for this study. We performed gene expression profiling (GEP; Affymetrix U133 Plus 2.0) of matched circulating peripheral PCs and bone marrow (BM) PCs from 13 patients. Whole exome sequencing (WES) was performed on purified CD138+ PCs from BM aspirates from 19 pPCL patients with a median depth of 61x. CD34+ sorted cells, taken at the time of stem cell harvest from the same 19 patients, were used as controls. Translocations and mutations were called using Manta and Strelka and annotated as previously reported. Copy number was determined by Sequenza. RESULTS/ANTICIPATED RESULTS: 1) GEP from the BM and circulating peripheral PCs showed that the expression patterns of the two samples from each individual clustered together, indicating that circulating PCs and BM PCs in pPCL result from the same clone and are biologically clearly related. 2) The clinical characteristics from the patient cohort used for WES analysis were as follows: median age was 58 years (range 36–77), females accounted for 74% (14/19), an elevated creatinine level was found in 78% (14/18) and an elevated LDH level in 71% (10/14). All patients presented with an ISS stage of III. Median OS of the whole dataset was poor at 22 months, which is consistent with OS from previously reported pPCL cohorts. 3) Primary Immunoglobulin translocations were common and identified in 63% (12/19) of patients, including MAF translocations, which are known to carry high risk in 42% (8/19) of patients [t(14;16), 32% and t(14;20), 10%] followed by t(11;14) (16%) and t(4;14) (10%). Furthermore, 32% (6/19) of patients had at least one MYC translocation, which are known to play a crucial role in disease progression. 4) The mutational burden of pPCL consisted of a median of 98 non-silent mutations per sample, suggesting that the mutational landscape of pPCL is highly complex and harbors more coding mutations than non-pPCL MM. 5) Driver mutations, that previously have been described in non-pPCL MM showed a different prevalence and distribution in pPCL, including KRAS and TP53 with 47% (9/19) and 37% (7/19) affected patients respectively compared to 21% and 5% in non-PCL MM. PIK3CA (5%), PRDM1 (10%), EP300 (10%) and NF1 (10%) were also enriched in the pPCL group compared to previously reported cases in non-pPCL MM. 6) Biallelic inactivation of TP53 – a feature of Double Hit myeloma - was found in 6/19 (32%) samples, indicating a predominance of high risk genomic features compared to non-pPCL MM. Furthermore, analysis of mutational signatures in pPCL showed that aberrant APOBEC activity was highly prevalent only in patients with a MAF translocation, but not in other translocation groups. DISCUSSION/SIGNIFICANCE OF IMPACT: In conclusion we present one of the first WES datasets on pPCL with the largest patient cohort reported to date and show that pPCL is a highly complex disease. The aggressive disease behavior can, at least in part, be explained by a high prevalence of MAF and MYC translocations, TP53 and KRAS mutations as well as bi-allelic inactivation of TP53. It is of interest that only KRAS but not NRAS mutations are highly enriched in pPCL. From all highly prevalent genomic alterations in pPCL, only KRAS mutations offer a potential for already available therapeutically targeting with MEK inhibitors, which should be further explored.

In multiple myeloma malignant plasma cells expand within the bone marrow. Since this site is well-perfused, a rapid dissemination of “fitter” clones may be anticipated. However, an imbalanced distribution of multiple myeloma is frequently observed in medical imaging. Here, we perform multi-region sequencing, including iliac crest and radiology-guided focal lesion specimens from 51 patients to gain insight into the spatial clonal architecture. We demonstrate spatial genomic heterogeneity in more than 75% of patients, including inactivation of CDKN2C and TP53, and mutations affecting mitogen-activated protein kinase genes. We show that the extent of spatial heterogeneity is positively associated with the size of biopsied focal lesions consistent with regional outgrowth of advanced clones. The results support a model for multiple myeloma progression with clonal sweeps in the early phase and regional evolution in advanced disease. We suggest that multi-region investigations are critical to understanding intra-patient heterogeneity and the evolutionary processes in multiple myeloma., In multiple myeloma, malignant cells expand within bone marrow. Here, the authors use multi-region sequencing in patient samples to analyse spatial clonal architecture and heterogeneity, providing novel insight into multiple myeloma progression and evolution.

Background: Structural variants are key recurrent molecular features of myeloma (MM) with two types of complex rearrangement, chromoplexy and chromothripsis, having been described recently. The contribution of these to MM prognosis, rapid changes in clinical behavior and punctuated evolution is currently unknown as is the mechanism by which they deregulate gene function. Methods: We analyzed two sets of newly diagnosed MM data: 85 cases with phased whole genome sequencing; and 812 cases from CoMMpass where long-insert whole-genome sequencing was available. Patient derived xenografts from five MM cases were used to generate epigenetic maps for the histone marks, BRD4, MED1, H3K27Ac, H3K4me1, H3K4me3, H3K9me3, H3K36me3 and H3K27me3. Results: In the 10X data the median number of structural events per case was 25 (range 1 - 182); with a median of 14 intra-chromosomal events (range 1 - 179; P Using an elbow test defined cutoff, we identified cases with high structural variant load in 10% of cases. Chromoplexy called by "Chainfinder" was seen in 18% of cases. Chromothripsis called by "Shatterseek" was seen in 9% of cases. Cases with a high structural load alone were not associated with an adverse outcome whereas cases with chromoplexy or chromothripsis were associated with adverse PFS and OS, p=0.001. A new high-risk subgroup comprising approximately 5% of cases was identified with chromoplexy, chromothripsis and a high structural load. Gene set enrichment analysis of cases with chromoplexy and chromothripsis showed an excess of MYC, E2F and G2M targets, and a reduction in RAS signaling. Interferon a and g responses, an excess of TP53 and reduction in TRAF3 mutations was associated predominantly with chromothripsis. How chromoplexy and chromothripsis are tolerated by the cell is unknown and the association with the cGAS/STING response is further being explored. To determine how chromoplexy may deregulate multiple genes we identified the full spectrum of structural variants to the immunoglobulin (Ig) and non-Ig loci. A range of genes are deregulated by Ig loci including MAP3K14 at a frequency of 2% confirming the importance of non-canonical NFkB signaling. A novel intra-chromosomal rearrangement to ZFP36L1 was upregulated in 10% of cases but was not prognostic. Gene upregulation by non-Ig super enhancers is frequent and targets include PAX5, GLI3, CD40, NFKB1, MAP3K14, LRRC37A, LIPG, PHLDA3, ZNF267, CENPF, SLC44A2, MIER1, SOX30, TMEM258, PPIL1, and BUB3. The topologically associating domain (TADs) containing super enhancers bringing about gene deregulation include TXNDC5, FOXO3, FCHSD2, SP2, FAM46C, CACNA1C, TLCD2 and PIK3C2G. These super enhancers frequently contain important MM genes, the coding sequence of which are disrupted by the rearrangement and could contribute to the clinical phenotype. Accurately reconstructing the structure of the complex rearrangements will allow us to identify the mechanism of gene deregulation and to distinguish between either gene stacking, receptor stacking or both. Conclusions: Upregulation of gene expression by super enhancer rearrangement is a major mechanism of gene deregulation in MM and complex structural events contribute significantly to adverse prognosis by a range of mechanisms as well as simple gene overexpression. Disclosures Boyle: Amgen, Abbvie, Janssen, Takeda, Celgene Corporation: Honoraria; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Davies:Amgen, Celgene, Janssen, Oncopeptides, Roche, Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Consultant/Advisor; Janssen, Celgene: Other: Research Grant, Research Funding. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding.

Background: An ever improving understanding of the heterogeneity in clinical behavior of multiple myeloma (MM) in older populations supports frailty-adapted therapy as a potential treatment approach. A deeper understanding of the evolutionary trajectory leading to full blown disease in elderly compared to younger patients may give insights into the interplay between mutations and frailty, allowing us to optimize therapy for this group. Methods: We analyzed next generation sequencing data from the Myeloma Genome Project (MGP) (n = 1273, mean age = 65) determining single nucleotide variants (SNV), copy number alterations (CNAs), and mutational signatures to determine age associated patterns. An initial analysis compared a population diagnosed older than age 75 (elderly patients, n = 232, mean age = 80 yrs) in comparison to a group diagnosed at age ≤ 74 (n = 1041, mean age = 62 yrs) and a younger subgroup of patients diagnosed at age ≤ 65 (n = 632, mean age = 57 yrs). Using RNASeq from the same dataset, we will analyze expression of TERT and other shelterin genes in an attempt to correlate changes to age, ATRX mutations, genomic instability, NHEJ, and telomere length as estimated using Telomere Hunter and TelSeq. The aim of this latter analysis being to highlight the importance of telomere biology in determining mutation patterns in the elderly population. Results: We identified age associated patterns in the distribution of mutations with patients age > 74 yrs, when compared to all younger patients showing a significantly greater proportion of SNVs or indels in DIS3 (14.2% vs 8.7%, p = 0.005), HIST1H1E (5.6% vs 3.3%, p = 0.044), and IRF4 (5.6% vs 2.4%, p = 0.005). There were fewer SNVs and indels in CDKN1B (0% vs 1.3%, p = 0.038), FAM46C (6.5% vs 9.7%, p = 0.043), HUWE1 (1.7% vs 6.1%, p = 0.004) and SP140 (0.4% vs 2.9%, p = 0.014). In addition the elderly patient population was found to have proportionally more copy number gains at 1q21: CKS1B (47.4% vs 40.7%, p = 0.031), 5q23: TNFAIP8 (58.2% vs 50.0%, p = 0.012), 5p15: ADCY2 (58.2% vs 50.4%, p =0.016), 6p21: TNXB (39.2% vs 31.4%, p= 0.011), and 17q22: AKAP1 (30.2% vs 23.9%, p = 0.035) along with copy number losses at 16q: CYLD (38.8% vs 32.6%, p = 0.035), 6q25: PARK2 (40.5% vs 33.9% p = 0.028) and 2p23: DNMT3A (28.4% vs 23.0% p = 0.038). A greater proportion of elderly patients exhibited MYC tandem duplications (9.0% vs 5.6%, p = 0.032) while fewer elderly patients harbored MYC translocations (26.2% vs 19.3%, p = 0.015). These differences were further enhanced in comparisons to patients presenting under the age of 65, with elderly patients exhibiting fewer t(4;14) translocations (9.1% vs 14.4%, p = 0.019). Recent data has shown a significant time delay between MM initiation and disease presentation, and mutational signatures have been defined at varying evolutionary trajectories. We have examined if these signatures are different in elderly compared to younger cases. We could not identify a difference in the APOBEC mutational signature between any of the age-based series. We will present further analysis of other signatures and the role of telomere length at the meeting. Conclusions: Our results show significant differences in the genetic alterations between older and younger myeloma patients. These difference may lead to important differences in clinical behavior. The findings suggest disease behavior in the elderly may be driven relatively more frequently by acquired copy number alterations occurring over a period of long disease latency. Ongoing analysis is determining the prognostic impact of mutations in different age strata, which mutational signatures are driving these differences and how these impact clonal structure in the older populations. These results suggest that it should be possible to integrate genetic data and frailty-adaptive risk models to aid in the treatment of multiple myeloma that presents late in life. Disclosures Boyle: Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses; Amgen, Abbvie, Janssen, Takeda, Celgene Corporation: Honoraria. Davies:Amgen, Celgene, Janssen, Oncopeptides, Roche, Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Consultant/Advisor; Janssen, Celgene: Other: Research Grant, Research Funding. Walker:Celgene: Research Funding. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership. Morgan:Celgene: Other: research grant, Research Funding; Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Background: Smoldering myeloma (SMM) is an asymptomatic plasma cell disorder, distinguished from monoclonal gammopathy of undetermined significance (MGUS) by a higher risk of progression to symptomatic multiple myeloma (MM). Studying the genetic makeup and sub-clonal architecture of bone marrow samples taken from the same case sequentially over time is an innovative strategy to define the evolutionary trajectory underlying myeloma initiation and progression through SMM to MM and may provide new strategies to identify progression and to intervene therapeutically before end organ damage develops. Methods: Sequential samples from 9 SMM patients (53 samples) with a median follow-up of 7 years (range: 3.5 to 12.8 years) were analyzed. DNA was obtained from CD138+ cells from the bone marrow of SMM patients. 100 ng of DNA was fragmented, end-repaired, and adapters ligated, before hybridization using MedExomePlus (Nimblegen) with an additional capture for the IGH, IGK, IGL, and MYC loci. After PCR amplification hybridized libraries were sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads. The median coverage was 93x (IQR 86-105) and 100x (IQR 95-103) for tumors and controls, respectively. Variant, translocations, and copynumber changes were called using Variant Effect Predictor (v.85), Manta (v0.29.6), and Sequenza respectively. Sub clonal architecture was determined using the Pyclone package and nNMF performed using the NMF package in R. Results: The median number of mutations per sample was 79 (range: 34-236) and increased with time from diagnosis with a trend suggesting that the mutation rate of progressors (n=6) was higher than of the non-progressors (F=3.9, p=0.052). Samples with hyperdiploidy had a higher mutational rate than other subgroups (F=9, p=0.009) in relation to higher DNA contents. We previously defined a set of 63 genes that drive myeloma; 7/9 patients had a mutation in one of these genes, independently from progression status. Four patients had more than one driver mutation, which were in different clones in two patients and in the same clone in two patients. The acquisition of bi-allelic inactivation of myeloma drivers immediately before progression was seen in genes such as DIS3 and TRAF3 indicating a role in progression to an active disease state. Translocations were detected in six patients from the initial time point. In one case, a t(8;14) was detected during follow-up, 5.9 years from diagnosis. Quantification of the rearranged MYC allele compared to the IGH rearranged locus was performed by ddPCR. This t(8;14) was not present at diagnosis, appeared in a small fraction (1%) 4.1 years after diagnosis and steadily increased over time reaching 45% in the last sample, 8.9 years from the initial diagnosis indicating growing dominance of a potentially progressive clone. It was possible to reconstruct the sub-clonal structure and how it varied overtime for eight patients. This analysis identified a median number of seven sub-clones per patient, most of them related via branching evolutionary patterns (7/8). In one case a linear pattern was identified. Ninety-five percent of the tumor contents was occupied by five clones in 6/8 cases, and six in 2/8 cases. The median number of minor clone ( Conclusion: A comprehensive analysis of multiple SMM samples over time offers new insight into the mechanisms of progression of SMM to MM including the role of events we have identified previously associated with relapse e.g. MYC translocations, clonal sweeps, and biallelic deletions and changes in the clonal architecture. Changes in sub-clonal structure occurred before progression providing a new tool to monitor SMM. Disclosures Boyle: Amgen, Abbvie, Janssen, Takeda, Celgene Corporation: Honoraria; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Davies:Janssen, Celgene: Other: Research Grant, Research Funding; Amgen, Celgene, Janssen, Oncopeptides, Roche, Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Consultant/Advisor. van Rhee:Takeda: Consultancy; Sanofi Genzyme: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy; Adicet Bio: Consultancy; Kite Pharma: Consultancy; Karyopharm Therapeutics: Consultancy. Facon:Sanofi: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees. Morgan:Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses; Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding. Walker:Celgene: Research Funding.