ALBERT

Description: Introduction We previously reported on the generation of highly activated/expanded natural killer cells (ENKs) after coculture with K562 cells modified to express membrane bound IL15 and 41BB-ligand. These cells have potent antimyeloma properties in vitro, in a NGS mouse model, and are safe when given to advanced multiple myeloma (MM) patients. (Szmania et al, J Immunother 2015) A potential obstacle to the effectiveness of ENK-based immunotherapy of MM is the evasion of immune recognition. We have generated 4 MM cell lines (OPM2, JJN3, ANBL6, and INA-6) which are resistant to ENK-mediated lysis to study mechanisms of resistance. These lines were derived from parental lines by repeated challenge with ENKs and maintained resistance long term when cultured without further exposure to ENKs.(Garg et al, Blood 2012, 120:4020) We have shown by stable isotope labeling with amino acids in cell culture-mass spectrometry, gene expression profiling (GEP), and flow cytometry that ICAM3 is downregulated in the ENK-resistant version of OPM2 (OPM2-R) compared to the parental OPM2. (OPM2-P; Garg et al, Blood 2013, 122:3105) We investigated OPM2-P and OPM2-R by whole exome sequencing (WES) and RNA sequencing (RNAseq) with a focus on ICAM3, evaluated ICAM3 cell surface expression on patient myeloma cells, and studied the importance of ICAM3 expression on ENK functionality. Methods DNA and RNA were extracted from OPM2-P and OPM2-R cells using the Qiagen AllPrep kit. WES libraries were prepared with the Agilent qXT and Agilent SureSelect Clinical Research Exome kits with additional baits covering the Ig and MYC loci. RNAseq libraries were prepared using the Illumina TruSeq stranded mRNA kit. Samples were sequenced 100bp PE on an Illumina HiSeq2500. Samples for WES were sequenced to a mean coverage of 〉120x and RNAseq to a target of 〉100M reads. WES data were aligned to the Ensembl GRCh37/hg19 human reference using BWA mem. Somatic variants were called MuTect. RNAseq data were analyzed using Tuxedo Suite. Data were aligned to the Ensembl GRCh37/hg19 human reference using TopHat with Bowtie2. Transcriptome reconstruction, quantification and differential analysis was performed using CuffLinks. ENK-mediated lysis of myeloma cells was measured by 4 hour chromium release assay in the presence of isotype or ICAM3 blocking antibody. Bone marrow aspirates were obtained from MM patients after informed consent in accordance with the Declaration of Helsinki. Primary myeloma cells were selected with CD138-coated immunomagnetic beads and ICAM3 expression was assessed by flow cytometry gated on viable CD138 positive cells. Results There was no mutation in ICAM3 in OPM2-R by WES, but RNAseq found a significant reduction in ICAM3 RNA in OPM2-R compared to OPM2-P (p

Description: Introduction Over the last 15 years gene expression profiling (GEP) has been used to define myeloma molecular subgroups and to determine clinical prognosis. Two major molecular subgroup classifications have been used: the UAMS which determines 7 subgroups and the TC classification based on the presence of IgH translocations and expression of D group cyclins. For prognosis, although a number of different GEP signatures have been defined, the widely used GEP70 identifies 15% of patients with high risk (HR) disease who have a median PFS and OS of 1.75 and 2.83 years. An ideal classification system would identify clinically relevant subgroups with distinct etiology and biology using standardized techniques. We have examined a large group of patients characterized at multiple genetic levels to optimize the diagnostic approach of newly diagnosed patients going forward. Materials and methods Study subjects included 1349 cases enrolled in Total Therapy trials (median follow up 7.5 years). Gene expression profiling was used to determine GEP70 risk status, molecular subgroup by UAMS and TC classifications, and to devise a new and extended TC classification (TC10). Interphase FISH associated with IgH translocations and 1q+ and 17p- were used to build GEP proxies. Data from mutational analysis generated by the FoundationOne targeted sequence panel was also incorporated. Results were validated on the UK MRC MyelomaIX and Hovon65/GMMG-4 studies. Results An initial agnostic analysis of GEP data using sparse k-means clustering verified the existence of TC based groups. Six groups were identified that corresponded overwhelmingly with known TC subgroups; CCND1-t(11;14), D1-HRD, D2-HRD, MMSET, MAF/CCND2, and CCND3. Further comparisons between the molecular subgroup and TC classifier revealed that the UAMS 7 subgroups clustered strongly within one predominant TC group: CD-1 and CD-2 to t(11;14), HY to D1, LB and PR to D2, MF to t(14;16) or t(14;20), and MS to t(4;14). As the UAMS molecular subgroups are largely contained within the TC framework, we aimed to extend the TC by developing the TC10. To extend the known TC subgroups, unsupervised clustering was applied to the 3 largest subgroups [t(11;14), D1, and D2] to determine the strongest single divisor within each respective subgroup. The dominant feature within the t(11;14) cases was CD20 expression, while the D1 and D2 subgroups both split according to RRAS2. CD20 is associated with PAX5 and VPREB3 expression, and RRAS2 is associated with decreased PTP4A3 and increased TNFAIP3 and BIRC3 expression. RRAS2 activation within D1 subgroup and CD20 activation within t(11;14) cases corresponds to an increased time to response to induction therapy suggesting they constitute important biological subgroups. The TC10 combines the known etiologic subgroups of the TC with functionally relevant subdivisions to create 10 novel subgroups: t(11;14) CD20+/-, D1: RRAS2+/-, D2: RRAS2+/-, t(4;14), t(14;16), t(14;20), and t(6;14). Analysis of mutational data revealed that RRAS2 and CD20 activation within the D1, D2, and t(11;14) subgroups reduced the number of mutations in the MAPK pathway. Further mutational analysis revealed that median mutational load was highest in t(14;16) and lowest in D2: RRAS2+ subgroups. The GEP70 score identifies 15% of patients with HR disease and is specific for this purpose. In an analysis of risk assessment methods, we compared GEP detected adverse lesions [t(4;14), t(14;16), t(14;20), 17p- and 1q+] with the GEP70 and revealed that GEP70 HR identified samples have lower OS rates than cases with more than one adverse lesion (validated in external sets). GEP70 HR segregates non-uniformly across molecular subgroups as over 40% of all HR cases are found in the TC10 t(4;14), t(14;16), and t(14;20) subgroups. GEP70 HR cases also have a higher mutational load than low risk cases. Furthermore, GEP70 HR is uniquely associated with 1q+ and 17p- as cases with at least one of these adverse lesions are 4.9 times as likely to be GEP70 HR as cases with neither. Conclusion GEP profiling has a central role in simplifying and standardizing the molecular subgroup designation and risk stratifying of MM patients. The GEP70 risk score reliably identifies HR cases and outperforms FISH in risk assessment, even in validation data sets. The TC10 provides a classification system that improves upon previous methods by defining both etiological and functionally meaningful subgroups. Disclosures Stein: University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Onyx: Consultancy. Heuck:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Other: Advisory Board; Millenium: Other: Advisory Board; Foundation Medicine: Honoraria. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding. van Rhee:University of Arkansa for Medical Sciences: Employment. Kaiser:Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; BristolMyerSquibb: Consultancy; Chugai: Consultancy. Sonneveld:Janssen-Cilag, Celgene, Onyx, Karyopharm: Honoraria, Research Funding; novartis: Honoraria. Goldschmidt:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: 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; Millenium: Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Weismann Institute: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Description: Introduction Functional imaging of Multiple Myeloma (MM) is redefining our knowledge of disease patterns. A pattern, termed macrofocal MM (macro MM), is defined by the presence of focal lesions and the absence of significant intervening bone marrow (BM) infiltration. At presentation, macro MM constitutes a distinct disease entity likely being associated with a favorable prognosis, although current evidence to support this is limited. Following first-line therapy, macrofocal patterns of disease emerge also in patients that initially presented with classical MM. In these patients the systemic BM involvement disappears in follow up examinations during treatment whereas focal lesions persist. In a third scenario, macrofocal patterns occur at overt relapse representing a patchy type of MM progression (Figure 1). The prognostic impact of a macrofocal pattern at these various disease stages is largely unknown. Therefore, we analyzed the clinical outcome and biological features of macro MM at different treatment stages. Patients and Methods 279 patients met the criteria of macro MM. Of those, 56 were at initial presentation, 48 at restaging following first line therapy, and 175 at relapse. Generally, macrofocal lesions were present in both positron emission tomography and magnetic resonance imaging. All first-line patients were treated with multi-agent induction therapy, autologous stem cell transplantation and received maintenance within prospective trials. Outcome results were compared to a set of cases with classical MM matched for age, gene-expression based (GEP) risk group, and treatment protocol. Results Macro MM at presentation is rare, constituting 6% of patients in the time period examined. The vast majority showed GEP-based low risk (94%). Age, Ig-type, and sex were not significantly different between macro MM and classical MM. With a median follow up of 8.6 years, only 10 of the macro MM patients relapsed. Compared to a matched-pair MM group, progression-free survival (PFS) and overall survival (OS) were significantly better in the macro MM group (P= 0.01 and 0.04 for PFS and OS, respectively). Thus macro MM at presentation constitutes a low risk form of MM. Focusing on the 10 macro MM cases who relapsed, no specific risk profile could be identified except 〉26 focal lesions on MRI was associated with a shorter PFS (P=0.04) but not with OS. Of note, although focal lesions frequently responded slowly, the time to response was not associated with outcome. To elucidate whether there are biological differences between MM cells in focal lesions and at differentially involved BM sites, we analyzed a set of 16 patients with paired samples from macrofocal lesions and iliac crest BM aspirates. No difference in a GEP based proliferation index was seen between the two sites. After correction for multiple testing we did not observe gene expression differences between them. A candidate gene study including a set of 27, myeloma relevant, adhesion molecules also did not reveal expression differences. In contrast to the situation at presentation, macrofocal patterns at restaging during initial therapy showed a 70% cumulative 24 months relapse incidence. The outcome of these cases was significantly worse in comparison to matched controls (P=0.02 and 0.02 for PFS and OS, respectively). Of note, all patients with macro MM showed an objective response at the time of imaging with 9 of 46 cases meeting the IMWG criteria for CR. Performing a similar analysis of patients with macro MM at relapse showed that 25% of patients presented with that pattern; a surprisingly high proportion. Extramedullary involvement was common (41%). Of note, 36% of patients repeatedly showed macrofocal patterns at subsequent relapses. PFS and OS at 2 years from macrofocal relapse were 24% and 39%, respectively. A matched group OS comparison was not possible since number of relapses and treatments were too different among the patients. Conclusions Macro MM at presentation seems to be an early stage of MM with an excellent prognosis. In contrast, a macrofocal pattern at restaging is associated with poor prognosis and early relapse. At this disease stage residual focal lesions may represent drug resistant clones. At overt relapse a macrofocal pattern was frequently seen, highlighting the need to integrate advanced imaging tools into the standard work up and indicating an important confounder of standard minimal residual disease diagnostics in MM. Disclosures Barlogie: Signal Genetics: Patents & Royalties. Davies:Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding; Univ of AR for Medical Sciences: Employment.

Description: Abstract 1814 Gene expression profiling (GEP) provides valuable information on molecular subclass and prognostic risk of multiple myeloma (MM). In addition to subclass designation, GEP also predicts many cytogenetic aberrations in MM (Zhou, Blood 119:e148). We re-examined GEP derived assignments of translocation-related molecular subgroups (CD1, CD2 MF, and MS) in correlation to FISH data. Focusing on 14q32 translocation, we used FISH probes to MMSET/FGFR3, CCND3, CCND1, MAF, and MAFB in combinations with probes specific to the immunoglobulin heavy chain (IGH) constant and viable regions (IGHC and IGHV) to identify translocations in myeloma cells from Total Therapy (TT) 4 and TT5 patients at baseline. Combining our FISH data with GEP expression of probe sets specific to the IgH translocation partners, we identified threshold expression levels; above this threshold the expression level was clearly indicative of genomic translocations of t(4;14)(p16;q32), t(6;14)(p21;q32), t(11;14)(q13;32), t(14;16)(q32;q23), or t(14;20)(q32;q11). Below these thresholds, increased signals reflected gene copy numbers. Overall, 42.2% (113/268) of patients were predicted and confirmed to have 14q translocations: 14.9% had t(4;14), 3.4% t(6;14), 19.4% t(11;14), 3.4% t(14;16), and 1.1% t(14;20). In cases of GEP indicated simultaneous spikes of several 14q translocation partners, we discovered that 14q translocation were indeed mutually exclusive - one 14q translocation per clone. The spikes of the other 14q partner gene(s) shown by GEP, reflected copy number driven increases, or in a few cases, the co-existence of distinct clones, each with a unique 14q translocation. Using threshold expression levels equivalent to those developed in the TT4 and TT5 training set, we predicted that 42.3% (335/792) of patients treated on TT2 and TT3 protocols had 14q translocations at baseline, as follows; 14.9% t(4;14), 1.4% t(6;14), 19.8% t(11;14), 4.4% (14;16), and 1.8% t(14;20). Next, we correlated the effects of 14q translocations with overall survival of patients treated with TT2 and TT3. Patients whose GEP spikes predicted MMSET/FGFR3 and CCND3 translocations significantly benefited from the inclusion of Bortezomib in TT3; and patients with CCND1 and MAFB translocations had improved median survivals comparing 8 years follow-ups of TT2 and TT3. In contrast, patients with high MAF expression clearly did not benefit from the inclusion of the proteasome inhibitor in TT3. In conclusion, by combining interphase FISH and GEP we established predictive threshold expression levels of 14q translocations in MM and identified a group of patients who do not benefit from Bortezomib. This single-gene approach to define molecular subgroups is expedient and provides important information of prognostic significance. Disclosures: Tian: University of Arkansas for Medical Sciences: Employment, under pending process, under pending process Patents & Royalties. Sawyer:University of Arkansas for Medical Sciences: Employment, under panding process, under panding process Patents & Royalties. Epstein:University of Arkansas for Medical Sciences: Employment, under pending process, under pending process Patents & Royalties. Barlogie:University of Arkansas for Medical Sciences: Employment, under pending process, under pending process Patents & Royalties.

Description: Introduction Gene expression and comprehensive genomic profiling (CGP) underscore the importance of multiple myeloma (MM) being driven by diverse genomic abnormalities and are increasingly being integrated into personalized treatment algorithms to optimize clinical outcomes, in particular that of high risk disease. Furthermore, CGP allow for ultra-deep sequencing of various clinically relevant and targetable genomic alterations using a single assay, with an advantage of detection of low frequency variants. Methods Samples from 578 patients (monoclonal gammopathy of undetermined significance, MGUS, (n=19); smoldering multiple myeloma, SMM, (n=42); or multiple myeloma, MM, (n=517; 87 newly diagnosed (NDMM), 107after treatment (TRMM), and 323 at relapse (RLMM)) were analyzed using the FoundationOne® Heme (F1H) assay. 50 ng of DNA and RNA from CD138+ selected cells were analyzed for genomic alterations including base substitutions, indels, copy number alterations, and rearrangements. Sequencing was performed to a median depth of 468x in 405 genes, as well as selected introns of 31 genes involved in rearrangements. Additionally, matched Gene Expression Profiling (GEP) was performed using Affymetrix U133 Plus 2 array, and GEP70-defined risk status and molecular subgroups were calculated. Results Results of the F1H assay revealed the most common alterations in MM to be: KRAS (28.8%), NRAS (23.2%), TP53 (17.4%), BRAF (6.8%), CDKN2C (6.0%), RB1 (5.8%), TRAF3 (5.8%), DNMT3A (3.9%), TET2 (3.7%) and ATM (2.5%), including mutations, homozygous loss and rearrangements. When these frequencies were split across GEP70 risk groups, TP53, CDKN2C/FAF1, RB1, and the t(4;14) were significantly different (p

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

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

Description: Introduction Ex vivo activated/expanded natural killer (ENK) cells can induce myeloma cell lysis both in vitro and in murine models and are currently being studied clinically in the setting of high-risk relapsing disease and asymptomatic disease at high risk of progression. This prompted us to study, in myeloma cell lines, whether intrinsic resistance to ENK cell lysis exists, whether repeated challenge with ENK leads to increased resistance, and what the underlying mechanisms of resistance are. Of 11 myeloma cell lines tested in standard 4h chromium release assays, 8 were avidly killed (78-89% lysis, E:T Ratio 10:1) whereas 3 lines were less sensitive (41-65% lysis). Repeated exposure to ENK challenge decreased sensitivity in 4 of 11 lines, that was at least in part due to down-regulation of Tumor Necrosis Factor-Related Apoptosis Inducing Ligand-Receptors on the myeloma cell surface (Garg et al, Blood 2012, 120:4020). In this study we investigated the resistance issue further via metabolomics, gene expression profiling (GEP) and flow cytometry analysis of OPM2, which was intrinsically resistant and developed further resistance after challenge with ENK cells. Methods Metabolomics was studied using a quantitative proteomic strategy entailing stable isotope labeling with amino acids in cell culture – mass spectrometry (SILAC-MS). Resistant and parental OPM2 cells were grown either in medium with heavy amino acids (13C6 L-Lysine and 13C6 L-Arginine) or with light amino acids (12C6 L-Lysine and 12C6L-Arginine). Reverse labeling with heavy or light amino acids was also done to confirm the results. Cell lysates from heavy and light amino acid labeled cells were pooled, simultaneously resolved on SDS-PAGE, protein bands were excised and analyzed on a mass spectrometer after trypsin digestion. GEP was performed using the Affymetrix U133 Plus 2.0 microarray platform (Santa Clara, CA). The fold change of signal intensity for genes and proteins in resistant vs. parental OPM2 was calculated. The most differentially expressed genes (top 150-fold up or down) and proteins (up or down by 1.3-fold) were compared for commonality. Cell surface protein expression was determined via flow cytometry. The ability of ENK to lyse myeloma cell targets in the presence of isotype control or ICAM-3 blocking antibody was tested in 4h chromium release assays. Results Metabolomics identified 〉3800 proteins and revealed that the abundance of 352 proteins was significantly altered in resistant myeloma cells. These altered proteins were mainly associated with cell cycle, morphology, organization, cellular compromise, immune response, and survival. Further, a comparison of these differentially expressed proteins with GEP data revealed 3 commonly up-regulated molecules: TBC1D8B, HSPA1A and IFI16; and 2 down-regulated molecules: intercellular adhesion molecule (ICAM-3) and BAI3. Of these, ICAM-3, a ligand for leukocyte function-associated antigen-1 (LFA-1) and a potent signaling molecule, was selected for further studies. Flow cytometry confirmed that ICAM-3 cell surface expression was 〉 8-fold lower on resistant versus parental OPM2 cells. Further, blocking of ICAM-3 in cytotoxicity assays resulted in decreased lysis (43% blocked, E:T ratio 5:1), suggesting that this molecule is functionally important and takes part in ENK cell-mediated killing. Conclusion In conclusion, quantitative proteomic analysis demonstrated dynamic changes in the ENK-resistant OPM2 myeloma cells that correlated with GEP and differences in ICAM-3 expression may have functional implications. Studies evaluating the expression of ICAM-3 in myeloma patients at diagnosis and relapse are in progress. Myeloma cells may down-regulate ICAM-3 as a mechanism of escape from immune surveillance and therefore, ICAM-3 may be a useful biomarker to predict sensitivity to ENK cell-mediated killing and aid in the selection of patients most likely to benefit from ENK cell therapy. Disclosures: Barlogie: Celgene: Consultancy, Honoraria, Research Funding; Myeloma Health, LLC: Patents & Royalties.

Description: Key PointsHits in driver genes and bi-allelic events affecting tumor suppressors increase apoptosis resistance and proliferation rate–driving relapse. Excessive biallelic inactivation of tumor suppressors in high-risk cases highlights the need for TP53-independent therapeutic approaches.

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