Citations

Introduction Waldenstrom macroglobulinemia (WM) is a rare, incurable, indolent non-Hodgkin lymphoma that is preceded by an IgM monoclonal gammopathy of undetermined significance (IgM MGUS). In recent years, better understanding of the pathobiology of WM and IgM MGUS has strengthened the hypothesis that they may represent different stages of the same disease. Studying the relationship between these two conditions is the essential first step to understanding the mechanisms driving the malignant transformation from IgM MGUS to WM. Recently long non-coding RNAs (lncRNAs) have garnered significant recognition for their roles in the regulation of gene expression and cellular function. lncRNA can regulate gene expression of neighboring protein-coding genes through multiple mechanisms such as by recruiting chromatin modifiers and increasing accessibility of genes to transcription proteins, or by directly binding to enhancers or promoters. Here we present the first analysis of the lncRNA expression profile in patients with WM compared to IgM MGUS. Methods Twenty-one patients with untreated WM (n=14) and IgM MGUS (n=7) seen at Mayo Clinic were included in this analysis. Malignant cells were isolated from bone marrow samples using CD19 and CD38 positive selection. Total RNA was extracted and sequencing was performed using the Illumina HiSeq4000 NGS platform. Differentially expressed lncRNAs in WM compared to IgM MGUS were defined as log2 fold change (FC) > 1.0 (upregulated) or < -1.0 (downregulated) and a false discovery rate (FDR) < 0.05. The nearest gene to each lncRNA was also identified and their gene expression was detected. Using the Ingenuity Pathway Analysis software and taking into account the transcriptional expression and known function of these genes, a correlation between cell function and disease analysis was performed. Results A total of 119 lncRNAs were differently expressed between WM and IgM MGUS (17 transcripts were upregulated in WM, while 102 were downregulated in WM). Sixteen unique genes were identified as the nearest gene to 17 upregulated lncRNAs, while 18 genes were identified as the nearest genes to the top 18 downregulated lncRNAs in WM compared to IgM MGUS. Several of the upregulated lncRNA in WM were in proximity to known oncogenes. The well-known oncogenic lncRNA PVT1 was found to be upregulated in WM compared to IgM MGUS (log2 FC 1.35, FRD 0.04). PVT1 is overexpressed in several malignancies and associated with inhibition of apoptosis and increased cell proliferation. PVT1 is in close proximity to MYC and co-expression of PVT1-MYC has been described in multiple cancers. PVT1 also potentiate MYC amplification by increasing MYC protein stability, therefore promoting malignant cell growth. The lncRNA TCB1D27, also upregulated in WM compared to IgM MGUS, is located in close proximity to the oncogene TNFRSF13B (TACI), which was found to be overexpressed in WM patients (log2 FC 1.61, FDR <0.001). TACI is a receptor for BLyS/BAFF, which modulates the development of normal B-cells and plays a role in various B-cell malignancies. In fact, BLyS/BAFF level is known to be increased in WM and to promote survival and proliferation of WM B cells. The upregulated lncRNA RP11-25K21.1 in WM is located in close proximity to the oncogene FCGR2B (CD32B), which was also upregulated in WM compared to IgM MGUS (log2 FC 1.59, FDR 0.001). Overexpression of CD32B was previously shown to be associated with inferior outcomes in DLBCL and FL patients, presumably secondary to an increased internalization and clearance of rituximab due to increased CD32B expression on malignant cells. Several other upregulated lncRNAs in WM compared to IgM MGUS are also in proximity of other oncogenes such as PCED1B, POLD3, CLLU1, and SMAD7. Finally, we considered the expression of the nearest genes and their reported cell function and association with diseases. Several genes that are known to have a role in the cell proliferation and/or neoplasm development were noted to be upregulated in WM (figure 1). Conclusion We present the first report of lncRNAs expression in WM and IgM MGUS in addition to a concurrent analysis of the gene expression of the nearest protein-coding genes. Several known oncogenes are in proximity of overexpressed lncRNAs in WM compared to IgM MGUS that could indicate potential implications in the pathobiology of these diseases and the mechanism driving the malignant transformation to WM. Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Dasari:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kapoor:Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Ailawadhi:Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Celgene: Consultancy. Gertz:Amyloidosis Foundation: Research Funding; i3Health: Other: Development of educational programs and materials; Medscape: Consultancy, Speakers Bureau; Ionis/Akcea: Consultancy; Celgene: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Teva: Speakers Bureau; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties; International Waldenstrom Foundation: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Affimed: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding.

Abstract Introduction Immunoglobulin M monoclonal gammopathy of undetermined significance (IgM MGUS) is a relatively common pre-malignant condition that may progress to Waldenstrom macroglobulinemia (WM), an incurable low-grade non-Hodgkin lymphoma. The acquisition of genomic alterations in a multistep process of progression is likely a central mechanism responsible for the progression of IgM MGUS to WM, leading to increased tumor burden and end-organ damage. The exact role of the immune system in this process is still poorly characterized. The modulation of the immune system by malignant cells leading to anti-lymphoma immunity or immune evasion is a well-stablished principle associated with lymphoma survival and progression, respectively. The understanding of the pathobiology of IgM MGUS and WM has grown significantly over the last few years; however, most of the past research has focused on genetics and flow cytometric analysis of malignant cells. The introduction of CyTOF, a novel platform coupling mass spectometry with single-cell flow cytometry using antibody-metal isotope pairs, has allowed a broader single-cell analysis and the study of functional profiles. We here report the use of CyTOF technology to comprehensively profile the immune system of patients with IgM MGUS and WM. Methods Viably-cryopreserved (DMSO 10%) single-cell suspensions containing peripheral blood mononuclear cells from 15 patients (WM n=9 and IgM MGUS n=6) were used in this study. Recovered cells (1-3 x 106 per sample) were stained with a 30-parameter surface protein CyTOF panel designed to characterize multiple cell types and profile the immune system. Nucleated cellular events were identified using a DNA intercalator conjugated to natural abundance iridium (191Ir and 193Ir). Cisplatin (195Pt) was used for dead-live cell discrimination and calibration beads containing natural abundance cerium (140/142Ce), europium (151/153Eu), holmium (165Ho), and lutetium (175/176Lu) were used for normalization of the instrument signal. CyTOF analysis of normalized files was carried out using the Astrolabe Diagnostics platform. Cell clusters were assigned using the FlowSOM package and differential abundance analysis was performed using the edgeR package. Analysis is adjusted for multiple comparisons. Results The median live single-cell count for the entire cohort was 573,300 (range 50,681 to 869,727), representing a median of 81% (range 39% to 87%) of the total event counts [median 820,800 (range 130,452 to 1,000,000]. The cell count and the proportion of event count was similar in patients with WM or IgM MGUS. T cells represented 45.8% of the total events; granulocytes represented 31.3%; NK cells represented 8.6%; monocytes represented 8.1%; B cells represented 2.4% of the total events for the entire cohort. Figure 1 shows a heat map of different immune cell sub-types proportion per patient sample. A difference was noted in the proportion of monocytes (CD14+, CD16+) and B-cells when comparing patients with IgM MGUS and WM (figure 2). Patients with WM had a higher proportion of monocytes compared to IgM MGUS patients (p=0.05). A higher proportion of B-cells was noted in patients with IgM MGUS when compared to WM (4.2% vs 0.6%, p=0.01). When B-cells were sub-classified as naïve, transitional, non-switched memory, switched memory, and plasmablasts, the proportion of non-switched memory B-cells was higher in IgM MGUS compared to WM (p=0.05). While the results of this pilot study suggest important differences between IgM MGUS and WM as regards the immune repertoire, confirmatory studies are in progress in a larger cohort of patients. Conclusion To our knowledge, this is the first study of mass cytometry in patients with WM and IgM MGUS patients. A higher proportion of monocytes and lower proportion of memory B-cells was noted in patients with WM compared to IgM MGUS. CyTOF study of the immune profile in these patients is feasible and can potentially uncover relationships between cell types not typically associated with the disease progression continuum (IgM MGUS to WM). While definitive conclusion cannot be made using this dataset due to the small sample size, CyTOF is a powerful tool in the study of the immune profile of WM patients. Disclosures Gertz: Amgen: Consultancy; Abbvie: Consultancy; Apellis: Consultancy; annexon: Consultancy; spectrum: Consultancy, Honoraria; Teva: Consultancy; Alnylam: Honoraria; Physicians Education Resource: Consultancy; Ionis: Honoraria; janssen: Consultancy; celgene: Consultancy; Prothena: Honoraria; Research to Practice: Consultancy; Medscape: Consultancy. Kapoor:Celgene: Research Funding; Takeda: Research Funding. Ailawadhi:Takeda: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Pharmacyclics: Research Funding. Reeder:Affimed: Research Funding. Ansell:Seattle Genetics: Research Funding; Regeneron: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Merck & Co: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Celldex: Research Funding; Bristol-Myers Squibb: Research Funding.

Abstract Immunomodulatory drugs (IMiDs) are widely used in the treatment of patients with Multiple Myeloma (MM) however, only 30% of relapsed MM patients respond to single agent therapy and most patients eventually develop drug resistance. The molecular target of IMiDs in MM is cereblon but other parallel pathways or downstream events which enhance or preclude drug responsiveness are unknown. We therefore conducted a genome scale small interfering RNA (siRNA) lethality study in MM in the presence of increasing concentrations of lenalidomide. Primary screening was performed in a single-siRNA-per-well format with the human druggable genome siRNA set V4 comprising four siRNAs targeting each of 6,992 genes (total 27968 siRNAs). Lenalidomide was added 24 hours post transfection and cell viability was measured by ATP-dependent luminescence at 144 hours after transfection. Primary screen data was rigorously evaluated for multiple quality control metrics and found to exceed all expected performance parameters with >98% global transfection efficiency, <0.25 CV values, and minimal plate-to-plate and set-to-set variations observed. Hit selection was performed by analysis of IC50 value shift in the presence of each testing siRNA compared with three different control siRNA oligos. 160 candidate genes that enhance lenalidomide sensitivity upon silencing (sensitizers) were selected and re-screened with four siRNA oligos targeting each gene. 50 genes were identified as reproducible lenalidomide sensitizers including three Peroxisome (PEX) family proteins (PEX1, PEX10 and PEX7) and seven RAB family proteins (RAB17, RAB1A, RAB26, RAB30, RAB36, RAB4A and RAB8A). Four kinase genes were also identified in sensitizer hits and two of these, I-Kappa-B Kinase-Alpha (IKK1 or CHUK) and ribosomal protein S6 kinase (RPS6KA3 or RSK2), encode proteins that associate with significance together with a phosphorylation dependent transcription factor (CREB1) in Toll signaling pathways (p-value 0.0068). RSK2 is a serine/threonine-protein kinase that acts downstream of oncogenic FGFR3 mediated signaling and is phosphorylated by ERK (MAPK1/ERK2 and MAPK3/ERK1 signaling) during hematopoietic transformation. Phosphorylated RSK2 was previously reported to be frequently expressed in myeloma cell lines and primary myeloma cells. Using lentiviral shRNA expression, we demonstrated that knockdown of RSK2 in three genetically variable MM cell lines induced cyctocytoxiticy and consistently sensitized to lenalidomide. Two selective small molecular inhibitors of RSK2 (SL 0101-1 and BI-D1870) were then demonstrated to synergize with lenalidomide to induce myeloma cell cytotoxicixity. To further understand the mechanism underlying sensitization, immunoblotting analysis was performed to look at downstream changes after either RSK2 knockdown or RSK2 inhibition by BI-D1870. We found that both RSK2 knockdown and BI-D1870 treatment, mimicking lenalidomide treatment or cereblon inhibition, induced downregulation of both IRF4 and MYC in MM cells. The combination of lenalidomide and BI-D1870 not only produced a substantial synergistic effect inducing MM cytotoxicity, but also demonstrated a significant enhancement of downregulation of IRF4 and MYC. Forced overexpression of RSK2 attenuated the synergistic effects of lenalidomide and BI-D1870. In summary, our high throughput screen identified multiple gene targets that associate with increasing sensitivity to IMiDs in MM cells, of which, RSK2 was further validated by both shRNA silencing and specific inhibitors as an effective target to cooperate with IMiDs to induce myeloma cytotoxicity. Clinical studies of RSK2 inhibition in concert with IMiD (cereblon inhibitor) therapy would be appropriate. Disclosures: Stewart: Onyx: Consultancy, Research Funding; Millennium: Honoraria, Research Funding; Celgene: Honoraria; BMS: Honoraria.

Abstract Despite recent advances and improvement in treatment options, chronic lymphocytic leukemia (CLL) is still an incurable disease. Recent advancement in parallel sequencing technology has provided better understanding of genetic landscape and clonal architecture underlying disease course. Furthermore, mutation status of TP53, SF3B1, NOTCH1, and BIRC3 has improved current CLL risk stratification. In order to better understand the genetic basis and clonal evolution underlying disease progression we analyzed a homogeneously treated cohort of CLL cases at multiple time points before and after therapy. We performed WES in 30 sequential samples from 12 CLL cases. All 12 cases received PCR (pentostatin, cyclophosphamide and rituximab) based chemoimmunotherapy as initial treatment. Of these 12 cases, two had samples available both >6 months prior to enrollment and at treatment initiation, seven patients who experienced relapse had samples available at both treatment initiation and at and the time of relapse, and three patients had samples >6 months prior to enrollment, at treatment initiation, and at relapse. These cases were previously characterized by aCGH/FISH. R function Mclust and kernel density estimate (KDE) plots were used to identify subclonal heterogeneity. An average of 102-fold depth coverage was obtained with an average of 80% of the targeted regions being covered by at least 30X. Overall, we detected a total of 219 nonsynonymous Single Nucleotide Variants (SNV) and indels (average 19, range 11-33). Del13q (42%) was identified as most prevalent copy number abnormality followed by trisomy 12 (33%), del11q and del17p (17% each). Recurrent mutations were identified in NOTCH1 (33%), DDX3X (25%), TP53, SF3B1 XPO1 and MED12 (17% each). Other tumor implicated genes such as CARD11, NOTCH2, NOTCH4, DIS3, TRAF2, NFKBID, CHK2 and RB1 were found mutated in individual cases. In all 12 cases we identified at least one relevant cytogenetic abnormalities (del11q32, del13q14, del17p13, trisomy 12) and/or a mutated driver gene. Interestingly we found NOTCH1, DDX3X and XPO1 mutated at a significantly higher prevalence than previous studies (10%, 2% and 4%, respectively). Since all cases in our cohort correspond to individuals with progressive disease, a plausible explanation for the higher prevalence of mutations in our cohort is that these mutations are associated with disease progression. By WES we identified multiple subclones in 58% of cases (7 of 12 cases), as compared to previously identified 33% (4 of 12 cases) by CGH/FISH. In 5 chemotherapy naïve cases, no significant increase in genomic complexity was detected with disease progression. In contrast, 5 out of 10 cases with samples analyzed before CIT and at the time of relapse had changes in clonal dynamics, with initially dominant subclones diminishing in response to therapy and the available empty niche being occupied by expansion of another “fitter” subclone at the time of relapse. No acquisition of tumor associated gene mutations was seen post therapy or associated with relapse in 9 of 10 cases. In the remaining case we identified an interesting example of convergent evolution. The pre-therapy sample was comprised of a unique clone characterized with del11q13, mutations in SF3B1 (G742D) and DDX3X (pE196fs). After therapy that clone was either eradicated or reduced to below detection level. Interestingly another clone emerged after relapse that harbored an independent del11q22 and mutations affecting different aminoacids of SF3B1 (K700E) and DDX3X (pL21fs). The strong selection of impairment in these genes during disease progression suggests a key role of these pathways/genes in the pathogenesis of this patient. In conclusion, these studies provide additional insight into the genomic landscape and clonal architecture in a cohort of homogeneously treated CLL cases before and after therapy. Stable clonal architecture was seen in prior to treatment, whereas clonal evolution leading to increased tumor heterogeneity occurred after the under selection selective pressure of therapy in relapsed cases. Early identification of tumoral heterogeneity and underlying genetic abnormalities might provide better targets for therapy to decrease the likelihood of relapse. Disclosures: Fonseca: Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties. Shanafelt:Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

Abstract Clonal hematopoiesis of indeterminate potential (CHIP) is defined as somatic mutations in clonal blood cells of individuals without any other hematological abnormalities and have been identified using whole exome sequencing (WES) from pre-specified genes. CHIP is reportedly associated with aging, reduced overall survival, cardiovascular disease, and hematologic disease, mainly myeloid cancers. Additionally, whole genome sequencing and WES studies in CLL cases have identified putative CLL driver genes with recurrent somatic variants or copy number alterations. Here, we investigate the associations of CHIP and mutations in CLL somatic driver genes with risk of CLL and MBL, a precursor to CLL. Using WES, 571 individuals from 132 CLL families were sequenced from two different sites, Mayo Clinic (92 families) and National Cancer Institute (NCI, 40 families), including 251 CLLs, 74 MBLs, and 246 unaffected relatives. DNA was extracted from buccal cells (Mayo Clinic) and peripheral blood (NCI). Allele counts from gVCF files were extracted for specific genes and analyzed. Variants were annotated using the Variant Effect Predictor in Ensembl, genome build GRCh37 (http://useast.ensembl.org/index.html). Highly-sensitive flow cytometry of the blood was used to screen for MBL. CHIP was defined as in Genovese et al., 2014 as the presence of any of the following: A) disruptive mutations in ASXL1, TET2, or PPM1D; B) missense mutation p.617F in JAK2; C) disruptive or missense mutations in DNMT3Alocalized to exons 7 to 23D) any mutations reported at least 7 times in hematological malignancies in the Catalogue of Somatic Mutations in Cancer (COSMIC); and (E) No variant allelic fraction (VAF) threshold. CLL driver genes included disruptive or missense mutations in ATM, BIRC3, MYD88, NOTCH1, SF3B1, TP53, EGR2, POT1, NFKBIE, XPO1, or FBXW7with VAF greater than 0.1. Mutations from COSMIC and CLL driver genes were excluded if the minor allele frequency in 1000 Genomes was greater than 0.5%. Data was analyzed using logistic regression adjusting for age, sex, and site. Odds ratios (OR) and 95% confidence intervals (CI) were estimated. Median age for CLL, MBL, and unaffected was 63, 61.5, and 54, respectively. The unaffected relatives were significantly younger than both the CLL and MBL groups (p<0.0001), with no difference between CLL and MBL (p=0.46). A total of 70 (12%) individuals from 54 different families had CHIP. CHIP was characterized in the majority of samples as mutations in DNMT3A, ASXL1, and TET2with 43, 9 and 8 individuals, respectively. As expected, CHIP was associated with age (p<0.001) but not sex (p=0.87). CHIP was present in 42 (17%) CLLs, 9 (12%) MBLs, and 19 (8%) unaffected relatives. There was a significant association between the presence of CHIP and CLL risk compared to unaffected relatives (OR= 2.19, 95% CI: 1.20 - 4.13, p=0.013) but not with MBL risk (OR=1.34, 95% CI: 0.54 - 3.11, p=0.51). Using the CLL driver genes, we found mutations in 119 (47%) CLL, 20 (27%) MBL, and 70 (29%) unaffected relatives. Mutations in CLL driver genes were associated with CLL risk compared to unaffected relatives (OR = 2.51, 95% CI: 1.64 - 3.87, p<0.0001), but there was no evidence for increased MBL risk (OR = 1.00). These results held when we subset the analysis to the Mayo only data where the DNA was extracted from buccal cells. This study further supports the association between mutations in CHIP related genes and risk of hematologic malignancies, in particular CLL. This is the first report of the prevalence of CHIP in MBLs. Although MBL and CHIP represent clonal population of blood cells, our data suggest the clones are different. Mutations in CLL driver genes are also associated with CLL risk but show little to no evidence with increased MBL risk. Interestingly, our unaffected relatives of CLL cases also had a high frequency of CLL driver mutations. Future studies are needed to understand these acquired mutations (both in CHIP- and CLL driver- related genes) in the unaffected to MBL to CLL continuum. Disclosures Parikh: Abbvie: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Research Funding. Ding:Merck: Research Funding. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Shanafelt:Jansen: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; GlaxoSmithKline: Research Funding. Kay:Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Background The precise molecular mechanism and targets of lenalidomide specific anti-Multiple Myeloma (MM) activity remain largely unknown, although, cereblon (CRBN) has been identified as a central mediator of activity with IRF-4 acting as a downstream molecule. Lenalidomide resistance in MM cells which, despite CRBN depletion, are able to restore their IRF-4 levels suggests the existence of alternative pathways. Methods To measure apoptosis and gene expression analysis 1x 106cells were incubated with 10μm to 50μm of lenalidomide for 24 to 96 hours before they were examined by annexin-PI and FACS analysis. Gene and protein expression were measured by RT-PCR, western blot, and immunohistochemistry. For lenalidomide followed bortezomib serial studies, cells were incubated with 10μm of lenalidomide for 48 hours and later treated with bortezomib for 24 to 48 hrs. CRBN and immunoglobulin light chain knock-down were performed by lentiviral mediated shRNA. Results We found lenalidomide induces Endoplasmic reticulum stress (ERS) via accumulation of intracellular immunoglobulin proteins, but only in the presence of CRBN in a panel of MM cell lines. This accumulation of intracellular proteins resulted in ERS as documented by western blot and RT-PCR in OPM2 and MMIS cell lines. We next utilized OPM2 cells knocked down for CRBN and control, non-target (NT), and treated with different doses of lenalidomide. After three days of treatment an ERS response was occurring in OPM2-NT cells but not in CRBN knockdown cells. Using Western blot we found decreased XBP-1u and increased XBP-1s, ERS marker proteins, in CRBN positive (NT) cells after 72 hours of lenalidomide treatment, but not in CRBN knock-down cells. Another ERS marker GRP78/BiP was also clearly accumulated after lenalidomide treatment in CRBN positive cells. Inactivation of the tumor suppressor p53 by degradation is a mechanism utilized by cells to adapt to ERS has been reported. We also found that p53 protein down regulated after lenalidomide treatment in CRBN positive OPM2 and MMIS cells. We have analyzed other isogenic cell lines MM1.S (lenalidomide-sensitive) and isogenic MM1.Sres (lenalidomide-resistant) HMCLs and found that lenalidomide induces ERS in MM1.S but not MM1.Sres cells. CRBN is important for lenalidomide to induce immunoglobulin protein accumulation and ERS was further proved by over-expressing CRBN in OCI-My5 MM cell line and subsequent lenalidomide treatment. To test the effect of light chain accumulation as ER stressors, we knocked down immunoglobulin light chain lambda in human MM cell line OPM2. Knock-down of lambda light chain in OPM2 cells resulted in lenalidomide resistance and it induced endoplasmic reticulum stress mediated anti-myeloma activity. Our preliminary data reveals that lenalidomide mediated progressive ERS can positively enhance bortezomib induced apoptosis in an in-vitro MM model. We pre-treated myeloma cell lines with lenalidomide and subsequently treated with bortezomib. MM cells pre-treated with lenalidomide for two days and there after treated with bortezomib clearly shown increased sensitivity to bortezomib induced apoptosis than non-pre-lenalidomide treated cells. Conclusion Here we show that lenalidomide causes accumulation of immunoglobulin light chains, inducing progressive ER stress, resulting in anti-myeloma activity and that effect depends on the presence of CRBN. Proteasome inhibitors such as bortezomib also sensitize MM cells to endoplasmic reticulum stress-mediated apoptosis. Our data demonstrated the synergistic effect of lenalidomide and proteasome inhibitors, likely due to the augmented protein stress. This data support the use of serial therapy utilizing pre-lenalidomide treatment followed by bortezomib or carfilzomib treatment. Disclosures: Fonseca: Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties.

Abstract Abstract 5093 A relative increase in proliferation is now accepted as the most important measure in risk stratification of myeloma. Attempts to include measures of proliferation in different stratification strategies have been met with obstacles largely influenced by their practicality and economic viability in the clinical setting. The plasma cell labelling index (PCLI) is the gold standard used in myeloma; however it is still only used in a minority of centers across the globe. New surrogate markers of proliferation, such as serum biomarkers and transcription-based methods, are under investigation but few have evolved to become part of a uniform strategy in the stratification of myeloma. The gene-expression based proliferation indices (gPI) described by Bergsagel et al, Shaughnessy et al and Hose et al all show similar correlation with PCLI. Studies comparing all currently available methods are lacking. In a cohort of 70 patients, PCLI and gPI were compared with two immunohistochemistry-based methods of measuring proliferation, Ki67 and a more specific measure of S-phase entry – phosphorylation of minichromosome maintenance protein 2 (pMcm2) at serine 40/41, combined with dual staining of CD138. We observed a similar correlation of gPI wth PCLI to that previously reported (r value; CI – 0.43;0.21–0.61). Both Ki67 and pMcm2 staining showed a stronger correlation with PCLI than with gPI and PCLI (r value; CI – 0.81;0.71–0.88 and 0.86; 0.78 – 0.91 respectively). PCLI and gPI have already been shown to be an independent predictor of time to progression and overall survival. Although this sample size was too small to test for time to progression or overall survival and thus independence, a logical extrapolation would indicate similar independence and predictions on survival and time to progression. As Ki67 is routinely used for more proliferative cancers in the clinic, the extension of its use as part of a new stratification system in myeloma provides interesting possibilities for further exploration. Disclosures: O'Dwyer: Novartis: Honoraria, Research Funding.

Abstract Abstract 2838 The combination of novel agents and chemo-immunotherapy (CIT) has led to a significant increase in both overall and complete response rates for chronic lymphocytic leukemia (CLL). There is, however, significant clinical heterogeneity in the overall effectiveness of CIT. Specifically, some patients are long-term responders who experience durable clinical remission while others, even though responding well initially, have less durable remissions. It is unclear if this heterogeneity is a reflection of an incomplete cytotoxic effect or whether certain leukemia sub-populations (i.e. subclones) are resistant to the effects of CIT. Comprehensive analysis of tumor cells has provided, in some malignancies, evidence of extraordinary genomic complexity and intraclonal genetic diversity. Conversely, little is known of the genetic architecture of the leukemic clone in CLL patients and the extent to which CLL tumor cells differentiate genetically at the subclonal level. In order to gain insight into the genetic architecture and clonal evolution underlying disease progression/relapse, we performed a sequential, longitudinal whole genome analysis by array-based comparative genomic hybridization (aCGH) in 22 CLL cases with evidence of clinical progression and/or clinical relapse post therapy. These patients received standard purine analogue, cyclophosphamide and rituximab (PCR) for progressive disease. For the purposes of this study, we classified samples as; time point 1 (TP1) collected >6 months before starting first-line CIT for disease progression; time point 2 (TP2) consisting of tumor samples at the time of initial progression requiring treatment; and time point 3 (TP3) consisting of tumor samples collected >6 months after initial treatment but prior to subsequent treatments. Overall, aCGH assay was performed in 54 samples from 22 cases (2 to 4 time points analyzed per patient): 6 cases were analyzed at TP1 and TP2, 8 at TP2 and TP3, 2 at TP1 and TP3, and the remaining 6 cases were analyzed at TP1, TP2 and TP3. In cases showing genetic changes between time points, FISH analyses were performed in 32 additional samples to assess intra-clonal diversity and provide a quantification of the clone size. There was a small increase in genomic complexity in later time points with a mean of 4 (median 4, range 0–12) CNAs detected at the first time point for each patient, increasing to a mean of 5.3 (median 4.5, range 0–21) CNAs at the last time point analyzed. Diverse patterns of changes in genetic architecture were observed. The simplest change in genetic architectures was the linear sequence, found in 2 cases (9%) and characterized by the maintenance of the initial abnormalities and the subsequent acquisition of additional CNAs in the same subclone. The pattern of changes in genetic architecture in another 4 patients (18%) demonstrated marked heterogeneity including multiple genetic subclones evolving either in succession or in parallel. In all 4 cases with multiple subclones, a marked shift in the dominant subclone was observed at the time of disease relapse, with the minor subclones at TP1 and TP2 becoming the dominant subclones at TP3. In addition, the subclonal evolution continued following secondary therapies, and in all 4 cases the emerging subclone showed resistance to multiple treatments. This argues strongly in favor of subclones with specific genetic structures becoming dominant with the selective evolutionary pressure imposed by therapy. In summary, this sequential genetic study reveals that CLL leukemic B cells exhibit an unanticipated level of heterogeneity including a variable number of genetically distinct subclones, which display dynamic behavior in disease progression. The characterization of this intraclonal architecture is of critical importance to better understand the mechanisms of disease progression and relapse as well as to ultimately inform us of individualized treatment strategies. Disclosures: Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

Abstract Massively parallel sequencing analyses have revealed a common mutation within the MYD88 gene (MYD88L265P) occurring at high frequencies in many non-Hodgkin lymphomas (NHL) including the rare lymphoplasmacytic lymphoma, Waldenström’s macroglobulinemia (WM). Using whole exome sequencing, Sanger sequencing and allele-specific PCR, we validate the initial studies and detect the MYD88L265P mutation in the tumor genome of 97% of WM patients analyzed (n=39). MYD88L265P was detected at lower frequencies in other indolent lymphomas including LPL (0%), MALT (4%), nodal MZL (5%) and splenic MZL (8%); all but one MYD88L265P was heterozygous. Due to the high frequency of MYD88 mutation in WM and other NHL, and its known effects on malignant B cell survival, therapeutic targeting of MYD88 signaling pathways may be useful clinically. However, while the effects of MYD88L265P on the activity of IRAK1/4 and NF-κB are have been studied previously, we are lacking a thorough characterization of the role of intermediary signaling proteins such as TRAF6 and TAK1 on the biology of MYD88L265P-expressing B cells. A better understanding of the proteins involved in MYD88L265P signaling may lead to the development of more targeted and effective therapeutic approaches. In an attempt to identify MYD88L265P –specific therapeutic targets we first wanted to characterize the role of intermediary signaling proteins that facilitate the downstream activation of NF-κB. Upon activation of TLRs or IL-1b receptors, MYD88 forms a homodimer and recruits IRAK1/4 and TRAF6 into a complex resulting in association and phosphorylation of TAK1 followed by activation of NF-κB. We monitored the formation of a complex comprised of MYD88, IRAK1, IRAK4 and TRAF6 and immunoprecipitation of either endogenous IRAK4 or IRAK1 revealed constitutive association of IRAK with TRAF6 and MYD88L265P. To assess if the formation of a MYD88L265P/IRAK/TRAF6 complex results in downstream activation of TAK1, constitutive TAK1 phosphorylation was measured and detected in all three cell lines that express MYD88L265P. An association between TAK1 and TRAF6, another measure of TAK1 activation, was also detectable. When a similar analysis of TAK1 was performed in DLBCL cells expressing wild-type MYD88, no phosphorylation of TAK1 was detected, nor was TAK1 associated with TRAF6. IRAK1, IRAK4, TAK1, TRAF6, and MYD88 were expressed at similar levels in all cell lines studied and therefore did not contribute the differences in MYD88 complex formation observed between cell lines. These studies were further confirmed using HEK 293T cells that were transduced with either a vector control plasmid or HA-tagged MYD88WT or MYD88L265P expression plasmids. Together, these studies suggest that MYD88L265P forms a complex with IRAK and TRAF6 resulting in constitutive activation of TAK1 and NF-κB. To confirm the significance of TAK1-mediated MYD88L265P signaling on lymphoma cell growth, the effect of the selective TAK1 inhibitor, (5Z)-7-Oxozeaenol, on cell proliferation was tested. All MYD88L265P-expressing cell lines were sensitive to TAK1 inhibition in a dose-dependent manner (0-10 μM). In contrast, NHL cells expressing MYD88WT were found to be insensitive to TAK1 inhibition. We next tested the impact of the TAK1 inhibitor on a MYD88L265P positive WM patient sample. Similar to what was seen in the WM cell lines, the TAK1 inhibitor inhibited WM cell growth and survival in a dose dependent manner. Additionally, the TAK1 inhibitor significantly reduced the level of IL-10 secreted by each of the cell lines. Together, these data suggest that MYD88L265P drives cell proliferation and cytokine secretion through a TAK1-dependent mechanism. In conclusion, we are the first to validate by NGS in a large patient cohort the high prevalence and specificity of MYD88L265P in WM. Cells harboring the L265P mutation but not wild-type MYD88 exhibit constitutive signaling leading to the hyperactivation of NF-κB. We have established the role of TAK1 as an integral component of MYD88L265P signaling in both WM and DLBCL cell. Our data suggest that targeting TAK1 clinically may be an effective strategy for the treatment of WM and other lymphomas driven by MYD88L265P signaling. Disclosures: Fonseca: millennium: Consultancy; amgen: Consultancy; Binding site: Consultancy; onyx: Consultancy; medtronic: Consultancy; Genzyme: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; lilly: Consultancy; Onyx: Research Funding; cylene: Research Funding.

Abstract Abstract 2881 Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. Significant efforts have been made to identify genetic changes and signatures that can predict clinical outcome and include them in the routine clinical care. Gene expression profiling (GEP) studies have achieved a central role in the study of multiple myeloma (MM), as they become a critical component in the risk-based stratification of the disease. To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells (obtained from the immunobead selection of CD138+ cells) from 489 MM samples in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, UAMS class, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of 57 samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. A macrophage index was calculated and used as a surrogate measurement of non-plasma cell contamination. A total of 49 samples (10%) were excluded from subsequent analysis as the macrophage index indicated a significant contamination with no plasma cells, hence potentially compromising the results. The percent of high-risk disease patients identified from different signatures ranged from 26.4% by using high proliferation index to 28.8% with high centrosome signature and 31.3% with high 70-gene index. This percent of high-risk cases based on the 70-gene index is similar to what was found in Total therapy 2 (TT2) and TT3 cohorts. A third of patients (33.2%) were classified as D1 in the TC class, followed by 11q13 (19.3%), D2 (16.4%), 4p16 (13.8%), MAF (6.1%), None (4.7%), D1+D2 (4.5%) and 6p21 (1.8%). The NF-kB pathway was likely activated in 45.5% to 59.5% of cases, depending on the index used for its calculation. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02) and D2 (p=0.05) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations with unknown partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making. Disclosures: Stewart: Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding. Fonseca:Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.