Abstract: Smoldering myeloma (SMM) is associated with a high-risk of progression to myeloma (MM). We report the results of a study of 82 patients with both targeted sequencing that included a capture of the immunoglobulin and MYC regions. By comparing these results to newly diagnosed myeloma (MM) we show fewer NRAS and FAM46C mutations together with fewer adverse translocations, del(1p), del(14q), del(16q), and del(17p) in SMM consistent with their role as drivers of the transition to MM. KRAS mutations are associated with a shorter time to progression (HR 3.5 (1.5–8.1), p  = 0.001). In an analysis of change in clonal structure over time we studied 53 samples from nine patients at multiple time points. Branching evolutionary patterns, novel mutations, biallelic hits in crucial tumour suppressor genes, and segmental copy number changes are key mechanisms underlying the transition to MM, which can precede progression and be used to guide early intervention strategies.

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

Abstract: Circulating cell‐free DNA (cfDNA) has the potential to capture spatial genetic heterogeneity in myeloma (MM) patients. We assessed whether cfDNA levels vary according to risk status defined by the 70 gene expression profile (GEP70). cfDNA levels in 77 patients were significantly higher in the GEP70 high‐risk (HR) group compared to the low‐risk (LR) group and correlated weakly with clinical markers including lactate dehydrogenase, β 2 ‐microglobulin, and ISS. Patients with high cfDNA levels were associated with a worse PFS (hazard ratio 6.4; 95% CI of ratio 1.9‐22) and OS (hazard ratio 4.4; 95% CI of ratio 1.2‐15.7). Circulating tumor DNA (ctDNA) was elevated in the HR group and ctDNA correlated strongly with GEP70 risk score (Spearman r  = .69, P  = .0027). cfDNA concentrations were significantly elevated between days 3‐5 after chemotherapy before falling back to baseline levels. ctDNA in two patients showed a similar spike in levels between days 3 and 5 after chemotherapy with a concomitant increase in allele fraction of KRAS mutations. We assessed cfDNA levels in 25 patients with smoldering myeloma with serial samples and showed increased allele fraction of mutated KRAS at progression in cfDNA. Our study shows that cfDNA is a dynamic tool to capture genetic events in myeloma.

Abstract: Primary plasma cell leukemia (pPCL) is a rare and aggressive form of multiple myeloma (MM) that is characterized by the presence of ≥20% circulating plasma cells. Overall survival remains poor despite advances of anti-MM therapy. The disease biology as well as molecular mechanisms that distinguish pPCL from non-pPCL MM remain poorly understood and, given the rarity of the disease, are challenging to study. In an attempt to identify key biological mechanisms that result in the aggressive pPCL phenotype, we performed whole-exome sequencing and gene expression analysis in 23 and 41 patients with newly diagnosed pPCL, respectively. The results reveal an enrichment of complex structural changes and high-risk mutational patterns in pPCL that explain, at least in part, the aggressive nature of the disease. In particular, pPCL patients with traditional low-risk features such as translocation t(11;14) or hyperdiploidy accumulated adverse risk genetic events that could account for the poor outcome in this group. Furthermore, gene expression profiling showed upregulation of adverse risk modifiers in pPCL compared to non-pPCL MM, while adhesion molecules and extracellular matrix proteins became increasingly downregulated. In conclusion, this is one of the largest studies to dissect pPCL on a genomic and molecular level.

Abstract: 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 ( 〈 10% of tumor content) at diagnosis was estimated to be 3 (range: 1-5). In 7/8 patients a minor clone increased to more than at least 15% of tumor content and in 5/8 patients at least 20%. All patients that had more than 2 minor clones that increased to more than 15% progressed or had progressed (4/8). The only patient that progressed and did not display these clonal changes progressed within 4 months from the initial SMM sample, suggesting the clonal sweep had already occurred. Significant changes in sub-clonal structures were also seen in all samples at least one year prior to progression. 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.

Abstract: 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 〈 0.05). Regarding structural changes, there was no difference in the incidence of Tx (including those involving MAF and MAFB) but significantly fewer del(1p), del(12p) and del(14q) cases in SMM (p 〈 0.05). There was no difference in the incidence of MYC translocations (19% of cases) and MYC rearrangements (23%). The 4-year progression rate was 25 percent. The presence of KRAS mutations (n=9) and del(6q) (n=11) were statistically associated with shorter progression free survival (PFS) [median 49m (26-∞) vs 147m (67.6-∞) and 26m (9.6-∞) vs 147m (80-∞) for KRAS and del(6q) respectively] and treatment free survival (TFS) [median 6m (9.6-∞) vs 19m (9.6-∞) and 9 m (13.4-∞) vs 16m (13.4-∞) for KRAS and del(6q) respectively] . MYC alterations and NF-κB alterations (BIRC2 and BIRC3 loss, TRAF2 and TRAF3 mutation or gain, CYLD loss, MAP3K14 mutations) did not influence progression. There were no double-hit patients in this cohort defined by bi-allelic-TP53 or ISS III with amp(1q). Ten percent of patients were identified as high-risk according to GEP4 risk-score. 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.

Abstract: 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 〈 2% of patients were not considered for further analysis. Risk groups including international staging system (ISS), revised-ISS, IMWG risk groups, and Double Hit MM (biallelic TP53 or amp1q with ISS III) were defined. 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 〈 0.0001), and OS 3 vs. 13 years (P=0.0003). With long follow-up and deep sequencing additional mutated genes associated with adverse outcome were identified including BRAF (11%), DIS3 (9%), LRP1B (6%) and KMT2C (3%). Further, inactivation of NF-κB regulators (CYLD, BIRC2/3) were associated with poor PFS or OS. Patients with a BRAF mutation had a median PFS of 2 vs. 7 years (P=0.003), and OS of 6 vs 13 years (P=0.0004), indicating a potential useful intervention for BRAF inhibitors. 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.

Abstract: Multiple Myeloma (MM) is a hematological malignancy with genomic heterogeneity and poor survival outcome. Apart from the central role of genetic lesions, epigenetic anomalies have been identified as drivers in the development of the disease. Methods Alterations in the DNA methylome were mapped in 52 newly diagnosed MM (NDMM) patients of six molecular subgroups and matched with loci-specific chromatin marks to define their impact on gene expression. Differential DNA methylation analysis was performed using DMAP with a ≥10% increase (hypermethylation) or decrease (hypomethylation) in NDMM subgroups, compared to control samples, considered significant for all the subsequent analyses with p 〈 0.05 after adjusting for a false discovery rate. Results We identified differentially methylated regions (DMRs) within the etiological cytogenetic subgroups of myeloma, compared to control plasma cells. Using gene expression data we identified genes that are dysregulated and correlate with DNA methylation levels, indicating a role for DNA methylation in their transcriptional control. We demonstrated that 70% of DMRs in the MM epigenome were hypomethylated and overlapped with repressive H3K27me3. In contrast, differentially expressed genes containing hypermethylated DMRs within the gene body or hypomethylated DMRs at the promoters overlapped with H3K4me1, H3K4me3, or H3K36me3 marks. Additionally, enrichment of BRD4 or MED1 at the H3K27ac enriched DMRs functioned as super-enhancers (SE), controlling the overexpression of genes or gene-cassettes. Conclusions Therefore, this study presents the underlying epigenetic regulatory networks of gene expression dysregulation in NDMM patients and identifies potential targets for future therapies.