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Abstract Introduction Although allogeneic hematopoietic cell transplantation (HCT) is an expensive modality of treatment for hematological disorders, very little is known about the financial burden for the patients themselves. We analyzed factors associated with financial burden and its impact on health behaviors of allogeneic HCT recipients. Patients and Methods A questionnaire was mailed to adult patients who were alive after allogeneic HCT performed between 1/06 to 6/12 at Mayo Clinic to collect information regarding financial concerns, household income, employment status, insurance, out-of-pocket expenses and health and functional status. A multivariable logistic regression analysis was performed to determine socio-demographic and clinical factors associated with financial hardship. Results Of 482 patients, 268 responded (response rate 56%). Respondents were more likely to be older and Non-Hispanic Whites. No significant differences were seen in disease or transplant characteristics between the two groups except that respondents were more likely to have received reduced intensity conditioning. Median follow-up after HCT was 2.3 years (range 0.7-6.7 years). All the patients for whom the insurance information was available (missing n=13) were insured. Median physical and mental component score, as derived from the SF-12, were 36.8 [Interquartile range (IQR) 29.3-49.0] and 53.1 (IQR 42.1-57.1) respectively, where the general population score is 50, and lower scores indicate worse functioning. The proportion of patients that reported being on medical disability increased from 8% prior to HCT to 29% after HCT. 73% of the respondents reported that being sick had hurt them financially. 54% experienced adverse financial consequences such as decrease in household income by >50% due to the HCT, need to sell/mortgage home, withdraw money from retirement accounts or having to pay more for medical care than they could afford. 3% declared bankruptcy. 12% had out-of pocket costs for the past 3 months that were> $5000. Median monthly medication copayments were $100 (range $ 0-3000). 36% patients felt that their insurance coverage was poor which was strongly correlated with adverse financial consequences (p=0.0013). Other risk factors for financial burden were poor physical functioning reflected by the physical component score <40 (OR 3.2; p=0.005), lower household income (OR 3.08; p=0.006) and lack of commercial/private insurance (OR 4.2; p<0.001) while adjusting for patient and transplant related characteristics. Time from transplant was a significant predictor (OR 1.2; p=0.03) in the univariate analysis, but lost its significance in the multivariate analysis. 18 to 27% patients reported deleterious health behaviors due to financial constraints such as not taking prescribed medications, not using a recommended medical service or not making physician appointments. Conclusions A significant proportion of allogeneic HCT survivors experience adverse financial consequences with a few reporting severe outcomes like bankruptcy, despite having health insurance coverage. Future research should investigate potential interventions such as early financial counseling and connecting with sources of financial support to help at-risk patients and prevent long-term adverse financial outcomes after this life-saving procedure. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: Treatment options for elderly acute myeloid leukemia (AML) patients, despite various novel therapies, are still limited. Relapse is common after chemotherapy, and progression on lower intensity treatment with hypomethylating agents is inevitable in all patients even after initial response. Lysine deacetylase inhibitors (KDIs or HDAC inhibitors) have shown limited single-agent clinical activity in AML. Therefore, to develop potential novel treatment options for AML, we aimed to identify synergistic KDI-based drug combinations to build upon the single-agent activity and clinical-availability of KDIs. We performed RNA-interference (RNAi) drug modifier screens of the kinome and phosphatome with the KDI vorinostat in AML cells in order to identify targets to inhibit in combination with KDIs. Based on pathway analysis of RNAi screen results that highlighted an unexpected role for p38/JNK/SAPK signaling in vorinostat activity in AML, we explored the putative p38 substrate-selective MK2 inhibitor CMPD1 combined with KDIs, and demonstrated potent synergistic activity in AML regardless of molecular, cytogenetic or lineage background. As published evidence suggests that CMPD1 can also act as a microtubule-destabilizing/targeting agent (MTA), and that MK2 can associate with microtubules and cytoskeleton-associated proteins, we examined several MTAs in combination with KDIs in AML, including vinca alkaloid microtubule-destabilizing agents vinblastine/vincristine, and taxol as a microtubule-stabilizing agent. Results: Vinblastine and vincristine dose-dependently synergized with vorinostat similarly to CMPD1 in all AML cell lines tested, with vinblastine being more potently synergistic than vincristine. Taxol only potentiated KDI activity in approximately half of AML cell lines. Importantly, preliminary data suggest preferential, dose-dependent synergy of KDIs and vinca alkaloid MTAs in the CD34+ progenitor AML cell fraction, as compared to the paired CD34-depleted fraction from the same primary AML sample in ex vivo culture. This preferential CD34+ activity was similar to that observed with CMPD1 combined with KDIs in ex vivo models. Consistent with synergistic activity of MTAs and KDIs, multiple RNAi screen hits also converged on microtubule dynamics. Subsequently, we identified the LKB1 signaling network, a critical integrator of cellular stress metabolism and hematopoietic stem cell quiescence/homeostasis, as a determinant of vorinostat activity in AML. Of 14 known kinases directly regulated by LKB1, 4 belong to microtubule-associated protein (MAP) or microtubule-affinity regulating kinases (MARK) families (e.g. screen hit MARK4). RNAi screen hits also converged on regulation of microtubule proteins, or directly on microtubule proteins themselves, (e.g. atypical MAPK4 and MAPK6 which phosphorylate microtubule-associated protein 2, or TAOK2 which has a kinase-independent role in stabilizing microtubules). Additionally, several RNAi hits such as PACSIN1 and -2, and MAST2 and -4 link the actin cytoskeleton to microtubule dynamics. Discussion: Herein we show that several types of clinically-used MTAs (i.e. vinca alkaloids or taxanes) potently synergize with KDIs in AML, and that this synergy can occur preferentially in primary CD34+ cells, as compared to the CD34-negative AML compartment. Although at high in vitro doses microtubule-destabilizing agents such as vinca alkaloids, and microtubule-stabilizing agents such as taxanes, can both induce apoptosis via mitotic perturbation, many publications show that low therapeutic MTA doses can induce apoptosis in tumor cells in vivo independent of mitosis. Therefore, we hypothesize that clinically-available KDIs (i.e. vorinostat, panobinostat) and MTAs (i.e. vinca alkaloids or taxanes) represent novel combinations with the potential to target the AML progenitor pool even in a non-proliferate state, and thus have the potential to eradicate minimal residual disease. Disclosures Mesa: Celgene: Research Funding; Incyte Corporation: Research Funding; Ariad: Consultancy; Galena: Consultancy; Novartis: Consultancy; Promedior: Research Funding; CTI: Research Funding; Gilead: Research Funding. Tibes:Italfarmaco S.p.A.: Consultancy, Honoraria.

Abstract Gene expression profiling has shown that diffuse large B-cell lymphoma (DLBCL) clusters into three major subtypes based on similarity in expression patterns to their cell of origin (COO): germinal center B-cell-like (GCB-DLBCL), activated B-cell-like DLBCL (ABC-DBLCL) and primary mediastinal B-cell lymphoma (PMBCL). These subtypes of DLBCLs are associated with distinctly different overall survival rates after standard immunochemotherapy. However, clinical and prognostic heterogeneity remains within COO subsets and strategies are needed to further stratify patients to identify and target high-risk subsets. A comprehensive genomic analysis of COO on a clinically defined set of DLBCL cases has not been performed and the aim of this study was to use whole-exome sequencing (WES) data from 58 paired tumor-normal DLBCL samples to assess association of known DLBCL genomic alterations with cell COO as well as for identification of novel and relevant genetic biomarkers. To investigate genomic alterations associated with DLBCL subsets, we analyzed WES and genome wide copy number data from 58 paired tumor-normal DLBCL tumors. Gene expression profiling or Hans classification was performed to determine DLBCL COO subtype; 31 patients were classified as GCB and 27 as non-GCB. The WES data were used to 1) assess the association of known DLBCL genomic alterations with COO, and 2) identify novel alternations associated with COO. Statistical analysis was performed and the data were ranked by significance (p≤ 0.05) within each DLBCL subtype. In total, 45 genomic abnormalities were analyzed for their association with either GCB, non-GCB or both. Mutations in CREBBP, EZH2, MEF2B, FOXO1 and REL have also been reported as GCB driver mutations and we observed GCB patients with these mutations, but the mutation clustering was not wholly associated with GCB. MYC double-hits were exclusively found in the GCB-subtype group. For the non-GCB cases we found that mutations in MAP2K3 and MYD88 were significantly associated with this subtype(p< 0.05). In addition to mutational patterns, we identified several copy number alterations (CNA) across both groups. Chromosomal losses in GCB patients were found at chromosomes 10q11.21-10q24.23, 4q12-4q35.2, 3q12.1-3q29, 4p12-4p16.3, 10p11.21-10p15.3, and 14q11.2-14q24.3 whereas gains were localized to 7q11.1-7q36.3, 7p11.2-7p22.3, and 1q21-1q32.1 (p < 0.05). No CNA was observed to directly associate with non-GCB patients, however, a loss at 9p21 and gains at 9q24.1 and 18q21.33 trended with the non-GCB subtype, supporting previous reports. Loss at 10q23.31 or a gain in 2p13-2p12 have been reported as being specific for GCB and our data confirmed the association of 10q23.31 with GCB while a gain at 2p13-2p12 (REL)was found in both subtypes. To further understand genomic differences between DLBCL subtypes, we evaluated the relative percentage of each genomic feature. 18/45 (40%) were only observed in GCB patients whereas 2/45 (5%) were specific to the non-GCB subtype. The majority (25/45, 55%) overlapped between the two subtypes. Throughout our analysis we noted that 7 non-GCB cases lacked any of the driver mutations analyzed in the study. While all cases carried mutations, they consisted of low frequency mutations that were not specifically associated with COO. 2/7 cases had a gain at 9p24.1 that included CD274 and JAK2. Because 9p24.1 gains have not been fully defined in DLBCL, we reviewed all cases and identified 4 (7%) with a 9p24.1 gain, 3 of which were non-GCB and 1 GCB. One non-GCB case was EBV+ and none of the cases showed evidence of PMBCL. Of the 9p24.1 cases, three had RNAseq data available and we found that PDL1 and JAK2 expression was elevated (12 fold p< 0.01 and 7 fold p< 0.01, respectively) when compared to the 9p24.1 normal cases (n=32). While outcome was not the focus of the study, we did note that 6/7 cases that lacked driver mutations achieved event free survival at 24 months (EFS24). Taken together, this analysis has further characterized the genetic profile of each COO subtype and has identified novel GCB CNAs which require independent replication. Additionally, we identify a subgroup of non-GCB DLBCL patients that do not harbor known driver mutations and require further genomic study to better resolve the biology of these tumors. Together, these data provide insight on the genetic heterogeneity of DLBCL and identify genetic variants that may inform subtype specific therapy. Disclosures Nowakowski: Morphosys: Research Funding; Celgene: Research Funding; Bayer: Consultancy, Research Funding. Rimsza:NCI/NIH: Patents & Royalties: L.M. Rimsza is a co-inventor on a provisional patent, owned by the NCI of the NIH, using Nanostring technology for determining cell of origin in DLBCL..

Abstract Introduction Venetoclax (ABT-199) is an approved BCL-2 inhibitor for the treatment of patients with chronic lymphocytic leukemia (CLL). Multiple clinical trials are underway to explore its efficacy in additional indications. While venetoclax demonstrated high remission rates in combination with azacitidine in early stage clinical trials, the question of durability of responses and primary and acquired resistance remain, especially given the modest activity and rapid development of resistance as a single agent. One reported mechanism of intrinsic resistance is high expression of other BCL-2 family proteins, including MCL-1. We and others have demonstrated that the CDK9 inhibitor, alvocidib, can mediate transcriptional repression of anti-apoptotic MCL-1. It has also been shown that alvocidib can increase pro-apoptotic BIM, a dual activator and sensitizer BH3-only protein that can directly induce apoptosis and simultaneously inactivate anti-apoptotic BCL-2 family proteins such as MCL-1 and BCL-2, thus having the same effect on mitochondria-associated apoptosis as MCL-1 down-regulation, with the potential to directly induce apoptosis. An alvocidib-containing cytotoxic chemotherapy regimen demonstrated favorable remission rates in high-risk AML patients over standard therapy in a randomized Phase 2 trial indicating its potential role and safety in AML. We hypothesized that alvocidib and venetoclax would synergize against AML cells by shifting the overall balance of pro- and anti-apoptotic BCL-2 proteins in favor of apoptosis and thus represent a novel active treatment regimen in AML. Aims This study seeks to examine the efficacy of a treatment regimen containing alvocidib and venetoclax in multiple preclinical studies, including in vivo models of AML. Methods Cell viability assays interrogating alvocidib and venetoclax activity in cell lines were performed using CellTiter-Glo according to manufacturer's protocol. mRNA/miRNA expression changes were assessed using standard RT-PCR technique. Protein expression changes were assessed using standard western immunoblotting technique. To assess the efficacy of an alvocidib and venetoclax combination on tumor growth in an in vivo model, the OCI-AML3 xenograft mouse model and ex vivo studies with AML patient samples were performed. Results Herein we demonstrate that alvocidib inhibits both mRNA and protein expression of MCL-1 in a time and concentration-dependent fashion in 3 out of 4 AML cell lines analyzed, while in cells where alvocidib did not reduce MCL-1 protein levels (i.e. MOLM-13) a dose-dependent decrease in miR17-92, and concomitant increase in BIM protein was observed after 24 hours of alvocidib treatment. The alvocidib-venetoclax combination resulted in very strong synergistic reductions of cell viability (with combination indices [CI] of 0.4 to 0.7), both in venetoclax-sensitive and resistant cells. The venetoclax-sensitive lines, MV4-11 and MOLM-13, exhibited 5- to 10-fold reduction of venetoclax EC50 values in the low nM range when combined with only 80 nM alvocidib. Importantly, venetoclax-resistant lines, OCI-AML3 and THP-1, exhibited at least 20-fold reduction of venetoclax EC50 values from near 1 µM to 10-50 nM, when combined with 80 nM alvocidib.In the venetoclax-resistant OCI-AML3 xenograft model, single agent alvocidib and venetoclax achieved tumor growth inhibition (TGI) of 9.7 and 31.5%, respectively, while the combination achieved 87.9% TGI at the same dose levels of individual drugs. Conclusions Taken together, our data suggest that the combination of alvocidib with venetoclax is highly synergistic in vitro and in vivo, in both venetoclax-sensitive and -resistant AML across a heterogeneous genomic background. The particularly high level of synergy achieved in venetoclax-resistant cell lines highlights the central importance of both BCL-2 and MCL-1-mediated cell survival in AML. Importantly, the addition of alvocidib to venetoclax treatment reduced IC50s to clinically achievable concentrations. Therefore, we conclude that an alvocidib/venetoclax combination may be a novel approach for the treatment of AML and warrants further pre-clinical and clinical validation. Disclosures Whatcott: Tolero Pharmaceuticals: Employment. Kim:Tolero Pharmaceuticals: Employment. Haws:Tolero Pharmaceuticals: Employment. Mesa:Celgene: Research Funding; Galena: Consultancy; Promedior: Research Funding; Ariad: Consultancy; Novartis: Consultancy; CTI: Research Funding; Incyte: Research Funding; Gilead: Research Funding. Peterson:Tolero Pharmaceuticals: Employment. Siddiqui-Jain:Tolero Pharmaceuticals: Employment. Weitman:Tolero Pharmaceuticals: Employment. Bearss:Tolero Pharmaceuticals: Employment, Equity Ownership, Patents & Royalties. Warner:Tolero Pharmaceuticals: Employment, Equity Ownership, Patents & Royalties.

Abstract Abstract 1749 BACKGROUND: We have previously reported that transformation of a myeloproliferative neoplasm (MPN) to acute leukemia or blast phase (MPN-BP) is accompanied by a median survival of 2.6 months (range 0–24.2 months) (Mesa et. al. Blood 2005) despite treatment. Allogeneic stem cell transplantation (ASCT) offers a potential for cure. We reviewed 13 patients with MPN – BP who were candidates for ASCT, and 8 of those patients received ASCT after induction chemotherapy. METHODS: Retrospective analysis of individuals who had a clear antecedent myeloproliferative neoplasm (essential thrombocythemia (ET), polycythemia vera (PV), or myelofibrosis (MF) (either primary (PMF) or post ET/PV)), transformed to MPN-BP, and were candidates for ASCT. Clinical characteristics at each phase of illness, therapy at each phase of illness, and outcome of ASCT were assessed. RESULTS: Patients (MPN details): Thirteen patients (8 male, 5 female, median age at diagnosis of MPN 42 years in those who underwent ASCT and 64 years in those who did not receive ASCT (combined median 53 years, range 26–70)) were identified who met the inclusion criteria, of whom 2 had ET, 4 PV, 2 Post-ET MF, 1 Post-PV MF and 4 PMF. Jak2-V617F mutation was present in 7 of 8 patients tested. Treatments used during the MPN phase included hydroxyurea in 9 (+ busulfan in 1 and + anagrelide in 1), and thalidomide/prednisone in 2 (two patients received no treatment prior to MPN-BP). Patients (MPN-BP details): Transformation of an MPN to blast phase (from diagnosis of MPN) occurred after a median of 11 years in those who underwent ASCT and 8 years in non-ASCT (range 3 months to 30 years). At the time of MPN-BP diagnosis, 9 patients had an abnormal karyotype, 3 with complex karyotypes, 3 with trisomy 8 (sole abnormality), 1 with del(7q), 1 with del(13q). Leukemic induction consisted of standard anthracycline/cytarabine combination therapy (+/− etoposide), except for one patient received a study medication voreloxin. One patient who did not receive ASCT had remission after induction, but later expired from relapse, two expired soon after leukemic transformation, and two were too ill to receive induction. In the ASCT group (N=8), 5 patients achieved complete remission (CR) of their leukemia/return to MPN chronic phase (one of the five patients required a second induction to achieve CR), while 3 patients were considered primary induction failures due to persistence of > 5% blasts in the blood (2) or marrow (1) at the time of pre-transplant response assessment. The median time from diagnosis of MPN-BP to ASCT was 128 days. ASCT Details and Outcomes: Patients (median age of 55 years at time of ASCT; range 44–73) underwent ASCT (all HLA identical, 3 matched unrelated donors, 5 matched sibling donors) with either reduced intensity conditioning (n=6) or fully myeloablative conditioning (n=2). After a median follow-up of 488 days (range 127 – 1206), 6 of 8 patients are alive and free of disease. MPN-BP recurred in two patients at day 69 and 77 post-transplant; both patients eventually died of progressive leukemia, one after a second transplant. Acute GVHD occurred in 3 patients (1 grade III, 2 grade II), and chronic GVHD in 4 (2 mild, 2 moderate). All five patients who achieved CR (marrow and PB blasts < 5%) after induction therapy remain in continuous CR, while 2 of the 3 patients with evidence of persistent leukemia relapsed and died of MPN-BP. No patient died of transplant-related causes and all patients engrafted promptly. CONCLUSION: Rapid induction followed by ASCT can offer a curative path for individuals with MPN-BP. Although limited by small numbers, our data suggest that leukemic clearance (marrow blast percentage < 5%) prior to ASCT is associated with improved outcome. Disclosures: Mesa: Incyte: Research Funding; Lilly: Research Funding; SBio: Research Funding; Astra Zeneca: Research Funding; NS Pharma: Research Funding; Celgene: Research Funding.

Abstract Abstract 1977 Introduction: Transplant options are limited for adult patients (pts) who lack a fully matched related or unrelated donor. We hypothesized that in-vivo T-cell depletion with r-ATG would allow safe allo-SCT from mismatched, unrelated donors, thus expanding the potential donor pool for pts with hematologic malignancies who require allo-SCT. Patients and Methods: Thirty seven adult pts (age 20 – 70, median 45) underwent a first unrelated, mismatched, allo-SCT between 1/1/2006 and 6/30/2011 at Mayo Clinic Arizona for hematologic malignancy (35 PBSC, 2 marrow). All pts had at least a one allele or one antigen mismatch (MM) at HLA-A, -B, -C, or -DRB1, and all except one pt received r-ATG as part of the GVHD prophylaxis strategy (dose range 2.5 – 10 mg/kg in daily dose of 2.5 mg/kg depending on degree of mismatch, with the last dose generally given on day −1). One pt received Campath after experiencing anaphylaxis to r-ATG. Pts were transplanted for acute myeloid leukemia (n = 19; 10 CR1, 6 CR2, 3 other), acute lymphoblastic leukemia (n = 8; 4 CR1, 4 CR>/= 2), chronic myeloid leukemia (n = 1), myelodysplastic syndrome (n = 5), or non-Hodgkin lymphoma (n = 4). Conditioning was myeloablative in 20, reduced intensity in 16, and non-myeloablative in 1. Mismatches were as follows: 1-allele MM (n = 7); 1 antigen MM (n = 15); 2 allele MM (n = 4); 1 antigen, 1 allele MM (n = 7); and 2 antigen MM (n = 4). Additional GVHD prophylaxis included tacrolimus plus either methotrexate (n =21), mycophenolate mofetil (n = 14), or other (n = 2). Results: The median follow-up for surviving pts is 12 months. As of 6/30/11, 31 pts were alive, and 6 had died of the following causes: multiorgan failure (n = 1), relapse (n = 3), and veno-occlusive disease (n = 2). To date, there have been no deaths related to acute or chronic GVHD. The 1- and 2-year estimated rates of overall survival are 84.5%/78.8% (Fig. 1); of progression-free survival 79.7%/72.0%. The estimated rate of relapse at 1 and 2 years is 11.8%/18.7%, and of non-relapse mortality 8.1%/10.5%. Four pts (10.8%) have developed severe (grades III-IV) acute GVHD by day 100 (days 19, 27, 30, 43; one pt after withdrawal of prograf due to transplant-associated microangiopathy). No late onset severe acute GVHD has been seen. Moderate to severe NIH-defined chronic GVHD occurred in a single pt at risk (cumulative incidence estimate 2.4% at 1 year, 4.7% at 2 years; Fig. 2). Four pts have reactivated EBV, with one developing PTLD (all have been treated with Rituximab). CMV reactivation was seen in 24 pts (65%), CMV disease in 4, with no deaths directly related to CMV. Conclusions: In vivo T-cell depletion with r-ATG abrogates severe acute and chronic GVHD, and allows use of mismatched unrelated donors for allo-SCT in adult pts with otherwise incurable hematologic malignancies. Long-term survivors are generally free of severe chronic GVHD, with good quality of life. There does not appear to be an increased incidence of disease relapse, and non-relapse mortality is low. This approach is safe, effective, and considerably expands the donor pool for adult pts who require allo-SCT. Disclosures: Reeder: Celgene: Research Funding; Millennium Pharmaceuticals Inc.: Research Funding; Novartis: Research Funding. Mesa:Incyte: Research Funding; Lilly: Research Funding; SBio: Research Funding; Astra Zeneca: Research Funding; NS Pharma: Research Funding; Celgene: Research Funding.

Abstract To date, there are no effective strategies to treat patients with a particularly lethal form of diffuse large B-cell lymphoma (DLBCL) that over-express both B-cell lymphoma gene-2 (BCL2) and cellular-myelocytomatosis (MYC) oncogenes. Here, we provide a novel approach for simultaneously targeting BCL2 and MYC directly through secondary DNA structures. These structures act as molecular switches, turning gene expression on or off, and serve as scaffolds for protein and small molecule interaction. Previously identified compounds IMC-76 and GQC-05, recognize the BCL2 or MYC promoter region DNA structures, respectively, and independently lower mRNA and protein expression of each oncogene.For utility in sensitizing aggressive lymphoid tumors that concurrently dual express BCL2 and MYC, we examined the efficacy ofIMC-76 and GQC-05 in the presence and absence of a standard chemotherapy, cyclophosphamide (CPA)to inhibit tumor growth in a severe combinedimmunodeficient U2932 DLBCLxenograft mouse model. Unlike IMC-76, effects of GQC-05 in a xenograftmouse model are unknown; therefore, our initial in vivo studies involved GQC-05 alone and with IMC-76 in combination with CPA to determine the concentration for MYCdown-regulation and a maximum tolerated dose (MTD). At 2.5 and 5 mg/kg GQC-05 with 50 mg/kg CPA, we observed no effect on mean mouse weight and while the 5 mg/kg GQC-05-CPA combination group exhibited some mouse death, LD50 was not reached. Tumors from mice treated with GQC-05 and CPA were compared to diluent or CPA alone treated mice for MYC mRNA level and found to have significantly less MYC expression. As expected, there was no effect on BCL2 mRNA levels. Next, we carried over the 5 mg/kg GQC-05 to a subsequent in vivo experiment where we also tested a 10 mg/kg GQC-05 dose and included IMC-76 at 10 mg/kg, a previously established efficacious dose, to determine if we would reach an MTD, but still retain the repression of MYC. While the mice were able to tolerate the 5 mg/kg GQC-05 dose when IMC-76 and/or CPA was added to the treatment regimen, the LD50 was reached and surpassed in mice treated with the 10 mg/kg of GQC-05 combinations. However, both concentrations of GQC-05 when co-treated with IMC-76 and CPA resulted in a notable decrease of tumor MYC expression relative to untreated and CPA only treated mice as well as a detectable inhibition of BCL2. We then conducted a follow-up study to evaluate effects on tumor burden using concentrations of GQC-05 below the LD50, 5 mg/kg and 7.5 mg/kg, and also reduced the dose of CPA to 30 mg/kg. The co-treatment of mice with IMC-76 (10 mg/kg) and GQC-05 (7.5 mg/kg) in combination with CPA delayed tumor growth and decreased tumor size by 50%, 46%, and 37% at 9, 12, and 15 days post-first-day drug administration compared to the diluent and 50%, 67%, and 44% smaller tumors relative to CPA only treated mice (Figure 1). Overall, there was a prominent decrease in tumor burden in these mice as determined by area under the curveand the tumors displayed a lower expression of BCL2 and MYC at both the mRNA and protein levels. Interestingly, tumors of mice that received IMC-76 and GQC-05 also showed knock-down of BCL2 and MYC, but no effect on tumor burden unless CPA was present indicating these transcriptional inhibitors act as chemo-sensitizers. None of the various drug regimens resulted in a significant loss of mouse weight and while survival for GQC-05 7.5 mg/kg treated mice was reduced, LD50 was not reached. These are the first studies to demonstrate two differentpromoterDNA secondary structures can be targeted at the same time for direct transcriptional inhibition that leads tochemo-sensitization and slowed tumor growth in mice.Our findings demonstrate the potential utility of a dual-targeted, precision medicine-based strategy to improve the response of DLBCL patients to current chemotherapy and overcome resistant disease. Figure Tumor burden of DLBCL xenograft mice Figure. Tumor burden of DLBCL xenograft mice Disclosures Gokhale: Tetragene: Equity Ownership. Hurley:Tetragene: Consultancy, Equity Ownership. Rimsza:NCI/NIH: Patents & Royalties: L.M. Rimsza is a co-inventor on a provisional patent, owned by the NCI of the NIH, using Nanostring technology for determining cell of origin in DLBCL..

Abstract Background: Large case-control studies have identified common epidemiologic exposures associated with risk of AML development, including obesity [BMI ≥30 kg/m2] associated with 2-fold increase risk. However it is not known whether individual risk factors are associated with unique AML genetic lesions. We have recently identified a significant association of some common exposures including obesity with unique disease cytogenetic subgroups in a cohort of n=295 consecutive AML patients diagnosed and treated at Mayo Clinic [Cancer Epidemiol 39:1084, 2015]. Aberrant DNA methylation is a hallmark of cancer and has been reported in AML, and we therefore performed an analysis of global DNA methylation in patients in this cohort to determine whether specific epidemiologic exposures are associated with a unique pattern of DNA methylation. Methods: Patients in the initial cohort were previously reported. An available baseline AML bone marrow cytogenetic pellet from diagnosis was identified for n=148 patients (50.2%). Following IRB approval and using previously reported techniques [Leukemia 24:1283, 2010], leukemia DNA was isolated from the remnant diagnostic cytogenetic samples and processed on the HumanMethylation450 BeadChip array (Illumina). Individual CpG dinucleotides were assigned a Beta value (i.e. methylation value) ranging from 0 (unmethylated) to 1 (methylated). We excluded those with minor methylation changes (<0.2 between any two samples), resulting in 383,524 CpG's. For the purposes of this first analysis we have focused only on CpG's with an inter-quartile range (IQR, i.e. difference between 75th and 25th percentiles) of greater than 0.25, comprising a total of 39,657 CpG's. An unsupervised cluster analysis of differential methylation was generated ("Heatmap", Figure). Univariate and multivariable analyses were performed adjusting for age as a continuous variable to determine the association of individual exposures with unique methylation clusters (primary endpoint), and we also evaluated the association of clinical characteristics and clinical outcomes after therapy (secondary endpoint). The median age of patients was 69 yrs (range 19-89), male 65%, and Caucasian 89%. The median BMI was 27.2 (range 16.9-59.9), and n=47 patients (32%) had BMI ≥30. Standard cytogenetic risk groups were applied [favorable (n=8), normal (n=61), intermediate abnormal (n=32) and poor (n=47)]. Results: We identified 4 distinct methylation clusters (Figure: Cluster #1, Black horizontal bar; Cluster #2, Green; Cluster #3, Blue; and Cluster #4, Red). Multivariate analysis of select variables is shown in Table. Patients in cluster #1 were significantly more likely to have a BMI ≥30 [Odds Ratio (OR) 2.49, p=0.017] and more likely to have favorable risk cytogenetics although this was not statistically significant [OR 6.71, p=0.088], while those in cluster #2 were significantly more likely to be have a history of regular NSAID use [OR 3.26, p=0.03] and less likely to have normal [OR 0.40, p=0.044] and possibly intermediate abnormal cytogenetics [OR 0.34, p=0.051]. Those in cluster #3 were significantly younger [median age 48 yrs, p<0.001] and were also more likely to have prior history MDS although this was not statistically significant [OR 3.7, p=0.082]. Cluster #4 was not correlated with any distinct exposure apart from possible decreased alcohol use [OR 0.3, p=0.079]. History of past or current tobacco use, regular acetaminophen use, documented myelotoxin exposure, family history, and secondary AML did not correlate with a unique methylation cluster. There was no independent association of methylation cluster with overall survival (p=0.46), although among patients receiving intensive therapy cluster #2 was associated with lower complete remission rate [OR 0.28 (95% CI 0.09-0.86), p=0.026]. Conclusion: This first analysis of AML molecular epidemiology and clinical outcome suggests that specific disease risk factors (esp. obesity and age) are associated with unique leukemia DNA methylation clusters. In addition, aberrant methylation is associated with some clinical features and with outcome after therapy, suggesting that epidemiologic risk factors may have a prognostic impact. This provides novel insight into leukemogenesis, and will guide prospective studies of AML epidemiology and outcome. Figure. Figure. Disclosures Foran: medscape: Honoraria; pfizer: Honoraria; Millennium Pharmaceuticals, Inc.: Research Funding; novartis: Honoraria; karyopharm: Honoraria; boehringer ingelheim: Research Funding; agios: Research Funding; Cellerant: Research Funding.

Abstract Background Myeloid neoplasms (MN) occurring secondary to treatment of non-malignant disease, especially autoimmune diseases (AID), and the overlap between them, is an increasing clinically important observation. Confounding variables for myeloid transformation include the nature of autoimmune disease as an incessant, chronically activated inflammatory cascade that has been associated with an increased risk of myeloid malignancy, and disease modifying therapies, often with cytotoxic and immunosuppressive drugs. Thus, the addition of therapeutic agents for treatment of AID theoretically amplifies the opportunity for MN to develop in genetically susceptible individuals. To our knowledge, there is no large study evaluating a comprehensive primary AID population that developed MDS or AML with detailed assessment of therapeutic interventions for AID. The presented study queries the association of cytotoxic, anti-inflammatory, and immune-modulating agents to treat AID as risk factors for developing myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML). Methods In a 10-year period from 2004-2014, a total of 40,011 patients with an ICD-9 coded diagnosis of AID were seen at Mayo Clinic Arizona and Florida campuses; 311 patients from this population had a concomitant coded diagnosis of MDS and/or AML. In an IRB approved, retrospective chart review, we confirmed 86 cases meeting strict inclusion criteria for AID and confirmed MDS or AML. We performed a case-control match from the same primary autoimmune population for age, gender, and autoimmune specific diagnosis at a 2:1 ratio querying for therapeutic interventions. Results Myeloid neoplasm specifics in our case cohort of 86 patients were 55 MDS, 21 de novo AML, and 10 AML with history of MDS. Average age was 72 years with a slight male predominance (57%). Rheumatoid arthritis (26.4%), psoriasis (20.7%), systemic lupus (13.8%), was the most common autoimmune profiles. Median onset of autoimmune disease to diagnosis of myeloid neoplasm was 6 years (range 1-54 years). Up to 26% controls documented no systemic agent for treatment of autoimmune disease and similarly 12.8% of the case cohort (p=0.142). Case and control populations treated with systemic therapies had similar exposures by category for immune-modulating agents (33.7% and 37.8% respectively) and cytotoxic therapies (47.7 and 44.8% respectively). A total of 57/86 cases (66.3%) received either a cytotoxic or immune-modulating agent. In comparison, 105/172 (61.1%) controls received either agent; p=0.495. Azathioprine was the only statistically significant agent exposure observed more frequently in the study cohort than controls with an OR of 7.05 (p= < 0.001). There was no increased incidence of MDS or AML in TNF-antagonist treated patients. No significant correlation for duration of agent exposure by drug category was observed. Discussion In a large primary AID population, azathioprine exposure was associated with a 7 fold risk of MDS or AML (p=< 0.001). Notable, but not statistically significant case cohort trends among cytotoxic agents was exposure to cyclophosphamide (OR 3.58) followed by mitoxantrone (OR 2.73). Methotrexate, mercaptopurine, and mycophenolate had favorable odd ratios, but these were not statistically significant. No association was found for TNF-inhibitory agents. Finally, there was no association of drug exposure time to the incidence in development of MDS or AML. Disclosures Dueck: Bayer: Honoraria. Mesa:Gilead: Research Funding; Novartis: Consultancy; Celgene: Research Funding; Incyte Corporation: Research Funding; Galena: Consultancy; Ariad: Consultancy; CTI: Research Funding; Promedior: Research Funding. Al-Kali:Onconova Therapeutics, Inc.: Research Funding; Celgene: Research Funding. Foran:Millennium Pharmaceuticals, Inc.: Research Funding; novartis: Honoraria; karyopharm: Honoraria; medscape: Honoraria; pfizer: Honoraria; boehringer ingelheim: Research Funding; agios: Research Funding; Cellerant: Research Funding.

Abstract Abstract 3803 Background: The MMRC is a non-profit, disease-focused consortium founded in 2004 comprised of 13 North American centers with expertise in multiple myeloma (MM). The MMRC Inc. (Norwalk, CT), MMRC's member institutions, and several pharmaceutical partners work closely to speed early development of new treatment options for MM patients. In December 2007, MMRC headquarters staff implemented multiple project management (PM) business solutions to address trial barriers that delay activation of our phase I-II clinical trials and established trial metric benchmarks considered attainable at our member institutions. In November 2009, we reported initial data1 on trial activation: MMRC trials initiated between Sep08-Jul09 (n=5) demonstrated a 38% decrease in mean time to first patient dosed (FPFD), compared with the Early Group trials (EG) initiated before PM solutions (Jun06-Sept08; n=7 trials). These results also confirmed improvement over published metrics from Dilts et al 2,3. We present additional data on time to FPFD and now enrollment, to assess impact of the MMRC PM resources and processes on trial efficiency. Methods: Twenty-one (21) trials conducted within the MMRC from May 2006 to June 2010 had sufficient trial data for review. Data were collected by MMRC-funded project managers at the clinical centers using a web-based clinical trial management system (CTMS). FPFD was defined as the time from the member institutions' receipt of the final protocol (FP) from the trial sponsor, to the time the first patient was dosed on the trial at any participating MMRC member institution. With respect to enrollment, pre-study enrollment commitment (EC) established between MMRC and the study sponsor was defined as the total number of subjects committed to receive at least one dose of study drug across all participating MMRC centers on a trial; baseline enrollment timeline (BET) was prospectively defined as the target time period to attain EC. Results: Mean time to FPFD was 181 calendar days for the early group trials (EG, n=7) and 122 days for the recent group (RG) trials (n=14, Sep08 – Jun10), representing a 32% reduction of time to FPFD in the RG. Additionally, time from final protocol to first patient dosed at all MMRC centers on a trial decreased 18% from a mean of 7.7 months (EG) to 6.3 months (RG). With respect to MMRC trial enrollment, data was available for 16/21 trials (5 EG and 11 RG). 2 EG trials were missing data and 3 RG trials were still enrolling as of June 2010. The mean MMRC pre-study EC was 39 subjects per trial (n=16 trials; 626 enrollment target); the mean actual MMRC enrollment was 49 subjects per trial (n=16; 783 enrolled through Jun 29, 2010) representing a 25% increase in actual versus committed enrollment. Two trials did not meet MMRC EC: MMRC investigators discontinued their involvement in these trials at approximately 30% target EC due to trial complexity or low patient enrollment. 14/16 evaluable trials (88%) met their EC; 11/16 trials met EC within BET (69%) of which 8/16 trials (50%) reached EC 34% faster than their BET (representing a mean reduction of 4.5 months). The overall pre-study mean BET for 16 trials was 13.1 months. MMRC's actual mean enrollment timeline was 11.3 months for the group of 14 evaluable trials representing improvement over the original BET by a mean of 1.9 months (14%). We believe FP to FPFD is a more meaningful metric beyond that of first patient consented. Moreover, we believe that if all participating trial centers focus their efforts on dosing the first patient and within a targeted timeframe, it may improve our research efforts overall. Conclusion: Development of drugs in the clinical setting has become time and resource intensive. Activating and enrolling trials promptly is a priority for drug development. The MMRC's standardization of processes and support for site-based PM resources results in improved trial metrics. MMRC member centers met or exceeded pre-specified enrollment targets in 88% of the trials analyzed to date. Ongoing monitoring of trial conduct continues to reveal areas where increased focus is needed to realize further trial efficiencies. These trial metrics and measures to improve efficiency may be applied with similar expected benefit in all oncology disciplines. Disclosures: Wear: Multiple Myeloma Research Consortium (MMRC): Employment. Richardson:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Millennium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees. Revta:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Vij:Multiple Myeloma Research Consortium (MMRC): Research Funding; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Fiala:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Lonial:Bristo-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millenium Pharmaceuticals Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Onyx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Francis:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Siegel:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Speakers Bureau; Millennium: Speakers Bureau. Schramm:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jakubowiak:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC, Research Funding; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Speakers Bureau; Centocor Ortho-Biotech: Honoraria, Speakers Bureau; Exelixis: Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Harvey:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Reece:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Honoraria, Research Funding; Ortho Biotech: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Facet: Research Funding; Otsuka: Honoraria, Research Funding. Gul:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jagannath:Celgene: Honoraria; Millenium: Honoraria; Orthobiotec: Honoraria; Onyx Pharma: Honoraria; Merck: Honoraria; Proteolix: Honoraria; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. La:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Hofmeister:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jansak:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Stewart:Millennium: Consultancy; Celgene: Honoraria; Multiple Myeloma Research Consortium: Member Institution of the MMRC. Hagerty:Mayo Clinic: Employment; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Wolf:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Speakers Bureau; Millennium: Speakers Bureau; Novartis: Speakers Bureau; Orthobiotech: Speakers Bureau. Davis:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Krishnan:Celgene: Speakers Bureau; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Duarte:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Zimmerman:Millennium, Celgene: Speakers Bureau. Cisneros:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Kumar:Celgene: Consultancy, Research Funding; Millennium: Research Funding; Merck: Consultancy, Research Funding; Novartis: Research Funding; Genzyme: Consultancy, Research Funding; Cephalon: Research Funding; Bayer: Research Funding; Multiple Myeloma Research Consortium: Member Institution of the MMRC. Birgin:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Ott:Multiple Myeloma Research Consortium (MMRC): Employment. Tasca:Multiple Myeloma Research Consortium (MMRC): Employment. Kelley:Multiple Myeloma Research Consortium (MMRC): Employment. Anderson:Millennium: Consultancy; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Consultancy; Novartis: Consultancy; Onyx: Consultancy; Merck: Consultancy; Bristol-Myers Squibb: Consultancy; Acetylon: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Giusti:Multiple Myeloma Research Consortium (MMRC): Employment.