Abstract: Background: The main genetic features of myeloma identified so far have been the presence of balanced translocations at the immunoglobulin heavy chain (IGH) region and copy number abnormalities. Novel methodologies such as massively parallel sequencing have begun to describe the pattern of tumour acquired mutations detected at presentation but their biological and clinical relevance has not yet been fully established. Methods: Whole exome sequencing was performed on 463 presentation patients enrolled into the large UK, phase III, open label, Myeloma XI trial. DNA was extracted from germline DNA and CD138+ plasma cells and whole exome sequencing was performed using SureSelect (Agilent). In addition to capturing the exome, extra baits were added covering the IGH, IGK, IGL and MYC loci in order to determine the breakpoints associated with translocations in these genes. Tumour and germline DNA were sequenced to a median of 60x and data processed to generate copy number, acquired variants and translocation breakpoints in the tumour. Progression-free and overall survival was measured from initial randomization and median follow up for this analysis was 25 months. These combined data allow us to examine the effect of translocations on the mutational spectra in myeloma and determine any associations with progression-free or overall survival. Results: We identified 15 significantly mutated genes comprising IRF4, KRAS, NRAS, MAX, HIST1H1E, RB1, EGR1, TP53, TRAF3, FAM46C, DIS3, BRAF, LTB, CYLD and FGFR3. By analysing the correlation between mutations and cytogenetic events using a probabilistic approach, we describe the co-segregation of t(11;14) with CCND1 mutations (Corr 0.28,BF=1.5x106 (Bayes Factor)) and t(4;14) with FGFR3 (Corr=0.40, BF=1.12x1014) and PRKD2 mutations (Corr=0.23, BF=3507). The mutational spectrum is dominated by mutations in the RAS (43%) and NF-κB (17%) pathway, however they are prognostically neutral. We describe for the first time in myeloma mutations in genes such as CCND1 and DNA repair pathway alterations (TP53, ATM, ATR and ZFHX4 mutations) that are associated with a negative impact on survival in contrast to those in IRF4 and EGR1 that are associated with a favourable overall-survival. By combining these novel risk factors with the previously described adverse cytogenetic features and ISS we were able to demonstrate in a multivariate analysis the independent prognostic relevance of copy number and structural abnormalities (CNSA) such as del(17p), t(4;14), amp(1q), hyperdiploidy and MYC translocations and mutations in genes such as ATM/ATR, ZFHX4, TP53 and CCND1. We demonstrate that the more adverse features a patient had the worse his outcome was for both PFS (one lesion: HR=1.6, p=0.0012, 2 lesions HR=3.3, p 〈 0.001, 3 lesions HR=15.2, p 〈 0.001) and for OS (one lesion: HR=2.01, p=0.0032, 2 lesions HR=4.79, p 〈 0.001, 3 lesions HR=9.62, p 〈 0.001). When combined with ISS, we identified 3 prognostic groups (Group 1: ISS I/II with no CNSA or mutation, Group 2: ISS III with no CNSA or mutation or ISS I/II/III with one CNSA or mutation, Group 3: Two CNSA or mutation regardless of their ISS) thus identifying three distinct prognostic groups with a high risk population representing 13% of patients that both relapsed [median PFS 10.6 months (95% CI 8.7-17.9) versus 27.7 months (95% CI 25.99-31.1), p 〈 0.001] and died prematurely [median overall survival 23.2 months (95% CI 18.2-35.3.) versus not reached, p 〈 0.001] regardless of their ISS score. Finally, we have also identified a list of potentially actionable mutations for which targeted therapy already exists opening the way into personalized medicine in myeloma. Conclusion: We have refined our understanding of genetic events in myeloma and identified clinically relevant mutations that may be used to better stratify patients at presentation. Identifying high risk populations or patients that may benefit from targeted therapy may open the way into personalized medicine for myeloma. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.

Abstract: 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 〈 0.001). 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.

Abstract: Introduction: Malignant transformation of normal to tumour cells is a multistep process followed by sequential aggregation of hits at different molecular levels. Genetic events including single nucleotide variants (SNVs), insertion-deletion changes (indels) as well as copy number variants (CNVs) affect the phenotype of the tumour population and consequently patient prognosis. Transformation from a symptomless state, monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) can be used as a unique model for cancer development studies. To date, there is very little data regarding the mechanisms leading to disease progression at molecular level. In our study, we performed exome sequencing together with SNP array analysis on 33 MGUS patients to describe the premalignant phenotype and compared these to advanced tumour cells at the DNA level. We hypothesised that increased genetic instability indicated MGUS patients with a high risk of progression to MM. Methods: 33 MGUS patients (M:F 1.5:3; median age 61, range: 35-86) were included in this study. Plasma cells were isolated from bone marrow by FACSAria (BD Biosciences) system using CD138, CD19 and CD56 markers to obtain a pure abnormal plasma cell population with a purity 〉 90%. Tumour DNA was isolated using Gentra Puregene Kit and amplified using REPLI-g Midi Kit (both Qiagen); control DNA was gained from peripheral white blood cells by MagNA Pure System (Roche Diagnostics). For exome sequencing, NEBNext kit (NEB) and SureSelect Human All Exon V5 (Agilent Technologies) were used and samples were sequenced by HiSeq2000 (Illumina) using 76-bp paired end reads. Unbalanced CNVs were tested by SurePrint G3 CGH+SNP, 4x180K (Agilent Technologies). Results were compared to 463 MM patients. Results: In our analysis, we found acquired SNVs in 100% (33/33) MGUS patients with a median of 89 (range 9-315) SNVs per patient. Non-synonymous SNVs (NS-SNVs) were present in 97% (32/33) cases with a median 19 (range 0–70) NS-SNVs per patient. Overall, 42 genes were recurrently mutated in at least 2 patients and 6 genes were mutated in at least 3 cases including MUC16, IGK, TTN, KLHL6, AKAP9 and NPIPL2. We identified 7 genes which were significantly mutated in MM in our previous study including KRAS (n=2), HIST1H1E (n=2) and NRAS, DIS3, EGR1, LTB, PRKD2 (all n=1). IGH translocations were identified in 27% (9/33) of patients: t(11;14) in 12% (4/33), t(4;14) in 9% (3/33), t(14;16) in 3% (1/33) and t(14;20) in 3% (1/33). We did not find any translocations involving MYC (8q24.21) or the light chain loci IGK (2p12) and IGL (22q11.2). Using SNP arrays, unbalanced CNVs were presented in 67% (22/33) of MGUS patients and detected CNVs showed similarity to MM across the cohort. As previously described in MM, only one type of IGH translocation was found per patient and all 9 cases with IGH translocation did not have additional hyperdiploidy. Furthermore, we identified a patient with two CCND1 (p.K50T, p.E51D) mutations and a t(11;14), a case with a DIS3 (p.D488N) mutation and a 13q loss. Moreover, we noticed a co-segregation of cases t(4;14) and t(14;16) who all had a 13q loss (100%, 4/4). In contrast none of the patients (0/5) with a t(11;14) or a t(14;20) had a 13q loss. Of note 29% (7/24) patients without any IGH translocation had a 13q loss. Sixty seven percent (2/3) of patients with a t(4;14) and the one case with a t(14;16) also had a 1q gain. In comparison, none of patients with a t(11;14) (0%; 0/4) had a 1q gain. Unlike what has previously been described in MM, neither of the 2 MGUS patients with a KRAS (p.Q61L and p.A146T) mutations had a t(11;14). We also identified a patient with both a KRAS (p.Q61L) and an NRAS (p.G13R) mutation which are although not mutually exclusive, negatively correlated in MM. Importantly, we did not find any mutations in TP53, ATM, ATR and ZFHX4 genes involved in DNA repair pathway alterations which were identified as unfavourable factors in survival of MM patients. Summary: We have performed the first comprehensive analysis of 33 MGUS patients using exome sequencing together with SNP arrays and described the main genetic events that are already present in this premalignant state. We found similarities to MM in terms of SNVs, CNVs and their correlations. We identified 6 MGUS cases with NS-SNVs in potential key genes that could indicate a potential high risk to progression. Support: IGA MH CZ NT13492, OPVK CZ.1.07/2.3.00/20.0183. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.

Abstract: Introduction Multiple myeloma (MM) is consistently preceded by an asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM). Here, we present the first comprehensive whole-genome sequencing (WGS) analysis of patients with MGUS and SMM. Methods To characterize the genomic landscape of myeloma precursor disease (i.e. SMM and MGUS) we performed WGS of CD138-positive bone marrow mononuclear samples from 32 patients with MGUS (N=18) and SMM (N=14), respectively. For cases with low cellularity resulting in low amounts of extracted DNA (N=15), we used the low-input enzymatic fragmentation-based library preparation method (Lee-Six et al, Nature 2019). Myeloma precursor disease samples were compared with 80 WGS of patients with MM. All WGSs (N=112) were investigated using computational tools available at the Wellcome Sanger Institute. Results After a median follow up of 29 months (range: 2-177), 17 (53%) patients with myeloma precursor disease progressed to MM (13 SMM and 4 MGUS). To interrogate the genomic differences between progressive versus stable myeloma precursor disease we first characterized the single base substitution (SBS) signature landscape. Across the entire cohort of plasma cell disorders, all main MM mutational signatures were identified: aging (SBS1 and SBS5), AID (SBS9), SBS8, SBS18, and APOBEC (SBS2 and SBS13). Interestingly, only 2/15 (13%) stable myeloma precursor disease cases showed evidence of APOBEC activity, while 14/17 (82%) and 68/80 (85%) patients with progressive myeloma precursor disease (p=0.0058) and MM (p=0.004), respectively, had APOBEC mutational activity. The two stable cases with detectable APOBEC were characterized by a high APOBEC3A:3B ratio, a feature which defines a group of MAF-translocated MM patients whose pathogenesis is characterized by intense and early APOBEC activity (Rustad et al Nat Comm 2020) and is distinct from the canonical ~1:1 APOBEC3A:3B mutational activity observed in most cases. When exploring the cytogenetic landscape, no differences were found between progressive myeloma precursor disease and MM cases. Compared to progressors and to MM, patients with stable myeloma precursor disease were characterized by a significantly lower prevalence of known recurrent MM aneuploidies (i.e. gain1q, del6q, del8p, gain 8q24, del16q) (p & lt;0.001). This observation was validated using SNP array copy number data from 78 and 161 stable myeloma precursor disease and MM patients, respectively. To further characterize differences between progressive versus stable myeloma precursor disease, we leveraged the comprehensive WGS resolution to explore the distribution and prevalence of structural variants (SV). Interestingly, stable cases were characterized by low prevalence of SV, SV hotspots, and complex events, in particular chromothripsis and templated insertions (both p & lt;0.01). In contrast, progressors showed a genome wide distribution and high prevalence of SV and complex events similar to the one observed in MM. To rule out that the absence of key WGS-MM defining events among stable cases would reflect a sample collection time bias, we leveraged our recently developed molecular-clock approach (Rustad et al. Nat Comm 2020). Notably, this approach is based on pre- and post-chromosomal gain SBS5 and SBS1 mutational burden, designed to estimate the time of cancer initiation. Stable myeloma precursor disease showed a significantly different temporal pattern, where multi-gain events were acquired later in life compared to progressive myeloma precursor disease and MM cases. Conclusions In summary, we were able to comprehensively interrogate for the first time the whole genome landscape of myeloma precursor disease. We provide novel evidence of two biologically and clinically distinct entities: (1) progressive myeloma precursor disease, which represents a clonal entity where most of the genomic drivers have been already acquired, conferring an extremely high risk of progression to MM; and (2) stable myeloma precursor disease, which does not harbor most of the key genomic MM hallmarks and follows an indolent clinical outcome. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; National Cancer Institute: Research Funding; AbbVie: Consultancy. Landgren:Pfizer: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Merck: Other; 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; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Merck: Other; Seattle Genetics: Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Bolli:Celgene: Honoraria; Janssen: Honoraria.

Abstract: Introduction: Systemic light chain amyloidosis (AL) is characterized by the deposition of immunoglobulin light chains as amyloid fibrils in different organs, where they form toxic protein aggregates. The underlying disease is a plasma cell disorder, likely a monoclonal gammopathy, but limited data are available on the biology of the plasma cell clone underlying AL and existing studies have concentrated on chromosomal abnormalities. We report the final findings of the first exome sequencing to define the plasma cell signature in AL and compared this to other mature lymphoid malignancies. Methods: Whole exome sequencing was performed on 27 newly diagnosed, histologically proven amyloidosis patients. DNA was extracted from peripheral blood and CD138+ plasma cells and whole exome sequencing was performed using SureSelect (Agilent). In addition to capturing the exome, extra baits were added covering the IGH, IGK, IGL and MYC loci in order to determine the breakpoints associated with translocations in these genes. Tumour and germline DNA were sequenced and data processed to generate copy number, acquired variants and translocation breakpoints in the tumour. Patient demographics: The median age at diagnosis was 69 (range: 41-81) years old. All cases were histologically proven, newly diagnosed AL amyloid. 74% were lambda restricted and 26% kappa with median respective median involved sFLC were 180 mg/L (range: 58.9-986 mg/L) and 730 mg/L (609-3190 mg/L) respectively. The median plasmocytosis was 17.5% (range: 2-90%). 78% of them had evidence of heart involvement, 70% had renal involvement and 33% had liver involvement. Mutation load: The median number of acquired non-synonymous variants per sample was 65 (range 7-285) with 40 (4-251) potentially disease causing variants per sample. Mutational landscape: Although no genes were significantly mutated, the genes closest to significance were NRAS, PIM1, and HIST1H3F. We identified 2 cases with NRAS mutations in the codon 61 (Q61R and Q61H) but no KRAS mutations were seen. Interestingly, there were mutations in some of the significantly mutated genes in myeloma such as EGR1 (Q95R), DIS3 (M505L and D317E) and TRAF3 (splice site). One patient bore a CARD11 (R1077W) mutation, more commonly seen in non-Hodgkin’s lymphoma. Although 22% of our samples had a t(11;14) translocations we did not observe any mutations in CCND1. We identified a t(1;14) (p36;q32) previously described in non-hodgkin lymphoma in one patient. We also identified a Myc translocation in a patient who met the criteria for smouldering myeloma. As previously described in myeloma, both DIS3 mutants occurred in patients with a del(13q). Finally, there was no APOBEC signature in our small samples cohort butwe identified an unspecific mutational signature that was related to age. When comparing the spectrum of mutated genes in both amyloidosis (n=27) and previously sequenced myeloma samples (n=463), we identified 948 genes in common between myeloma and amyloidosis. Four hundred and forty two genes were only mutated in amyloidosis most of them being in housekeeping genes. The clustering of the most frequent and significantly mutated genes in each B-cell malignancy, suggests amyloidosis resembles myeloma and MGUS more than other B-cell malignancies. Discussion: The mutational landscape of amyloidosis resembles myeloma with no disease defining mutations but a variety of mutations occurring in different pathways such as RAS and NF-kB. Two samples had an NRAS mutation, which is a known driver mutation also found in MM. We identified a non-canonical IgH translocation that is a rare event in myeloma. There was little overlap in mutated genes indicating a diverse spectrum of mutations, which is in common with MM. Given the diverse mutational spectrum it will be necessary to study a large cohort to fully understand the genetic complexity of the disease. Conclusion: We conclude that exome sequencing identifies a genetic signature of AL amyloidosis which is similar to other plasma cell disorders in terms of translocations and non-synonymous mutations. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.

Abstract: Molecular dissection of inborn errors of immunity can help to elucidate the nonredundant functions of individual genes. We studied 3 children with an immune dysregulation syndrome of susceptibility to infection, lymphadenopathy, hepatosplenomegaly, developmental delay, autoimmunity, and lymphoma of B-cell (n = 2) or T-cell (n = 1) origin. All 3 showed early autologous T-cell reconstitution following allogeneic hematopoietic stem cell transplantation. By whole-exome sequencing, we identified rare homozygous germline missense or nonsense variants in a known epigenetic regulator of gene expression: ten-eleven translocation methylcytosine dioxygenase 2 (TET2). Mutated TET2 protein was absent or enzymatically defective for 5-hydroxymethylating activity, resulting in whole-blood DNA hypermethylation. Circulating T cells showed an abnormal immunophenotype including expanded double-negative, but depleted follicular helper, T-cell compartments and impaired Fas-dependent apoptosis in 2 of 3 patients. Moreover, TET2-deficient B cells showed defective class-switch recombination. The hematopoietic potential of patient-derived induced pluripotent stem cells was skewed toward the myeloid lineage. These are the first reported cases of autosomal-recessive germline TET2 deficiency in humans, causing clinically significant immunodeficiency and an autoimmune lymphoproliferative syndrome with marked predisposition to lymphoma. This disease phenotype demonstrates the broad role of TET2 within the human immune system.

Abstract: 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 & gt;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 & lt;0.0001). Importantly, 67% (207/308) of cases with a MYC translocation involving 5 or less chromosomes had one of the commonly known super-enhancers involved in the translocation. Gene expression analysis was used to explore the impact of these events on downstream gene expression patterns. The results showed that inter- and/or intra-chromosomal rearrangements were associated with a significantly (P & lt;0.0001) higher MYC expression (4.1-fold). In patients where rearrangements were associated with additional copies of MYC there was higher expression of MYC in comparison to cases with a translocation but lacking copy number gain (P=0.04). To identify downstream genes deregulated by MYC rearrangements we compared gene expression between those with and without a translocation, independently of hyperdiploidy. Genes that showed & gt;2-fold change in expression (P & lt;0.01) included MYC and the non-protein coding oncogene PVT1 that is located next to MYC . Genes with significantly lower levels of expression were involved in B-cell biology including CD79A and AHR, or were associated with cell proliferation, migration, adhesion, apoptosis and/or angiogenesis (FGF16, ADAMTS1, FBXL7, HRK, PDGFD, and PRKD1) . 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 & gt;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.