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Background The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases. Methods and findings Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5–29, 49–69 versus 70–82 months, χ2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI –1 to 31, 92–120 versus 113–129 months, χ2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8+ T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6–36, 49–73 versus 74–90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies. Conclusions In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies., Author summary Why was this study done? The cells around a tumor, also known as the tumor microenvironment (TME), can help a tumor grow by suppressing the immune system or fight a tumor by mounting an immune response. Most studies of multiple myeloma (MM) have focused on the tumor itself, rather than the bone marrow (BM) TME in which the tumor is growing. We hypothesized that the MM TME held clues that could help us better treat patients. What did the researchers do and find? We used a gene-expression–based computational technique to determine which cell types were present in patient BM. Patients with BM lacking a family of innate immune cells called granulocytes presented with worse outcomes compared to other patients. As MM progresses from a predisease to a cancerous state, the percentage of granulocytes decreases; the patients with the fewest granulocytes had more serious diseases. What do these findings mean? If granulocytes help myeloma patients respond to therapy, then addressing the decline in granulocytes may improve MM treatment. Patients with MM and few granulocytes in their BM should be watched for worse outcomes.

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

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.

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.

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.

Introduction: Multiple myeloma (MM) cell dormancy and proliferative states, particularly in standard risk patients, are regulated by the BM niches and factors they secrete. Mesenchymal stem cells (MSCs) and their differentiated progeny are key microenvironmental components in MM. We established a reproducible experimental system in which normal MSCs were co-cultured with BM-dependent MM lines for 5 days, at which point MM cells were removed through a trypsinization and replating process (primed MSCs). MSCs cultured alone were similarly processed (unprimed MSCs). Conditioned media (CM) from primed MSCs, but not unprimed MSCs, consistently promoted the growth of primary MM cells from 12 newly diagnosed patients with low-risk MM and 6 patients with high-risk MM (p Methods: CD138+ MM cells from 27 newly diagnosed patients were used for growth experiments and molecular characterization. BM-dependent MM lines were established through passaging MM cells from patients with advanced MM in the SCID-rab model. Unprimed and primed MSCs were molecularly characterized by global gene expression profiling (GEP) and growth factor content in CM was quantified using protein arrays. Proliferation of MSCs was determined by Ki67 immunostaining and cellular senescence by beta-galactosidase (SA-beta-Gal). MM growth was assessed after culturing primary MM cells with 50% CM from unprimed MSCs, primed MSCs or fresh media for 5 days. Cell survival and proliferation were determined by MTT assay and by detection of annexin V- and Ki67-positive MM cells by flow cytometry. Ultra low depth WGS was performed to assess copy number variation. MM cells were molecularly characterized by GEP, followed by pathway analyses using Ingenuity. Rictor activity was detected in MM cells by Western Blot and double immunostaining for Ki67 and Rictor. Adenoviral-based vectors and siRNA were used for transient RICTOR overexpression and gene expression silencing, respectively. Results: Fewer Ki67+ proliferating cells and increased numbers of SA-beta-Gal senescent cells were detected in primed MSCs compared to unprimed MSCs. Primed MSCs expressed a higher level of secreted factors such as CCL5, FGF1, IL6 and IL1B, and reduced expression of FGF7, CXCL12 (SDF1) and IGFBP2 compared to unprimed MSCs. CM of primed MSCs had a higher level of IL6, FGF1 and a lower level of IGFBP2 than CM of unprimed MSCs. There was no significant difference in the proportion of annexin V+ apoptotic MM cells cultured in CM from unprimed and primed MSCs, whereas the proportion of Ki67+ proliferating cells was 5 fold higher in MM cells treated with primed MSCs CM (p The top genes overexpressed in MM cells treated with primed MSCs CM versus unprimed MSCs CM were related to proliferation, whereas underexpressed genes were related to dormancy including BCL2, RICTOR, and CXCR4. Pathway analyses identified oxidative phosphorylation with mitochondrial dysfunction, cell cycle, mitosis and p53 as the most significantly altered pathways in MM cells treated with primed MSCs CM. WGS revealed similar copy number variation in MM cells treated with unprimed and primed CM, suggesting other mechanisms produced the observed gene expression changes. mTOR signaling is controlled by major MM growth factors such as IL6 and IGF1, therefore we investigated the role of the mTOR2 component, Rictor, in MM growth. Blocking IL6 or IGF1 with the use of neutralizing antibodies against their receptors inhibited the stimulatory effect of primed MSCs CM on MM cell growth (p Transient overexpression of RICTOR inhibited the growth of 3 MM cell lines by 2-fold (p Conclusions: Primed MSCs possess a senescence phenotype but produce MM growth factors capable of shifting MM cell status from a dormant to proliferative state through downregulation of Rictor expression in MM cells. Disclosures Walker: Celgene: Research Funding. van Rhee:Takeda: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy; Adicet Bio: Consultancy; Kite Pharma: Consultancy; Karyopharm Therapeutics: Consultancy; Sanofi Genzyme: Consultancy.

The purpose of this study is to identify prognostic markers and treatment targets using a clinically certified sequencing panel in multiple myeloma. We performed targeted sequencing of 578 individuals with plasma cell neoplasms using the FoundationOne Heme panel and identified clinically relevant abnormalities and novel prognostic markers. Mutational burden was associated with maf and proliferation gene expression groups, and a high-mutational burden was associated with a poor prognosis. We identified homozygous deletions that were present in multiple myeloma within key genes, including CDKN2C, RB1, TRAF3, BIRC3 and TP53, and that bi-allelic inactivation was significantly enriched at relapse. Alterations in CDKN2C, TP53, RB1 and the t(4;14) were associated with poor prognosis. Alterations in RB1 were predominantly homozygous deletions and were associated with relapse and a poor prognosis which was independent of other genetic markers, including t(4;14), after multivariate analysis. Bi-allelic inactivation of key tumor suppressor genes in myeloma was enriched at relapse, especially in RB1, CDKN2C and TP53 where they have prognostic significance.

To elucidate the mechanisms underlying relapse from chemotherapy in multiple myeloma, we performed a longitudinal study of 33 patients entered into Total Therapy protocols investigating them using gene expression profiling, high-resolution copy number arrays, and whole-exome sequencing. The study illustrates the mechanistic importance of acquired mutations in known myeloma driver genes and the critical nature of biallelic inactivation events affecting tumor suppressor genes, especially TP53, the end result being resistance to apoptosis and increased proliferation rates, which drive relapse by Darwinian-type clonal evolution. The number of copy number aberration changes and biallelic inactivation of tumor suppressor genes was increased in GEP70 high risk, consistent with genomic instability being a key feature of high risk. In conclusion, the study highlights the impact of acquired genetic events, which enhance the evolutionary fitness level of myeloma-propagating cells to survive multiagent chemotherapy and to result in relapse.

Introduction Hyperhaploid multiple myeloma is a rare numerical aberration group defined by a range of 24-34 chromosomes. We have previously shown that hyperhaploid myeloma is associated with a poor prognosis with a 5-year survival rate of 23.1%, compared to 64% for hyperdiploid myeloma, and 80.4% for those with a normal karyotype. It is known that hyperhaploid myeloma frequently has monosomy of chromosome 17, making it a high risk group, but no data are currently available on the mutational status of this interesting sub-group, or how the copy number difference arises. Methods We analyzed data from whole genome, whole exome, and targeted panel sequencing from 1141 newly diagnosed myeloma patients. Internal samples were selected for whole exome or targeted sequencing based on previous karyotype information, or were identified in the process of other sequencing studies. The CoMMpass dataset was screened for the presence of hyperhaploidy. Hyperhaploid samples without prior karyotype information were identified by conflicting copy number profile and B allele frequency information, where the samples had incorrectly been normalized to a diploid copy number. These samples were re-normalized to a haploid copy number. Copy number, B allele frequency, and mutations of key genes were examined. Results In the entire dataset 9 hyperhaploid samples were identified, of which 2 came from the CoMMpass dataset. From those with gene expression array data, 5/7 were GEP70 high risk and all belonged to the D1 hyperdiploid gene expression subgroup. Samples had a median of 13 monosomies (range 9-14), which in general were those not associated with trisomies in hyperdiploid samples. The chromosomes traditionally trisomic in hyperdiploid myeloma were disomic in hyperhaploid myeloma. We examined the B allele frequency of these disomic chromosomes and saw that they all retained heterodisomy. Retention of heterodisomy indicates that the method of generating hyperhaploidy is through deletion of the monosomic chromosomes, rather than reverting to a haploid genome followed by duplication of some chromosomes. Retention of heterodisomy was also seen on chromosome 18, which is not normally trisomic in hyperdiploid samples, indicating that heterodisomy of chromosome 18 may be essential for a viable plasma cell clone. We examined the hyperhaploid samples for frequently mutated genes and found that 8/9 (88.8%) of hyperhaploid samples had a mutation in TP53. This rate of mutation far exceeds the overall rate of mutation in newly diagnosed patients (5.5%), indicating an oncogenic dependency in this group. The sample without mutation of TP53 had only 9 monosomies, fewer than the other samples (12-14 monosomies), indicating there may be a prognostic difference that is dependent on the total chromosome count. All samples with TP53 mutation also had monosomy of chromosome 17, indicating bi-allelic inactivation of TP53. The variant allele frequency of the TP53 mutations was high (median=0.94), indicating that bi-allelic inactivation was a clonal event. No other significant mutations were found, including those that encode chromosome segregation or kinetochore proteins. Conclusions We have previously described bi-allelic inactivation of TP53 as Double Hit myeloma, and here we identify that hyperhaploid myeloma belongs to this poor prognosis group. The method of generating the hyperhaploid clone is through deletion of chromosomes, which may happen in a way that is similar to gain of chromosomes in hyperdiploid myeloma. These Double Hit patients may be good candidates for new therapies, but using next generation sequencing techniques researchers must be careful when normalizing data to correctly identify them as hyperhaploid rather than hyperdiploid, using copy number and B allele frequency data. Disclosures Davies: MMRF: Honoraria; TRM Oncology: Honoraria; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy; ASH: Honoraria. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding.

Introduction: Multiple myeloma (MM) is characterized by the over-expression of D-cyclin genes and the expression is tightly linked to cytogenetic subgroups. For instance, overexpression of CCND1 in the t(11;14) and CCND3 in the t(6;14) is direct through IgH super-enhancer translocation, and CCND2 overexpression in t(4;14), t(14;16) and t(14;20) indirectly, presumably as a result of transcription factor or epigenetic regulation. In hyperdiploid MM, CCND1 is highly expressed in patients with trisomy of chr.11 and CCND2 in those without trisomy 11. Here, we combine DNA methylation with CTCF or super-enhancers (SE) binding and global histone profile to describe a novel perspective of epigenetic regulation of CCND1/2 expression in MM. Methods: Newly diagnosed MM patient CD138 sorted bone marrow plasma cells (PCs), consisted of those with a t(14;16) (n=17), t(14;20) (n=7), t(4;14) (n=9), t(11;14) (n=10) and hyperdiploidy (n=19, separated into D1-HRD (n=12) or D2-HRD (n=7), and were compared to PCs from healthy donors (n=4). A reduced representation bisulfite sequencing was performed to identify differentially methylated regions (DMRs). Histone modifications were determined using ChIP-seq in reference to KMS11 cells for t(4;14), U266 for t(11;14), and MM.1S for the MAF group patients. Additionally, enrichment of SEs in MM1.S and CTCF in Delta47 cells were determined to assess the collaborative epigenetic impact on altered CCND1/2 expression (FDR Results: In the t(4;14) subgroup, CCND2 DMRs (n=21) at the promoter expressed marginal differences ( CCND1 showed a reducing hypomethylation gradient (MDM: 12% to 0.05%, p1%, p Conclusion: The disparities in CCND1/2 expression among MM subgroups are not the singular consequence from epigenetic events but broadly depends on other genotypic features such as alterations in copy number or chromosome structure. For instance, CCND1 DNA-demethylation does not always correlate with expression in t(4;14), D2-HRD, or the MAF cluster, implying that the predominant effect is through juxtaposition of the IgH-SE next to CCND1 in the t(11;14). In contrast, suppression of CCND2 expression in t(11;14), compared to t(4;14)/MAF may be explained by the enrichment of inactivating H3K27me3 marks at the body of CCND2 in the U266 cells. In summary, DNA hypomethylation at promoters or gene bodies facilitate the formation of open chromatin to enhance interactions with SE/CTCF or histones to constitute a three-dimensional epigenetic regulatory network, which potentially influence gene expression and identify novel variants in signaling pathways in MM, as evidenced here with CCND1/2. Disclosures Roy Choudhury: University of Arkansas for Medical Sciences: Employment, Research Funding. Davies:MMRF: Honoraria; TRM Oncology: Honoraria; ASH: Honoraria; Abbvie: Consultancy; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria.

Introduction: Multiple myeloma (MM) cells from patients with smoldering MM (SMM) and low-risk (LR) MM harbor genetic alterations typically seen in patients with high-risk (HR) disease. To test whether the bone marrow (BM) microenvironment plays a role in controlling growth of LR MM cells, we established an experimental model that mimics a HR microenvironment by co-culturing normal mesenchymal stem cells (MSCs) with HR MM cells. We previously have shown that MSC conditioned media (CM) promotes growth of MM cells more effectively than cell-cell contact, as adhesion to MSCs often promotes survival at the expense of proliferation. Therefore, we utilized CM and hypothesized that MSC CM is enriched with bioactive growth factors that facilitate proliferation of LR MM cells. The aim of the study was to test the effect of CM from unprimed and primed MSCs on the survival, growth, and molecular properties of LR MM cells, and identify molecular pathways that mediate these effects. Methods: Primed MSCs were prepared by co-culturing normal MSCs with BM-dependent MM lines for 5 days. MSCs were trypsinized, replated for 40 min followed by serial washing to remove MM cells. Molecularly classified CD138-selected LR MM cells from 8 newly diagnosed patients were treated with 50% primed CM or unprimed CM, or growth media (CONT) for 5 days. Growth and survival of primary MM cells was assessed by MTT assay and detection of annexin V/PI and KI67 by flow cytometry. Microarrays were performed on primed and unprimed MSCs (n=7) and on primary LR MM cells treated with primed and unprimed MSCs CM (n=3). Pathways were analyzed using Ingenuity. Ultra low depth WGS was performed to assess copy number variation. Protein arrays were performed to test levels of secreted factors in CM (n=7). Results: Growth of primary LR MM cells (n=8) was increased by primed CM 5.1±0.05 (p Primed MSC CM caused MM cell GEP70 score to increase resulting in change from LR to HR in 2 experiments and from an ultra LR score to an intermediate score in another. Pathway analyses on genes differentially expressed between primed CM- and unprimed CM-treated MM cells identified oxidative phosphorylation with mitochondrial dysfunction, cell cycle, mitosis and p53 as the most significantly altered pathways. Top transcription regulators included FOXO3, TP53, E2F4, MYC and E2F1, whereas mir-16-5p and let-7 were the top microRNAs. Top significantly upregulated genes (>2 fold) by primed MSC CM included proliferation-related factors (MKI67, TOP2A, CCNB1, BIRC5 and RRM2), whereas underexpressed genes (< 2 fold) involved regulators of cell dormancy including BCL2 (survival), RICTOR (mTOR), HEY1 (NOTCH), JUN (AP-1) and CXCR4 (adhesion). Four genes we reported to powerfully predict progression of SMM to MM (Khan et al., Haematologica 2015) were highly upregulated in MM cells by primed MSC CM. WGS revealed similar copy number variation in MM cells treated with unprimed and primed CM, suggesting other mechanisms produced the observed gene expression changes. IGF1 is a central MM growth factor and IGF binding proteins (IGFBPs) control its bioavailability. We recently reported that mesenchymal cells are the main source of IGFBPs in BM, with IGFBP2 being the most downregulated gene in MM bone (Schinke et al., CCR 2018). Expression and secretion of IGFBPs (particularly IGFBP2) by MSCs were significantly reduced by priming these cells with MM cells, whereas IGF1 levels remained unchanged. IGFBP2 markedly blocked IGF1-induced MM cell growth (p Conclusions: MSCs primed by HR MM cells mimic a HR microenvironment, reflected by reduced level of factors that restrain bioavailability of MM growth factors such as IGF1, resulting in shutdown of master regulators of cell dormancy, which then enable a MM cells to proliferate. Such a scenario is particularly applicable in SMM and LR disease where MM cells exhibit a low proliferative index and their expansion is accelerated in distinct HR BM microenvironmental niches such as focal lesions. Disclosures Epstein: University of Arkansas for Medical Sciences: Employment. Davies:Abbvie: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; MMRF: Honoraria; Janssen: Consultancy, Honoraria; TRM Oncology: Honoraria. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding.

Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: 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. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

Introduction: Invasive bone marrow sampling is used in multiple myeloma (MM) diagnosis to obtain biological material, which can then be used to generate prognostically important genetic features. Physically sampling the bone marrow can be uncomfortable for the patient. Also, spatial heterogeneity is a common feature in MM, with multiple focal lesions (FLs) occurring throughout the skeleton, meaning a single sample from the iliac crest may be insufficient to capture intrapatient heterogeneity. An alternative strategy is to extract data directly from diagnostic positron emission tomography-computed tomography (PET-CT) scans of patients. These radiomic features can be used as a proxy from which to infer molecular and clinical phenotypes. Compared to physical sampling, there are several advantages, including rapid analysis, minimalizing patient discomfort, reduced cost and widespread availability of the required scanning equipment in hospitals. Methods: A series of 439 newly diagnosed MM patients were selected, all of which had diagnostic PET-CT scans. A radiologist examined these data and identified focal lesions in the axial skeleton of 136/439 (31%) patients. Focal lesions were manually segmented from the PET portion of the original DICOM data using a density-based thresholding method in 3DSlicer version 4.9.0. Pyradiomics version 1.3 was used to resample the voxels in the PET data to 4x4x4 mm and extract radiomic features from each FL. A combination of 10 filters and 7 feature classes were used and a total of 1679 radiomic features were generated per lesion. Radiomic features were a mixture of first order characteristics such as maximum intensity, shape characteristics and gray level matrix features. Hierarchical clustering was applied to the radiomic features, using the Pearson correlation between features as the distance metric and Ward's method for clustering. Next generation sequencing (NGS) data was available for samples from 58/136 (43%) patients with FLs in whole genome (WGS), whole exome (WES) or targeted panel (TP) modalities. The NGS data was used to detect translocations, copy number aberrations and somatic mutations. Results: There were 789 FLs identified in 136 patients, with each patient containing an average of 5.8 FLs. The median FL volume was 4350 mm3, with a median maximum 3D diameter of 29 mm. Hierarchical clustering across all FLs and radiomic features separated the FLs into 5 discrete clusters associated with various clinical and molecular features. However, clustering appeared to be independent of other classification systems based on gene expression profiling (GEP), including the UAMS classification system and GEP70 risk score. Clustering was also independent of the International Staging System (ISS) status suggesting that it can add additional prognostic information. Clusters also appeared to be independent of somatic mutations in genes previously reported as significantly mutated in MM. Patients commonly had FLs occurring in multiple clusters, suggesting that this method takes into account the heterogeneity between lesions in the same patient. Larger FLs were grouped primarily into two clusters consistent with them having distinct features that can be recognized by this approach. Looking across the different clusters distinct differences in clinical outcome were seen between the groups, with significant differences in both PFS (p=0.007) and overall survival (p=0.005), with worse prognosis being led by a cluster of smaller lesions. Conclusions: Radiomics provides a novel method to extract potentially important data from PET-CT scans which can define individual clusters that have different clinical, molecular and prognostic features. This can provide a novel non-invasive method to assess FLs based on both their physical and radiomic characteristics. Larger study sizes will be needed to confirm the differences in outcomes seen between groups. Disclosures Boyle: Celgene: Honoraria, Other: travel grants; Janssen: Honoraria, Other: travel grants; La Fondation de Frace: Research Funding; Abbvie: Honoraria; Amgen: Honoraria, Other: travel grants; Gilead: Honoraria, Other: travel grants; Takeda: Consultancy, Honoraria. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Davies:TRM Oncology: Honoraria; MMRF: Honoraria; Abbvie: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria.

Introduction: Clustering of gene expression signatures at diagnosis has identified a number of distinct disease groups that correlate with outcome in multiple myeloma (MM). Some of these are defined by an etiologic genetic event whereas others, such as the proliferation cluster (PR) and GEP70 risk relate to acquired clinical behaviors regardless of the underlying background. The PR cluster has a number of important features, including markers of proliferation, and has been associated with an adverse outcome. This logic led us to study how gene expression patterns change over time with the aim of gaining insight into acquired features that could be targeted therapeutically or be used to predict outcome. Methods: We followed 784 newly diagnosed MM patients from the Total Therapy trials over a median of 9.5 years for whom repeated GEP of CD138+ plasma cells using Affymetrix U133 Plus 2.0 plus arrays were obtained. Raw data were MAS5 normalized and GEP70-based high-risk (HR) scores, translocation classification (TC) and molecular cluster classification were derived, as previously reported. Results: At diagnosis, 85.9% percent of patients (666/784) were identified as low-risk (LR). Among them, 23.1% (154/666) went on to develop HR status (defined by a GEP70 score > 0.66) at least once after initial diagnosis. Among the non-PR cases, 28.5% (193/677) were seen to develop a PR phenotype at some point during follow-up. Similarly, among the PR patients (n=107), we observed that 43.1% (25/58) identified as LR by GEP70 at presentation eventually develop HR status at least once during follow-up. We further analyzed 147 patients with paired diagnosis and relapse samples. Seventeen percent of patients (25/147) were PR at diagnosis. Most patients were from favorable TC prognostic groups [80% D1-D2, 8% t(11;14), 8% t(4;14) and 4% t(14;20)]. Seventy-six percent of PR patients remained PR at relapse (19/25) whereas 23% switched cluster in accordance to their translocation group. Fifteen percent of patients (22/147) became PR at relapse. They originated from four clusters and three TC groups [77% from the D1-D2, 14% t(4;14) and 9% from the t(11;14)]. Overall-survival from the time of relapse was inferior for patients categorized as PR at relapse compared to other subgroups (p< 0.0001); among PR patients at relapse, there was no difference in outcome between patients classified as PR or non-PR at diagnosis (p= 0.74). When looking at GEP70 defined risk scores, the incidence of HR status rose from 23% to 39% between diagnosis and relapse with a significant increase in mean GEP70 scores using paired t-test (p Discussion: Following the introduction of therapeutic regimens aimed at maximizing response, long term survival in MM has improved. This also led to an apparent increase in the development of more aggressive disease patterns at relapse including extra-medullary disease and plasma cell leukemia. Here we show, that HR features both in terms of PR and GEP70 risk status, develop as a variable over time. At relapse, most acquired HR cases originate from standard-risk presentation cases, suggesting selective pressure for HR features. Moreover, we show that the detection of such behaviors is associated with an adverse outcome from the time of relapse. These data also suggest that repeating GEP during follow-up adds precision to better comprehend individual risk and may help identify patient specific therapeutic strategies. Indeed, understanding how these patterns develop, which genes are implicated, and their impact on the immune microenvironment should allow us to effectively utilize a wide array of treatment approaches ranging from immune-therapies to novel cell-cycle targeting agents to specifically address this type of aggressive behavior. Conclusion: The acquisition of high risk patterns captured by GEP70 risk and PR status is an ongoing process from initial diagnosis. Such high risk prognostic features have an adverse outcome from the time of development. Repeating GEP during follow-up may therefore help better predict outcome and identify patient specific therapeutic strategies. Disclosures Boyle: Janssen: Honoraria, Other: travel grants; Takeda: Consultancy, Honoraria; Gilead: Honoraria, Other: travel grants; Abbvie: Honoraria; Celgene: Honoraria, Other: travel grants; La Fondation de Frace: Research Funding; Amgen: Honoraria, Other: travel grants. Dumontet:Janssen: Honoraria; Roche: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria. Facon:Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Barlogie:Celgene: Consultancy, Research Funding; Multiple Myeloma Research Foundation: Other: travel stipend; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; Dana Farber Cancer Institute: Other: travel stipend; Millenium: Consultancy, Research Funding; 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; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend. Davies:TRM Oncology: Honoraria; Janssen: Consultancy, Honoraria; ASH: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria.

Introduction: Chromothripsis and chromoplexy are gross structural events that deregulate multiple genes simultaneously and may help explain rapid changes in clinical behavior. Previous screening studies in multiple myeloma (MM) using copy number arrays have identified chromothripsis at a low frequency (1.3%) and suggested it adversely impacts prognosis. Here, using whole genome sequencing (WGS) data we have identified a higher frequency of these events, suggesting they are more common than previously thought. Methods: 10X ChromiumWGS (10XWGS) from 76 newly diagnosed MM (NDMM) patients were analyzed for structural rearrangements using Longranger. Oxford Nanopore long read sequencing was performed on 2 samples. Long insert WGS data from 813 NDMM patient samples from the Myeloma Genome Project (MGP) were analyzed for structural rearrangements using Manta. Whole exome sequencing was available for 712 samples. RNA-seq was available for 643 samples. Chromothripsis was determined by manual curation of breakpoint and copy number data. Chromoplexy was defined as rearrangements within 1 Mb of one another involving 3 or more chromosomes. Results: Chromoplexy was detected in 33/76 (46%) cases using 10XWGS data, and cross validated in the MGP WGS dataset being found in 30% (247/813) of samples and was most frequent on chromosomes 8 (11.7% of samples), 14 (10.6%), 11 (9.6%), 1 (9.5%), 6 (8.0%), 22 (7.6%), 12 (6.7%), and 17 (6.7%). The gene regions most involved in chromoplexy events were MYC (chr8; 7.3%), IGH (chr14, 8.8%), IGL (chr22; 4.6%), CCND1 (chr11; 3.9%), TXNDC5 (chr6; 1.7%), FCHSD2 (chr11; 1.4%), FAM46C (chr1; 1.2%), MMSET (chr4; 1.2%), and MAP3K14 (chr17; 0.7%). Chromoplexy samples involved pairings of super-enhancer donors (IGH, IGL, FAM46C, TXNDC5) and oncogenic receptors (CCND1, MMSET, MAP3K14, MYC) implicating transcriptional deregulation. To confirm, RNASeq showed an elevation of expression over median in the oncogenic receptors when paired with a donor: CCND1 (median expression = 12.0 vs. median expression with donor = 17.9), MAP3K14 (10.8 vs. 14.7), MYC (12.7 vs. 14.1) and MMSET (11.9 vs. 16.7). We also identified elevated expression of PAX5 (8.23 vs. 13.79) and two cases where BCL2 (13.32 vs. 14.68) partnered with MYC, one involved IGH similar to follicular lymphoma. To determine if chromoplexy events were happening on the same allele, we performed long read sequencing using Oxford Nanopore on a sample with a t(2;6;8;11) event. We observed a read mapped to chromosome 2, with secondary alignment to chromosomes 6 and 8. This single 32 kb read was a continuous t(2;6;8) event, proving these events occurred on the same allele. However, despite close proximity, the data did not put the t(8;11) in the same read meaning this event occurred on a different allele or sub-clone, suggesting ongoing genomic instability. Chromothripsis was detected in 16/76 (21%) cases using 10XWGS, and was consistent in MGP data, (170/813; 21%). Chromothripsis occurred on all chromosomes but at different frequencies where chromosome 1 had most events (5.1%), followed by 14 (2.4%), 11 (2.3%), 12 (2.2%), 20 (1.9%), 17 (1.9%), and 8 (1.9%). We hypothesized the presence of both chromoplexy and chromothripsis could be associated with ineffective DNA repair and indeed, using WES data, patients with both events show more mutations in TP53 (19% vs. 5%) and ATM (10% vs. 4%) implicating homologous recombination deficiency as an etiologic mechanism. Gene set enrichment analysis showed significant enrichment and positive normalized enrichment score (NES) for the DNA Repair (P = 0.01; NES = 1.7) and MYC pathways (P = 0.01; NES = 3.2) consistent with previous results. In relation to prognosis, chromoplexy and chromothripsis have a negative impact on progression free survival (28.6 months vs. 42.8 months, P=0.03 and 28.6 months vs. 40.7 months P=0.01, respectively). When patients with both chromoplexy and chromothripsis (9%) were examined there was a pronounced effect on PFS (40.7 months vs. 22.7 months, P Conclusion: Complex structural events are seen frequently in MM and could help explain disease progression. Severe cases with both chromoplexy and chromothripsis are associated with acquired genomic instability and an adverse impact on prognosis either directly or due to their association with DNA repair abnormalities. This opens the possibility of specifically therapeutically targeting the underlying DNA abnormalities. Disclosures Flynt: Celgene Corporation: Employment, Equity Ownership. Ortiz:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene Corporation: Employment, Equity Ownership. Gockley:Celgene Corporation: Employment. Davies:Janssen: Consultancy, Honoraria; TRM Oncology: Honoraria; Abbvie: Consultancy; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees. Thakurta:Celgene Corporation: Employment, Equity Ownership. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding.

Introduction: Mutations in the components of the spliceosome have been shown to occur at relatively high frequency in many cancers such as chronic lymphocytic leukemia, myelodysplastic syndromes and breast cancer. One component in particular, encoded by SF3B1, has hotspot missense mutations that result in a significant increase in alternatively spliced transcripts. RNA splicing in Multiple Myeloma (MM) has not been investigated and in particular the extent of mutations in SF3B1 and its effects on the transcriptome. Methods: Using the MMRF CoMMpass dataset (N=1273) of newly diagnosed MM patients, samples with whole exome sequencing (WES) were analyzed for mutations using Strelka and Mutect, and samples with SF3B1 mutations identified. A range of approaches were used to explore the effect of the SF3B1 mutations on the transcriptome and to determine possible downstream effects. Using RNA-seq with matched WES samples (n=615), the splice junction usage of SF3B1 mutants was compared against non-mutated samples which were matched for key MM molecular sub-types. The RNA-seq data was analyzed using a pipeline that included STAR and Salmon, aligning to human reference genome hg38, gene and transcript differential expression analysis tools DESeq2 and StringTie/Ballgown, differential splicing exon usage tools JunctionSeq/QoRTs, DEXSeq, and SUPPA and for Gene Set Enrichment Analysis (GSEA) the R package FGSEA was used. Results: From the WES data 1.7% (22/1273) of samples had mutations in SF3B1 of which 5 had mutations in the hotpot codons of K666 and K700. Differential isoform analysis of the 22 SF3B1 mutant samples compared to non-mutated samples did not identify any transcripts. However, when the analysis was restricted to the 5 samples with hotspot mutations differential gene expression identified 146 genes that were significantly differentially expressed at an adjusted p-value Results of differential gene analysis between the control and SF3B1 mutants were used in GSEA and significant normalized enrichment scores (NES) identifying increased protein secretion (p-value =0.009, NES= 1.9) and unfolded protein response (UPR) (p-value = 0.02, NES = 1.52) pathways. Conversely GSEA identified decreased apoptosis (p-value = 0.008, NES = -1.76), KRAS signaling (p-value = 0.008, NES = -1.92), TNFA signaling via NF-κB (p-value = 0.008, NES= 2.12) pathways in SF3B1 mutant samples. Investigation of splicing loci revealed that novel splice loci were significantly more abundant in the SF3B1 mutants versus control samples. Differential splicing analysis detected 474 genes to be significantly differentially spliced and of those 311 were not found to be differentially expressed at the gene level, indicating that alternative splicing is as important alternative mechanism to gene expression differences. 59 novel splice sites were identified, as well as 152 known splice sites and 218 exon significant differential usage with a p-value of < 0.05. The genes with most significant levels of alternative splicing and found by more than one approach were DYNLL1, TMEM14C, CRNDE, BRD4 and BCL2L1, several of which are also seen in other cancers with mutated SF3B1. Conclusions: Hotspot mutations in SF3B1 result in alternative splicing of genes as well as the introduction of novel splice sites. The confirmation that SF3B1 hotspot mutations in MM increases alternative splicing as well as the identification of the genes undergoing alternative splicing may present novel therapeutic targets. Gene expression analysis of these samples identifies key deregulated pathways, perhaps in response to alternative splicing, including the UPR and protein secretion pathways. These analyses indicate that disruption of these pathways are potential avenues of therapeutic intervention in patients with SF3B1 mutations. Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene Corporation: Employment, Equity Ownership. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria.

Introduction: Primary Plasma Cell Leukemia (pPCL) is a rare form of multiple myeloma (MM) that is characterized by an aggressive disease course with >20% peripherally circulating plasma cells (PCs) and poor clinical outcome. Despite the advances of modern anti-MM therapy, pPCL patients continue to experience low median overall survival (OS) suggesting a distinct biological background. Due to its low incidence of 1-2% of all MM patients, studies on physiopathology remain challenging and are limited. The aim of this study was to elucidate the differences in biology and outcome between non-pPCL MM and pPCL, to determine the genetic landscape of pPCL and to identify distinct signatures and pathways that potentially could be used as therapeutic targets. Methods: 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: 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. 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. 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. MYC breakpoints (8q24) were identified in 25% with Ig partner loci including IGH (5%), IGK (10%), and IGL (10%). The remaining samples had partner loci including FAM46C (5%), MYNN (5%), SPARC (5%), QRSL1 (5%), RNF126 (5%), PLXNA4 (5%) and CDH7 (5%). 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. 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. 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. Conclusion: 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. Disclosures Davies: Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; TRM Oncology: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria; Abbvie: Consultancy; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria. Barlogie:Multiple Myeloma Research Foundation: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: 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. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding.

There is currently no standard approach to classify high-risk newly diagnosed Multiple Myeloma (ndMM). Various efforts have yielded approaches based on single molecular data types, including gene expression (GE), mutation (SNV), copy number alteration (CNA), and structural variation (SV) profiling. A comprehensive classification integrating heterogeneous molecular information may improve prognosis and treatment of high-risk ndMM patients. Clinical data (ISS, Age, Sex), genomics datasets, and clinical outcomes were assembled. 18 common structural variants were derived from whole genome and whole exome data. CNAs were aggregated into cytobands and SNVs into pathways. Two multi-omics integrative clustering methods (Cluster of Clusters and iClusterPlus) were applied to the integrated dataset to identify patient subgroups. Each algorithm was run 1000 times, using patient and feature resampling and optimized over a range of pre-defined clusters (K=[2,20]). Selection of clusters was based on Bayesian Information Criterion and Normalized Mutual Information. Patient subgroups defined using coherence across the outputs. Biological interpretation of the high-risk group was performed using differential expression analysis (voom-limma), gene set enrichment of canonical pathways, master regulator (MR) inference to identify putative drivers, protein-protein interaction network (PPIn) analysis for identification of downstream effects, and Fisher's test for SNV, SV and CNA association. A relevance network topology-based method (DART) was used to compare results against previous high-risk GE signatures. Hypergeometric tests were applied to identify DNA signature enrichment across patient subgroups. Multi-dimensional unsupervised analysis identified 12 stable patient subgroups (≥~5% of samples) including patient tumors enriched in t(4;14) and t(11;14), various patient subgroups stratified by CNAs including Del1p, Del13q, Del14q, Amp1q and amplifications of odd chromosomes, and distinct GE patterns. Association of patient subgroups with PFS identified one high-risk group (median PFS < 505 days, referred to here as C8) which contains 11% of samples and is defined by a combination of genomic features including Del1p/13q/14q/17p and Amp1q, combination of t(4;14) and t(11;14) translocations, down-regulation of related gene expression profiles, and up-regulation of cell cycle related genes. An MR analysis of C8 tumors versus all others identified 10 potential driver genes including E2F2 and CKS1B. C8 displays significant enrichment in GE signatures including EMC92, UAMS70, UAMS PR (proliferative) and M9. Published high-risk GE signatures overlap substantially with genes downstream of the MRs identified as driving C8, suggesting regulatory association with previously defined signatures (based on supervised analysis). C8 also showed a significant enrichment on t(4;14) with low FGFR3 expression versus the rest of t(4;14) patients, suggesting that not all t(4;14) patients are high-risk. Finally, the amp1q+ISS3 group and one third of bi-TP53 is significantly associated to C8, indicating that the bi-TP53 factor is a high-risk marker but not a driver of a patient sub-group. C8 showed significant association to previously described high-risk signatures; importantly however, not all patients with high-risk markers (eg, t(4;14) and Double Hit patients) were present in this subgroup. The separation of t(4;14) and Double Hit patients into multiple subgroups with differing clinical outcome suggests a separation between high-risk biology and key poor prognosis markers. While these high-risk prognostic biomarkers may impact clinical outcome, our unsupervised analysis suggests that there is an interplay between these biomarkers and biological disease drivers that determine the poor prognosis of these patients. Analysis of SVs, CNAs and GE differences identified 12 patient subgroups driven by SVs, CNAs, and GE profiles. In contrast, SNVs contributed less to the variance observed across and between MM subgroups. These analyses revealed potential biological drivers underlying a high-risk patient sub-group. The novel molecularly-defined subgroups identified will be further validated. This analysis provides an opportunity to identify biological drivers within molecularly-defined patient subgroups that can lead to development of novel therapies in MM. Disclosures Ortiz: Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Towfic:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Jang:Celgene Corporation: Employment, Equity Ownership. Wang:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria. Munshi:OncoPep: Other: Board of director. Thakurta:Celgene Corporation: Employment, Equity Ownership.

Introduction: Poor prognosis and drug resistance in multiple myeloma (MM) is associated with increased mutational load. APOBEC3B is a major contributor to mutagenesis, especially in myeloma patients with t(14;16) MAF subgroup. It was shown recently that presence of the APOBEC signature at diagnosis is an independent prognostic factor for progression free survival (PFS) and overall survival (OS). We hypothesized that high levels of APOBEC3B gene expression at diagnosis may also have a prognostic impact in myeloma. To consider APOBEC3B as a potential target for therapy more studies are necessary to understand how APOBEC3B expression is regulated and how APOBEC3B generates mutations. Methods: Gene expression profiling (GEP, U133 Plus 2.0) of MM patients was performed. APOBEC3B gene expression levels were investigated in plasma cells of healthy donors (HD; n=34), MGUS (n=154), smoldering myeloma (SMM; n=219), MM low risk (LR; n=739), MM high risk (HR; n=129), relapsed MM (RMM; n=74), and primary plasma cell leukemia (pPCL; n=19) samples. The samples from relapse were taken on or after the progression/relapse date but within 30 days after progression/relapse from Total Therapy trials 3, 4, 5 & 6. GEP70 score was used to separate samples into LR and HR groups. We also investigated APOBEC3B expression in different MM molecular subgroups and used logrank statistics with covariate frequency distribution to determine an optimal cut off APOBEC3B expression value. Gene expression was compared in cases with low expression of APOBEC3B (log210), and an optimal cut-point in APOBEC3B expression was identified with respect to PFS. To explore the role of MAF and the non-canonical NF-ĸB pathway we performed functional studies using a cellular model of MAF downregulation. TRIPZ lentiviral shRNA MAF knockdown in the RPMI8226 cell lines was used to explore MAF-dependent genes. NF-ĸB proteins, p52 and RelB, were investigated in the nuclear fraction by immunoblot analysis. Results: Expression of APOBEC3B in HD control samples (log2=10.9) was surprisingly higher than in MGUS (log2=9.51), SMM (log2=9.09), and LR (log2=9.40) and was comparable to HR (log2=10.4) and RMM (log2=10.6) groups. Expression levels of APOBEC3B were gradually increased as disease progressed from SMM to pPCL. The high expression of APOBEC3B in HD places plasma cells at risk of APOBEC induced mutagenesis where the regulation of APOBEC3B function is compromised. The correlation between APOBEC3B expression and GEP70 score in MM was 0.37, and there was a significant difference in APOBEC3B expression between GEP70 high and low risk groups (p=0.0003). An optimal cut-point in APOBEC3B expression of log2=10.2 resulted in a significant difference in PFS (median 5.7 yr vs.7.4 yr; p=0.0086) and OS (median 9.1 yr vs. not reached; p Conclusions: Increased expression of APOBEC3B is a negative prognostic factor in multiple myeloma. MAF is a major factor regulating expression of APOBEC3B in the t(14;16) subgroup. NF-ĸB pathway activation is most likely involved in upregulation of APOBEC3B in non-t(14;16) subgroups. Disclosures Davies: TRM Oncology: Honoraria; MMRF: Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding; Takeda: Consultancy, Honoraria.

Introduction: Despite novel International Myeloma Working Group (IMWG) criteria, Smoldering Myeloma (SMM) remains a heterogeneous disease for which correctly identifying patients that will eventually progress to myeloma (MM) is essential. The genetic and molecular factors that underlie disease progression are not well elucidated, therefore, we examined samples from SMM patients in order to identify molecular determinants of progression. Methods: CD138-sorted and control samples from 77 non-treated SMM patients according to IMWG 2014 underwent targeted sequencing and gene expression profiling (GEP). The median follow-up was 4.81 years (95% CI: 4.19-6.16). Targeted sequencing consisted of 140 genes and additional regions of interest for copy number, as well as tiling of the immunoglobulin and MYC loci for detection of translocations and was performed on a NextSeq500 using 75 bp paired end sequencing. Results were aligned to the hg19 genome and mutations, translocations and copy number were determined. Nonnegative matrix factorization (NMF) (NMF package in R) was used to identify mutation signatures. The median mean coverage was 365 (88-696) and 783 (161-1559) for translocations (Tx) and mutations respectively. We compared these samples to 199 newly diagnosed MM samples. Results: Significant differences in the frequencies of mutated genes were seen, including fewer NRAS, KRAS, FAM46C, LRRK2 and TP53 mutations and more PCLO and MAFB mutations than expected in comparison to MM (p Pearson correlation was performed between patients that progressed (n=24) against those who did not (n=53) for genetic events with n≥6. Del(6q) [χ2=0.32, p=0.004], LRP1B [χ2=0.27, p=0.015] and KRAS mutations [χ2=0.28, p=0.01] were positively correlated to progression, but only del(6q) remained significant after Bonferroni adjustment. Of particular interest, we did not identify the APOBEC mutational signature in the t(14;16) SMM samples, which is heavily associated with a poor prognosis in t(14;16) MM (4/11 in MM and 0/5 in SMM). Discussion: As previously reported, copy number changes, Tx and mutations predate MM. The lower frequencies of copy number changes and mutations suggest an ongoing process whereby cells acquire successive events eventually leading to MM. KRAS and del(6q) were significant predictors of both PFS and TFS with hazard ratios of 2.8 and 3.71, respectively. We comprehensively analyzed both the NF-κB pathway mutations and copy number changes, that did not bear, unlike previous reports, any clear relationship to PFS. Although we are limited by the power of this analysis, this supports the idea that the NF-κB dependency preexists symptomatic myeloma and is present throughout disease stages. Further analysis of the NF-κB 11-gene signature expression are ongoing. This is the first broad analysis of both MYC rearrangements and Tx in SMM. Previous studies have focused on FISH analysis of IGH-MYC Tx that underestimate the extent of MYC rearrangements present. Finally, our data also shows that absence of an APOBEC signature in SMM may account for the rather indolent phenotype of MAF and MAFB Tx in comparison to MM. Conclusion: KRAS mutations as well as del(6q) were associated with shorter PFS and TFS in this dataset. The absence of APOBEC signature may explain part of the indolent phenotype of the MAF and MAFB translocation SMM patients. Disclosures Boyle: Gilead: Honoraria, Other: travel grants; Amgen: Honoraria, Other: travel grants; Celgene: Honoraria, Other: travel grants; Abbvie: Honoraria; Takeda: Consultancy, Honoraria; La Fondation de Frace: Research Funding; Janssen: Honoraria, Other: travel grants. Facon:Karyopharm: 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; Amgen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: 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; Sanofi: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees. Dumontet:Janssen: Honoraria; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding; Sanofi: Honoraria. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding. Davies:Abbvie: Consultancy; MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; Janssen: Consultancy, Honoraria.

Introduction: The study of multiple myeloma (MM) genomics has identified many abnormalities that are associated with poor progression free survival (PFS) and overall survival (OS). Copy number abnormalities have been extensively studied in many datasets with long follow-up, however, the prognostic impact of mutations have not been extensively studied and available datasets have generally had a relatively short follow-up of 22-25 months, with one dataset being up to 5.4 years. These analyses have identified a range of mutations that are associated with prognosis, making it important to extend these observations in larger studies with robust diagnostic technologies. Methods: Samples from newly diagnosed MM patients enrolled in Total Therapy trials (n=199) were sequenced on a targeted panel consisting of 140 genes and additional regions of interest for copy number, as well as tiling of the Ig and MYC loci for detection of translocations. Samples were sequenced to a median depth of 452x using 2x75 bp paired end reads. Reads were aligned to hg19 and mutations called using Strelka and filtered with fpfilter. Translocations were called by Manta, and copy number determined by read depth ratio and loss of heterozygosity comparison with a patient matched non-tumor sample. Additional copy number data were generated by ultra-low pass whole genome sequencing (median 0.5x). Events in Results: The median follow-up for this dataset was 8 years, with a median PFS of 6 years and OS of 11 years. The median age was 60.6 years and risk groups were comparable to other studies with 29.1% of patients with ISS III and 20% with high IMWG risk status. In a univariate analysis the markers with highest hazard ratios (HR) for PFS were Double Hit (9%, HR 5.2; 95% CI 2.79-9.76), abnormal BIRC3 (5%, 2.89; 1.32-6.32), ISS III (29%, 2.88; 1.65-5.02), mutation BRAF (11%, 2.26; 1.3-3.93), mutation LRP1B (6%, 2.23; 1.39-3.58), mutation DIS3 (9%, 2.2; 1.22-3.97), bi-allelic inactivation CYLD (10%, 2.04; 1.01-4.10), and high IMWG risk (20%, 2.01; 1.29-3.13). For OS the markers with highest HR were ISS III (5.21; 2.46-11.07), mutation KMT2C (3%, 4.4; 1.37-14.14), t(14;16) (4%, 3.83; 1.38-10.62), mutation EGR1 (4%, 3.58; 1.28-10.00), Double Hit (3.24; 1.65-6.40), mutation BRAF (2.89; 1.57-5.33), mutation LRP1B (2.49; 1.19-5.24), rearrangements surrounding MYC (46%, 2.49; 1.50-4.11), and high IMWG risk (2.11; 1.26-3.53). In a multivariate analysis for PFS Double Hit (HR 4.37, 95% CI 2.31-8.26), loss of BIRC2/3 (5%, 3.95; 1.69-9.21); mutation LRP1B (3.21; 1.53-6.72), mutation DIS3 (2.44; 1.31-4.53), ISS III (2.29; 1.22-4.32), mutation BRAF (2.28; 1.24-4.18) contributed to the model. For OS, ISS III (3.15;1.40-7.06); 1q21 amp (6%, 2.988; 1.01-8.86); mutation LRP1B (2.90; 1.33-6.35), Double Hit (2.51; 1.05-6.01), deletion CDKN1B (10%, 2.44; 1.15-5.16), and mutation BRAF (2.25; 1.13-4.48) contributed to the model. Conclusion: We confirm the clinical relevance of Double Hit risk status that constitutes 9% of patients; median PFS of 2 vs. 7 years (P Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: 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; Dana Farber Cancer Institute: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Millenium: Consultancy, Research Funding; Multiple Myeloma Research Foundation: Other: travel stipend. Thakurta:Celgene Corporation: Employment, Equity Ownership. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding.

Introduction: The proto-oncogene MYC (locus 8q24.21) is a key transcription factor in multiple myeloma (MM) resulting in significant gene deregulation and impacting on many biological functions, including cell growth, proliferation, apoptosis, differentiation, and transformation. Chromosomal rearrangement and copy number change at the MYC locus are secondary events involved in MM progression, which are thought to lead to aggressive disease. Current analyses of the MYC locus have not been large and have reported rearrangements in 15% of new-diagnosed MM. However, more recent studies using advanced genomic techniques suggest that the frequency of MYC rearrangements may be much higher, and that a full reassessment of the role of MYC in MM pathogenesis may be critical. In this study, we analyzed 1280 MM patients to provide a better understanding of the role of this important genomic driver in MM pathogenesis. Methods: In total, 1280 tumor normal pairs of CD138 sorted bone marrow plasma cells and their germline control samples were analyzed by: 1. Targeted sequencing of 131 genes and 27 chromosome regions (n=100) with 4.5 Mb captured region surrounding MYC ; 2. Exome sequencing (n=461) with 2.3 Mb captured region surrounding MYC ; 3. Whole genome sequencing (n=719). Normalized tumor/germline depth ratio in targeted-sequencing cases and MANTA were used for detection of somatic copy number and structural variants. Expression analysis was performed using RNA-seq or microarrays. Results: MYC translocations were found in 25% (323/1280) of patients and occurred most frequently as inter-chromosomal translocations involving 2-5 chromosomes (90%, 291/323). Of the remaining cases, 5% (17/323) of the translocations involved inversion of chromosome 8 and 5% (15/323) were complex, affecting more than 5 chromosomal loci. The proportion of MYC translocations involving 2, 3, 4, and 5 loci was 62% (200/323), 23% (74/323), 8% (26/323) and 3% (8/323), respectively. Using abnormal rearranged cases (29/100), we found copy number imbalances >14.2 kb in size associated with a MYC translocation in 76% (22/29). Another 7% (2/29) of cases with translocations showed complex intra-chromosomal rearrangement. A region of 2.0 Mb surrounding MYC was identified as a translocation breakpoint hot-spot incorporating 96% of breakpoints. This region also contained two hotspots for chromosomal gain and tandem duplications. MYC rearrangements were not randomly distributed across the spectrum of MM with an excess being seen in hyperdiploidy (76% of rearranged samples, P 2-fold change in expression (P Conclusions: This study confirms the central role of MYC in the pathogenesis of clinical cases of MM, and as such defining it as a critical therapeutic target. We will be able to target MYC better if we understand how it is deregulated and in this respect we show that the MYC locus rearrangements are complex and it is a hot-spot for heterogeneous inter- as well intra-chromosomal rearrangements, including complex rearrangements involving >5 chromosomes. These events lead to increased MYC expression consistent with it being a driver of disease progression, particularly in the hyperdiploid subset of MM. Disclosures Mavrommatis: Celgene Corporation: Employment. Trotter: Celgene Corporation: Equity Ownership; Celgene Institute for Translational Research Europe: Employment. Davies: Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria. Thakurta: Celgene Corporation: Employment, Equity Ownership. Morgan: Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Myers: Consultancy, Honoraria.

Introduction Gene expression and comprehensive genomic profiling (CGP) underscore the importance of multiple myeloma (MM) being driven by diverse genomic abnormalities and are increasingly being integrated into personalized treatment algorithms to optimize clinical outcomes, in particular that of high risk disease. Furthermore, CGP allow for ultra-deep sequencing of various clinically relevant and targetable genomic alterations using a single assay, with an advantage of detection of low frequency variants. Methods Samples from 578 patients (monoclonal gammopathy of undetermined significance, MGUS, (n=19); smoldering multiple myeloma, SMM, (n=42); or multiple myeloma, MM, (n=517; 87 newly diagnosed (NDMM), 107after treatment (TRMM), and 323 at relapse (RLMM)) were analyzed using the FoundationOne® Heme (F1H) assay. 50 ng of DNA and RNA from CD138+ selected cells were analyzed for genomic alterations including base substitutions, indels, copy number alterations, and rearrangements. Sequencing was performed to a median depth of 468x in 405 genes, as well as selected introns of 31 genes involved in rearrangements. Additionally, matched Gene Expression Profiling (GEP) was performed using Affymetrix U133 Plus 2 array, and GEP70-defined risk status and molecular subgroups were calculated. Results Results of the F1H assay revealed the most common alterations in MM to be: KRAS (28.8%), NRAS (23.2%), TP53 (17.4%), BRAF (6.8%), CDKN2C (6.0%), RB1 (5.8%), TRAF3 (5.8%), DNMT3A (3.9%), TET2 (3.7%) and ATM (2.5%), including mutations, homozygous loss and rearrangements. When these frequencies were split across GEP70 risk groups, TP53, CDKN2C/FAF1, RB1, and the t(4;14) were significantly different (p In order to identify independent prognostic genomic alterations, we performed a multivariate Cox regression analysis on all the gene alterations that were present in at least 5% of the patient cohort, resulting in identification of four significant alterations: the t(4;14), mutation/loss of TP53, CDKN2C/FAF1 or RB1. Alterations in CDKN2C and RB1 were associated with the PR group. When the MM samples were split according to type (NDMM, TRMM, RLMM) the effect on survival of each of these alteration was more pronounced at relapse, but still present at diagnosis for CDKN2C and t(4;14). Bi-allelic events in CDKN2C, TP53 and RB1 were examined, by both homozygous deletion and monosomy with accompanying mutation, showing the rate of inactivation increased from 9.2% in NDMM to 17.9% at relapse, indicating that bi-allelic inactivation of these genes are correlated with relapse. CDKN2C and TP53 are known prognostic markers but the prognostic significance of RB1 has been debated. Previous data have shown that the association of t(4;14) with del(13q) results in insignificance of del(13q) as a prognostic marker in multivariate analyses. Here, we confirmed that the prognostic effect of RB1 is not due to association with t(4;14), and show that patients with either the t(4;14) or alteration of RB1 have a poor prognosis, which is worse when both lesions are present. Conclusions Using the F1H assay, we establish the mutational spectrum in MM, identifying lesions associated with high risk. This is the first study in MM to identify and confirm the poor prognostic effect of RB1 driven by bi-allelic inactivation, which is more prevalent at relapse. Furthermore, we determined the gene alterations that are independent prognostic markers in relapsed MM, thereby identifying novel therapeutic targets. Disclosures He: Foundation Medicine, Inc: Employment, Equity Ownership. Bailey:Foundation Medicine, Inc: Employment, Equity Ownership. Ashby:University of Arkansas for Medical Sciences: Employment. Zhong:foundation medicine: Employment. Nahas:Foundation medicine: Employment. Ali:Foundation Medicine: Employment, Equity Ownership. Vergillo:Foundation Medicine, Inc: Employment. Ross:Foundation Medicine, Inc: Employment. Miller:Foundation Medicine: Employment, Equity Ownership. Stephens:Foundation Medicine: Employment, Equity Ownership. Barlogie:Signal Genetics: Patents & Royalties. Mughal:Foundation Medicine: Employment, Equity Ownership. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding; Univ of AR for Medical Sciences: Employment.

Introduction Segmenting multiple myeloma (MM) into subgroups with distinct pathogenesis and clinical behavior is important in order to move forward with advancements in therapy and implement a targeted therapy approach. Current technologies have elucidated five major translocation groups, which have a varying effect on prognosis: t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20) along with recurrent copy number changes including deletion of CDKN2C (1p32.3) and TP53 (17p13.1) as well as gain or amplification of 1q21. However, minor translocation and mutational groups are poorly described because sample numbers are limited in small datasets. The availability of multiple sets of high quality mutation data associated with clinical outcomes has provided a unique opportunity in MM whereby clustering mutational data with chromosomal aberrations in the context of gene expression we can develop a molecular classification system to segment the disease into therapeutically meaningful subgroups. The Multiple Myeloma Genome Project (MGP) is a global collaborative initiative that aims to develop a molecular segmentation strategy for MM to develop clinically relevant tests that could improve diagnosis, prognosis, and treatment of patients with MM. Materials and methods We have established a set of 2161 patients for which whole exome sequencing (WES; n=1436), Whole Genome Sequencing (WGS; n=708), targeted panel sequencing (n=993) and expression data from RNA-Seq and Gene Expression arrays (n=1497) were available. These data were derived from the Myeloma XI trial (UK), Intergroupe Francophone du Myeloma/Dana-Faber Cancer Institute (MA), The Myeloma Institute (AR) and the Multiple Myeloma Research Foundation (IA1 - IA8). We assembled all data on a secure site and analyzed it using a streamlined and consistent pipeline using state of the art tools. First, BAM were converted to FASTQ using Picard tools v2.1.1 to extract read sequences and base quality scores. Next, all reads were realigned to the human genome assembly hg19 using BWA-mem. Duplicate marking and sorting was performed using Picard tools v2.1.1. For QAQC we use FASTQC and Picard tools. We identified somatic single nucleotide variants and indels with Mutect2 using default parameters. Translocations and large chromosomal aberrations were identified using MANTA and breakdancer and inferred copy number abnormalities and homozygous deletions using Sequenza v2.1.2 and ControlFreeC. Results We have begun to integrate these diverse large genomic datasets with various correlates. Samples were stratified by RNA-seq expression values and WES/WGS to identify the main cytogenetic groups with high concordance. In addition to the main translocation groups, translocations into MAFA, t(8;14), were detected in 1.2% of samples by both RNA-seq and WES/WGS. RNA-seq also detected fusion transcripts, including the known Ig-WHSC1 transcript in t(4;14). However, a proportion of identified in-frame fusion genes involved kinase domains consistent with activation of the Ras/MAPK pathway, which may be clinical targets for therapy. The main recurrent mutations included KRAS and NRAS, and negative regulators of the NF-κB pathway. In addition we identified recurrent copy number abnormalities and examined the interaction of these with mutations. This highlighted the interaction of the recurrent changes at 1p, 13q, and 17p with mutation of genes located within these regions, specifically indicating bi-allelic inactivation of CDKN2C, RB1 and TP53. Using WGS and RNA-Seq data we identified recurrent translocations and fusion genes that can be used to instruct therapy. Based on these data and the presence of homogeneous inactivation of key tumor expressed genes we will present clinically relevant clusters of MM that can form the basis of future risk and molecular targeted trials. Interaction of mutation with expression patterns has identified distinct expression signatures associated with mutational groups. Conclusions We have established the largest repository of molecular profiling data in MM along with associated clinical outcome data. Integrated analyses of these are enabling generation of clinically meaningful disease segments associated with differing risk. The MGP intends to build a global network by expanding collaboration with leading MM centers around the world and incorporating additional datasets through current and new collaborations. Disclosures Mavrommatis: Discitis DX: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Employment, Equity Ownership. Ashby:University of Arkansas for Medical Sciences: Employment. Ortiz:Celgene: Employment. Towfic:Celgene: Employment, Equity Ownership; Immuneering Corp: Equity Ownership. Amatangelo:Celgene: Employment, Equity Ownership. Yu:Celgene: Employment, Equity Ownership. Avet-Loiseau:celgene: Consultancy; janssen: Consultancy; sanofi: Consultancy; amgen: Consultancy. Jackson:Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau. Thakurta:Celgene: Employment, Equity Ownership. Munshi:Takeda: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Merck: Consultancy; Pfizer: Consultancy; Oncopep: Patents & Royalties. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.

INTRODUCTION In multiple myeloma (MM) samples for diagnostics, prognostication and response evaluation are most commonly obtained from the patients' posterior iliac crest due to its accessibility and safety, assuming a homogenous spread throughout the bone marrow. However, imaging studies revealed a highly imbalanced distribution of the disease in the majority of the patients, presenting with accumulations of malignant plasma cells (PC) in restricted areas in the bone marrow (BM), so called focal lesions (FL). In line with this pattern, our recently reported preliminary results of paired FL and random BM (RBM) samples strongly indicate an unequal distribution of sub-clones in the BM. Spatial genomic heterogeneity has not been systematically analyzed in MM thus far, although its existence would have a high impact on interpretation of drug resistance studies, risk stratification and personalized treatment based on genomic markers. Here we report on an extended genomic analysis of regional heterogeneity in paired FL and RBM samples including 42 newly diagnosed and 11 extensively treated MM patients with 10 of these patients also being studied longitudinally. MATERIAL & METHODS MM PCs were CD138-enriched. Leukapheresis products were used as controls. For whole exome sequencing (WES) we applied the qXT kit and the SureSelect Clinical Research Exome bait design (Agilent). Paired-End sequencing was performed on an Illumina HiSeq 2500. Sequencing data were aligned to the GRCh37/hg19 reference using BWA. Somatic single nucleotide variants (SNV) were identified using MuTect. Copy number aberrations (CNA) were derived from Illumina HumanOmni 2.5 bead chip data using ASCAT. Subclonal reconstruction was performed using SciClone. Gene expression profiles (GEP) were generated using Affymetrix U133plus2 microarrays. Statistical analyses were carried out using the R software package 3.1.1. RESULTS In 42 newly diagnosed patients we detected a median number of 86 (34 to 807) mutations per patient with up to 42% (median 5%) of them being unique to a specific site (non-ubiquitous). Among known MM driver genes, BRAF (n=2) and KRAS (n=4) were the genes that most often showed non-ubiquitous mutations at baseline. In treated patients mutations in KRAS, NRAS and RB1 contributed to regional heterogeneity in one patient each. Furthermore, we found temporal heterogeneity in mutations affecting ATRin two patients, aberrations recently associated with poor outcome. Analyzing chromosomal aberrations with known prognostic value we observed three newly diagnosed patients with a site-specific del(1p) affecting CDKN2C and/or FAM46Cwith two of these patients also showing regional heterogeneity in del(17p13). Non-ubiquitous gain(1q) or amp(1q) was seen in two patients at baseline. Of note, in all of these cases the unique event was detected in a FL and one case with a unique gain(1q) at baseline presented with this aberration in subsequent samples. These observations strongly support the concept of FLs being sites of resistant clones able to cause relapse. In four patients a MYC translocation was seen at only one site. In the longitudinal analysis we found one patient in whom a MYC translocation clone was replaced by a clone with a different MYC translocation, indicating that events at the MYC locus are secondary and can be sub-clonal. In contrast, primary IgH translocations were always shared, confirming that they are initiating events. Paired samples from RBM and FLs derived from three newly diagnosed patients showed discordant GEP risk profiles, further supporting the existence of site-specific high risk (HR) clones. To investigate the clinical relevance of this finding we analyzed outcome data of 263 newly diagnosed patients with paired GEP data. The 34 cases with discordant GEP based risk scores showed no significant differences in outcome compared to cases with HR at both sites, suggesting that HR sub-clones drive prognosis even if they are not ubiquitously present. CONCLUSIONS We show that spatial genomic heterogeneity is common in MM. The existence of site-specific sub-clones highlights the importance of heterogeneity analyses for accurate risk prediction, detection of suitable targets for precision medicine and identification of aberrations contributing to treatment resistance. As a result we strongly recommend to include FL examinations into routine diagnostics and follow-up analyses in MM. Disclosures Ashby: University of Arkansas for Medical Sciences: Employment. Barlogie:Signal Genetics: Patents & Royalties. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.

Background Deletion of chromosome 17p (del17p) is detected in 10% of multiple myeloma (MM) patients at diagnosis and is associated with both a dismal prognosis and increased prevalence after treatment. Even though this suggests that it might be a driver of disease progression, relatively little is known about the genomic landscape of these tumors. With this study, we aimed to identify recurrent aberrations in a large cohort of del17p MM patients and to assess their prognostic value within this patient group. Methods The study design consisted of a discovery phase and a validation phase. For the discovery phase, 44 newly diagnosed MM (NDMM) patients were included with a del17p in at least 50% of plasma cells, as detected with fluorescent in situ hybridization (FISH). From 12 del17p patients, 2 or 3 tumor samples were available to analyze clonal evolution during disease progression. DNA was isolated from peripheral blood and CD138+ enriched bone marrow mononuclear cells, followed by a custom capture of the whole exome, Chr17p and the IgH, Igk, IgL and MYC regions (SeqCap EZ Exome Plus, Nimblegen) and paired-end sequencing. Significantly mutated genes were determined with MutSigCV and significantly deleted or amplified genomic regions with GISTIC2. Single nucleotide variants (SNVs) in TP53, FAM46C, KRAS, NRAS, DIS3 and BRAF were validated using a custom amplicon panel (TruSeq Custom Amplicon Assay v1.5, Illumina), followed by deep sequencing on a MiSeq System. Findings were validated in a cohort of 463 NDMM patients of the UK Myeloma XI trial, from which whole exome sequencing data were available for paired tumor and germline DNA (Walker et al. - J Clin Oncol 2015). All samples in both the discovery and the validation cohort were reanalyzed with one bioinformatic pipeline, from alignment to variant calling. The copy number status of TP53 was determined with Sequenza, followed by selection of del17p samples after manual inspection of the results. From a third cohort of 233 MM patients with progressive disease (PDMM) treated at the University of Arkansas for Medical Sciences (UAMS), 406 cancer-related genes were paired-end sequenced from tumor DNA with the FoundationOne Heme assay (He et al. - Blood 2016). Results In the discovery cohort, we identified a commonly deleted region (CDR) on Chr17p of 235 kb, in which one or more somatic, nonsilent aberrations (SNSA) in TP53 were detected in 25/44 (57%) patients (adj. p In the UK Myeloma XI cohort, we detected a del17p in 32/463 (7%) of patients. Of these, 15/32 (47%) had an SNSA in the remaining allele. Follow-up data were available of 73/76 del17p NDMM patients from both cohorts (n=46 transplant-eligible (TE), n=27 nontransplant-eligible (NTE)), with a median follow-up time of 17 months. TP53 mutated patients had a worse overall survival (OS) than TP53 wildtype patients (p=0.02), of which the strongest effect on OS was seen of TP53 missense mutations (p We went on to look in the FoundationOne cohort of PDMM patients and detected a del17p in 37/233 (16%) of patients and an SNSA in TP53 in 25/37 (68%) of del17p PDMM patients, which is a higher rate than in the 2 NDMM cohorts. Conclusions (1) TP53 is somatically mutated in half of del17p MM patients at diagnosis, validated in 2 independent cohorts, and this percentage is higher in del17p MM patients at progression. (2) Particularly TP53 missense mutations have a significant, negative impact on both PFS and OS in del17p MM patients. (3) The rest of the genomic landscape of del17p MM is characterized by significant mutations in FAM46C and KRAS, as well as deletions of RB1, TRAF3 and FAM46C. Disclosures Ashby: University of Arkansas for Medical Sciences: Employment. He:Foundation Medicine, Inc: Employment, Equity Ownership. Bailey:Foundation Medicine, Inc: Employment, Equity Ownership. Mughal:Foundation Medicine: Employment, Equity Ownership. Waage:Novartis, Amgen, Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen: Speakers Bureau; Celgene: Consultancy, Honoraria. Avet-Loiseau:celgene: Consultancy; sanofi: Consultancy; janssen: Consultancy; amgen: Consultancy. Jackson:Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau. Morgan:Univ of AR for Medical Sciences: Employment; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Bristol Meyers: Consultancy, Honoraria. Sonneveld:Amgen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding.

Introduction: Molecular assessment using conventional karyotyping, interphase FISH and gene expression profiling (GEP) has revealed multiple subgroups of myeloma with distinct pathogenesis and clinical course. While these technologies have tremendously impacted risk assessment they have had little contribution to the identification of therapeutic targets. Next generation sequencing (NGS) technology can identify mutations in genes of key cancer pathways, which impact outcome and are targetable by new drugs. Targeted gene panels can analyze clinical samples in sufficient depth affording the opportunity to incorporate NGS into clinical decision making in a meaningful way. Using the FoundationOne Heme test (F1H), we aimed to determine the mutational spectra of cancer-associated genes in multiple myeloma (MM), their association with disease risk and their effect on clinical outcome. Methods: DNA and RNA were extracted from CD138-selected MM cells. Comprehensive genomic profiling (CGP) using F1H was performed by Foundation Medicine, Inc (Cambridge, MA). Sequencing to an average depth of 470x (range: 5-3781) was performed on a HiSeq2500 sequencer. Sequences were analyzed for base substitutions, insertions, deletions, copy number alterations, and rearrangements in frequently altered genes. Annotated germline variants (dbSNP135) were removed. Somatic alterations in COSMIC (v62) and inactivating variants in tumor suppressor genes were called as biologically significant. GEP of CD138-selected MM cells using Affymetrix U133 2.0 plus arrays was performed as described. Overall survival analysis was done using log-rank tests. Results: CGP was performed on a total of 630 patients (3.4% MGUS, 6.5% SMM, 24.9% newly diagnosed MM, 24.9% relapsed MM, 18.8% MM in remission). We found increasing mutation load in from MGUS to relapsed MM. Later stages of the disease had an increased frequency of mutations in genes coding for epigenetic modulators and proteins involved in DNA repair. Alterations of TP53 and RB1 among others weresignificantly more frequent in GEP-defined high-risk (HR) disease and after relapse. Patients of the GEP-defined MF molecular subgroup carried a significantly greater mutation load. While there was no difference in the frequency of altered RAS/MAPK pathway genes between newly diagnosed and relapsed patients, we found an increased average mutant allele frequency in relapsed patients, indicating clonal selection. Using paired GEP data we identified gene expression signatures for patients with RAS/MAPK activation and patients with loss-of-function mutations in the DNA repair pathway, suggesting a functional relevance of these mutations. Mutations in either of these pathways were associated with significantly worse overall survival (OS) (Figure 1). Presence of DNA repair gene mutations resulted in significantly worse OS within the GEP-defined low-risk subgroup. Among the 630 patients who underwent CGP, 396 had clinically relevant alterations, which were associated with either an FDA approved drug or a clinical trial. For example, 316 patients had alterations of the RAS/MAPK pathway. Recently we have shown clinical benefit of MEK directed therapy in this patient population. 39 patients had alterations in the mTOR pathway, suggesting benefit from mTOR inhibitors. 426 patients with MM had mutations in epigenetic modulators. For 37 of them therapy with demethylating agents was recommended. Many more epigenetic targeted drugs, such as EZH2 or Bromodomain inhibitors are currently in development. Conclusion: Using the F1H test we demonstrate a negative impact of somatic mutations of the MAPK and DNA repair pathways on outcome. In tandem, for 396 patients we identified genomic alterations, which suggest benefit from targeted treatment. Thus targeted therapy, guided by comprehensive genomic profiling, may be applied to the majority of MM patients, with the potential of significantly improving clinical outcomes. Comprehensive genomic profiling should therefore be considered in the routine work-up, especially for HR patients where outcomes remain poor. Figure 1. Inferior outcome of patients with mutations in the MAPK or DNA repair pathway. Panels A) and C) mutation of MAPK pathway; Panels B) and D) mutation of the DNA repair pathway. Overall survival is measured from time of disease diagnosis in panels A) and B) and is shown from sample date in panels C) and D) Figure 1. Inferior outcome of patients with mutations in the MAPK or DNA repair pathway. Panels A) and C) mutation of MAPK pathway; Panels B) and D) mutation of the DNA repair pathway. Overall survival is measured from time of disease diagnosis in panels A) and B) and is shown from sample date in panels C) and D) Disclosures Heuck: Millenium: Other: Advisory Board; Janssen: Other: Advisory Board; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment; Foundation Medicine: Honoraria. Chavan:University of Arkansas for Medical Sciences: Employment. Stein:University of Arkansas for Medical Sciences: Employment. Tytarenko:University of Arkansas for Medical Sciences: Employment. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Ali:Foundation Medicine, Inc.: Employment, Equity Ownership. Miller:Foundation Medicine, Inc.: Employment, Equity Ownership. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Schinke:University of Arkansas for Medical Sciences: Employment. Mohan:University of Arkansas for Medical Sciences: Employment. Sawyer:University of Arkansas for Medical Sciences: Employment. Peterson:University of Arkansas for Medical Sciences: Employment. Bauer:University of Arkansas for Medical Sciences: Employment. Ashby:University of Arkansas for Medical Sciences: Employment. Johann:University of Arkansas for Medical Sciences: Employment. van Rhee:University of Arkansa for Medical Sciences: Employment. Waheed:University of Arkansas for Medical Sciences: Employment. Davies:Millenium: Consultancy; Onyx: Consultancy; Janssen: Consultancy; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:CancerNet: Honoraria; Weismann Institute: Honoraria; MMRF: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Introduction: Recent next generation sequencing studies have defined the mutation spectrum in multiple myeloma (MM) and uncovered significant intra-clonal heterogeneity, showing that clinically relevant mutations are often only present in sub-clones. Longitudinal analyses demonstrated that tumor clones under therapeutic pressure behave in a "Darwinian" fashion, with shifting dominance of tumor clones over time. Recently, stratification of clonal substructures in distinct areas of the tumor bulk has been shown for multiple cancer types. So far, spatial genomic heterogeneity has not been systematically analyzed in MM. This stratification in space is becoming increasingly important as we begin to understand the contribution of Focal Lesions (FL) to tumor progression and emergence of drug resistance in MM. We have recently shown that high numbers of FL are associated with gene expression profiling (GEP) defined high risk (HR). A comparison of GEP data of 170 paired random bone marrow (RBM) and FL aspirates showed differences in risk signatures, supporting the concept of spatial clonal heterogeneity. In this study we have extended the analysis by performing whole exome sequencing (WES) and genotyping on paired RBM and FL in order to gain further insight into spatial clonal heterogeneity in MM and to find site-specific single nucleotide variant (SNV) spectra and copy number alterations (CNA), which contribute to disease progression and could form the basis of adaptation of the tumor to therapeutic pressure. Materials and Methods: We included 50 Total Therapy MM patients for whom paired CD138-enriched RBMA and FL samples were available. Leukapheresis products were used as controls. For WES we applied the Agilent qXT kit and a modified Agilent SureSelect Clinical Research Exome bait design additionally covering the immunoglobulin heavy chain locus and sequences located within 1Mb of the MYC locus. Paired-End sequencing to a minimum average coverage of 120x was performed on an Illumina HiSeq 2500. Sequencing data were aligned to the Ensembl GRCh37/hg19 human reference using BWA. Somatic variants were identified using MuTect. For detection of CNA we analyzed Illumina HumanOmni 2.5 bead chip data with GenomeStudio. Subclonal reconstruction was performed using PhyloWGS. Mutational signatures were investigated using SomaticSignatures. The GEP70 risk signature was calculated as described previously. Informed consent in accordance with the Declaration of Helsinki was obtained for all cases included in this study. Results: Analyzing RBM and FL WES data, we detected between 100 and 200 somatic SNVs in covered regions, with approximately 30% of them being non-synonymous, and less than 5% stop gained or splice site variants. A comparison of paired RBM and FL WES data showed different extents of spatial heterogeneity. Some pairs had very similar mutation profiles with up to 90% shared variants, whereas others demonstrated marked heterogeneity of point mutations. We did not detect differences in mutational signatures between RBM and FL using the 'SomaticSignatures' package. We found site-specific driver mutations with high variant allele frequencies, indicating replacement of other clones in these areas. For example we observed a clonal KRAS mutation exclusively in the RBM, whereas a NRAS variant was only identified in the paired FL. The same holds true for large-scale CNAs (>1 Mb). We identified a case in which the high risk CNAs gain(1q) and del(17p) were only detectable in the FL. Further examples for site-specific CNAs were a del(10q21) and a gain(4q13) detected in FLs only. As a prominent pattern, we observed outgrowth of sub-clonal RBM CNAs as clonal events in the FL. Based on mutation and CNA data we identified different forms of spatial evolution, including parallel, linear and branching patterns. Of note, a stratified analysis by GEP70-defined risk showed that a more pronounced spatial genomic heterogeneity of SNVs and CNAs was associated with HR disease. Conclusion: We show that spatial heterogeneity in clonal substructure exists in MM and that it is more pronounced in HR. The existence of site-specific HR CNAs and driver mutations highlights the importance of heterogeneity analyses for targeted treatment strategies, thereby facilitating optimal personalized MM medicine. Disclosures Weinhold: University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Heuck:Millenium: Other: Advisory Board; Janssen: Other: Advisory Board; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment; Foundation Medicine: Honoraria. Stephens:University of Arkansas for Medical Sciences: Employment. Tytarenko:University of Arkansas for Medical Sciences: Employment. Bauer:University of Arkansas for Medical Sciences: Employment. Peterson:University of Arkansas for Medical Sciences: Employment. Ashby:University of Arkansas for Medical Sciences: Employment. Stein:University of Arkansas for Medical Sciences: Employment. Johann:University of Arkansas for Medical Sciences: Employment. Johnson:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. van Rhee:University of Arkansa for Medical Sciences: Employment. Zangari:Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding; University of Arkansas for Medical Sciences: Employment. Schinke:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Davies:Millenium: Consultancy; Onyx: Consultancy; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment; Janssen: Consultancy. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:University of Arkansas for Medical Sciences: Employment; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Weismann Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Introduction: Next generation sequencing of over 800 newly diagnosed multiple myeloma (NDMM) cases has established the mutational landscape and key cancer driver pathways. The mutational basis of relapse has not been systematically studied. Two previous studies (Keats et al.; Bolli et al.) identified 4 patterns of clonal evolution. Neither study included uniformly treated patients and looked at the impact of therapy on clonal structure at relapse. Understanding the mutational patterns underlying relapse and how they relate to specific therapies is crucial in order to improve MM outcomes, especially for high-risk (HR) MM. In this study we compare the clonal structure at presentation (PRES) and at relapse (REL), after exposure to Total Therapy (TT). Materials and Methods: We studied 33 pairs of tumor samples collected at PRES and REL. 9 patients were treated on TT2, 13 on TT3, 10 on TT4 and 1 on TT5-like regimen. Eleven patients had HR disease at PRES. DNA was extracted from CD138+ selected cells from random bone marrow aspirates. Germline controls were obtained from leukapheresis products. Whole exome sequencing libraries were prepared using the Agilent qXT kit and the Agilent SureSelect Clinical Research Exome kit with additional baits covering the Ig and MYC loci. All samples were sequenced on an Illumina HiSeq2500 to a median depth of 120x. Sequencing data were aligned to the Ensembl GRCh37/hg19 human reference using BWA. Somatic variants were called using MuTect. Translocations were identified using MANTA. Copy number variations were inferred using TITAN. Gene expression profiles (GEP), generated using the Affymetrix U133plus2 microarray, were available for all tumor samples. Nonnegative matrix factorization (NMF) was used to define mutation signatures. Results: The median time to progression was 30 months with a median follow up of 9.5 years. 22 cases achieved a complete remission (CR) or near CR. There were 11 cases of HR at PRES. Of the 22 cases with low risk (LR) MM, 7 relapsed with HR disease. There were on average 478 SNVs per sample at PRES and 422 at REL. All but 2 cases had evidence of new mutations at REL. There were no consistent patterns or number of mutation associated with REL or GEP-defined risk. Patients of the MF molecular subgroup had more mutations compared to other molecular subgroups (657 vs. 379) and were enriched for mutations with an APOBEC signature. We did not detect any mutation signature consistent with chemotherapy-induced alterations, providing evidence that TT itself does not cause additional mutations. Primary recurrent IgH translocations called by MANTA were confirmed by GEP data. A number of new translocations were identified , several only at REL. In particular we demonstrate a case with a newly acquired MYC translocation at relapse, indicating that it contributed to progression. We identified 5 patterns of clonal evolution (Figure 1): A) genetically distinct relapse in 3 patients, B) linear evolution in 8 patients, C) clonal selection in 9 patients, D) branching evolution in 11 patients, and E) stable clone(s) in 2 patients. Patterns A (distinct) and B (linear) were associated with low risk and longer survival, whereas patterns D (branching) and E (stable) were associated with high risk and shorter time to relapse and overall survival (Table 1). Conclusion: This is the first study to systematically analyze the pattern of clonal evolution using NGS in patients treated with combination chemotherapy and tandem ASCT. We identified 5 patterns of evolution, which correlate with survival. We identified 3 cases with a loss of the original clone and emergence of a new clone, suggesting high effectiveness of Total Therapy for those patients. The persistence of major clones despite multi agent chemotherapy in most other cases supports a concept of a tumor-initiating cell population that persist in a protective niche, for which new therapies are needed. | Pattern of Evolution | GEP70 Pres. (high risk: ≥0.66) | Proliferation Index Pres. | GEP70 Rel. (high risk: ≥0.66) | Proliferation Index Rel | Mean OS | Mean TTR | | -------------------- | ------------------------------------- | ------------------------- | ------------------------------------ | ----------------------- | ------- | -------- | | A: distinct (n=3) | -0.690 | -3.34 | -0.015 | 2.04 | 8.18 | 5.00 | | B: linear (n=8) | -0.171 | -0.34 | 0.618 | 9.22 | 5.70 | 4.05 | | C: selection (n=9) | 0.366 | 3.20 | 0.569 | 6.97 | 3.95 | 2.64 | | D: branching (n=11) | 0.710 | 5.17 | 1.173 | 11.15 | 3.84 | 2.21 | | E: stable (n=2) | 1.532 | 7.42 | 1.124 | 2.54 | 0.96 | 0.35 | * Pres.: Presentation; Rel.: Relapse; OS: Overall Survival; TTR: Time to Relapse Table 1. ![Figure 1.][1] Figure 1. Patterns of Relapse Disclosures Heuck: Foundation Medicine: Honoraria; Millenium: Other: Advisory Board; Janssen: Other: Advisory Board; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment. Weinhold: Janssen Cilag: Other: Advisory Board; University of Arkansas for Medical Sciences: Employment. Peterson: University of Arkansas for Medical Sciences: Employment. Bauer: University of Arkansas for Medical Sciences: Employment. Stein: University of Arkansas for Medical Sciences: Employment. Ashby: University of Arkansas for Medical Sciences: Employment. Chavan: University of Arkansas for Medical Sciences: Employment. Stephens: University of Arkansas for Medical Sciences: Employment. Johann: University of Arkansas for Medical Sciences: Employment. van Rhee: University of Arkansa for Medical Sciences: Employment. Waheed: University of Arkansas for Medical Sciences: Employment. Johnson: University of Arkansas for Medical Sciences: Employment. Zangari: University of Arkansas for Medical Sciences: Employment; Millennium: Research Funding; Onyx: Research Funding; Novartis: Research Funding. Matin: University of Arkansas for Medical Sciences: Employment. Petty: University of Arkansas for Medical Sciences: Employment. Yaccoby: University of Arkansas for Medical Sciences: Employment. Davies: University of Arkansas for Medical Sciences: Employment; Millenium: Consultancy; Janssen: Consultancy; Onyx: Consultancy; Celgene: Consultancy. Epstein: University of Arkansas for Medical Sciences: Employment. Barlogie: University of Arkansas for Medical Sciences: Employment. Morgan: Weismann Institute: Honoraria; MMRF: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. [1]: pending:yes