325 Clonal Evolution in Newly Diagnosed Multiple Myeloma Patients: A Follow-up Study from the MMRF CoMMpass Genomics Project Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Genomics Sunday, December 10, 2017: 7:30 AM
Bldg B, Lvl 3, B302-B303 (Georgia World Congress Center)
Alessandro Lagana, PhD1,2*, David Melnekoff, BS, MSc1,2,3*, Itai Beno, PhD1,2*, Violetta Leshchenko, PhD3*, Deepak Perumal,
PhD3*, Jonathan J. Keats, PhD4, Mary DeRome, MS5*, Jennifer Yesil, MS6*, Daniel Auclair, PhD6*, Deepu Madduri, MD3*,
Ajai Chari, MD3, Hearn Jay Cho, MD, PhD7, Bart Barlogie, MD, PhD3, Sundar Jagannath, MD3, Joel Dudley, PhD1,2,8* and
Samir Parekh, MD3,9
1Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY
2Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
3Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
4Translational Genomics Research Institute, Phoenix, AZ
5Multiple Myeloma Research Foundation, Norwalk, CT
6Multiple Myeloma Research Foundation (MMRF), Middletown, CT
7Department of Hematology and Medical Oncology, Tisch Cancer Institute, Tisch Cancer Institute, New York, NY
8Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY
9Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY Multiple Myeloma (MM) is an incurable malignancy of bone marrow plasma cells characterized by wide molecular diversity and complex clonal and subclonal architecture. Intra-tumor heterogeneity reflects the evolution of the disease. Clonal structure may affect response to treatment, which, in turn, may shape tumor evolution and, consequently, contribute to drug resistance. Thus, analysis of tumor clonality has profound implications for personalized medicine. Using our recently developed network model of newly diagnosed MM from the MMRF CoMMpass study (Lagana et al, Leukemia 2017), MMNet, we have identified patterns of gene co-expression significantly associated with tumor clonality and showed that clonality is correlated with mutational burden and relapse.
In order to characterize the evolution of clonal structure from diagnosis to relapse, we analyzed Whole-Exome data (WES) from 19 patients enrolled in CoMMpass for whom sequencing data for multiple time points was available. The cohort included both male (12; 63%) and female (7; 37%) patients of white/caucasian (14; 73%) and black/african- american (5; 27%) origin. Patients were distributed among five of the ten classes defined by MMNet: CCND1 (4; 21%), MAF (1; 5%), MMSET (2; 10%), HY/NRAS (4; 21%), and IMM (4; 21%) (4 were unassigned). Also, six patients had the t(11;14) and two had the t(4;14) chromosomal translocations. All patients had received induction therapy, consisting of combinations of proteasome inhibitors (PI) (Bortezomib: 19, 100%; Carfilzomib: 3, 16%), immunomodulatory drugs (IMiDs) (Lenalidomide: 13, 68%; Pomalidomide: 2, 10%; Thalidomide: 1, 5%), steroids (18, 95%) and monoclonal antibodies (Daratumumab: 1, 5%). Best responses to therapy were CR/sCR (3; 16%), VGPR (7; 37%) and PR (9; 47%). Fourteen (68%) patients had SD or PD at the second sequencing time point, with a median PFS of 456 days.
Our analysis inferred subclonal tumor composition and evolution based on somatic mutations and copy number alterations (CNA) using PhyloWGS (Deshwar et al, Genome Biol 2015). Patients had one (14; 74%), two (4; 21%) or three (1; 5%) founding clones at baseline, and numerous subclones. Trajectories of cancer cell populations revealed dramatic changes in most patients in terms of clonal and subclonal cell fractions between baseline and relapse. In most patients we observed emergence of new competing clones at relapse, where one or more subclones decreasing in size were replaced by other subclones, indicating selective pressure introduced by therapy (Fig. 1).
Subclones were characterized in terms of mutations and CNA and were labeled as stable/resistant or sensitive based on their trajectories from baseline to relapse. Our analysis revealed significant inter-patient heterogeneity in terms of mutations and clonal composition, with few overlaps. We identified subclonal drivers by screening the observed mutations and CNA against a database of known cancer drivers and analysis of co-occurrence and mutual exclusivity. Our analysis revealed that stable/resistant clones were characterized by concurrent deletion of 17p and 13q, and/or mutations in NRAS. In particular, 7 out of 19 patients had at least one mutation in NRAS, observed at baseline in 5 cases, and with two patients carrying two different mutations in two different clones. Our findings support earlier adoption of targeted therapy against RAS (e.g. Trametinib). Other drivers found exclusively in resistant clones included DIS3, FAM46C, ROBO1 and CCND1.
Overall, our analysis provides genomics characterization of relapsed patients in CoMMpass following induction therapy, reveals heterogeneous clonal and subclonal composition and trajectories from baseline to relapse, and defines specific somatic mutations (e.g. NRAS) and CNA as drivers of resistance to induction therapy.
Disclosures: Madduri: Foundation Medicine, Inc.: Consultancy. Chari: Janssen: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Millennium Pharmaceuticals, Inc.: Consultancy, Research Funding; Amgen: Honoraria, Research Funding; Celgene Corporation: Consultancy, Research Funding. Cho: Bristol Myers-Squibb: Other: advisory board, Research Funding; Agenus, Inc.: Research Funding; Genentech: Other: advisory board, Research Funding; Ludwig Institute for Cancer Research: Research Funding; Multiple Myeloma Research Foundation: Research Funding. Barlogie:Millenium Pharmaceuticals: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Jagannath: Celgene: Consultancy; Bristol-Meyers Squibb: Consultancy; Merck: Consultancy; Medicom:Speakers Bureau; MMRF: Speakers Bureau; Novartis: Consultancy. Dudley: GlaxoSmithKline: Consultancy; Janssen Pharmaceuticals, Inc.: Consultancy; Ayasdi, Inc.: Equity Ownership; Ecoeos, Inc.: Equity Ownership; NuMedii, Inc.: Equity Ownership, Patents & Royalties; Ontomics, Inc.: Equity Ownership; Personalis: Patents & Royalties; AstraZeneca: Consultancy.
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*signifies non-member of ASH
326 Integrative Analysis of the Genomic Landscape Underlying Multiple Myeloma at Diagnosis: An MMRF CoMMpass Analysis Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Genomics Sunday, December 10, 2017: 7:45 AM
Bldg B, Lvl 3, B302-B303 (Georgia World Congress Center)
Austin Christofferson, BS1*, Sara Nasser1*, Jessica Aldrich1*, Daniel Penaherrera2*, Christophe Legendre1*, Brooks Benard,
BS1*, Sheri Skerget, PhD1*, The MMRF CoMMpass Network3*, Jennifer Yesil, MS4*, Daniel Auclair, PhD4*, Winnie Liang,
PhD1*, Sagar Lonial, MD5 and Jonathan J. Keats, PhD1
1Translational Genomics Research Institute, Phoenix, AZ
2Translational Genomics Research Institute, Phoenix
3MMRF, Norwalk
4Multiple Myeloma Research Foundation (MMRF), Middletown, CT
5Winship Cancer Institute/ Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA Multiple myeloma is a hematological malignancy characterized by an expansion of clonal plasma cells in the bone marrow. Our understanding of the pathogenesis of this disease has improved dramatically with the development of whole genome analysis technologies. However, to date no study has comprehensively analyzed a large cohort of myeloma patients. The Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) is a longitudinal study of 1143 patients with newly-diagnosed multiple myeloma patients from clinical sites in the United States, Canada, Spain, and Italy. To be enrolled on the study each patient must be diagnosed with myeloma and must receive a treatment regimen within 30 days after providing a bone marrow aspirate with a minimum of 1% plasma cells and a recovery of >250,000 CD138 positive plasma cells. The initial treatment regimen is required to contain a proteasome inhibitor, immunomodulatory agent, or both. Clinical parameters are collected at study enrollment and every three months through the eight-year observation period. Each baseline and progression tumor specimen is characterized using Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), and RNA sequencing (RNAseq).
This analysis includes all data available as of January 1, 2017 from interim analysis 11, which represents the first data freeze with the complete molecular analysis of all baseline specimens. This dataset includes 1143 enrolled patients of whom 1003 are molecularly characterized with 982 having some level of baseline characterization. As part of the baseline characterization we released WGS on 890, WES on 926, and RNAseq on 714 patients, respectively. In total 591 patients had complete characterization of the provided baseline bone marrow specimen.
The median follow-up of the cohort now exceeds 2 years which has identified a median PFS of 36 months for the cohort. The median OS has still not been reached. Interestingly, there is a significant difference in PFS and OS, with males performing worse than females, p<0.001 and p<0.001, respectively, that is largely associated with dramatic differences for patients in ISS stage I.
We identified a median of 153 non-immunoglobulin related mutations per patient, a median of 29 structural events, and a median of 133 copy number segments per tumor. To identify a set of significantly mutated genes we applied a consensus-based approach resampling 80% of the cohort 1000 times, which identified 60 distinct genes mutated in at least 1% of the cohort. Many of these genes are already implicated in myeloma but many are novel including; BMP2K, PABPC1, PANK3, PTPN11, and RPS3A that all have mutations at recurrent amino acid positions, characteristic of an oncogene. We performed a similar consensus clustering approach on the copy number profiles and gene expression profiles, identifying 14 and 12 distinct subtypes respectively. To generate an integrative view of the genetics underlying myeloma we identified potential loss-of-function and gain-of-function genes using an integrated approach leveraging somatic copy number alterations, copy neutral loss-of-heterozygosity, mutations, structural abnormalities, gene expression alterations, and constitutional inherited gene defects. This approach identified numerous genes known to be involved in myeloma like CCND1, NRAS, TRAF3, and IRF4. But unlike other methods it found numerous novel genes missed by other strategies like DPYD and PSPC1. This also highlighted the critical importance of chromosome 13 and X loss, each with 15 genes with complete bi-allelic loss in multiple patients. Moreover, the complete loss of DPYD identifies a subset of patients that may have dramatic responses to 5-FU through an Achilles heel based mechanism. Overall, this represents the most comprehensive analysis of the genetic underpinnings of myeloma.
Disclosures: No relevant conflicts of interest to declare.
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61 Comprehensive Identification of Fusion Transcripts in Multiple Myeloma: An MMRF CoMMpass Analysis Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Transcriptional Regulatory Circuitries of Multiple Myeloma Saturday, December 9, 2017: 7:45 AM
Bldg B, Lvl 4, B401-B402 (Georgia World Congress Center)
Sara Nasser1*, Austin Christofferson, BS1*, Christophe Legendre1*, Jessica Aldrich2*, Brooks Benard, BS2*, The MMRF
CoMMpass Network3*, Jennifer Yesil, MS4*, Daniel Auclair, PhD4*, Winnie Liang, PhD2*, Sagar Lonial, MD5 and Jonathan
J. Keats, PhD2
1TGen, Phoenix
2Translational Genomics Research Institute, Phoenix, AZ
3MMRF, Norwalk
4Multiple Myeloma Research Foundation (MMRF), Middletown, CT
5Winship Cancer Institute/ Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA Multiple myeloma is a hematological malignancy characterized by the clonal expansion of plasma cells in the bone marrow. Unlike many other blood cancers there is a diverse array of somatic alterations with patients being split into two large groups of non-hyperdiploid and hyperdiploid based on recurrent trisomies of the odd number chromosomes in ~58% of patients. Though the remaining non-hyperdiploid patients are frequently characterized by immunoglobulin translocations these rarely create fusion transcripts like the quintessential BCR-ABL fusions seen in CML, as the majority represent promoter or enhancer replacements driving the ectopic expression of a target gene with oncogenic potential. The only well defined fusion transcripts in myeloma are the IGH-MMSET fusions created in the majority of patients with t(4;14) translocations. However, these fusions are simple byproducts of the enhancer replacement event and do not create hybrid poly-peptides. We have used the comprehensive nature of the MMRF CoMMpass dataset with matched tumor RNA sequencing and tumor/normal whole genome sequencing to define the landscape of fusion transcripts in myeloma. In this IA11 analysis of the MMRF CoMMpass dataset, which represents the first release of the complete baseline cohort, we utilized the RNA sequencing data from 806 samples with matching WGS for 704 specimens. Our approach leverages a discovery step using Tophat-Fusion to identify potential fusion candidates. These potential fusions are then validated with three independent processes: Tophat-Fusion post, a guided assembly based approach of the RNA sequencing reads to create the fusion transcript in silico, and genomic validation using the WGS data to confirm a somatic structural event exists that could explain the observed fusion.
In this large cohort we identified 45,769 potential fusions from 806 samples, Tophat-Fusion Post filters the list to 36,267, with numerous highly recurrent false positive between highly expressed genes. The guided assembly was able to construct 17,420 fusion transcripts with many being proximal fusions of adjacent genes that likely occur in normal cells. Genomic validation identified somatic structural events occurring proximal and in the correct orientation to create 1192 of the observed fusions. All three processes validate 930 fusion events.
This analysis found the expected fusions between IGH-JH segments and WHSC1 as the most common fusion event detected in 100 patients. In addition, we observed several other fusion transcripts associated with common IgH rearrangements; CCND1/MYEOV and MYC plus several novel IgH fusions with TOP1MT and MAP3K14. There are very few recurrent gene pairs outside of novel fusions of KANSL1-ARL17A and TFG-GPR128. However, there are several genes like FCHSC2, TXNDC11, TXNDC5, MAP3K14, NEDD9, and TNFRSF17. Several like FCHSC2 and TNXDC5 are clear promoter replacements putting a strong B-cell promoter in front of myeloma promoting genes like CD40, LTBR, or MYC. Others appear to be the target gene of the fusion such as those involving MAP3K14 that removes the protein degradation region leading constitutive non-canonical NF-kB signaling. Another frequent set of fusions observed were highly random events associated with inactivation of known tumor suppressor genes in myeloma like TRAF3, RB1, FAM46C, and NF1. In conclusion, our comprehensive analysis of the landscape of fusion transcripts across several hundred samples with in-silico validation in RNA and DNA brings forth several fusion genes in myeloma. These genes are informative of the malignant processes occurring in multiple myeloma. Further investigation is warranted to understand the significance of the recurrently observed gene pairs or hub genes to understand how they contribute to the development and pathogenesis of multiple myeloma.
Disclosures: No relevant conflicts of interest to declare.
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*signifies non-member of ASH
1754 A Molecular Analysis of Cereblon-Related Immunomodulatory Drug Resistance in CoMMpass Multiple Myeloma Patients Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster I Saturday, December 9, 2017, 5:30 PM-7:30 PM
Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center)
Sheri Skerget, PhD1*, Brooks Benard, BS1*, Austin Christofferson, BS1*, Christophe Legendre1*, Jessica Aldrich1*, Sara
Nasser1*, Jennifer Yesil, MS2*, Daniel Auclair, PhD2*, Winnie Liang, PhD1*, Sagar Lonial, MD3 and Jonathan J. Keats, PhD1 1Translational Genomics Research Institute, Phoenix, AZ
2Multiple Myeloma Research Foundation (MMRF), Middletown, CT
3Winship Cancer Institute/ Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA Immunomodulatory drugs (IMiDs) are commonly used in first-line therapy to treat multiple myeloma (MM), a plasma cell malignancy. Previous studies have shown that cereblon (CRBN) is critical for the efficacy of IMiDs, such as lenolidomide, pomalidomide and thalidomide, in MM. In a small cohort of 9 patients with lenalidomide-resistant MM, CRBN expression decreased 20-90% after treatment. In a MM cell line, acquired deletion of CRBN was the primary genetic event conferring IMiD resistance. Although CRBN expression is copy number (CN) dependent, CN aberrations affecting CRBN are thought to be rare, with less than 2% of patients exhibiting monoallelic deletion of CRBN. We analyzed clinical and molecular data from the Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) IA11 release to better understand the molecular basis underlying CRBN-related IMiD resistance.
IMiDs were used in first-line therapy for 75% (728/971) of patients in the CoMMpass cohort. We identified five CRBN mutations in 5/891 (<1%) patients with available whole exome sequencing data at baseline, before therapeutic intervention. Four baseline CRBN mutations were nonsynonymous; two occurred in exon 3, and one each in exon 10 and 11, which are involved in forming the IMiD binding pocket. None of the four patients with both follow up data and CRBN mutations at baseline had relapsed, with follow up ranging from 18-30 months. Despite IMiD treatment in 68/84 (81%) patients between serial timepoints, no patient developed a CRBN mutation at relapse, suggesting that acquired mutations in CRBN do not play a large role in the development of IMiD resistance in treated patients.
RNAseq data was available for 32 serial patients of which 23 (72%) were IMiD treated. Only a subset of IMiD-treated patients (13/23, 57%) exhibited a decrease (ranging from 5-75%) in CRBN expression at relapse, however the decrease from baseline (mean = 41.4 transcripts per million (TPM)) to relapse (mean = 26.8 TPM) was significant (p=0.004) in this group. We analyzed the change in transcript expression of nine noncoding and four protein-coding CRBN transcripts from baseline to relapse in this patient subset and found four differentially expressed non-coding transcripts and two differentially expressed protein coding transcripts; CRBN-001 ENST00000231948 (17.4 versus 10.7 mean TPM; p = 0.04) which codes the full length CRBN protein, and CRBN-010 ENST00000450014 (3.3 versus 2.1 mean TPM; p = 0.02) which is missing exons 10 and 11. Reduced expression of CRBN-001 plays the greatest role in the overall reduction of CRBN and since it contains the IMiD binding motifs, likely also plays a greater role in CRBN resistance in IMiD-treated patients. Of the 13 patients who exhibited a decrease in CRBN expression at relapse, 10 had whole genome sequencing (WGS) CN data at both timepoints and 2/10 (20%) patients experienced CRBN CN loss from baseline to relapse. This suggests that CRBN CN loss may account for acquired IMiD resistance in a larger subset of patients than previously reported.
Across the cohort, 14/871 (2%) patients with WGS data exhibit CRBN CN loss at baseline, while 342/871 (39%) patients exhibit CRBN CN gain, primarily whole chromosome gains related to trisomies of chr3 in a subset of hyperdiploid patients at baseline. Survival analyses revealed that IMiD-treated patients with CRBN CN gains at baseline exhibit better progression free survival (PFS) (median = 43 months) and overall survival (OS) (median = 55 months) than non IMiD-treated patients with CRBN CN gains (median PFS = 24 months, p<0.0001, median OS not reached, p<0.001). Although not significant, IMiD-treated patients with CRBN CN gains exhibit better PFS than IMiD- treated CN-normal patients (median = 39 months), suggesting that patients with CRBN CN gains at baseline may benefit from an IMiD based treatment regimen. Overall, these results highlight that chr3/CRBN CN gain can be a predictor of good outcome in IMiD treated patients and demonstrate the clinical benefit of obtaining CRBN CN and expression data throughout a MM patient’s disease course.
Disclosures: No relevant conflicts of interest to declare.
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*signifies non-member of ASH
3027 FGFR3 Mutations Are an Adverse Prognostic Factor in Patients with t(4;14)(p16;q32) Multiple Myeloma: An MMRF CoMMpass Analysis Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster II Sunday, December 10, 2017, 6:00 PM-8:00 PM
Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center)
Brooks Benard, BS1,2*, Austin Christofferson, BS2*, Christophe Legendre2*, Jessica Aldrich2*, Sara Nasser2*, Jennifer Yesil,
MS3*, Daniel Auclair, PhD3*, Winnie Liang, PhD2*, Sagar Lonial, MD4 and Jonathan J. Keats, PhD2
1Stanford School of Medicine, Stanford University, Palo Alto, CA
2Translational Genomics Research Institute, Phoenix, AZ 3Multiple Myeloma Research Foundation (MMRF), Middletown, CT
4Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA Multiple Myeloma is a hematological malignancy of terminally differentiated post-germinal center B-cells (plasma cells) genetically characterized by recurrent aneuploidy patterns and translocations. One common translocation is t(4;14)(p16.3;q32.2), which occurs in ~13% of patients and results in overexpression of FGFR3 and WHSC1/MMSET/ NSD2. We previously found that all t(4;14) tumors dysregulated WHSC1, however only 75% express FGFR3, suggesting that FGFR3 is not the primary target of this structural rearrangement. Studies have reported known activating mutations in FGFR3 in 5-10% of t(4;14) tumors and the lack of prognostic significance in t(4;14) based on FGFR3 expression. FGFR3 mutations have been linked with the constitutive activation of downstream signaling pathways such as PI3K, mTOR, RAF, RAS, MAPK, and STAT which has led to clinical trials of FGFR3 inhibitors. Here, we show the first indication of adverse clinical prognosis of t(4;14) patients overexpressing mutated FGFR3 isoforms. As part of the interim analysis 11 of the MMRF CoMMpass trial (NCT145429), 506 patients with combined clinical data, Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), and RNA sequencing (RNAseq) assays performed at diagnosis were analyzed regarding FGFR3 mutation, expression, t(4;14) status, and clinical outcome. Analysis of the WGS dataset for translocations between an immunoglobulin loci and 4p16 identified classic t(4;14) events in 13% of patients, plus one translocation involving the kappa locus and none with the lambda locus. Besides these events, we identified non-immunoglobulin translocations creating novel fusion genes between WHSC1 and SUB1, HTT, FUT8, CREB3L2, or CXCR4. Receiver operator characteristic (ROC) analysis was performed using RNAseq data to identify the optimal expression threshold for defining a structural event targeting WHSC1 and/or FGFR3. This empirical threshold defined the percent of t(4;14) patients with FGFR3 expression as 79% (59/74). Confirming previous reports, survival analysis yielded a nonsignificant (p = 0.7) association of survival for t(4;14) patients based on FGFR3 expression. WES detected non-synonymous FGFR3 mutations in 20% of t(4;14) patients (16/80) compared to previous reports of only 10%. FGFR3 mutations were only observed in FGFR3-expressing t(4;14) patients and not all FGFR3 mutated patients express pure mutated isoforms, suggesting the mutations, at least in some patients, are late events occurring after the translocation. Additionally, non-synonymous WHSC1 mutations were observed in five patients: three in FGFR3-expressing t(4;14)+ and two in t(4;14)-, with one t(4;14)+ patient harboring both FGFR3 and WHSC1 mutations. We next investigated the correlation of FGFR3 mutations, expression and survival within t(4;14) by stratifying patients into four categories: t(4;14)+ with expressed FGFR3mut (n = 12), t(4;14)+ with expressed FGFR3wt (n = 29), t(4;14)+ without FGFR3 expressed (n = 16), and t(4;14)- patients (n = 449).
Our analysis shows a statistically significant (p = 0.02) correlation of adverse prognosis in t(4;14)+ FGFR3mut expressing patients (median survival = 2.8 years) compared to t(4;14)+ FGFR3wt expressing patients (median survival not reached).
The detection of six non-classical translocation events, all targeting WHSC1 and not FGFR3, provide additional genetic evidence that WHSC1 is the target of t(4;14). Although not the primary target of t(4;14), we propose that mutated FGFR3 is a gain-of-function event which leads to worse disease in t(4;14) patients. In the future it will be important to determine if clones with mutated FGFR3 have a competitive advantage, as evidenced by an increase in the relative proportion of the mutant clones at progression in patients expressing both mutated and unmutated FGFR3 at baseline. Altogether, these results support the feasibility of FGFR3 inhibitors as potentially invaluable agents targeting a subset of high-risk myeloma patients.
Disclosures: No relevant conflicts of interest to declare.
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2178 Comparative Analysis of Gene Expression Status to Predict Response to Initial Multiple Myeloma Treatment: Utility of the MMRF CoMMpass Database in Validating Historical Findings Program: Oral and Poster Abstracts Session: 904. Outcomes Research—Malignant Conditions: Poster I Saturday, December 9, 2017, 5:30 PM-7:30 PM
Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center)
Aneel Paulus, MD1, Marie Coignet2*, Alak Manna, PhD3*, Mayank Sharma4*, Taimur Sher, MD1*, Vivek Roy, MD1, Asher A.
Chanan-Khan, MD1 and Sikander Ailawadhi1
1Division of Hematology-Oncology, Mayo Clinic, Jacksonville, FL
2Roswell Park Cancer Institute, Buffalo
3Mayo Clinic Florida, Jacksonville, FL
4Mayo Clinic Florida, Jacksonville Introduction: Treatment of multiple myeloma (MM) has evolved tremendously, resulting in significant improvement of overall survival in patients. Frontline therapy can be broadly categorized into: proteasome inhibitor (PI)-based, immunomodulatory (IMiD)-based or PI+IMiD-based. Despite their widespread benefit, a considerable proportion of patients achieve suboptimal response to treatment. It would be clinically valuable to understand disease biology prior to initiating therapy and be able to predict the likelihood of response. Prior studies have attempted such a process in which response to PI and/or IMiD-based treatments were retrospectively examined in correlation to genomic testing from pre-treatment patient samples. The MMRF CoMMpass study provides a unique opportunity for interrogation of genes predictive of response to PI and/or IMiD-based regimens. Here, we performed a comparative analysis of genes previously reported to be associated with response to treatment or survival in MM patients with data recorded from the CoMMpass trial. Methods: Previous studies with reported relationship of gene expression and response/survival outcome to PI and/ or IMiD-based regimens were identified (Table 1). Next, we interrogated the CoMMpass database and abstracted patients by Best Response to First Line Therapy. Therapy class (PI and/or IMiD-based) was simplified to match the comparator studies in Table 1. We analyzed the genes reported in studies from Table 1 against our PI-based, IMiD- based and PI+IMiD-based CoMMpass patient cohorts. Logistics regression modeling and Bonferroni correction methods were applied adjusting for patient age, gender and ISS stage and gene odds ratios were calculated. An alpha of <0.00029 was considered significant due to multiple comparison of genes between the groups. We also compared mRNA expression for the genes identified in these 3 different treatment cohorts that had an alpha of ≤0.05 between patients classified as Responders (≥PR) and Non-responders ( Disclosures: Sher: LAM Therapeutics, Inc: Research Funding. Ailawadhi: Pharmacyclics: Research Funding; Novartis: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. See more of: 904. Outcomes Research—Malignant Conditions: Poster I See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 3034 Identifying High-Risk Phenotypes Using Three-Way Feature Interactions in Multiple Myeloma: An Analysis of Publically Available MMRF CoMMpass Study Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster II Sunday, December 10, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Caleb K Stein, MS1*, Yan Asmann, PhD2 and P. Leif Bergsagel, MD3 1Division of Hematology and Oncology, Mayo Clinic, Scottsdale, AZ 2Mayo Clinic, Jacksonville, FL 3Division of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ The introduction of next generation sequencing has exponentially increased the number of potential disease targets from a limited panel of probes to markers across the entire genome. Previous studies of NGS data in multiple myeloma have identified key features associated with outcome in additive models, typically showing involvement of TP53, gain of 1q, MYC translocations, and others. We modified the multi-factor dimensionality reduction method (MDR) method, a computationally efficient method for detecting non-linear patterns of gene-gene interactions in genome-wide association studies (GWAS) first proposed by Richie et al, to identify three-way interactions significantly associated with outcome in MM. Specifically, we altered the ranking strategy of the survival adapted Cox-MDR method of Lee et al from balanced accuracy (average of sensitivity and specificity) to the absolute sum of deviance residuals. Due to the high level of censoring (94 events in 559 cases), balanced accuracy ranking favored interactions that identified few high risk cases and failed to validate well on external data. The publically available MMRF CoMMpass trial database (release version IA10) was utilized as primary training set for our MDR method. We included in our comparisons 164 RNA-SEQ genes with significant association with overall survival, the 20 most abundant non-synonymous, non-IG variants, and 24 estimated whole chromosome arm copy number gains or loss calls from the long insert WGS data of 559 baseline cases with outcome and clinical data (median follow-up 1.8 years). We searched for all three-way interactions across 210 total features resulting in the ranking of over 1.5 million unique three-way interactions, adjusted for age, ISS, creatinine level, and treatment by modified MDR. The top three-way RNA-expression interactions from our discovery phase were validated on 540 cases with GEP microarray and clinical data from UAMS Total Therapy 2 and 3 with median follow-up of 4.66 years (publically available data from GSE2658, GSE31161, and GSE24080). Of the top 100 three-way gene expression interactions discovered that we could validate on Affymetrix GEP data, 84 were significant in the UAMS TT2 and TT3 data set (log rank p-value < 0.05). HMGB3, a proposed inhibitor of myeloid differentiation, and DDX51, known to be differentially methylated in ALL, were the most commonly involved genes in the top significant interactions across both data sets. Pathway analysis of the 150 most frequently observed genes across the top 5,000 interactions in CoMMpass revealed an enrichment of DNA replication and cell cycle associated genes: RFC5, MCM2, MCM3, MCM4, MCM6, CCNA2, CDK2, etc. Within the top interactions that included whole arm copy number variants, gain of 1q and loss of 13q were the most frequently observed. We note that the top three-way interactions with gain of 1q paired exclusively with ALYREF, a transcriptional promoter, and one additional gene. Of the top interactions that included a SNV, mutations of DIS3 and DNAH5 were the most frequently observed. Many of the top interactions including DIS3mutations paired with HMGB3 or TRIB3, a negative regulator of NF-kB and sensitizer to apoptosis, and one additional gene. MDR models are capable of identifying complex interactions of features that are not strictly additive. We observed a variety of interaction models, and present below an example where presence of 2 or more features resulted in overall negative outcome while presence of 1 or fewer did not. This situation was observed for the interaction of high expression of HMGB3, DDX51, and PSMB2, a bortezomib sensitizer in MM cell lines, where high expression in 2 or more of these genes imparted a negative outcome in CoMMpass and TT2/TT3 data sets. In the TT2/TT3 data set, cases identified as high risk from this interaction were enriched for the PR subtype and GEP70 HR. This modified MDR method is an effective tool at mining large genomic data sets for all k-way interactions associated with outcome among binary or multi-level features, adjusted for clinical covariates. Our modifications to MDR method allow us to identify more relevant interactions and increase reliability in validation data. This method could potentially further knowledge of the complex relationships between key features in genomic landscape of MM by discovering novel interactions that identify high-risk phenotypes. Disclosures: Bergsagel: Phosplatin Therapeutics: Research Funding. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster II See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 267 A Seven Gene Signature to Distinguish Bortezomib- and Lenalidomide-Responsive Myeloma: Rnaseq from the Padimac Study Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis Saturday, December 9, 2017: 4:30 PM Bldg B, Lvl 2, B211-B212 (Georgia World Congress Center) Michael Chapman, PhD, MRCP, FRCPath1, Jonathan Sive2*, John Ambrose3*, Claire Roddie, PhD, MD3*, Nicholas Counsell4*, Paul Smith5*, Nivette Braganza5*, Toyin Adedayo5*, Rakesh Popat, MD, PhD6*, James D Cavenagh7, Matthew J. Streetly, BMBS, PhD, MRCP, FRCPath8, Roger G. Owen, MD9*, Stephen Schey, MBBS10, Mickey Koh, MD, PhD11*, Josephine Crowe12*, Michael F. Quinn, FRCPath, MB, MD, MRCP13*, Shirley D'Sa, MD, FRCP, FRCPath14*, Andres E. Virchis, FRCP, FRCPath15, Gordon Cook, PhD16*, Charles R Crawley17, Guy Pratt, MD, FRCP, FRCPath18, Mark Cook, MBChB, PhD19, Laura Clifton-Hadley, PhD, BSc5*, Javier Herrero, PhD20* and Kwee Yong, PhD21* 1University of Cambridge, Cambridge, United Kingdom 2St. Bartholomew's Hospital, London, GBR 3University College London, London, United Kingdom 43Cancer Research UK & UCL Cancer Trials Centre, London, United Kingdom 5Cancer Research UK & UCL Cancer Trials Centre, London, United Kingdom 6Department of Haematology, University College London Hospital, London, United Kingdom 7St. Bartholomew's Hospital, London, United Kingdom 8Clinical Haematology, Shepley Towers, London, United Kingdom 9Haematological Malignancy Diagnostic Service, St. James’s University Hospital, Leeds, United Kingdom 10Kings College Hospital, London, GBR 11Department of Haematology, St George's Hospital NHS Foundation Trust, London, United Kingdom 12Royal United Hospital, Bath, United Kingdom 13Belfast City Hospital, Belfast, GBR 14University College Hospital, London, GBR 15Haematology, University College London Hospitals NHS Foundation Trust, London, United Kingdom 16St James's University Hospital, Leeds, United Kingdom 17Haematology, Cambridge University Hospitals NHS Trust, Cambridge, United Kingdom 18Centre for Clinical Haematology, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom 19Centre for Clincal Haematology, University Hospitals Birmingham, Birmingham, United Kingdom 20Cancer Institute, Bill Lyons Informatics Centre, University College London, London, United Kingdom 21Department of Haematology, UCL Cancer Institute, United Kingdom Background The treatment of multiple myeloma has benefited from the adoption of several highly active novel therapies over the last 20 years. This has led to the challenge of how to rationally choose the best drug regimen for an individual patient. Two of the most important novel anti-myeloma drugs are the proteasome inhibitor (PI), bortezomib, and the immunomodulatory drug (IMiD), lenalidomide. However, whilst patients often respond much better to one or other of these agents, there are very few means for rationally choosing between them. The application of precision medicine to guide this choice would improve efficacy and reduce adverse drug effects. Microarray data can identify poor prognosis disease very effectively, but is agnostic to treatment and does not inform drug selection. RNA sequencing (RNAseq) offers several potential advantages over microarray profiling. However, it also poses specific challenges for deriving prediction signatures and RNAseq-based signatures have not so far been described in myeloma. Methods In the phase 2 PADIMAC trial, newly diagnosed transplant-eligible myeloma patients were treated with bortezomib, doxorubicin, and dexamethasone (PAD) therapy and stratified to receive autograft in partial remission or no further treatment ("watch and wait") in very good partial remission (VGPR) or better. We extracted somatic RNA from CD138- selected cells prior to treatment and performed massively parallel RNAseq. We employed a novel combination of published techniques to transform the data and to perform training, validation, and testing. Results Nine of 41 patients with RNAseq data achieved ≥VGPR sustained for over 12 months with no further treatment. We used these RNAseq data as a training/validation set to develop a signature for predicting good responses to bortezomib-based therapy in the absence of an IMiD. We developed several signatures, cross-validated within the PADIMAC dataset, and tested performance in identifying good responders to bortezomib-based therapy from an independent test set of 109 patients from the CoMMpass study. The signatures performed extremely well in testing (p=2.3x10-14, p=0.001, p<2.2x10-16, and p<2.2x10-16 for sensitivity, specificity, F-measure, and accuracy, respectively). Based on cross-validation performance, we identified a single seven-gene signature, trained it on the PADIMAC data, and applied it to the 109 bortezomib-treated patients within CoMMpass. Predicted good responders had superior survival to the remaining patients (p=0.016; Figure 1A). To investigate whether the signature identified generic good risk disease or whether it was treatment-specific, we tested it on 31 patients treated with lenalidomide-based therapies (without PI) within the CoMMpass study. To our surprise, when applied to lenalidomide-treated patients, the signature performed significantly worse than expected by chance, as did several other signatures trained on the PADIMAC data (p=3.2x10-9, p=3.6x10-6, p=3.8x10-12, and p=1.7x10-13 for sensitivity, specificity, F-measure, and accuracy, respectively). This implied that expectation of a good response to bortezomib was associated with a poor response when the patient received lenalidomide and vice-versa. We could not assess survival in lenalidomide-treated patients because of small numbers, so we instead trained our seven-gene signature on lenalidomide-treated patients and tested it on bortezomib-treated patients. This confirmed the reciprocal relationship; patients expected to respond well to lenalidomide, but receiving bortezomib, had an inferior survival to other bortezomib-treated patients (p=0.008; figure 1B). This reciprocity across independent datasets implies that our signature could select between bortezomib- based or lenalidomide-based therapies for individual patients. Conclusions We have identified a seven-gene RNAseq signature that can predict if newly diagnosed myeloma patients would benefit from initial bortezomib-based or lenalidomide-based therapy, both in terms of response and survival. This is, to our knowledge, the first example of such a signature in MM and could be employed for precision medicine in the future. Disclosures: Chapman: Takeda: Other: Travel sponsorship; Janssen: Honoraria; Celgene: Other: Travel sponsorship; Celgene: Honoraria. Popat: Janssen: Honoraria, Other: Travel support for meetings; Celgene:Honoraria, Other: Travel support for meetings; Amgen: Honoraria; Takeda: Honoraria, Other: Travel support for meetings. Cavenagh: Novartis: Consultancy; Janssen: Honoraria; Celgene: Consultancy; Takeda: Consultancy. Owen: J anssen: Consultancy, Other: Travel support; Takeda: Honoraria, Other: Travel Support; Celgene:Consultancy, Honoraria, Research Funding. D'Sa: Janssen Cilag: Consultancy, Honoraria, Other: Education grant, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Cook: Amgen: Honoraria, Other: Travel support; Janssen: Honoraria, Other: Travel support, Research Funding; Janssen: Honoraria, Other: Travel support, Research Funding; Celgene: Honoraria, Other: Travel support, Research Funding; Celgene: Honoraria, Other: Travel support, Research Funding; Amgen: Honoraria, Other: Travel support; Takeda: Honoraria; Takeda:Honoraria; Myeloma UK: Membership on an entity's Board of Directors or advisory committees; Myeloma UK:Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria; Jazz Pharmaceuticals: Honoraria. Yong: Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 395 Utility of Clinical-Grade Sequencing of Relapsed Multiple Myeloma Patients; Interim Analysis of the Multiple Myeloma Research Foundation (MMRF) Molecular Profiling Protocol Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Genomics of The Pathogenesis and Progression of Multiple Myeloma Sunday, December 10, 2017: 10:30 AM Bldg B, Lvl 3, B304-B305 (Georgia World Congress Center) Daniel Auclair, PhD1*, Sikander Ailawadhi2, Jesus G. Berdeja, MD3, Xuhong Cao4*, Craig E. Cole, MD5, Craig C Hofmeister, MD6, Sundar Jagannath, MD7, Andrzej J. Jakubowiak, MD8, Amrita Krishnan, MD9, Shaji K. Kumar, MD10, Moshe Levy, MD11, Sagar Lonial, MD12, Gregory Orloff, MD13*, Dan Robinson, PhD4*, David Siegel, MD, PhD14, Suzanne Trudel, MD, FRCP(C)15, Saad Z Usmani, MD16, Ravi Vij, MD MBA17, Jeffrey Lee Wolf, MD18, Jennifer Yesil, MS19*, Jeffrey A Zonder, MD 20, Arul Chinnaiyan, MD21* and P. Leif Bergsagel, MD22 1Multiple Myeloma Research Foundation, Norwalk, CT 2Division of Hematology-Oncology, Mayo Clinic, Jacksonville, FL 3Hematology, Sarah Cannon Research Institute, Nashville, TN 4U. of Michigan, Michigan Center for Translational Pathology, Ann Arbor, MI 5Division of Hematology/Oncology, University of Michigan School of Medicine, Ann Arbor, MI 6Comprehensive Cancer Center, The Ohio State University, Columbus, OH 7Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 8Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, Chicago, IL 9City of Hope Medical Center, Duarte, CA 10Division of Hematology, Mayo Clinic, Rochester, MN 11Baylor University Medical Center at Dallas, Dallas, TX 12Department of Hematology and Medical Oncology, Emory University - Winship Cancer Institute, Atlanta, GA 13Virginia Cancer Specialists, Fairfax, VA 14John Theurer Cancer Center at Hackensack University Medical Center, Hackensack, NJ 15University of Toronto, Princess Margaret Cancer Centre, Toronto, ON, Canada 16Levine Cancer Institute/Carolinas HealthCare System, Charlotte, NC 17Washington University School of Medicine, St Louis 18University of Cal. SF, San Francisco, CA 19Multiple Mu, Norwalk, CT 20Karmanos Cancer Institute, Detroit, MI 21University of Michigan, Ann Arbor, MI 22Division of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ INTRODUCTION: Multiple myeloma (MM) is the second most prevalent blood cancer, representing approximately 1% of all cancers. Although overall survival has improved in recent years due to new approved agents, the vast majority of MM patients ultimately stop responding to treatment. Whereas current therapeutic approaches have focused mostly on the plasma cell biology of the disease, seminal genomic sequencing research efforts, such as the MMRF CoMMpass study, have highlighted that a large number of MM cases harbor potentially actionable oncogenic molecular alterations. Published reports on small numbers of cases suggest that Precision Medicine (PM) interventions clinically targeting such actionable alterations might be of benefit to MM patients who are running out of options. In order to study on a larger scale the potential and clinical utility of PM approaches in myeloma, the MMRF Molecular Profiling Protocol (NCT02884102) was opened in 2016 across the entire Multiple Myeloma Research Consortium (MMRC) with the goal of enrolling and following 500 relapsed patients that would be molecularly profiled using clinical-grade sequencing performed on the Michigan Oncology Sequencing (MI-ONCOSEQ) platform. METHODS: Bone marrow aspirates (BMAs) and matched normal peripheral blood (PB) are shipped overnight to the Michigan Center for Translational Pathology (MCTP) Clinical Sequencing Lab where CD138 enrichment is carried out. The MCTP sequencing lab is CLIA-CAP certified. DNA and RNA are isolated from MM cells and matched normal. Libraries are generated and subjected to the Oncoseq1500 gene exome capture. Deep targeted re- sequencing (>600x) is carried out on HiSeq2500 run in rapid mode. Data is computationally analyzed for mutation status. A molecular report highlighting actionable findings is produced, reviewed internally by a genomic Tumor Board and returned within 10 days. RESULTS: We are reporting on 228 consecutive cases analyzed with 84% of the sequenced samples (192) showing a very good tumor content. Importantly, 76% of cases were found to harbor at least one potentially actionable alteration. Of those cases, 53% had alterations in the MAPK pathway, 14% in the CCND1 and cyclin-dependent kinase (CDK) pathways, 6% had activating FGFR3 mutations followed by a group of events at 3% or less. In this cohort (n>2 priors on average), 16% of cases presented with TP53 mutations of which 1/4 could also be detected in blood. A search for other genes where a significant percentage of mutations were also detected in PB identified a small number of those including, among others, SF3B1, TET2, ASLX1, ASLX2 and DNMT3A with such mutations (typically subclonal) often co- occurring in the same specimen. In all, 10% of all cases presented with this mutational signature in both BMAs and PB of genes generally associated with MDS, AML and other myeloid disorders. With regards to actionability, in 10% of cases the treating clinician acted upon the information with the indicated targeted agent. Examples of the responses obtained will be presented. Analysis of progression-free survival and overall benefit for this cohort is ongoing. CONCLUSION: Actionable alterations were identified in over three quarter of cases analyzed. Deep sequencing of both BMAs and normal blood could also identify events that would have been missed had sequencing been only performed on the marrow. Although clinical applicability has been limited so far by the lack of availability of targeted agents for myeloma patients, the results suggest that Precision Medicine approaches in MM are possible and should be further studied clinically. To that end, we are launching MyDRUG, a master protocol aimed at developing new myeloma regimens based on individual patient’s genomics. Disclosures: Ailawadhi: Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Novartis:Consultancy, Honoraria; Pharmacyclics: Research Funding. Berdeja: Constellation: Research Funding; Amgen:Research Funding; BMS: Research Funding; Bluebird: Research Funding; Abbvie: Research Funding; Celgene:Research Funding; Vivolux: Research Funding; Teva: Research Funding; Takeda: Research Funding; Novartis:Research Funding; Janssen: Research Funding; Curis: Research Funding. Hofmeister: Takeda: Research Funding; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Karyopharm: Research Funding; Thrassos:Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies:Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding; Celgene: Research Funding. Jagannath: Merck: Consultancy; Novartis: Consultancy; MMRF: Speakers Bureau; Celgene: Consultancy; Medicom: Speakers Bureau; Bristol-Meyers Squibb: Consultancy. Jakubowiak: University of Chicago: Employment; Amgen Inc., BMS, Celgene, Janssen, Karypharm, Millennium-Takeda, Sanofi, SkylineDX:Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Krishnan: Sutro: Consultancy; Onyx: Speakers Bureau; Takeda: Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Celgene: Consultancy, Equity Ownership, Speakers Bureau. Kumar: Celgene, Millennium, BMS, Onyx, Janssen, Noxxon, AbbVie, Amgen, Merck, Oncopeptides, Skyline Diagnostics, Takeda: Consultancy; Skyline: Honoraria; Celgene, Millennium/Takeda, Onyx, AbbVie, Janssen, Sanofi, Novartis, Amgen, Genentech, Merck, Oncopeptides, Roche, Skyline Diagnostics: Research Funding. Levy: Actinium Pharmaceuticals: Equity Ownership; Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Research Funding; Takeda: Consultancy, Speakers Bureau. Siegel: Merck: Consultancy; Celgene, Takeda, Amgen Inc, Novartis and BMS: Consultancy, Speakers Bureau. Trudel: Takeda: Honoraria; Celgene:Consultancy, Honoraria; GlaxoSmithKline: Research Funding; Amgen: Consultancy, Honoraria; Janssen: Research Funding; Astellas: Research Funding. Usmani: Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Speakers Bureau; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Array BioPharma: Honoraria, Research Funding. Vij: Takeda, Onyx: Research Funding; Celgene, Onyx, Takeda, Novartis, BMS, Sanofi, Janssen, Merck: Consultancy. Zonder: Pharmacyclics: Other: Data Safety Monitoring Committee; Takeda: Consultancy; Prothena: Consultancy; BMS: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy. Chinnaiyan: Tempus: Consultancy. Bergsagel: Phosplatin Therapeutics: Research Funding. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Genomics of The Pathogenesis and Progression of Multiple Myeloma See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> 1771 Biological and Prognostic Impact of Apobec-Induced Mutations in the Spectrum of Plasma Cell Dyscrasias Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster I Saturday, December 9, 2017, 5:30 PM-7:30 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Francesco Maura1,2*, Mia Petljak1*, Marta Lionetti3,4*, Ingrid Cifola5*, Winnie Liang, PhD6*, Eva Pinatel5*, Ludmil Alexadrov7*, Anthony Fullam1*, Inigo Martincorena1*, Kevin J Dawson1*, Nicos Angelopoulos, PhD1*, Raphael Szalat, MD8*, Paolo Corradini, MD9,10, Kenneth C. Anderson, MD11, Stephane Minvielle12*, Mehmet K. Samur, PhD11*, Antonino Neri3,4*, Hervé Avet-Loiseau, MD, PhD13*, Jonathan J. Keats, PhD6, Peter J Campbell, MD, PhD1*, Nikhil Munshi, MD8 and Niccolò Bolli1,4,10* 1Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom 2Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy, Italy 3Hematology, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy 4Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy 5Institute for Biomedical Technologies, National Research Council, Milan, Italy 6Translational Genomics Research Institute, Phoenix, AZ 7Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos 8Dana-Farber Cancer Institute, Boston, MA 9University of Milan, Milan, Italy 10Department of Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy 11Dana–Farber Cancer Institute, Harvard Medical School, Boston 12Université de Nantes, Nantes, France 13Genomics of Myeloma Laboratory, L’Institut Universitaire du Cancer Oncopole, Toulouse, France In multiple myeloma (MM), whole exome sequencing (WES) studies have revealed four mutational signatures: two associated with aberrant activities of APOBEC cytidine deaminases (Signatures #2 and #13) and two clock-like signatures associated with “cancer age” (Signatures #1 and #5). Mutational signatures have not been investigated systematically in larger series, nor in other primary plasma cell dyscrasias such as monoclonal gammopathy of unknown significance (MGUS) or primary plasma cell leukemia (pPCL). Finally, while APOBEC activity has been correlated to increased mutational burden and poor-prognosis MAF/MAFB translocations in MM at diagnosis, this has never been confirmed in multivariate analysis in an independent series. To answer these questions, we mined 1151 MM samples from public WES datasets, including samples from the IA9 public release of the CoMMpass trial. The CoMMpass data were generated as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiatives. We also analyzed 6 MGUS/Smoldering MM as well as 5 previously published pPCLs. Extraction of mutational signatures was performed using the NNMF algorithm as previously described (Alexandrov et al. Nature 2013). NNMF in the whole cohort extracted the known 4 signatures pertaining to distinct mutational processes: the two clock-like processes (signatures #1 and #5) and aberrant APOBEC deaminase activity (signatures #2 and #13). While the clock-like processes were more prominent in the cohort as a whole (median 70%, range 0-100%), the APOBEC showed a heterogeneous contribution, more visible in samples with the highest mutation burden. In fact, the absolute and relative contribution of APOBEC activity to the mutational repertoire correlated with the overall number of mutations (r=0.71, p= < 0.0001). As previously described, APOBEC contribution was significantly enriched among MM patients with t(14;16) and with t(14;20) (p<0.001), but the association between relative APOBEC contribution and mutational load remained significant across all cytogenetic subgroups with the exception of t(11;14). In the MGUS/ SMM series, APOBEC contribution was generally low. Conversely, APOBEC activity was preponderant in three out of five pPCL samples, all of them characterized by the t(14;16)(IGH/MAF); in the remaining two pPCL the absolute number of APOBEC mutations was similar to MM. Overall, the APOBEC contribution was characterized by a progressive increment from MGUS/SMM to MM and pPCL. We next went on to investigate the prognostic impact of APOBEC signatures at diagnosis. Patients with APOBEC contribution in the 4th quartile had shorter PFS (2-y PFS 47% vs 66%, p<0.0001) and OS (2-y OS 70% vs 85%, p=0.0033) than patients in quartiles 1-3 (Figure 1a-b). This was independent from the association of APOBEC activity with MAF translocations and higher mutational burden, as shown by multivariate analysis with Cox regression (Figure 1c-d). ISS stage III was the only other variable that retained its independent prognostic value for both PFS and OS. We therefore combined both variables and found that co-occurrence of ISS III and APOBEC 4thquartile identifies a fraction of high-risk patients with 2-y OS of 53.8% (95% CI 36.6%-79%), while their simultaneous absence identifies long term survivors with 2-y OS of 93.3% (95% CI 89.6-97.2%). In this study, we provided a global overview on the contribution of mutational processes in the largest whole exome series of plasma cell dyscrasias investigated to date by NNMF. We propose that cases with high APOBEC activity may represent a novel prognostic subgroup that is transversal to conventional cytogenetic subgroups, advocating for closer integration of next-generation sequencing studies and clinical annotation to confirm this finding in independent series. Disclosures: Corradini: Amgen: Honoraria; Janssen: Honoraria; Novartis: Honoraria; Celgene: Honoraria; Takeda: Hon oraria; Roche: Honoraria; Gilead: Honoraria; Sanofi: Honoraria. Anderson: Oncopep: Other: scientific founder; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees; MedImmune: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Other: scientific founder; Millenium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees. Avet-Loiseau: Celgene, Janssen, Amgen, Bristol-Myers Squibb, Sanofi: Honoraria, Speakers Bureau; Celgene, Janssen:Research Funding; Janssen, Sanofi, Celgene, Amgen: Consultancy. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster I See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 393 MYC Translocations Identified By Sequencing Panel in Smoldering Multiple Myeloma Strongly Predict for Rapid Progression to Multiple Myeloma Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Genomics of The Pathogenesis and Progression of Multiple Myeloma Sunday, December 10, 2017: 10:00 AM Bldg B, Lvl 3, B304-B305 (Georgia World Congress Center) Niamh Keane, MB, MRCP1,2*, Caleb K Stein, MS3*, Daniel Angelov, MSc, MB3*, Shulan Tian4*, David Viswanatha, MD5, Shaji K. Kumar, MD5, Angela Dispenzieri, MD5, Veronica Gonzalez De La Calle, MD3*, Kristine Misund, PhD3,6*, Robert A Kyle, M.D5, Michael E O'Dwyer, MD2, Rafael Fonseca, MD3, A. Keith Stewart, MBChB, MBA7, Esteban Braggio, PhD8, Yan Asmann, PhD4, S. Vincent Rajkumar, MD5 and P. Leif Bergsagel, MD8 1Mayo Clinic, Scottsdale, AZ 2National University of Ireland Galway, Galway City, Ireland 3Division of Hematology and Oncology, Mayo Clinic, Scottsdale, AZ 4Mayo Clinic, Jacksonville, FL 5Division of Hematology, Mayo Clinic, Rochester, MN 6KG Jebsen Center for Myeloma Research, Trondheim, AZ, Norway 7Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 8Mayo Clinic Arizona, Scottsdale, AZ Introduction Smoldering Myeloma (SMM) is a heterogeneous asymptomatic stage between Monoclonal Gammopathy of Unknown Significance (MGUS) and Multiple Myeloma (MM). Risk of progression to MM is 10% in the first 5 years following diagnosis and greatly diminishes thereafter (Kyle et al NEJM 2007). Early intervention in SMM patients at high risk of progression extends survival (Mateos et al NEJM 2013). The IMWG 2014 Updated Criteria establish a subset of SMM patients who are at ultra-high risk for progression to MM within 2 years, and therefore merit treatment (Rajkumar et al Lancet Oncol 2014). Analyses of the genetic and molecular landscape of SMM to date report a near identical picture to MM, however most patients in these studies progress rapidly and are therefore not representative of the entire group. We performed a comprehensive analysis of SMM and MGUS patients to determine markers of high risk of progression that could identify patients to benefit from early treatment Methods MGUS not progressing after at least 10 years of follow up and SMM in which follow up data were available were extracted from the Plasma Cell Dyscrasia Biobank. A clinically applicable Custom Capture MM-specific sequencing platform was developed for detection of the most frequently mutated pathways in MM based on analysis of CoMMpass dataset. Coding exons of actionable genes, clinically relevant copy number abnormalities, and regions surrounding IgH (0.5Mb), IgK (0.1Mb), IgL (0.1Mb) and MYC loci (1.6Mb) to identify relevant structural variants (SVs) were included, with combined design 2.2Mb. 12 samples were pre-pooled before capture, and 2 captures were sequenced per lane of Illumina HiSeq4000. Paired-end 150bp reads were mapped to hg19 using BWA-MEM. Single nucleotide variants (SNVs) and small INDELs in capture regions were identified using the GenomeGPS analytic pipeline following Broad GATK variant discovery practices. Copy number variants (CNVs) were identified by patternCNV. SVs of translocations, inversions, large INDELs, and segmental duplications were called by the SnowShoes-SV algorithm developed in-house. False positive SVs, polymorphic SVs, and other artefacts were filtered out using in-house normal SV database. Results We identified and sequenced 128 patients including 32 MGUS patients not progressing after 10 years. Of 96 SMM patients included 36 had not progressed to SMM after minimum follow up of 5 years, while 37 and 23 progressed to MM in less than 2 years and between 2-5 years, respectively. The genetic subtype of each patient was determined and verified by clinical FISH. Proportions in each genetic subgroup in MM and SMM/MGUS were similar, indicating that these are primary genetic lesions occurring early in MM pathogenesis. Median SMM time to progression (TTP) was 46 months. As in other series, HRD with IGH translocation, and t(4;14) predicted shorter TTP. Analysis of CoMMpass dataset found frequent MYC SV (38%) in untreated MM with higher frequency in HRD versus NHRD MM: 53% versus 28% (Misund, ASH 2016). No MYC SV were detected in MGUS cohort, SMM non-progressors at >5 years or SMM progressing between 2-5 years. By contrast, MYC SV were detected in 49% SMM that progressed within 2 years, 55% in HRD and 41% NHRD. SMM with MYC SV had a significantly shorter median TTP compared to patients without MYC SV (11.5 months vs 61 month; p<0.0001). Multivariate analysis with high risk genetic groups and biomarkers for progression (BMPCs >/=60% and FLC ratio 100) confirm MYC SV as an independent variable for progression to MM (hazard ratio=7, 95% confidence interval 3.6-13.7, p=0.00001). RAS and NFKB pathway mutations were observed with similar frequencies in MM and SMM progressing within 5 years of diagnosis, but with lower frequency in those not progressing by 5 years follow up, and were not observed in the MGUS cohort. A trend toward shorter TTP was observed in patients with RAS pathway mutations but did not reach statistical significance. Conclusion In conclusion, we describe MYC translocations as a genetic marker of and likely cause of progression to MM that are absent in MGUS and SMM with TTP >2 years. In contrast MM and SMM early progressors (TTP <2 years) share a similar genetic landscape. Identification of MYC translocations at diagnosis of SMM predicts short TTP to MM, defining a novel ultra-high risk category that merits validation in prospective clinical trials. Disclosures: Kumar: Skyline: Honoraria; Celgene, Millennium/Takeda, Onyx, AbbVie, Janssen, Sanofi, Novartis, Amgen, Genentech, Merck, Oncopeptides, Roche, Skyline Diagnostics: Research Funding; Celgene, Millennium, BMS, Onyx, Janssen, Noxxon, AbbVie, Amgen, Merck, Oncopeptides, Skyline Diagnostics, Takeda:Consultancy. Dispenzieri: Celgene, Millenium, Pfizer, Janssen: Research Funding. Fonseca: Novartis:Consultancy; Merck: Consultancy; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; AMGEN: Consultancy; Celgene Corporation: Consultancy, Research Funding; Sanofi:Consultancy; Mayo Clinic & Dr Fonseca: Patents & Royalties: Prognostication of myeloma via FISH, ~$2000/year; Jansen: Consultancy; Bristol-Myers Squibb: Consultancy; Bayer: Consultancy; Pharmacyclics: Consultancy; Takeda: Consultancy. Stewart: Bristol-Myers Squibb: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Roche: Consultancy; Amgen: Consultancy. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Genomics of The Pathogenesis and Progression of Multiple Myeloma See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> 3029 Multiple Myeloma Drivers of High Risk and Response to Stem Cell Transplantation Identified By Causal Machine Learning: Out-of- Cohort and Experimental Validation Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster II Sunday, December 10, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Leon Furchtgott, PhD1*, Arnold Bolomsky, PhD2*, Fred Gruber, PhD1*, Mehmet Kemal Samur, PhD3, Jonathan J. Keats, PhD4, Jennifer Yesil, MS5*, Kathrin Stangelberger, MSc2*, Michel Attal, MD, PhD6, Philippe Moreau7*, Hervé Avet-Loiseau, MD, PhD8*, Karl Runge, PhD1*, Diane Wuest, PhD1*, Kelly Rich1*, Iya Khalil, PhD1*, Boris Hayete, PhD1*, Heinz Ludwig, MD9, Nikhil Munshi, MD3 and Daniel Auclair, PhD5* 1GNS Healthcare, Cambridge, MA 2Department of Medicine I, Wilhelminenspital, Wilhelminen Cancer Research Institute, Vienna, Austria 3Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 4Translational Genomics Research Institute, Phoenix, AZ 5Multiple Myeloma Research Foundation, Norwalk, CT 6Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France 7Department of Hematology, Nantes University Hospital, Nantes, France 8IUC-Oncopole, Unite de Genomique du Myelome, Toulouse, France 9Wilhelminenspital, Vienna, Austria Introduction:Multiple Myeloma (MM) is a plasma cell malignancy characterized by high polyclonality, heterogeneity of treatment response, and long-term adaptation to treatment. The apparent complexity of underlying biological networks of MM necessitates a systems approach to its study. Advances in instrumentation, machine learning, and computing make the systems approach possible and enable novel insights into MM disease drivers, patient subpopulations, and potential therapies. We have developed a high-dimensional network model of MM based on data from 645 patients (pts) in the Interim Analysis 9 (IA9) MMRF CoMMpass trial dataset (NCT0145429). This model, developed using the REFS™ causal inference engine, consists of an ensemble of 256 Bayesian networks, each representing the inferred causal relationships between 30,084 clinical and genomic variables. The results from this model include the identification of a pathway driving high-risk disease (progression or death within 18 months) and the characterization of a subpopulation of patients with increased progression-free survival (PFS) after stem cell transplantation (SCT). We have now tested the overall IA9 model and its key results using the IFM/DFCI 2009 dataset (NCT01191060), as well as performed experimental pre-clinical validation of core molecular drivers in the high-risk status pathway. Methods: RNA-Seq, demographic and clinical data modalities in the IFM/DFCI dataset were processed and normalized using the same pipeline as the IA9 dataset. Among the 30,084 variables in the IA9 model, 24,559 were present in the IFM/DFCI dataset, and a total of 323 pts had complete clinical and molecular data. Results: The IA9 model contains 121,708 edges (causally enriched statistical associations) that appear in at least 25% of the inferred networks in the ensemble. 93,636 of these edges (77%) were tested in the IFM/DFCI dataset and 81,155 edges (87%) had significant q-value (< 0.05) and effect sizes of the same sign between the two datasets. The effect sizes between datasets were highly correlated: among all edges, Pearson’s r = 0.89; among validated edges, r = 0.93. With regards to predictors of high risk disease, the IA9 model contains a subnetwork of 17 RNA-Seq variables driving high-risk status, notably involving cell-cycle regulators CDK1, MELK and PLK4, known targets of inhibitor drugs (Figure 1). In order to verify the robustness of these potential drivers, we tested the association between their gene expression and high-risk status in the IFM/DFCI dataset, including SCT as a covariate. We performed logistic regression for each of the variables, and 13 genes, including MELK, FOXM1, E2F1 and PLK4, maintained statistical significance (q < 0.05). The functional relevance of these potential drivers of high risk was confirmed pre-clinically in myeloma cell lines using targeted small molecule inhibitors of MELK, CDK1 and PLK4. Simulation of the IA9 model also revealed a patient subpopulation with increased PFS in response to SCT and decreased PFS in its absence. The top driver of this subpopulation was expression of CHEK1; other drivers included RUNX2 and MYBL2. We examined these genes in the IFM/DFCI dataset by defining cohorts of pts with high or low gene expression (above or below median). For the full cohort, the PFS Cox Hazard Ratio (HR) was 0.66, with 95% Confidence Interval (CI) 0.50-0.87. For CHEK1-low pts, a large PFS benefit was observed in response to SCT (HR=0.51; 95% CI 0.33-0.76, p=0.001), whereas no benefit was observed for CHEK1-high pts (HR=0.82; 95% CI 0.57-1.20, p=0.31). Cox survival modeling of PFS revealed a statistically significant interaction between CHEK1 expression and SCT (p = 0.026). RUNX2 and MYBL2 subpopulations showed only modest differences in HR (Figure 2). Conclusions: Together, these results confirm key predictive results of the IA9 computational model in an out-of- sample dataset. This model should now help researchers to focus on the most promising targets and pathways, as well as to address unanswered questions and unmet needs in myeloma, especially high risk disease. Disclosures: Furchtgott: GNS Healthcare: Employment. Gruber: GNS Healthcare: Employment. Attal: Sanofi:Consultancy; JANSSEN: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Celgene:Consultancy, Research Funding. Moreau: Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene, Janssen, Takeda, Novartis, Amgen, Roche: Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Honoraria; Onyx Pharmaceutical:Consultancy, Honoraria. Avet-Loiseau: Celgene, Janssen, Amgen, Bristol-Myers Squibb, Sanofi: Honoraria, Speakers Bureau; Janssen, Sanofi, Celgene, Amgen: Consultancy; Celgene, Janssen: Research Funding. Runge:GNS Healthcare: Employment. Wuest: GNS Healthcare: Employment. Rich: GNS Healthcare: Employment. Khalil:GNS Healthcare: Employment. Hayete: GNS Healthcare: Employment. Ludwig: AMGEN: Consultancy, Research Funding, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Celgene: Speakers Bureau; Bristol-Meyers: Speakers Bureau; Janssen-Cilag: Consultancy, Speakers Bureau. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster II See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 268 Adar-Mediated Aberrant a-to-I RNA Editing Is Driven By 1q Amplification and Contributes to Proteasome Inhibitor Resistance in Multiple Myeloma Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis Saturday, December 9, 2017: 4:45 PM Bldg B, Lvl 2, B211-B212 (Georgia World Congress Center) Alessandro Lagana, PhD1,2*, Violetta Leshchenko, PhD3*, David Melnekoff, BS, MSc1,2,3*, Itai Beno, PhD1,2*, Deepak Perumal, PhD3*, Jonathan J. Keats, PhD4, Mary DeRome, MS5*, Jennifer Yesil, MS6*, Daniel Auclair, PhD6*, Deepu Madduri, MD3*, Ajai Chari, MD3, Hearn Jay Cho, MD, PhD7, Bart Barlogie, MD, PhD3, Sundar Jagannath, MD3, Joel Dudley, PhD1,2,8* and Samir Parekh, MD3,9 1Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY 2Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 3Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 4Translational Genomics Research Institute, Phoenix, AZ 5Multiple Myeloma Research Foundation, Norwalk, CT 6Multiple Myeloma Research Foundation (MMRF), Middletown, CT 7Department of Hematology and Medical Oncology, Tisch Cancer Institute, Tisch Cancer Institute, New York, NY 8Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 9Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY Multiple Myeloma (MM) is an incurable malignancy of plasma cells characterized by wide and remarkable genetic heterogeneity. The amplification of 1q identifies a class of patients characterized by a more aggressive disease course and poor prognosis. While previous research has identified several oncogenes with prognostic relevance located in 1q, the biological mechanisms underlying the poor clinical outcome associated with this alteration is still unclear. ADAR (Adenosine Deaminase Acting on RNA) is an enzyme responsible for A-to-I editing, a post-transcriptional process which contributes to transcriptome and proteome diversity by conversion of specific adenosines into inosines in both coding and non-coding transcripts. Such changes may affect RNA stability and regulation and introduce mutations in the corresponding proteins. Recent research has reported dysregulation of RNA editing in cancer, revealing its oncogenicity and clinical relevance. Since ADAR is located in 1q21, the critical amplified region in MM, we sought to investigate the implications of ADAR activity in MM. We have previously shown increased ADAR expression and A-to-I RNA editing in a small group of newly diagnosed patients with 1q21 amplification enrolled in the MMRF CoMMpass study (Lagana et al, ASH 2016), and that ADAR expression could differentiate prognosis. To further investigate and characterize the role of ADAR and RNA Editing in MM, we have analyzed 590 RNAseq samples from newly diagnosed patients in CoMMpass. ADAR copy number gain was observed in 111 samples (19%). ADAR expression was significantly higher in patients with 1q21 gain (p < 0.0001), supporting the role of copy number gain as a driver of ADAR expression. Moreover, patients with high expression of ADAR and no CN alterations, showed significant activation of Interferon pathways (Fig. 1A). This can be explained by the fact that one isoform of ADAR (p150) is regulated by an interferon-inducible promoter. Multivariate regression of ADAR expression against ADAR CN and STAT1 expression, as a measure of interferon signaling activation, showed that both ADAR CN and STAT1 expression were significant independent predictors of ADAR expression. To investigate the functional impact of ADAR activity, we implemented a computational pipeline to calculate sample- wise Alu Editing Index (AEI). A-to-I RNA editing targets predominantly Alu elements, repetitive sequences abundantly interspersed throughout the human genome, mostly within introns and untranslated regions (UTRs). AEI has been developed and validated as a global measure of RNA editing activity at the sample level (Bazak et al, Genome Res 2014). Our analysis in MM revealed that AEI could discriminate between good and poor prognosis in terms of both PFS and OS (p < 0.0001). Moreover, AEI could critically stratify patients with 1q21 gain in terms of PFS (p < 0.01) (Fig. 1B). This finding suggests that RNA editing can be an adverse prognostic factor independently of 1q21 gain. Patients with high AEI were characterized by down-regulation of immunoglobulin transcripts and of the Unfolded Protein Response (UPR) pathway. In particular, patients with high AEI exhibited down-regulation of key activators of UPR such as NFYC, ATF4 and XBP1, and of transcriptional targets of XBP1s, the active spliced form of XBP1. Decreased UPR and low XBP1s expression in pre-plasmablasts have been shown to induce resistance to proteasome inhibitors (PI) (e.g. Bortezomib) through de-differentiation of plasma cells (Leung-Hagesteijn et al, Cancer Cell 2013). Among patients who received Bortezomib-based first line therapy, we found that AEI was significantly higher (p<0.01) in patients whose best response was stable disease (SD) or progressive disease (PD) (23; 4.5%) compared to patients who had complete (CR) or stringent complete response (sCR) (118; 23%) (Fig. 1C). siRNA meditated knockdown of ADAR increased sensitivity to Bortezomib in MM cell lines with 1q amplification and high ADAR expression (OPM2), consistently with our in-silico findings. In conclusion, we propose a model of MM where ADAR-mediated RNA editing is driven by gain of 1q21 and interferon signaling and contributes to disease aggressiveness and resistance to PI-based therapy through down- regulation of UPR (Fig. 1D). Disclosures: Madduri: Foundation Medicine, Inc.: Consultancy. Chari: Millennium Pharmaceuticals, Inc.:Consultancy, Research Funding; Amgen: Honoraria, Research Funding; Novartis: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Janssen:Consultancy, Research Funding. Cho: Agenus, Inc.: Research Funding; Genentech: Other: advisory board, Research Funding; Ludwig Institute for Cancer Research: Research Funding; Bristol Myers-Squibb: Other: advisory board, Research Funding; Multiple Myeloma Research Foundation: Research Funding. Barlogie:Millenium Pharmaceuticals: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Jagannath: Merck: Consultancy; Medicom: Speakers Bureau; Celgene: Consultancy; Bristol-Meyers Squibb: Consultancy; Novartis: Consultancy; MMRF: Speakers Bureau. Dudley: Ecoeos, Inc.: Equity Ownership; Ayasdi, Inc.: Equity Ownership; Ontomics, Inc.: Equity Ownership; Personalis: Patents & Royalties; AstraZeneca:Consultancy; NuMedii, Inc.: Equity Ownership, Patents & Royalties; Janssen Pharmaceuticals, Inc.: Consultancy; GlaxoSmithKline: Consultancy. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis See more of: Oral and Poster Abstracts 270 IKZF1/3 and CRL4CRBN E3 Ubiquitin Ligase Mutations Associate with IMiD Resistance in Relapsed Multiple Myeloma Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis Saturday, December 9, 2017: 5:15 PM Bldg B, Lvl 2, B211-B212 (Georgia World Congress Center) Santiago Barrio Garcia, PhD1*, Matteo Da Via', MD1*, Andoni Garitano-Trojaola, PhD1*, Yanira Ruiz-Heredia2*, Max Bittrich, MD1*, ChangXin Shi, PhD3*, Yuan Zhu4*, Nicola Lehners5*, Elias K Mai, MD5*, Marc S Raab, MD, PhD6*, Pieter Sonneveld, MD, PhD7, Joaquín Martínez2*, Andreas Rosenwald8*, Esteban Braggio, PhD9, Hermann Einsele10, A. Keith Stewart, MBChB, MBA11,12 and K. Martin Kortüm, MD1* 1Department of Internal Medicine II, University Hospital Wuerzburg, Wuerzburg, Germany 2Department of Hematology, Hospital Universitario 12 de Octubre, CNIO, Complutense University, Madrid, Spain 3Hematology and Oncology, Mayo Clinic Arizona, Scottsdale, AZ 4Division of Hematology and Oncology, Mayo Clinic Arizona, Scottsdale, AZ 5Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany 6Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany 7Erasmus Medical Center Rotterdam, Rotterdam, Netherlands 8Institute of Pathology, University of Wuerzburg and Comprehensive Cancer Center Mainfranken, Wuerzburg, Germany 9Mayo Clinic Arizona, Scottsdale, AZ 10Medizinische Klinik und Poliklinik II, Klinikum der Bayrischen Julius-Maximilians-Universität, Würzburg, Germany 11Division of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ 12Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ Immunomodulatory drugs (IMiDs) provide the backbone for various Multiple Myeloma (MM) treatment regimens. Cereblon (CRBN) is the known key modulator of the IMiD anti-tumor effects as it is the intermediate protein within the CRL4CRBN E3 ubiquitin ligase (CRL4) complex that targets the degradation of the two transcription factors, Ikaros (IKZF1) and Aiolos (IKZF3). We hypothesize that the formation of this complex (CRBN-DDB1-CUL4B-ROC1) is essential for the IMiD’s action and that mutations that occur in involved genes affect its assemblage, thus induce IMiD resistance. Targeted sequencing was performed using the M3P (v2.0 or v3.0) gene selection. Average sequencing depth was 700X. We have combined data from different cohorts analyzed with the M3P for a total of 501 patients, 337 newly diagnosed and 164 pretreated. We also included data from the CoMMpas trial (804 newly diagnosed MM analyzed by WES with 100X average depth). We analyzed a total of 1.305 MM cases and identified 53 mutations in 42 patients within CRBN, IKZF1, IKZF3 and CUL4B. Mutation frequency increased significantly after treatment (Z-score: 6.9, p<0.001), with 1.9% in untreated (22/1141) and 12.2% in (20 /164) pretreated cases. Of note, this analysis does not include either DDB1, one of the binding partners of CRBN in the complex, nor ROC1, in the pretreated cohort, as it was not part of the M3P gene selection. In untreated patients, however, mutation incidence was low with 4 out of 804 (0.5%) for DDB1 and no mutation was identified in ROC1 within patients included into the CoMMpass trial. CRBN was the most commonly mutated gene within the investigated CRL4 complex genes (21 mutations /15 MM patients), followed by CUL4B (15/13). Of note, in IKZF3 (11/11) and IKZF1 (6/6) we identified two new hotspots (IKZF3 p.G159R/A and IKZF1 p.A152T, two patient each). Furthermore, five of the patients harbored mutations in IKZF1/3 within 30 amino acids previously described to be essential for IMiD sensitivity in vitro (Krönke et al., Science, 2014). 91% (20/22) of the CRBN and CUL4B mutations found in treated patients were nonsense or were located within close proximity to the binding sites of the proteins (Figure), causing loss/weakening of lenalidomide (LEN)-CRBN, CRBN-DDB1, CUL4B-DDB1 or CUL4B-ROC1 interactions. This includes a patient having progressed on Thalidomide and Pomalidomide therapy with parallel evolution of 7 subclonal mutations in CRBN, 3 located in the LEN binding and 4 in the DDB1 binding site. Response data were available for 19 out of 20 treated patients with mutations in the CLR4 complex and, strikingly, 18 (95%) of them were clinically unresponsive to IMiD treatment. In summary, CLR4 mutations are predominantly selected by therapy and correlate with IMiD resistance. In our cohort of pretreated patients the incidence of CLR4 mutations was 12%, thus we provide evidence of a significant role of single point mutations as an IMiD drug resistance mechanism in MM. Figure: Mutations in CRL4CRBN E3 ubiquitin ligase clustered in the binding sides of complex units A) Mutations close to the binding side of Lenalidomide. B) Mutations in the interface CRBN-DDB1 and C) DDB1- CUL4B. D) Mutations in the binding side of ROC1. The structure of the CRL4 complex was produced by the alignments LEN-CRBN-DDB1 (PDB: 4CI2) and DDB1-CUL4B-ROC1 (PDB 4A0C). CRBN CTD (LEN binding) domain is shown in gray and HBD (DDB1 binding) in plun, LEN in orange, DDB1 in sky blue, CUL4B in pink and ROC1 in yellow. Disclosures: Mai: Mundipharma: Other: Travel grants; Janssen: Honoraria, Other: Travel grants; Onyx: Other: Travel grants; Celgene: Other: Travel grants; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Raab: Heidelberg Pharma GMBH: Research Funding. Sonneveld: Celgene Corporation, Amgen, Janssen, Karyopharm, PharmaMar, SkylineDx: Honoraria; Celgene Corporation, Amgen, Janssen, Karyopharm, SkylineDx, PharmaMar: Consultancy; Celgene, Amgen, Janssen, Karyopharm, Takeda: Consultancy, Honoraria, Research Funding. Stewart: Bristol-Myers Squibb: Consultancy; Roche: Consultancy; Celgene:Consultancy; Janssen: Consultancy; Amgen: Consultancy. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract *signifies non-member of ASH 1204 ADAR1-Mediated GLI1 Editing Promotes Malignant Self-Renewal in Multiple Myeloma Program: Oral and Poster Abstracts Session: 602. Disordered Gene Expression in Hematologic Malignancy, including Disordered Epigenetic Regulation: Poster I Saturday, December 9, 2017, 5:30 PM-7:30 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Leslie Crews, PhD1, Elisa Lazzari, PhD1*, Phoebe Mondala, BS1*, Nathan Delos Santos, BS2*, Amber Miller, PhD, BA3*, Gabriel Pineda, PhD2, Qingfei Jiang, PhD4, Heather Leu, BS2*, Shawn Ali, BA2*, Anusha Preethi Ganesan, MD, PhD5, Christina Wu, PhD2*, Caitlin L. Costello, MD2, Mark D. Minden, MD, PhD6, Raffaella Chiaramonte, PhD7*, A. Keith Stewart, MBChB, MBA8 and Catriona Jamieson, MD, PhD1 1Division of Regenerative Medicine, University of California, San Diego, La Jolla, CA 2University of California, San Diego, La Jolla, CA 3Mayo Clinic, Rochester, MN 4Moores Cancer Center, University of California San Diego, La Jolla, CA 5Rady Children's Hospital, San Diego, CA 6Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada 7University of Milan, Milan, Italy 8Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ Introduction Representing ten percent of hematologic malignancies, multiple myeloma (MM) is typified by clonal plasma cell proliferation in the bone marrow (BM) and may progress to therapy-resistant plasma cell leukemia (PCL). Despite many novel therapies, relapse rates remain high as a result of malignant regeneration (self-renewal) of MM cells in inflammatory microenvironments. In addition to recurrent DNA mutations and epigenetic deregulation, inflammatory cytokine-responsive adenosine deaminase associated with RNA (ADAR1) mediated adenosine to inosine (A-to-I) RNA editing has emerged as a key driver of cancer relapse and progression. In MM, copy number amplification of chromosome 1q21, which contains both ADAR1 and interleukin-6 receptor (IL-6R) gene loci, portends a poor prognosis. Thus, we hypothesized that ADAR1 copy number amplification combined with inflammatory cytokine activation of ADAR1 stimulate malignant regeneration of MM and therapeutic resistance. Methods and Results Analysis of MMRF CoMMpass RNA sequencing (RNA-seq) data revealed that high ADAR1 expression (n=162 patients) correlated with significantly reduced progression-free and overall survival compared with a low ADAR1 subset (n=159 patients). In contrast to lentiviral ADAR1 shRNA knockdown and overexpression of an editase defective ADAR1 mutant (ADAR1E912A), lentiviral wild-type ADAR1 overexpression enhanced editing of GLI1, a Hedgehog (Hh) pathway transcriptional activator and self-renewal agonist. Editing of GLI1 transcripts enhanced GLI transcriptional activity in luciferase reporter assays, and promoted lenalidomide resistance in vitro. Finally, lentiviral shRNA ADAR1 knockdown reduced regeneration of high-risk MM in humanized serial transplantation mouse models indicative of reduced malignant self-renewal capacity. These data demonstrate that ADAR1 promotes malignant self-renewal of MM and if selectively inhibited may prevent progression and relapse. Conclusions Deregulated RNA editing, driven by aberrant ADAR1 activation, represents a unique source of transcriptomic and proteomic diversity, resulting in self-renewal of MM cells in inflammatory microenvironments. In summary, both genetic (1q21 amplification) and microenvironmental factors (inflammatory cytokines, IMiDs) combine to drive GLI1- dependent malignant regeneration in MM. Thus, ADAR1 represents both a vital prognostic biomarker and therapeutic target in MM. Disclosures: Stewart: Amgen: Consultancy; Roche: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene:Consultancy; Janssen: Consultancy. See more of: 602. Disordered Gene Expression in Hematologic Malignancy, including Disordered Epigenetic Regulation: Poster I See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 4347 Role of MAX As a Tumor Suppressor Driver Gene in Multiple Myeloma Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster III Monday, December 11, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Santiago Barrio Garcia, PhD1*, Yanira Ruiz-Heredia2*, Matteo Da Via', MD1*, Miguel Gallardo, PhD2*, Andoni Garitano- Trojaola, PhD1*, Josip Zovko, MD1*, Marc S Raab, MD, PhD3*, Pieter Sonneveld, MD, PhD4, Esteban Braggio, PhD5, A. Keith Stewart, MBChB, MBA6,7, Hermann Einsele8, Joaquín Martínez2* and K. Martin Kortüm, MD1* 1Department of Internal Medicine II, University Hospital Wuerzburg, Wuerzburg, Germany 2Department of Hematology, Hospital Universitario 12 de Octubre, CNIO, Complutense University, Madrid, Spain 3Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany 4Erasmus Medical Center Rotterdam, Rotterdam, Netherlands 5Mayo Clinic Arizona, Scottsdale, AZ 6Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 7Division of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ 8Medizinische Klinik und Poliklinik II, Klinikum der Bayrischen Julius-Maximilians-Universität, Würzburg, Germany MYC associated factor X (MAX) is part of the basic-helix-loop-helix (bHLH) leucine zipper transcription factors. MAX forms homo- and hetero-dimers with other bHLH-like factors (e.g. MAD, MXL1 and MYC). The different dimers compete for CACGT DNA E-box consensus sequence (canonical binding site) and other binding sites (non-canonical binding sites) modulating transcriptional activation. The regulation of this process, among others, is based on the different affinity of the formed hetero-dimers to the canonical and non-canonical DNA binding sites and epigenetic modifications in these sequences. In the present study we aim to characterize and understand the role of MAX somatic mutations in the development of Multiple Myeloma (MM). Targeted amplicon sequencing was performed using the M3P (v2.0 or v3.0) gene selection with an average sequencing depth of 700X. We combined data from the different M3P cohorts for a total of 501 M3P patients, 337 newly diagnosed and 164 pre-treated. We also included data from the CoMMpas trial (804 newly diagnosed MM analyzed by WES with 100X average depth). In total 1.305 MM cases were analyzed. The MAX gene was mutated in 33 MM patients (2,5%). Incidence of mutations identified exceeded 5% variant read frequency (VR) and there was no difference in the incidence of the targeted M3P and the WES CoMMpass approach (M3P: 13 of 501 MM cases mutated [2.6%] vs. CoMMpass: 20 of 804 [2.5%]) or between newly diagnosed and pretreated cases (newly diagnosed M3P: 8 of 337 cases [2.4%] vs. pretreated M3P: 5 of 164; [3.0%]). In total, we identified 42 somatic mutations, with 7 patients showing more than one alteration. E.g. in a 73 y/o newly diagnosed MM patient we identified three missense mutations at known MAX hotspot location (p.E32V, p.R35L, p.R36K), all of them in subclonal range with variant reads of 26%, 21% and 10%, respectively. Each mutation exclusively occurred on a different sequencing read and no reads were shared between the mutations. This suggests parallel evolution of different subclones of the disease, and that mutant MAX was virtually present on all tumor cells (confirmed diploid karyotype by cytogenetics). In our cohort we did not observe incidence differences between newly diagnosed and pretreated patients, thus we believe MAX mutations are unlikely to be therapy-related. However, the existence of more than one variant in the gene in 21% of MAX mutated patients (7/33) may indicate a role in disease development. The loss of the protein activity of MAX may provide a survival advantage to the affected tumor clones, possibly due to the binding of other bHLH transcription factors (e.g. MYC) to the E-box, however underlying mechanisms need to be determined. Supporting this hypothesis, 16/42 MAX mutations (38%) affected the integrity of the protein (nonsense, stop gain/loss, tart loss and splice-site donor). The remaining were missense amino-acid changes located in the bHLH DNA binding domaine, including the hotspots R35 (8 mutations), R36 (6 mutations) and R60 (4 mutations), which, according to Wang et al. (Nucleic acid research, 2017) induce a loss of the DNA-binding capacity of the MAX protein. Thus, mutations in MAX in MM patients vastly induce the loss of function or the loss of the protein itself (at least 35/42 (85%)). This inactivation was related with disease development, confirming the role of MAX as a tumor suppressor driver gene in MM. Figure: A) Location of somatic missense mutations within MAX gene. All except E143G occurred in the DNA binding domaine. Mutations in a.a. G32, R35, R36 and R60 alter DNA binding properties (Wang d et al. Nucleic Acids Research, 2017). B) Three subclonal mutations in a single patient, located at the DNA binding site. The X-ray structure of MAX residues 22-107 bound to DNA (pdb: 5EYO) is shown. C) The mutations belong to different clones. Mutations were detected in independent sequencing reads, covered by the same amplicon, BAM file visualization in IGV sequencing reads aligned to hg19. Disclosures: Raab: Heidelberg Pharma GMBH: Research Funding. Sonneveld: Celgene Corporation, Amgen, Janssen, Karyopharm, SkylineDx, PharmaMar: Consultancy; Celgene Corporation, Amgen, Janssen, Karyopharm, PharmaMar, SkylineDx: Honoraria; Celgene, Amgen, Janssen, Karyopharm, Takeda: Consultancy, Honoraria, Research Funding. Stewart: Roche: Consultancy; Amgen: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy; Janssen: Consultancy. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster III See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> 60 Identification of Novel Oncogenes and Tumor Suppressor Genes in Newly Diagnosed Multiple Myeloma Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Transcriptional Regulatory Circuitries of Multiple Myeloma Saturday, December 9, 2017: 7:30 AM Bldg B, Lvl 4, B401-B402 (Georgia World Congress Center) Brian A Walker, PhD1, Christopher P. Wardell, PhD1*, Konstantimos Mavrommatis, PhD2*, Cody Cody Ashby, PhD1*, Michael Bauer, PhD1*, Mehmet Kemal Samur, PhD3, Fadi Towfic, PhD2*, Maria Ortiz4*, Erin Flynt2*, Matthew Trotter, PhD4*, Anjan Thakurta, PhD2*, Nikhil Munshi, MD5 and Gareth J. Morgan, MD, PhD1 1Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, AR 2Celgene Corporation, Summit, NJ 3Dana–Farber Cancer Institute, Harvard Medical School, Boston, MA 4Celgene Institute for Translational Research Europe (CITRE), Seville, Spain 5Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA Introduction. Identifying driver genes in multiple myeloma (MM) will have a number of key benefits as they can directly affect clinical behavior and define new targets for therapy, subgroups of disease and identify prognostic lesions. Here we focus on identifying key tumor suppressor (TSG) and oncogene (ONC) drivers of MM which could potentially act as novel targets for therapy. As the majority of mutations are present at <5% large datasets are required to identify these drivers. Rare targetable mutations are generally in ONCs and identifying their relevance requires knowledge of the mutational spectrum, sites of recurrent mutation and their downstream effects on protein function. We have taken a number of innovative strategies to identify such genes in the largest set of MM samples established to date. Methods. We established a set of 1277 newly diagnosed patient samples for which whole exome sequencing was available. Data were derived from the Myeloma XI trial, Dana-Faber Cancer Institute, The Myeloma Institute and the Multiple Myeloma Research Foundation CoMMpass study (IA1 - IA9). Mutations were called using Strelka and Mutect. Two strategies were used to identify drivers of MM pathogenesis 1. Determine significantly mutated genes in the entire cohort and in each subtype (MutSigCV) and 2. Identify ONCs and TSGs using curated cancer gene lists. ONCs were defined by identifying the proportion of recurrent missense mutations (ONC score >0.4) and TSGs by the proportion of nonsense and frameshift mutations (TSG score >0.2). If both thresholds were achieved the larger value was used as defining the gene type. Expressed somatic variants were analyzed using sample matched RNA-seq data. Results. A total of 26 statistically significantly mutated genes carrying single nucleotide variants (SNV) and indels were identified, by analyzing the dataset overall and within the major etiological subgroups. These results confirm significant mutations in 11 previously identified genes (KRAS, NRAS, DIS3, BRAF, TP53, MAX, TRAF3, CYLD, RB1, FAM46C, HIST1H1E) as well as 9 new significantly mutated genes including UBR5 (3.5%), PRKD2 (3.5%), SP140 (2.4%), TRAF2 (2.1%), PTPN11 (2.3%), RASA2 (1.3%), NFKBIA (1.3%), TG DS (1.3%), and CDKN1B (1.1%). Within cytogenetic sub-groups we found an additional 6 significantly mutated genes including IRF4 and HUWE1 in the t(11;14), ACTG1 in hyperdiploid cases, and FGFR3, IRF4, and MAFB in non- hyperdiploid cases. Using a curated list of 116 known driver genes plus the 26 significantly mutated genes, we determined their ONC and TSG score using the approach defined by Vogelstein. Using this strategy, we identified an additional 13 driver genes which included EGR1 (4.7%), CCND1 (2.9%) and MAF (1.6%), SF3B1 (1.8%, spliceosome factor), IDH1 (0.6%) and IDH2 (0.4%, increased DNA methylation). Surprisingly, DIS3 (9.9%) and TP53 (5.6%) were also classified as oncogenes due to their high proportion of recurrent missense mutations, 73% and 48% respectively. We determined if the key recurrent mutations in these oncogenes were expressed in the same cases. As expected, the recurrent variants in KRAS and NRAS were expressed, as were those in BRAF, IDH1, IDH2, EGR1, CCND1, PTPN11, IRF4, FGFR3, and SF3B1. Recurrent variants in TP53 (R248 (n=4), R175, G199, and Y234 (all n=3)) were also all expressed, as were those in DIS3 (R780 (n=11), M667, H691, R820 (all n=2)). Understanding the timing of when these variants arise is crucial to targeting them effectively and this can be determined by their cancer clonal fraction (CCF). Examining the CCF of these 40 driver genes, we saw an association with ONCs and a higher CCF and TSGs with a lower CCF, indicating activating mutations were either early events or were selected for, and inactivating mutations were later events. Although NRAS and KRAS mutations are most frequent they are not associated with early events, as indicated by intermediate CCF values (median 0.65). IDH2, EGR1, CCND1 and HIST1H1E had the most clonal mutations (>0.9). Conclusion. Oncogene activation through mutation is common in MM. We have identified new mutations in MM associated with oncogene activation including PTPN11, IDH1, IDH2, and SF3B1. Compared to tumor suppressor genes, mutations in oncogenes are more clonal and, therefore, associated with early events in the disease natural history. Fully characterizing driver genes in MM will enhance our ability to manage it effectively. Disclosures: Mavrommatis: Celgene Corporation: Employment. Towfic: Immuneering Corporation: Equity Ownership; Celgene Corporation: Employment, Equity Ownership. Flynt: Celgene Corporation: Employment. Trotter: Celgene Institute for Translational Research Europe: Employment; Celgene Corporation: Equity Ownership. Thakurta: Celgene Corporation: Employment, Equity Ownership. Morgan: Takeda: Consultancy, Honoraria; Bristol Myers: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Transcriptional Regulatory Circuitries of Multiple Myeloma See more of: Oral and Poster Abstracts Previous Abstract | Next Abstract >> *signifies non-member of ASH 592 Computational Modeling of Protein Networks Predicts Treatment Outcomes in Multiple Myeloma (MM) Program: Oral and Poster Abstracts Type: Oral Session: 652. Myeloma: Pathophysiology and Pre-Clinical Studies, excluding Therapy: Emerging New Targets and Mechanisms of Resistance Monday, December 11, 2017: 7:45 AM Bldg B, Lvl 2, B211-B212 (Georgia World Congress Center) Leylah M. Drusbosky, PhD1, Hemant S. Murthy, MD2, Helen L. Leather1, Himanshu Grover, MS3*, Yugandhara Narvekar, MS3*, Pallavi Kumari, MS3*, Sirisha Narayanabhatia, MS3*, Priyanka Bhowmick, MS3*, Taher Abbasi, MS, MBA4*, Shireen Vali, PhD4*, Qing Zhang, PhD5, Saad Z Usmani, MD6 and Christopher R. Cogle, MD7 1Department of Medicine/Division of Hematology Oncology, University of Florida, Gainesville, FL 2University of Florida Department of Medicine/Division of Hematology Oncology, Gainesville, FL 3Cellworks Research India Pvt. Ltd, Bangalore, India 4Cellworks Group Inc., San Jose, CA 5Department of Cancer Biostatistics, Levine Cancer Institute, Carolinas HealthCare System, Charlotte, NC 6Levine Cancer Institute/Carolinas HealthCare System, Charlotte, NC 7University of Florida, Gainesville, FL Background: MM is a highly refractory disease that is rarely cured. While introduction of novel therapies has improved survival and overall response rates (ORR), complete remission (CR) rates remain low (30%) and relapse is still frequent. Although nearly all newly diagnosed MM patients respond to standard of care (SOC) therapy, the depth of response varies. Triplet regimens, such as RVd (lenalidomide [R], bortezomib [V] and dexamethasone [d]), are accepted as SOC, but four- drug regimens are being investigated in an effort to improve CR rates. Doublet regimens are commonly initiated in older, transplant ineligible patients. Limitations of combination therapies include treatment-related adverse events, resulting in patients receiving suboptimal therapy. Unfortunately, no precise method exists to predict MM response to SOC, or to predict which patients will respond with two-drug combinations, making MM management challenging. Computational biological modeling (CBM) is a genomics-based tool to identify aberrant protein signaling networks within disease cells, and predict how each MMD case will respond to FDA-approved drugs and combinations. Predicting disease response would improve MM patient management and potentially reduce unnecessary treatment-related adverse events by identifying drug regimens with high potential for therapeutic activity against patient-specific, MM protein networks. Aim: To test the application of a genomics-informed CBM in MM patients treated with RVd. Methods: Forty newly-diagnosed patient profiles were identified from the publicly available MMRF CoMMpass dataset and divided into training (n=15) and test (n=25) cohorts. For each patient, all available genomic information was entered into computational biology program (Cellworks Group) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated protein networks. Digital drug simulations with standard of care (SOC) drugs were conducted by quantitatively measuring drug effect on a composite MM disease inhibition score (i.e. cell proliferation, viability, and apoptosis) to predict patient clinical outcomes. Additionally, the dynamics of antibody (heavy and light chains) transcription, translation, folding, and secretion were modeled to measure the extent that SOC therapy reduced paraprotein expression. Clinically, patients received SOC treatment and clinical responses were recorded. CR, VGPR, and PR were considered as responsive, while stable disease (SD) was considered non-response. Predictive values were calculated based on comparisons of the computer predictions and actual clinical outcomes. Results: The computational modeling correctly predicted 36 out of 40 clinical outcomes resulting in 94.12% PPV, 66.67% NPV (p=0.02), 94.12% sensitivity, and 66.67% specificity, and 90% accuracy. Additionally, 33 of 34 simulated paraprotein reductions were matched to actual clinical reductions in paraprotein levels, resulting in 97% accuracy. CBM was used to identify the minimum number of drugs each responder needed to achieve a simulated response, and selected the drug within the doublet or triplet regimen that had no therapeutic effect due to absence of the drug’s target within the disease profiles. Conclusions: Computational modeling and digital drug simulations using MM patient genomic data resulted in highly accurate matching of clinical response to SOC treatment. CBM also accurately predicted the extent by which each patient’s therapy reduced paraprotein levels, which can precede improved clinical outcomes. The computational approach identified each patient’s anchor drug in their combination therapy that was most likely responsible for achieving improved clinical outcome. For patients who were clinical non-responders, the CBM identified probable protein networks responsible for drug resistance and rapidly screened for alternative drug combinations with predicted efficacy in light of the patients’ drug-resistant pathways. Thus, CBM may be a useful tool for clinicians and translational scientists in search of personalized treatment or newer therapies for patients with MM. Disclosures: Grover: Cellworks: Employment. Narvekar: Cellworks: Employment. Kumari: Cellworks:Employment. Nara yanabhatia: Cellworks: Employment. Bhowmick: Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali: Cellworks Group Inc.: Employment. Usmani: Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Speakers Bureau; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Honoraria, Research Funding; Array BioPharma: Honoraria, Research Funding. Cogle: Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry. See more of: 652. Myeloma: Pathophysiology and Pre-Clinical Studies, excluding Therapy: Emerging New Targets and Mechanisms of Resistance See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 3037 Interleukin-6 Drives Multiple Myeloma Progression through Upregulating of CD147/Emmprin Expression and Its Sialylation Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster II Sunday, December 10, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Yanmeng Wang1*, Rong Fan1*, Li Lei, PhD1*, Aiying Wang1*, Xiaman Wang2*, Shen Ying2*, Hongli Chen2*, Caoilfhionn Connolly, MD3*, Kim De Veirman4*, Aili He, MD2*, Karin Vanderkerken, PhD5, Michael O'Dwyer, MD6and Jinsong Hu, PhD7* 1Department of Cell Biology and Genetics, Xi’an Jiaotong University Health Science Center, Xi'an, China 2Deparment of Heamatology of the Second Affiliated Hospital, Xi’an Jiaotong University Health Science Center, Xi'an, China 3Department of Hematology, National University of Ireland Galway, Galway, Ireland 4Department of Hematology and Immunology, Myeloma Center Brussels, Vrije Universiteit Brussel, Brussel, Belgium 5Department of Hematology and Immunology, Myeloma Center Brussels, Free University of Brussels, Brussels, Belgium 6Department of Haematology, National University of Ireland Galway, Galway, Ireland 7Department of Cell Biology and Genetics, Xi'an Jiaotong University Health Science Center, Xi'An, Shaanxi, China Multiple myeloma (MM) is a B-cell malignancy characterized by the clonal proliferation of plasma cells in the bone marrow (BM). CD147, known as extracellular matrix metalloproteinase inducer (EMMPRIN), is a type I transmembrane glycoprotein that belongs to the immunoglobulin superfamily. With regard to MM, it was recently documented that the aberrantly elevated expression of CD147 has been tightly correlated with MM cell colonization and proliferation. During malignant transformation, many glycoproteins undergo a wide range of glycosylation alterations, especially increased sialylation, have been associated with malignant transformation and metastasis. However, it is still unclear the mechanisms of regulating CD147 and its glycosylation in MM. In this study, we found that CD147 and its sialylation can be up-regulated by interleukin-6 (IL-6), which is derived from either autocrine or paracrine sources and plays an essential role in the malignant progression of MM. When serum-starved MM cell lines RPMI8226, MM1.S and NCI-H929 were stimulated with 20ng/mL of IL-6, we found that the expression of CD147 on MM cell membrane was significantly upregulated, as determined by both flow cytometric analysis and Western blotting. Importantly, the results of Western blotting also clearly show that CD147 is a glycoprotein with higher molecular weight bands than core CD147. By using PNGase F and sialidase to digest the protein samples, we further demonstrated that CD147 is typical N-linked glycoprotein with high level of sialylation. Moreover, we found that the expression of α-2,3 and α-2,6 sialic acid on the cell surface were significantly upregulated by IL-6 analyzed by flow cytometry using MAA and SNA lectin binding assay. Next, we investigated the signaling pathway involved in the IL-6 mediated upregulation of CD147 and its sialylation in MM cells. By using Real-Time PCR, we confirmed that CD147 was transcriptionally upregulated by IL-6. Simultaneously, several sialyltransferases like ST3GAL3, ST3GAL6 and ST6GAL1 were also transcriptionally upregulated by IL-6. By using Western blotting, we further confirmed that IL-6 can activate the JAK/STAT3 signaling pathway by inducing the phosphorylation of STAT3 at Tyrosine 705 in MM cells. Further bioinformatics and ChIP analysis demonstrated that the existing of transcription factor STAT3 binding sites in the promoter of CD147 and sialyltransferase (ST3GAL3, ST3GAL6 and ST6GAL1) genes.To further validate the role of JAK/STAT3 in regulating CD147 and its sialylation, we used a specific STAT3 inhibitor Cryptotanshinone to treat the MM cells and found that the expression of CD147 and its sialylation in MM cells was accordingly decreased. Furthermore, we validated the roles of high level of CD147 and its sialylation in MM biology by knocking-down CD147 or inhibiting its sialylation using 3Fax-Peracetyl Neu5Ac, which is a specific sialyltransferase inhibitor. We found that both siRNA mediated knock-down of CD147 and sialylation inhibition did not affect the proliferation in RPMI8226 and MM1.S cells, measured by EDU incorporation assay using flow cytometry. However, knock-down of CD147 and sialylation inhibition were found to significantly reduce the adhesion to BMSCs and HUVECs, and the migration to BMSC-conditioned media in vitro. Finally, to further validate the influence of IL-6 and the related sialyltransferase genes in MM patients, we assessed the effect of expression on survival of patients using GEP data from the CoMMpass trial (n=664), and noted a significantly reduced PFS (progression-free survival) for patients with high levels of expression of IL-6 and ST3GAL6 (P<0.0001). Taken together, our data provide evidence that IL-6 can up-regulate CD147 expression and its sialylation in a STAT3-dependent manner, and offer a compelling rationale for exploring this axis as a therapeutic target for MM. Disclosures: O'Dwyer: GlycoMimetics Inc: Research Funding; Onkimmune Ltd: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; Janssen: Consultancy, Honoraria, Research Funding. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster II See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> 3073 A B-Cell like Phenotype Is Associated with Sensitivity to Venetoclax in Multiple Myeloma Program: Oral and Poster Abstracts Session: 652. Myeloma: Pathophysiology and Pre-Clinical Studies, excluding Therapy: Poster II Sunday, December 10, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Vikas A. Gupta, MD, PhD1, Benjamin Barwick, PhD1*, Scott Newman, PhD2*, Jonathan J. Keats, PhD3, Daniel Auclair, PhD4*, Shannon Matulis, PhD1*, Michael R. Rossi, PhD5, Ajay K. Nooka, MD, MPH1*, Jonathan L. Kaufman, MD1, Sagar Lonial, MD6 and Lawrence H. Boise, PhD 1 1Winship Cancer Institute/ Hematology and Medical Oncology, Emory University, Atlanta, GA 2St. Jude Children's Research Hospital, Memphis, TN 3Translational Genomics Research Institute, Phoenix, AZ 4Multiple Myeloma Research Foundation (MMRF), Middletown, CT 5Emory University, Atlanta, GA 6Winship Cancer Institute/ Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA As B cells differentiate into plasma cells they switch from Bcl-2 dependent to Mcl-1 dependent. Multiple myeloma should therefore be resistant to the Bcl-2 inhibitor venetoclax. However an ongoing phase I trial of venetoclax has reported an ORR of 40% in a subset of myeloma characterized by the t(11;14) translocation. Nevertheless, 60% of patients with t(11;14) failed to respond to treatment, suggesting that factors beyond t(11;14) determine response. Our own testing of 15 cell lines and approximately 50 myeloma patient samples detected both venetoclax resistant t(11;14) samples as well as venetoclax sensitive non-t(11;14) samples. We have also used siRNA to knockdown cyclin D1, the protein overexpressed in t(11;14), and demonstrated that knockdown has no effect on venetoclax response in three venetoclax sensitive t(11;14) cell lines. Together these findings indicate that t(11;14) is neither necessary nor sufficient for response to venetoclax, and is therefore unlikely to be driving Bcl-2 dependence. To identify other factors that predict for response to venetoclax in myeloma, we used RNA sequencing data to analyze the differential gene expression in sensitive vs. resistant cell lines. We identified 148 genes whose mean expression differed by greater than 2 fold and with an fdr < 0.05. Interestingly, numerous B cell related genes were present among the genes up-regulated in the sensitive lines, including MS4A1 (CD20), CD79A, STAT5A, RASGRP2, FCGR2B, and CCR7. These B cell genes were not uniformly expressed in all of the sensitive cell lines, suggesting that no single marker is likely to be predictive. Expression of CD20, CD79A, and PAX5 have previously been reported in t(11;14) myeloma, however our analysis revealed expression of these markers in the non t(11;14) lines OCI-My5 and PCM6, both of which are t(14;16). A second set of genes highly upregulated in the sensitive lines includes interferon response genes. Notably, none of the master plasma cell transcription factors including Blimp1, IRF4, or XBP1 differed significantly between the sensitive and resistant lines. More importantly, neither Cyclin D1 nor any of the Bcl-2 family genes was differentially expressed, providing additional evidence that expression of the Bcl-2 family and the presence of t(11;14) cannot account for differences in venetoclax response. In order to further analyze the B cell-like properties of the venetoclax-sensitive cell lines, we applied gene set enrichment analysis using genes previously reported to be differentially expressed between mouse B cells and plasma cells. This revealed that B cell genes were enriched in venetoclax sensitive cell lines while plasma cell genes were depleted among the same cell lines. We also used the 148 differentially expressed genes associated with venetoclax response for linear discriminate analysis (LDA) on a larger panel of myeloma cell lines to predict their response to venetoclax. Our analysis correctly predicted the response for at least one cell line previously reported to be venetoclax sensitive (MOLP2) and multiple cell lines reported to be resistant (JJN3, L363, LP1, NCIH929, MOLP8, KMS26, KMS34), suggesting that this scoring system may be valid. We applied the same analysis to the CoMMpass data set, which currently includes RNAseq expression data from nearly 700 newly diagnosed myeloma patients. Many of the t(11;14) are predicted to be venetoclax sensitive, which is consistent with cell line and patient responses. Notably, a number of t(14;16) patients are predicted to be sensitive, mirroring our cell line results. In contrast, t(4;14) patients are predicted to be almost exclusively venetoclax resistant. Together this analysis demonstrates that there are patients with expression patterns similar to venetoclax-sensitive cell lines and more importantly, that such a scoring system may be clinically useful as well. Disclosures: Nooka: Amgen, Novartis, Spectrum, Adaptive: Consultancy. Kaufman: Amgen, Roche, BMS, Seattle Genetics, Sutro Biopharma, Pharmacyclics: Consultancy; Amgen, Novartis: Research Funding. Boise: Eli Lilly and Company: Research Funding; Abbvie: Consultancy. See more of: 652. Myeloma: Pathophysiology and Pre-Clinical Studies, excluding Therapy: Poster II See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> 4335 Overexpression of EZH2 in Multiple Myeloma Is Associated with Poor Prognosis Regardless of Treatment with Novel Agents or High- Dose Chemotherapy Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster III Monday, December 11, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Mark A. Schroeder, MD, Mark A. Fiala, MSW*, Armin Ghobadi, MD*, Keith E. Stockerl-Goldstein, MD, Tanya M. Wildes, MD, MSCI and Ravi Vij, MD MBA Division of Oncology, Washington University School of Medicine, Saint Louis, MO Background:EZH2 overexpression has been linked to the development and progression of a variety of cancers. Pawlyn et al (Blood Cancer J, 2017) recently reported that EZH2 overexpression among patients with multiple myeloma (MM) was associated poor prognosis. Their analysis included patients on varying treatment regimens over a number of years and, therefore, they were unable to determine if proteasome inhibitors (PIs), immunomodulatory drugs (IMIDs), or high-dose chemotherapy and transplantation (HDCT) can overcome the negative effect of EZH2 overexpression, as they have been shown to do for some other high-risk genetic profiles. Objectives: To independently confirm EZH2 overexpression is associated with poor outcomes and determine if PI, IMIDs, or HDCT can overcome this poor risk. Methods: Data was extracted from the open-access MMRF Researcher Gateway corresponding with interim analysis 10 from the CoMMpass study. The CoMMpass study enrolled 1000 newly diagnosed MM patients who are being tracked longitudinally for 5 years. CoMMpass collects sequential tissue samples as well as relevant clinical data. Eligibility requirements for CoMMpass include: symptomatic MM with measureable disease by SPEP (≥1.0g/dL), UPEP (≥200mg/ 24 hours), or SFLC (≥10mg/dL); receiving a PI and/or and IMID for initial MM treatment; and no prior malignancies in the past 5 years. RNA sequencing was performed on CD138-enriched bone marrow cells at MM diagnosis. EZH2 overexpression was defined as > 1 SD above mean expression. High-risk cytogenetics, defined as amp1q, del17p, t(4;14), t(14;16), t(14;20), were identified using custom SeqFISH software on long-insert whole genome sequencing data. All sequencing was performed by the Translational Genomics Research Institute (TGEN). Results: 646 patients in the dataset had EZH2 expression data and were included in the analysis. The median age was 63 at MM diagnosis, 58% were male and 76% were white. 33% were ISS stage I, 37% ISS stage II, and 30% ISS stage III. Cytogenetics was not available for 17% of cases. Of those with cytogenetics available, 53% had high- risk disease; 38% amp1q, 12% del17p, 12% t(4;14), 4% t(14;16), and 1% (14;20). 15% of patients (n = 97) were classified as EZH2 overexpressors. Patients with ISS stage III disease were more likely to have EZH2 overexpression (26%) than stage II (13%) and stage I (9%) (p < 0.0001). EZH2 overexpression was also more common among patients with high-risk cytogenetics than those without (19% compared to 9%, p = 0.0022). Of patients with high-risk cytogenetics available, overexpression was most common among patients with del17p (24%) and least common among patients with t(4;14) (11%). EZH2 expression was not associated with age, race, or gender. 629 patients had first-line treatment data available, of which 93% received a PI, 72% an IMID, 70% both. 45% of patients underwent HDCT during first-line treatment. In the multivariate analysis, EZH2 expression was consistently associated with poor prognosis regardless of regimen, with an increase in hazard ratio for progression or mortality ranging from 106%-173%. The results of the multivariate analysis are summarized in Table 1. Conclusions: We found that EZH2 expression was consistently associated with poor prognosis across all treatment regimens. It is important to note that we were unable to control for HRD by cytogenetics due to the high rate of missing data. EZH2 expression has previously been shown to predict response independent of HRD and, therefore, its omission should not alter the findings. Patients with myeloma overexpressing EZH2 are at increased risk of progression and death regardless of therapeutic regimen. Early-stage clinical trials of EZH2 inhibitors are currently ongoing in solid tumors and lymphomas and their testing should be expanded to MM. Disclosures: Wildes: Carevive Systems Inc: Consultancy; Janssen: Consultancy. Vij: Amgen: Honoraria, Research Funding; Celgene: Honoraria; Abbvie: Honoraria; Bristol-Meyers-Squibb: Honoraria; Takeda: Honoraria, Research Funding; Jazz: Honoraria; Konypharma: Honoraria; Janssen: Honoraria. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster III See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> 63 Transcriptional Plasticity Compensates for Ikaros and Aiolos Proteasomal Degradation and Mediates Resistance to IMiDs in Multiple Myeloma (MM) Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Transcriptional Regulatory Circuitries of Multiple Myeloma Saturday, December 9, 2017: 8:15 AM Bldg B, Lvl 4, B401-B402 (Georgia World Congress Center) Paola Neri, MD1, Ines Tagoug, PhD1*, Ranjan Maity, PhD1*, Caleb K Stein, MS2*, Madison Kong3*, Jonathan J. Keats, PhD4, David Soong, PhD5*, Christopher Chiu, PhD5, P. Leif Bergsagel, MD6 and Nizar J. Bahlis, MD1 1Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada 2Division of Hematology and Oncology, Mayo Clinic, Scottsdale, AZ 3Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada 4Translational Genomics Research Institute, Phoenix, AZ 5Oncology Translational Research, Springhouse, PA 6Mayo Clinic Arizona, Scottsdale, AZ Background: Immunoglobulin (IGH, IGL, IGK) and non-immunoglobulin (PVT1, TXNDC5, FAM46C, DUSP22, etc.) enhancers hijacking by variable genes (MYC, MAF, MAFB, CCND1/2/3, MMSET, IRF4) is a recognized oncogenic driver event in MM. However, the identity of the transcription factors (TFs) or transcriptional regulatory complexes binding and regulating the activity of these enhancers remains to be fully elucidated and may yield valuable therapeutic targets. As such the discovery of the BET family member BRD4 as the master histone acetyl mark reader at enhancers loci regulating MYC lead to promising therapeutic developments in MM and numerous other cancers. Immunomodulatory drugs (IMiDs) promote the proteasomal degradation of IKAROS (IKZF1) and AIOLOs (IKZF3) leading to the transcriptional repression of MYC and the suppression of MM cells survival and proliferation. However, acquired resistance to IMIDs and the loss of the transcriptional repression of MYC are nearly universal and occur in spite of sustained IKZF1/3 degradation suggesting that transcriptional rewiring may be sustaining hijacked enhancers activity and transcription of driver oncogenes. Methods and Results: In order to define how IMiDs repress MYC transcription, we first defined IKZF1, BRD4, the lysine acetyl transferase P300 and the mediator complex subunit MED1 mapping within the MM genome using ChIPseq. In MM cell lines (MM1S, RPMI8226, ARP1 and AMO1), IKZF1 predominantly mapped to intronic and intergenic loci which are typically enriched with enhancer and superenhancer elements. Indeed, IKZF1 mapping to the genome nearly completely (96.5%) overlapped that of P300, MED1 and BRD4 co-occupied enhancer and superenhancer loci. We also confirmed that in the MM1S sensitive cell lines IMiDs (lenalidomide 10 μM, 24h) exposure efficiently depleted IKZF1, BRD4, P300 and MED1 at enhancer loci with ensuing MYC (and MAF) downregulation. In contrast, in resistant cell lines (RPMI8226) and in spite efficient IKZF1 displacement, BRD4, P300 and MED1 were retained at the oncogenic enhancer (IGLL5) driving MYC (and MAF). These findings lead us to postulate that in IMiDs resistant cells retention of BRD4 and MED1 at oncogenic enhancers in the absence of IKZF1 likely results from rewiring of the TFs regulating MYC. To identify TFs that may co-localize with BRD4 and IKZF1, we analyzed the enrichment of DNA motifs at IKZF1and BRD4 co-occupied enhancers using the MEME suite motif-finding algorithms. This computational analysis revealed a strong enrichment at these MM enhancers of the GGAA motif recognized by the ETS family of TFs (P = 3.2 e-743) and other motifs boxes for the RUNX (P = 9.6 e-725), MYC/MYB (P = 8.8 e-52) and interferon regulatory (IRF) (P = 3.1 e-293) TFs. We next confirmed that the ETS family TF ETV4 was indeed expressed in IMiDs resistant, but not sensitive, MM cell lines. ChiPseq occupancy profiles in IMiDs resistant RPMI8226 cell line revealed co-localization of ETV4 with IKZF1, P300 and BRD4. As predicted, lenalidomide treatment induced global depletion of IKZF1 but not ETV4 at BRD4 occupied enhancers in resistant cell lines (RPMI8226 and ARP1). Importantly, Cas9- mediated knock out of ETV4 in RPMI8226 cells sensitized them to lenalidomide with ensuing MYC downregulation and cell death. Confirming its role in MM, ETV4 transcript was indeed detectable in primary patients’ samples in the CoMMpass data repository (ETV4 FPKM >1.0 in 112/724) and its expression was associated with significantly reduced survival outcomes (HR 0.64; P=0.0008). Similarly, high expression (top quartiles) of RUNX2 or MYB, TFs with enriched motifs at IKZF1 co-occupied enhancer loci, was also associated with decreased survival. Of note RNAseq analysis of paired patient samples pre- and post-IMiDs treatment (n=14 pairs) revealed significant upregulation of ETV4 at the time of acquired IMiDs resistance (7/14). Lastly transcriptome analysis of 101 patients enrolled in the RD arm (lenalidomide and dexamethasone) of the POLLUX trial (NCT02076009) confirmed the reduced survival of patients with top quartiles expression of ETV4 as well as MYB and RUNX2 (Fig.1) Conclusion: Transcriptional plasticity with expression of extra-lineage TFs such as the ETS family member ETV4 sustains oncogenic enhancers in MM overcoming IKAROS and AIOLOS dependency and promoting IMiDs resistance. Disclosures: Neri: Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Soong: Jannsen: Employment. Chiu: Janssen: Employment. Bahlis: Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Transcriptional Regulatory Circuitries of Multiple Myeloma See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> *signifies non-member of ASH 1780 High-Risk Myeloma Is Demarcated By Immunoglobulin Lambda Light Chain Translocations Program: Oral and Poster Abstracts Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster I Saturday, December 9, 2017, 5:30 PM-7:30 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Benjamin Barwick, PhD1*, Vikas A. Gupta, MD, PhD1, Daniel Auclair, PhD2*, Jonathan J. Keats, PhD3, Sagar Lonial, MD4, Paula M Vertino, PhD5* and Lawrence H. Boise, PhD 6 1Winship Cancer Institute/ Hematology and Medical Oncology, Emory University, Atlanta, GA 2Multiple Myeloma Research Foundation (MMRF), Middletown, CT 3Translational Genomics Research Institute, Phoenix, AZ 4Winship Cancer Institute/ Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA 5Radiation Oncology, Emory University, Atlanta, GA 6Division of BMT, Emory University, Atlanta, GA Multiple myeloma is the second most common hematological malignancy, and is the consequence of uncontrolled proliferation of antibody-secreting B cells, known as plasma cells. Myeloma causes lytic bone lesions and renal failure and is characterized by heterogeneous genetic mutations and clonal selection. Subsequently, understanding the complex mutational repertoire in the context of myeloma prognosis remains challenging. We report the translocation architecture of 826 newly diagnosed myeloma patients as part of the Clinical Outcomes in Multiple Myeloma to Personal Assessment (CoMMpass) study. These data are compared to other genetic mutations, gene expression, and patient outcome to better understand high-risk disease. Whole genome sequencing identified 31% of newly diagnosed myelomas as containing an Immunoglobulin heavy chain (IgH) translocation. The vast majority of IgH translocations occurred with CCND1, WHSC1, MYC, or MAF; all of which were upregulated upon translocation. IgH-CCND1 translocations were more likely to express low levels of IgH (25%), but the majority expressed high levels of IgG1 consistent with other myelomas. Conversely, IgH-WHSC, -MYC, and -MAF translocations were more likely to express IgA (31%) and only IgH-MAF translocated myeloma expressed IgA2. IgH translocations primarily occurred at sites of class-switch recombination except for IgH-MYC translocations that originated from IgH intergenic enhancers suggesting that these occur by a distinct mechanism. MYC translocations occurred in 18% of myelomas and in contrast to IgH, MYC translocation partners were scattered throughout the genome at regions proximal to highly expressed genes. MYC translocations corresponded with MYC amplification which was present in 15% of myelomas, yet half of these contained a translocation. Both types of genetic abnormalities corresponded with increased MYC expression, but neither MYC amplification and/or translocation, nor IgH translocation were prognostic of outcome. Immunoglobulin lambda light chain (IgL) translocations were the most common translocation (7.1%) that did not involve IgH or MYC. Unlike IgH and MYC translocations, IgL translocations were prognostic of poor progression-free and overall survival. IgL translocations occurred at IgL enhancers and most commonly involved MYC, MAP3K14, 6p24.3, CCND (1,2, and 3), and MAF. IgL translocated patients presented myeloma at a similar age and stage and were treated with similar therapies as compared to other patients. IgL translocated myelomas contained a mutational repertoire that mimicked other myelomas and poor prognosis was independent of the IgL translocation partner, suggesting that the prognostic effect was intrinsic to IgL. Interestingly, myelomas with IgL translocations expressed lower levels of inflammatory cytokines including CCL3 and CCL4, suggesting they are less likely to induce anti-tumor innate immune responses. Taken together these data have important insights into myeloma pathology and implications for identifying high-risk myeloma. Disclosures: Boise: Eli Lilly and Company: Research Funding; Abbvie: Consultancy. See more of: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster I See more of: Oral and Poster Abstracts << Previous Abstract | Next Abstract >> 836 PSMB9 Mediates Resistance to Bortezomib in Multiple Myeloma Program: Oral and Poster Abstracts Session: 605. Molecular Pharmacology, Drug Resistance—Lymphoid and Other Diseases: Poster III Monday, December 11, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Aneel Paulus, MD1, Sharoon Akhtar, MPhil2*, Thomas Caulfield3*, Alak Manna, PhD2*, Vivek Roy, MD1, Sikander Ailawadhi1 and Asher A. Chanan-Khan, MD1 1Division of Hematology-Oncology, Mayo Clinic, Jacksonville, FL 2Mayo Clinic Florida, Jacksonville, FL 3Mayo Clinic Florida, Jacksonville Introduction:Proteasomes are the chief degradation components of the ubiquitin proteasome system (UPS) and are comprised of several (�) structural and (ß) catalytic subunits. Targeting the ß5 subunit, with agents such bortezomib (Btz, 20S proteasome inhibitor, PI) has proven highly successful in MM pts. However, resistance to Btz (Btz-R) invariably ensues with disease evolution resulting in relapse and mortality. Our data highlight that in Btz-R cells, proteasome function is upregulated with increased reliance on other ß-catalytic subunits. We have uncovered a novel association between the ß5 subunit acting in concert with the ß1i proteasome subunit, to promote resistance to Btz. We hypothesized that targeting ß5 and ß1i together would be lethal to Btz-R MM cells. Methods: CD138+ cells from MM patients, Btz-sensitive (Btz-S) MM cell lines (OPM2, KMS11 and U266) and their isogenic Btz-R subclones were used in experiments. Fluorogenic peptide substrates were used to measure ß-subunit- specific enzymatic activity. Gene disruption was conducted using 2 different shRNA hairpins for PSMB5(ß5) and PSMB9 (ß9). Affymetrix HT12v4 gene expression array and NanoString mRNA quantification were used for gene expression profiling (GEP), followed by qPCR for confirmation. Novel dual ß5-ß1i PI were synthesized and tested in vitro. Apoptosis was determined by annexin-V/PI staining and MTS/CellTiter Glo assay used to determine cell viability. Results: Btz IC50 was noted to be 284nM in Btz-R vs. 4nM in Btz-S cells. Sanger seq. revealed no mutations in PSMB5 indicating that Btz should effectively bind the ß5 subunit. ß-subunit chymotrypic activity in Btz-R vs. Btz-S cells was significantly increased (3.5 fold, p<0.001) but remained amenable to downregulation with Btz (as well as other PI). This suggested that ß5-subunit-independent mechanisms may account for increased proteasome function and resistance to Btz. GEP analysis revealed modulation of several genes; in particular, PSMB9 (ß1i) in Btz-R vs. Btz- S MM cells. Proteomic analysis confirmed protein/enzymatic upregulation of PSMB5/ß5 in conjunction with PSMB9/ ß1i in primary cells from Btz-R MM patients and Btz-R MM cell lines. Clinical significance of PSMB9 upregulation was queried using the MMRF CoMMpass database (IA10), with analysis in pts. who received either Btz+dex or Len+Dex and did not respond to treatment. PSMB9 mRNA was significantly upregulated in Btz+Dex non-responders (n=42) vs. responders (n=92), whereas in Len+Dex pts. this difference was not significant. We hypothesized that downregulation of PSMB9 would resensitize Btz-R cells to Btz and noted a significant shift in IC50 (235nM to 108nM) in Btz-R- PSMB9 shRNA transfected cells. While ß1i knockdown alone was able to reduce Btz-R cell viability by ~25%, we posited that concurrent disruption of ß1i and ß5 would significantly reduce cell viability and proliferation. Targeted GEP in dual-ß5/ß1i knockdown Btz-R cells, showed >2-fold increase in CASP8, CASP9, CASP10, CDKN2A, CDKN1A, TNFRSF10B, FOXO4 and RNF43 (apoptosis genes) and decrease in CCB1, CCNA2, WNT5A, SKP2, CDC6 and FEN1 (cell cycle genes) vs. scramble-transfected controls. Indeed, both Btz-S and Btz-R MM cells demonstrated significantly compromised growth capacity and viability (Dual knockdown ß5/ß1i Btz-R cells: 0.32 million/mL vs. scramble Btz-R cells: 1.6 million/mL) (Fig 1). This prompted us to develop novel dual-ß5 and ß1i chemical inhibitors. Using a highly modified carbamate scaffold we developed 20 cmpds that display binding affinity for both ß5 and ß1i. In vitro screening of the top 5 cmpds showed >2 fold lower EC50 in Btz-R vs. Btz-S MM cell lines. 2 cmpds in particular showed ß1i and ß5 enzymatic inhibition of >50% at 1uM and 5uM, respectively and are being further optimized. Conclusions: We have found that upregulation of PSMB9/ß1i and PSMB5/ß5 is associated with resistance to Btz. These findings were confirmed in both MM cell lines as well as in primary MM cells from Btz-R pts. Moreover, increased PSMB9 mRNA expression in non-responding Btz+Dex pts. but not Len+dex MM pts. highlights this as a resistance mechanism unique to Btz. Direct genetic disruption or with novel dual-chemical inhibitors directed toward ß1i and ß5 induced lethality in Btz-R (as well as Btz-S) MM cells. Thus, our investigations point to development of agents, which inhibit ß1i and ß5-proteasome subunits to overcome resistance to Btz. Disclosures: Ailawadhi: Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Pharmacyclics:Research Funding; Amgen: Consultancy, Honoraria. See more of: 605. Molecular Pharmacology, Drug Resistance—Lymphoid and Other Diseases: Poster III See more of: Oral and Poster Abstracts 265 Crowdsourcing a High-Risk Classifier for Multiple Myeloma Patients Program: Oral and Poster Abstracts Type: Oral Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis Saturday, December 9, 2017: 4:00 PM Bldg B, Lvl 2, B211-B212 (Georgia World Congress Center) Andrew P Dervan1*, Michael Mason, PhD2*, Fadi Towfic, PhD1*, Michael Amatangelo, PhD1*, Daniel Auclair, PhD3*, Douglas Bassett, PhD4*, Hongyue Dai, PhD5*, William S. Dalton, PhD, MD5*, Samuel Danziger, PhD4*, Erin Flynt1*, Hartmut Goldschmidt, MD6, Justin Guinney, PhD2*, Dirk Hose, MD7, Konstantimos Mavrommatis, PhD1*, Gareth J. Morgan, MD, PhD8, Nikhil Munshi, MD9, Alexander Ratushny, PhD4*, Dan Rozelle, PhD10*, Mehmet Kemal Samur, PhD11, Frank Schmitz, MD, PhD4*, Kenneth H Shain, MD, PhD 12, Matthew Trotter, PhD13*, Brian A Walker, PhD8, Brian S. White, PhD2*, Thomas Yu, BS2* and Anjan Thakurta, PhD1* 1Celgene Corporation, Summit, NJ 2Sage Bionetworks, Seattle, WA 3Multiple Myeloma Research Foundation, Norwalk, CT 4Celgene Corporation, Seattle, WA 5M2Gen, Tampa, FL 6University Hospital Heidelberg and German Cancer Research Center, Heidelberg, Germany 7Department of Internal Medicine V, University Hospital Heidelberg, Heidelberg, Germany 8Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, AR 9J. Lipper Cancer Center for Multiple Myeloma, Dana Farber Cancer Institute, Boston, MA 10Rancho Biosciences, Boston 11Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 12Department of Malignant Hematology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 13Celgene Institute for Translational Research Europe (CITRE), Seville, Spain Background Multiple myeloma (MM) is a cancer of the plasma cells, and its clinical course depends on a complex interplay of clinical traits and molecular characteristics of the tumor. While current therapeutic combinations work well for a majority of patients, a subset of MM patients still rapidly progress or die within 18 months after diagnosis. Thus, there is an urgent need for a precise risk stratification model to identify these patients to allow selection of alternative therapeutic approaches. Current risk stratification practice is based on plasma cell cytogenetics and clinical stage at presentation, and, to a lesser degree, myeloma cell gene expression. The Myeloma Genome Project (MGP), an industry-academia collaborative project aims to develop highly accurate, clinically-implementable prognostic tests for MM patients. As part of the broader MGP effort, The MM DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge was conceived as an innovative approach to accelerate the development and evaluation of risk models in newly diagnosed MM. We collected large and diverse datasets from multiple collaborative groups and recruited a community of computational biologists, statisticians and leading experts in MM to build and test models (including the DREAM community of 20,000 solvers). Methods We assembled nine datasets comprised of 3,077 subjects with whole exome profiling (N = 1,273), gene expression profiling (N = 2,431), and clinical and cytogenetic data. Four data sets are publicly available and were provided for model training/development, while 5 data sets are previously unpublished and blinded to participants for model validation. Unpublished data were generously provided by an array of public, private and non-profit contributors including the University of Arkansas for Medical Sciences and Dana Farber Cancer Institute on behalf of the MGP, the Multiple Myeloma Research Foundation’s Personalized Medicine Initiative (www.theMMRF.org), Moffitt Cancer Center and M2Gen, and the University of Heidelberg. The Challenge goal was to accurately predict which newly diagnosed MM patients would progress (or die) within 18 months of diagnosis. We challenged the community to identify high- risk patients using DNA-based, RNA-based, or a combination of RNA, DNA, cytogenetic and demographic features. As an additional incentive, $150,000 in prizes will be awarded to top performing teams. By the first month of the competition, 290 teams from around the world had signed up for the challenge. We compare competitors’ performance against state of the art classifiers based on gene expression (e.g. UAMS-70, EMC-92), patient characteristics (e.g. International Staging System stage combined with age), and cytogenetics. These were used as a benchmark upon which winning models needed to improve. The challenge was hosted on Sage Bionetworks’ Synapse platform, which enabled development, submission and evaluation of models using an automated framework. A Docker framework was used to ensure portability and to allow secure use of unpublished validation data. Participants were evaluated on their ability to predict high risk patients using a variety of metrics including integrated AUC, AUC and precision-recall AUC. Results / Discussion Based on implementation of several published benchmark models on a preliminary set of validation data, the top performing published DNA-based model was based on mutation burden (Miller et al., AUC = 0.70, iAUC = 0.71), the top performing published RNA-based model was the UAMS-17 (Shaughnessy et al., AUC = 0.52), and the top performing clinical based model was a combination of age and ISS (AUC = 0.69, iAUC=0.68). The unique combination of over 3,000 patients’ worth of molecular and clinical data allowed precise model generation while the collection of multiple distinct (unpublished) validation cohorts prevented model over fitting. Top performing teams identified novel prognostic feature sets for high risk MM, establishing new benchmarks for the field, and final results from the top performing models will be presented. Such novel models will be integrated with the broader efforts within MGP for validation and clinical test development. The results of this effort demonstrate that data sharing coupled with the power of crowdsourcing can contribute to the development of robust and clinically-implementable models for risk-stratification of patients with MM. Disclosures: Dervan: Celgene Corporation: Employment, Equity Ownership; Twinstrand Biosciences: Equity Ownership. Towfic: Celgene Corporation: Employment, Equity Ownership; Immuneering Corporation: Equity Ownership. Amatangelo: Celgene Corporation: Employment. Bassett: Celgene Corporation: Employment. Dalton: M2Gen: Employment. Danziger: Celgene Corporation: Employment. Flynt: Celgene Corporation:Employment. Goldschmidt: Chugai: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis:Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Morphosys: Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Hose: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Research Funding; EngMab: Research Funding. Mavrommatis: Celgene Corporation:Employment. Morgan: Takeda: Consultancy, Honoraria; Bristol Myers: Consultancy, Honoraria; Celgene:Consultancy, Honoraria, Research Funding. Ratushny: Celgene Corporation: Employment. Rozelle: Rancho Biosciences: Employment. Schmitz: Celgene Corporation: Employment. Trotter: Celgene Corporation: Equity Ownership; Celgene Institute for Translational Research Europe: Employment. Thakurta: Celgene Corporation:Employment, Equity Ownership. 4533 High Rate of Sustained Minimal Residual Disease Negativity Predicts Prolonged Survival for the Overall Patient Population in the Phase 2 KRd Plus Autologous Stem Cell Transplantation MMRC Trial Program: Oral and Poster Abstracts Session: 731. Clinical Autologous Transplantation: Results: Poster III Monday, December 11, 2017, 6:00 PM-8:00 PM Bldg A, Lvl 1, Hall A2 (Georgia World Congress Center) Andrzej J. Jakubowiak, MD1, Noopur Raje, MD2*, Ravi Vij, MD MBA3, Donna Reece, MD4, Jesus G. Berdeja, MD5, Leonor Stephens6*, Kathryn M. Tinari6*, Cara A. Rosenbaum6*, Jagoda K. Jasielec7, Paul G Richardson, MD8, Sandeep Gurbuxani6, Jennifer Nam6*, Erica Severson6*, Amanda McIver6*, Brittany Wolfe6*, Sarah Major6*, Shaun Rosebeck6*, Andrew Stefka, MS6*, David Johnson6*, Dominik Dytfeld9*, Kent Griffith10*, Agata Turowski6*, Tyler Hycner6* and Todd M. Zimmerman, MD6 1University of Chicago Medical Center, Chicago, IL 2Massachusetts General Hospital, Boston, MA 3Washington University in St. Louis, St. Louis, MO 4University Health Network, Toronto, ON, Canada 5Sarah Cannon Center For Blood Cancers, Nashville, TN 6The University of Chicago, Chicago, IL 7Northshore University HealthSystem, Chicago, IL 8Dana-Farber Cancer Institute, Boston, MA 9University of Medical Sciences, Poznan, Poland 10University of Michigan, Ann Arbor, MI Introduction In a phase 2 study designed to assess efficacy of extended treatment (tx) with KRd (carfilzomib [CFZ], lenalidomide [LEN], and dexamethasone [DEX]) plus autologous stem cell transplantation (ASCT), we reported high rates of deep responses, including high rates of stringent complete response (sCR) and minimum residual disease (MRD) negativity in newly diagnosed multiple myeloma (NDMM) patients (pts) after a median of 18 cycles (C) and 25.5 months (mo) of follow-up (f/u). In this analysis, we evaluated the impact of MRD negativity on progression-free survival (PFS). Methods The study enrolled ASCT-eligible pts with NDMM requiring tx per International Myeloma Working Group (IMWG) criteria with no age limitation. Pts received initial four 28-day cycles of KRd induction: CFZ IV 20/36 mg/m2 on days (D) 1, 2, 8, 9, 15, and 16 (20 mg/m2 on D1, 2 of C1 only); LEN PO D1–21 at 25 mg; DEX PO 40 mg/week followed by stem cell collection using G-CSF and plerixafor, melphalan 200 mg/m2, and ASCT. KRd consolidation (C5–8) used the same doses and schedule, except LEN 15 mg in C5 with the option to escalate to prior dose and DEX reduced to 20 mg weekly. After C8, pts received maintenance KRd for an additional 10 cycles using the same doses as in C8, except CFZ on D1, 2, 15, and 16 only. Single-agent LEN was recommended off-study after C18. Primary endpoint was rate of sCR at the end of C8, with MRD among secondary endpoints. Response rates and MRD were evaluated as per current IMWG criteria. MRD was evaluated by next-generation sequencing (NGS), using the immunoSEQ® Platform (Sequenta/Adaptive Inc.) with a sensitivity of 10-5–10-6 for MRD negativity, and multiparameter flow cytometry (MFC) with 10-4–10-5 sensitivity at landmark time points: after KRd consolidation (after C8), at the end of KRd tx (after C18), and then yearly. MRD-negative status required CR, as per current IMWG criteria. Results As of July 1, 2017, enrollment was completed (76 pts); 74 pts completed KRd induction, 72 ASCT, 70 consolidation, and 64 KRd maintenance with median f/u at cutoff date of 35.2 mo (range 2.9–53.0). Median age was 59 years (yr; range 40–76), 57% stage II/III of the International Staging System, and 36% high-risk cytogenetics as per IMWG criteria. For this analysis, efficacy data were available for 76 pts and MRD data from ≥1 landmark time point for 46 (by NGS) and 40 pts (by MFC). On intent-to-treat, the rate of ≥very good partial response was achieved in 91%, ≥CR in 78%, and sCR in 75% for all enrolled pts. At the end of C8, MRD negativity by NGS combined with ≥CR was observed in 67% (n=36) (including 10 of 13 pts with high-risk disease) and at the end of C18 in 72% (n=32) (including 7 of 10 high-risk pts). As expected, MRD rates were slightly higher by MFC at 95% (n=37) and 96% (n=27). Paired MRD results for the end of C8 and C18 landmarks were available for 34 of these pts by NGS and for 31 pts by MFC. At the end of C18, MRD negativity was sustained for 91% of MRD-negative pts by NGS and for 96% by MFC. At the cutoff date, 30 pts completed 1-yr LEN maintenance, which followed C18 of KRd tx, with MRD results available for 17 pts by NGS and 21 pts by MFC, with MRD-negative rates of 82% and 90%, respectively. Paired MRD results for the end of C18 of KRd tx and 1-yr LEN maintenance landmarks were available for 17 of these pts by NGS and for 20 pts by MFC. MRD-negative results were sustained in 93% of pts by NGS and 94% of pts by MFC. After median f/u of 35.2 mo, 3-yr PFS for pts with sustained MRD at the end of C18 by NGS was 94%, and for all pts (n=76) 86%. Overall survival (OS) rates at 3 yrs for sustained MRD-negative disease by NGS were 100%, and for all pts 93% (Fig. 1). In a subset of pts with high-risk disease (n=27), 3-yr PFS and OS rates were 81% and 87%, respectively. Updated results, including a larger sample of MRD data, will be presented at the meeting, with an increasing number of pts completing respective landmarks of tx and MRD evaluations. Conclusions These results show that extended KRd tx with incorporated ASCT results in high rates of deep responses, including high rates of MRD-negative disease. The achievement of high rates of sustained MRD-negative status correlates with high rates of 3-yr PFS and OS in overall and high-risk pt populations. These observations provide rationale for using sustained MRD-negative rates as a reliable predictor of improved tx outcomes for the entire pt population, which will require validation in ongoing and planned randomized trials. Disclosures: Jakubowiak: University of Chicago: Employment; Amgen Inc., BMS, Celgene, Janssen, Karypharm, Millennium-Takeda, Sanofi, SkylineDX: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Raje: Millenium: Consultancy; Celgene: Consultancy; Onyx:Consultancy; Amgen: Consultancy. Vij: Bristol- Meyers-Squibb: Honoraria; Amgen: Honoraria, Research Funding; Celgene: Honoraria; Jazz: Honoraria; Janssen: Honoraria; Takeda: Honoraria, Research Funding; Abbvie:Honoraria; Konypharma: Honoraria. Reece: Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Research Funding; Otsuka: Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Berdeja: Teva: Research Funding; Takeda: Research Funding; Amgen: Research Funding; Novartis:Research Funding; Janssen: Research Funding; Curis: Research Funding; Constellation: Research Funding; BMS:Research Funding; Bluebird: Research Funding; Celgene: Research Funding; Vivolux: Research Funding; Abbvie:Research Funding. Stephens: TG Therapeutics: Employment, Equity Ownership. Rosenbaum: Celgene:Honoraria. Richardson: Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Oncopeptides AB: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals:Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Severson: University of Chicago: Employment. McIver: The University of Chicago Biological Sciences:Employment. Wolfe: University of Chicago Medicine: Employment. Johnson: The University of Chicago >> BSD MED - Hematology and Oncology Research Staff: Employment. Dytfeld: Amgen: Consultancy; Celgene:Consultancy; Janssen: Consultancy. Turowski: University of Chicago: Employment. Hycner: University of Chicago: Employment. Zimmerman: Abbvie: Employment.