Citations

Abstract A trait of inflammation pathogenesis in human atherosclerosis: inflammasome driven interleukin-1 signalling in complex atherosclerotic plaques via hyperlipidemia trained innate immunity Objectives We aimed to investigate interleukin (IL)-1 generation and the regulatory role of inflammasome in human advanced atherosclerosis. Background IL-1β is key contributor to the inflammatory process associated with atherosclerosis and its complications. Recent studies suggested that IL-1β blockade reduces the burden of inflammation and recurrence of cardiovascular events. Yet, other cytokines in IL-1 family and the regulation of IL-1 generation in patients with atherosclerosis remains poorly understood. Methods and results A focused transcriptomic analysis in human atherosclerotic specimens discovered that human atherosclerotic plaques host a broad reservoir of inflammasome components, characterised by expression of canonical inflammasome gene NLRP6, NLRP12, NLRC4, NLRP3 and non-canonical inflammasome gene caspase 4 significantly elevated in the symptomatic plaques versus the asymptomatic plaques. Upregulation of NLRP3 inflammasome expression in plaque validated by immunohistochemistry staining suggested it as a distinctive characteristic of plaque vulnerability and complexity. Functional studies on atherosclerotic explants obtained from patients undergoing carotid endarterectomy revealed constitutive generation of IL-1β accompanied by secretion of comparable levels of IL-1α from the majority of the plaques, while IL-18 and IL-33 generation from some of the plaques. Stimulation of the plaques with inflammasome activators showed an inducible generation of both IL-1α and IL-1β, not IL-18 or IL-33, mediated by specific canonical and non-canonical inflammasome pathways. Analysis on the medication records of these patients indicated that plaques from patients with suboptimally controlled hyperlipidemia, imaging signs for plaque instability and inadequate statins therapy possessed higher recruitable production of IL-1β, suggesting the conventional atherogenic factor in regulation of inflammasome immunity and disease activity. Mechanistic studies on tissue and cells isolated from atheromatous plaques demonstrate that generation of mature IL-1β is via a mechanism controlled by NLRP3 and the effector caspase-1. Conclusions The study supports a profound canonical and non-canonical inflammasomes mediated plaque IL-1α/β generation, via a key mechanism by NLRP3 and caspase-1. The results provide biological insights into the clinical merit of high-intensity cholesterol lowering and anti-IL-1 signalling-directed therapies in high-risk patients with atherosclerosis. Acknowledgement/Funding KI-Mayo collaboration project, the Swedish Research Council, the Swedish Heart-Lung Foundation, European Union FP7 projects, the NIH

Abstract Objective To design, develop, and evaluate a scalable clinical data normalization pipeline for standardizing unstructured electronic health record (EHR) data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification. Methods We established an FHIR-based clinical data normalization pipeline known as NLP2FHIR that mainly comprises: (1) a module for a core natural language processing (NLP) engine with an FHIR-based type system; (2) a module for integrating structured data; and (3) a module for content normalization. We evaluated the FHIR modeling capability focusing on core clinical resources such as Condition, Procedure, MedicationStatement (including Medication), and FamilyMemberHistory using Mayo Clinic’s unstructured EHR data. We constructed a gold standard reusing annotation corpora from previous NLP projects. Results A total of 30 mapping rules, 62 normalization rules, and 11 NLP-specific FHIR extensions were created and implemented in the NLP2FHIR pipeline. The elements that need to integrate structured data from each clinical resource were identified. The performance of unstructured data modeling achieved F scores ranging from 0.69 to 0.99 for various FHIR element representations (0.69–0.99 for Condition; 0.75–0.84 for Procedure; 0.71–0.99 for MedicationStatement; and 0.75–0.95 for FamilyMemberHistory). Conclusion We demonstrated that the NLP2FHIR pipeline is feasible for modeling unstructured EHR data and integrating structured elements into the model. The outcomes of this work provide standards-based tools of clinical data normalization that is indispensable for enabling portable EHR-driven phenotyping and large-scale data analytics, as well as useful insights for future developments of the FHIR specifications with regard to handling unstructured clinical data.

Abstract Background: We previously demonstrated that putative clinical epidemiologic exposures associated with leukemia risk are prevalent among AML patients, and some are associated with unique cytogenetic risk group and with clinical phenotype (Finn, Cancer Epidemiol, 2015). Herein, we studied genome-wide DNA methylation in a cohort of AML patients to evaluate the association of individual hyper- and hypo-methylated CpG sites with epidemiologic exposures and overall survival. Methods: The Mayo Clinic AML Epidemiology Cohort is a highly annotated retrospective case series of 295 consecutive patients (pts) with AML diagnosed and treated at Mayo Clinic Florida and Arizona, with central cytogenetics performed in all cases. The prevalence of clinical epidemiologic exposures, past medical and family history as well as medication use and lifestyle was systematically obtained. After IRB approval, we interrogated the cytogenetic database and successfully obtained leukemia DNA from available remnant diagnostic cytogenetic cell pellets in a cohort of 148 AML patients in the Mayo epidemiology case series and performed an assessment of genome-wide DNA methylation using the Infinium HumanMethylation450K BeadChip. Samples were processed using the R Bioconductor package 'minfi' using Subset Within Array Quantile Normalization (PMID: 22703947). Individual CpGs that did not reach a detection p-value of <0.05 were filtered out. An internal control was included on each array to control for significant batch effects. To determine differential methylation status of 473,864 individual CpG sites (after exclusion of 11,648 that did not pass detection p-value QC), CpG sites with low interpatient variability, a standard deviation <0.05 of methylation values, and an interquartile range <0.05 of methylation values and X/Y chromosome probes were excluded from analysis. Epidemiologic and important clinical variables occurring in at least 10 pts or previously-identified as of unique interest but in fewer than 10 pts were evaluated, including sex, BMI, performance status and comorbidities, tobacco and alcohol use, family history of hematologic malignancy, medications of interest, history of toxin exposure, and history of immunosuppression and/or solid organ transplant or of secondary and therapy-related AML (t-AML). Associations of differential hypo or hypermethylation at 281,259 CpG sites with epidemiologic exposures/clinical variables were evaluated using Spearman's test of correlation (continuous or ordinal exposures), a Wilcoxon rank sum test (dichotomous exposures), or a Kruskal-Wallis rank sum test (multi-category exposures). A Bonferroni correction was applied for multiple testing, after which p <1.8 x 10-7 was considered as significant. To reduce the likelihood of false-negative findings, we additionally considered p <5 x 10-6 as indicating suggestive evidence of an association. CpG sites and associated gene and function was determined using Illumina manifest file & www.ncbi.nlm.nih.gov/gene. Results: Statistically significant associations (p <1.8 x 10-7) with individual epidemiologic and clinical exposures were identified for 109 unique CpG sites, corresponding to differential methylation in the genes listed in Table. Specifically, obesity (predominantly hypomethylation), specific cytogenetic lesions and risk group, and gender were highly significantly associated with unique CpG methylation status, but not smoking, toxin exposure, family history, comorbidity or performance status, secondary or t-AML, immunosuppression, medication use, of family history in the analysis. A further 353 unique CpG sites had defined suggestive associations (ongoing analysis). We also identified 8 additional CpG sites where differential methylation was associated with overall survival (p <5 x 10-6, Table). Conclusion: Obesity, cytogenetic lesions, and sex are associated with differential methylation of unique CpG sites at AML diagnosis, using stringent univariate statistical criteria. Significant CpG sites were identified in genes previously linked to AML biology and prognosis (DOCK6, HOXB3, MIR10A, FOXN3/CHES1, GPX1, MYST2/KAT7, PTPRD), but some represent novel findings in AML. These preliminary results suggest an association of some AML risk factors and clinical variables with unique gene methylation and will undergo validation in a prospective AML epidemiology dataset. Disclosures Cerhan: Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Foran:Agios: Research Funding; Xencor, Inc.: Research Funding.

Abstract Background: Axi-cel is a US FDA-approved, autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy for the treatment of patients with relapsed or refractory large B cell lymphoma with ≥ 2 prior systemic therapies. This report focuses on high-risk patient populations as well as long-term durability of response, and B cell recovery. Methods: In ZUMA-1, eligible patients with refractory large B cell lymphoma (diffuse large B cell lymphoma, primary mediastinal B cell lymphoma, transformed follicular lymphoma) underwent leukapheresis and received low-dose conditioning followed by a target dose of 2 × 106 anti-CD19 CAR T cells/kg (Neelapu and Locke et al. NEJM. 2017; NCT02348216). Outcomes in patients with double-expressor B cell lymphoma (MYC [≥ 40%] and BCL-2 [≥ 50%] protein expression by immunohistochemistry [IHC]) or high-grade B cell lymphoma (HGBCL), defined as double- or triple-hit (MYC+ and BCL2+ and/or BCL6+ by fluorescence in situ hybridization) or not otherwise specified (MYC- and > 70% Ki67 by IHC) were examined by independent pathology review. Exploratory analyses, including normal B cell levels in peripheral blood over time and frequency of use of safety interventions of interest, were also performed. A long-term follow-up analysis will be conducted with a data cutoff of August 11, 2018 for all 108 patients, including the HGBCL subgroup. Results: As of August 11, 2017, all 108 patients had at least 1 year of follow-up, with a median follow-up of 15.4 months. The objective response rate (ORR) was 82% with a complete response (CR) rate or 58%. The CR rate was 53% (29/55) in patients with disease refractory to ≥ 2 consecutive prior lines of therapy and 72% (18/25) in patients who had relapsed within 12 months after autologous stem cell transplantation. High-risk genetics were assessed in the 47 evaluable pre-treatment tumor samples: 37 patients (79%) had HGBCL or double-expressor B cell lymphoma and had an ORR of 89% (33/37) including a CR rate of 68% (25/37). Forty-two percent of patients overall had ongoing responses with a median follow-up of 15.4 months including 49% (18/37) of patients with high-risk genetics. To investigate the relationship between B cell recovery and ongoing response, B cell levels were assessed over time. Overall, of the 87 evaluable patients, 47% had no detectable B cells at baseline, and the remainder had levels close to or below the lower level of quantification of the assay. In patients with ongoing responses at 12 months post axi-cel infusion, 19 of the 35 (54%) patients with evaluable samples had detectable B cells at 12 months. This suggests B cell recovery in some patients with ongoing response as only 6 of 40 (15%) patients with evaluable samples had detectable B cells at 3 months after axi-cel infusion. Grade ≥ 3 cytokine release syndrome (CRS) and neurologic events (NE) were generally reversible and reported for 12% and 31% of patients, respectively. Tocilizumab (45%) and/or steroids (28%) were used for the management of CRS and NE. The use of vasopressors, dialysis, and intubation were minimal, used in only 17%, 3%, and 3% of patients, respectively. Thirty-four percent of patients (37/108) had either no CRS/NE (6%) or only Grade 1 CRS with (12%) or without (16%) Grade 1 NE. For these 37 patients, the median onset of CRS and NE was 2 and 7 days after infusion, respectively. Patients with Grade 0 - 1 CRS/NE had similar efficacy (ORR, 86%; CR, 65%) but lower peak/AUC CAR T cell levels vs the overall population. Updated safety and efficacy results will be presented with a minimum follow-up of 2 years and a median follow-up of 27.1 months. Conclusion: High rates of durable response were observed in patients with HGBCL and double-expressor B cell lymphoma, with approximately half of these patients (18/37) maintaining CR at ≥ 1 year. Efficacy in these high-risk populations was comparable to the overall patient population in ZUMA-1. At 12 months, B-cell recovery was observed in over half the patients with ongoing remission. About one-third of patients had only Grades 0 - 1 CRS/NE yet showed comparable efficacy with the overall patient population. High-grade CRS/NE were largely managed with tocilizumab and steroids with very low use of intensive or invasive interventions. Updated outcomes with a minimum of 2 years of follow-up will be presented. Disclosures Neelapu: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellectis: Research Funding; Poseida: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Karus: Research Funding; Bristol-Myers Squibb: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Unum Therapeutics: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Miklos:Adaptive Biotechnologies: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pharmacyclics - Abbot: Consultancy, Research Funding; Kite - Gilead: Consultancy, Research Funding; Genentech: Research Funding; Janssen: Consultancy, Research Funding. Hill:Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Deol:Novartis: Consultancy; Kite Pharmaceuticals: Consultancy. Reagan:Seattle Genetics: Research Funding. Flinn:Agios: Research Funding; Forty Seven: Research Funding; Celgene: Research Funding; Calithera: Research Funding; Portola: Research Funding; Kite: Research Funding; Forma: Research Funding; Genentech: Research Funding; Verastem: Research Funding; BeiGene: Research Funding; Incyte: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Janssen: Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; ArQule: Research Funding; Constellation: Research Funding; Infinity: Research Funding; Novartis: Research Funding; Gilead: Research Funding; Curis: Research Funding; Pharmacyclics: Research Funding; Verastem: Consultancy, Research Funding; Takeda: Research Funding; Pfizer: Research Funding. McSweeney:Kite, a Gilead Company: Honoraria, Membership on an entity's Board of Directors or advisory committees. Munoz:Alexion: Consultancy; Genentech: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria; Janssen: Consultancy; Kite: Consultancy, Honoraria, Speakers Bureau; Bayer: Consultancy, Speakers Bureau; Gilead: Speakers Bureau; Pfizer: Consultancy; Bristol-Myers Squibb: Consultancy. Siddiqi:Juno Therapeutics: Other: Steering committee. Herrera:Bristol-Myers Squibb: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Seattle Genetics: Research Funding; Pharmacyclics: Consultancy, Research Funding; AstraZeneca: Research Funding; Gilead Sciences: Research Funding; Merck, Inc.: Consultancy, Research Funding; KiTE Pharma: Consultancy, Research Funding; Immune Design: Research Funding. Xue:Kite, a Gilead Company: Employment. Jiang:Kite, a Gilead Company: Employment. Bot:KITE: Employment. Rossi:KITE: Employment. Kim:Kite, a Gilead Company: Employment. Go:KITE: Employment. Locke:Cellular BioMedicine Group Inc.: Consultancy; Novartis Pharmaceuticals: Other: Scientific Advisor; Kite Pharma: Other: Scientific Advisor.

Abstract Background Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) characterised by the frequent presence of driver mutations in genes causing activation of JAK2 signalling pathways (JAK2, CALR and MPL). Additional mutations affecting epigenetic regulators and splicing machinery are common. Anaemia with RBC-transfusion-dependence is common in patients with advanced myelofibrosis and represents a major unmet need. The RESUME study assessed the rates of RBC-transfusion independence (TI) after therapy with Pomalidomide (POM) vs placebo in persons with MPN-associated myelofibrosis and RBC-transfusion dependence. 16% of patients in both the POM and placebo arms became TI. Aims The genetic landscape of strictly confirmed transfusion dependent MF is not fully characterised. Our aim was to analyse the genetics of transfusion-dependent myelofibrosis patients in the RESUME trial and correlate with clinical characteristics, outcome and therapy response. Methods DNA samples were available from 207 of 252 patients and analysed by targeted re-sequencing using a Fluidigm Access Array gene panel followed by next generation sequencing. The panel included JAK2, CALR, MPL, TET2, ASXL1, EZH2, DNMT3A, IDH1/2, CBL, IKZF1, U2AF1, CHEK2, TP53, SF3B1, SRSF2, SH2B3, BARD1, DAP3, HRAS, IRF4, KRAS, KIT, Mir662, NFE2, POLG, SCRIB, SETBP1, TCF12 and VPS45. Results 97% (95-99%) of subjects had a mutation in ≥1 targeted gene. 2 mutations were detected in 41% (34-48%) and ≥3 in 27% (21-33%). 7 had no detectable mutation. Mutations in JAK2V617F, CALR and MPL were identified in 66% (59-72%), 14% (8-19%) and 7% (4-11%) of subjects (Figure 1), with no driver mutation in 27 patients (13%; 9-18%) (triple-negative). 68% (61-74%) had additional non-driver mutations. 42% (35-48%) (N=86) had spliceosome mutations (U2AF1 [21%]; SF3B1 [11%]; SRSF2 [8%]; ZRSR2; [6%]). More spliceosome mutations were detected in men than women (47% [39-55%] vs 27% [15-40%]; p=0.009). Spliceosome mutations were mutually exclusive in 83 subjects and were less common in subjects with prior polycythaemia vera (17% [5-37%]) compared with prior essential thrombocythaemia (39% [22-58%]) and primary MF (46% [38-54%]; p=0.024). Mutations in epigenetic regulators (ASXL1, 28%; TET2, 8%; DNMT3A 5%; EZH2 4%) were detected at similar rates to those previously reported. High molecular risk (HMR) mutations (ASXL1, EZH2, IDH1/2, SRSF2) were detected in 36% [29-43%] of subjects. Only 10 of 105 subjects with an epigenetic regulator gene mutation had ≥1 related mutation. Subjects with JAK2V617F were significantly more likely than subjects with a CALR mutation to have: (1) ≥1 additional mutation (72% [64-79%] vs. 35% [18-54%], p=0.0001); (2) a spliceosome mutation (44% [36-53%] vs. 17% [6-36%], p=0.07), in particular a U2AF1 mutation (24% [17-32%] vs. 0%; p=0.004) and (3) a HMR mutation (38% [30-47%] vs. 21% [8-40%]; p=0.07). Survival at 1.5 years was 62% (55-67%) and was not significantly associated with the presence or number of mutations in this uniformly high-risk cohort. Survival in subjects without an SF3B1 mutation was better than those SF3B1-mutated (80% [56-91%]) vs. 59% [52-65%]; p=0.07). Driver mutation status did not influence the probability of achieving red blood cell (RBC) TI, regardless of therapy. Additional non-driver mutations were more often detected in those failing to achieve RBC-TI than those achieving RBC-TI (70% [63-77%] vs 56% [40-71%], p=0.07). Furthermore, those with additional non-driver mutations were less likely to achieve ≥50% reduction in RBC transfusions (24% [17-32%] vs. 39% [27-51%]; p=0.03). A significant correlation persisted in subjects receiving POM but not in those receiving placebo. There was also a significant correlation between U2AF1 mutations and RBC-TI in POM treated subjects compared with controls; U2AF1-mutated subjects were less likely to achieve RBC-TI (3% [1, 17%]) than U2AF1-unmutated subjects (25% [17, 34%], p=0.008). No other mutations were significantly correlated with response. Conclusion We found a high incidence of spliceosome mutations in persons with MPN-associated MF and RBC-transfusion-dependence. Mutation of U2AF1 correlated with response in subjects receiving POM but not in those receiving placebo. Incorporation of mutation profiling into clinical trial design may help to inform subgroups of patients that may benefit from the intervention. Disclosures Devos: Novartis: Consultancy; Celgene: Consultancy; Takeda: Consultancy. Gisslinger:Shire: Consultancy, Honoraria; Novartis: Honoraria, Research Funding; AOP Orphan Pharmaceuticals AG: Consultancy, Honoraria, Research Funding; Janssen Cilag: Consultancy, Honoraria. Kiladjian:Celgene: Membership on an entity's Board of Directors or advisory committees; AOP Orphan: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Mesa:Celgene: Research Funding; UT Health San Antonio - Mays Cancer Center: Employment; Pfizer: Research Funding; Gilead: Research Funding; NS Pharma: Research Funding; CTI Biopharma: Research Funding; Incyte Corporation: Research Funding; Novartis: Consultancy; Promedior: Research Funding; Genentech: Research Funding. Ribrag:argenX: Research Funding; BMS: Consultancy, Honoraria, Other: travel; NanoString Technologies: Consultancy, Honoraria; pharmamar: Other: travel; Infinity: Consultancy, Honoraria; MSD: Honoraria; Amgen: Research Funding; Gilead: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Roche: Honoraria, Other: travel; epizyme: Consultancy, Honoraria; Incyte Corporation: Consultancy. Schiller:Celator/Jazz Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding. Vannucchi:Celgene: Membership on an entity's Board of Directors or advisory committees; ITALFARMACO: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Reiser:Celgene: Employment. Zhong:Celgene: Employment. Mead:Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; ARIAD: Consultancy; Bristol-Myers Squibb: Consultancy; Cell Therapeutics: Consultancy; Celgene: Research Funding; Elstar: Research Funding; Evotek: Research Funding.

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: Aberrant B-cell receptor (BCR) and MYD88 signaling cooperatively drive Waldenstrom macroglobulinemia (WM) tumor cell survival. To sustain BCR-driven growth and maintain optimal cellular homeostasis, WM cells upregulate proteasome function. We have recently described the role of two distinct 19S proteasome-associated deubiquitinating enzymes (DUBs), USP14 and UCHL5, in maintaining WM-cell viability. Pharmacologic inhibition with b-AP15 (commercial compound) results in WM-cell death and accumulation of high molecular weight ubiquitinated protein conjugates; an event that is phenocopied by siRNA knockdown of USP14 and UCHL5. VLX1570 is a lead-optimized clinical grade analog of b-AP15 with enhanced solubility, stability and target residence time. We investigated the effect of VLX1570 on BCR components, WM cell survival proteins and its in vivo activity in a human xenograft model of WM. Materials: VLX1570 was obtained from Vivolux AB and bortezomib from Sellekchem. Human WM cell lines (BCWM.1, MWCL-1 and RPCI-WM1) and their drug-resistant derivatives were used (total n=12). Bone marrow aspirates from WM-patients and from healthy donors were obtained from the Mayo Clinic biospecimens core. PBMCs from healthy donors were used as comparators where needed. NOD/SCID mice were obtained from Taconic Labs and used for profiling the in vivo properties of VLX1570. Results: We first examined for USP14/UCHL5 protein expression by immunohistochemical analysis in primary tumor tissue from WM patients and compared it to healthy donor cells. WM patient-derived cells exhibited greater staining intensity of both DUBs. Preferential binding of VLX1570 to USP14 and UCHL5 in WM cells was probed in a WM cell using an enzyme-based competition assay (HA-Ub-Vs), which demonstrated time-dependent decrease in labeled USP14 and UCHL5 after treatment with VLX1570. In drug-resistant (bortezomib or ibrutinib-resistant) WM cells, VLX1570 induced >50% tumor-specific apoptosis and displayed synergistic activity with ibrutinib (median CI: 0.5). In addition to ER stress markers, we had previously observed that gene expression of BCR-signalosome components were reduced in VLX1570-treated WM cells (NanoString platform) and this was substantiated at the protein level where p-SYK, SYK and BTK decreased in a time-dependent manner. BCR end-effectors were probed in response to VLX1570 and a decrease in nuclear translocation of NFkB (p65) was noted. USP14 has been shown to control the availability of the chemokine receptor CXCR4, which regulates homing of WM cells to bone marrow. After VLX1570 treatment, we noted a significant decrease in CXCR4 surface receptor expression in all WM cell lines tested and primary WM cells. Lastly, to determine the in vivo anti-WM effects of VLX1570, we injected NOD/SCID mice (xenotransplanted with RPCI-WM1-Luc cells) with VLX1570 (I.P. route, 4.4mg/kg every other day) and measured tumor burden along with pharmacodynamic indicators. VLX1570-treated mice (n=7) demonstrated significantly lower tumor volumes by caliper measurement and bioluminescent signal (p=0.01 - 0.003) as well as reduced serum human-IgM, compared to vehicle-treated mice (n=7). Importantly, mice treated with VLX1570 survived longer vs. vehicle-treated mice (median survival, 62 vs. 44 days, respectively, p=0.0008). No unforeseen toxicities were noted and weight of the mice in vehicle vs. VLX1570-treated mice did not significantly change. IHC analysis in mice tumor tissues demonstrated reduced CXCR4, human-IgM, Ki67 and increased cleaved caspase-3 and ubiquitinated (k48-linked) protein level, the latter serving as a more specific indicator of USP14/UCHL5 inhibition. Conclusions: We provide substantial in vitro and in vivo evidence on the anti-WM effects of a novel first-in-class clinical grade USP14/UCHL5 inhibitor, VLX1570. In addition to anticipated induction of ER stress machinery, we describe for the first time VLX1570-mediated modulation of BCR components and WM cell survival proteins as well as demonstrate its robust activity in an aggressive human xenograft model of WM. These data provide the rationale for a clinical trial of single agent VLX1570 in patients with symptomatic WM. Disclosures Martin: Janssen: Consultancy, Honoraria; Acerta: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Bayer: Consultancy. Gullbo:Vivolux AB: Other: Shareholder. Linder:Vivolux AB: Other: Shareholder.

Abstract Background: Patients (pts) with refractory aggressive non-Hodgkin lymphoma (NHL) have poor outcomes with currently available therapies, with a complete response (CR) rate of 8%, a partial response (PR) rate of 18%, and median overall survival (OS) of 6.6 months (mo) as demonstrated in the 635 pt SCHOLAR-1 meta-analysis (Crump, ASCO 2016; abstract 7516). ZUMA-1 is the first multicenter trial of anti-CD19 chimeric antigen receptor (CAR) T cells in refractory, aggressive NHL (NCT02348216). The phase 1 portion of ZUMA-1 showed ongoing CRs at 12+ mos in 43% of pts (Locke, ESMO 2016; abstract 1048O). The pivotal phase 2 portion of ZUMA-1 comprises 2 cohorts based on tumor type: DLBCL (cohort 1) and primary mediastinal B-cell lymphoma or transformed follicular lymphoma (cohort 2). Here, we present results of a prespecified interim analysis from cohort 1. Methods: Pts received a target dose of 2 × 106 anti-CD19 CAR T cells/kg after a low-dose conditioning regimen of cyclophosphamide (500 mg/m2) and fludarabine (30 mg/m2) daily for 3 days. The primary endpoint is objective response rate (ORR) per 2007 IWG criteria. Key secondary endpoints include duration of response, frequency of adverse events (AEs), and levels of CAR T cells and serum cytokines. Key inclusion criteria include age ≥18 years, ECOG performance status (PS) 0-1, and refractory disease defined as progressive disease or stable disease as best response to last line of therapy, or disease progression ≤12 mos after autologous stem cell transplant (ASCT). Pts must have received a prior anti-CD20 antibody and an anthracycline-containing regimen. A prespecified interim analysis was to be conducted to determine early efficacy with a nominal alpha level of 0.017 in 50 treated pts in cohort 1 with a minimum follow-up of 3 mos. Results: In total, 111 pts from 22 institutions were enrolled and leukapheresed, and 101 pts received KTE-C19. As of August 24, 2016, 51 pts in cohort 1 were eligible for analysis. Median age was 58 years (range, 25-76), 73% were male, 71% had ECOG PS 1, 78% were refractory to ≥2 lines of therapy, 20% relapsed ≤12 mos of ASCT, and 61% were treated with ≥3 lines of prior therapy. KTE-C19 was successfully manufactured in 99% of pts enrolled. Average turnaround time from apheresis to receipt of KTE-C19 at the clinical site was 17.4 days. With an ORR of 76%, the study met the primary endpoint (P<0.0001; exact binomial test comparing observed ORR to a historical control assumption of 20%), with 47% CRs and 29% PRs. 92% of responses occurred within the 1st mo, and 39% of pts had ongoing responses (CR in 33%) at 3 mos. Responses were seen across key covariates, including refractory subgroup (refractory to chemotherapy=76%, relapse post ASCT=80%). Kaplan-Meier estimates of progression-free survival at 1 and 3 mos were 92% and 56%, respectively. The most common grade ≥3 treatment-emergent AEs were neutropenia (67%), anemia (39%), thrombocytopenia (29%), febrile neutropenia (27%), and encephalopathy (24%). Grade ≥3 cytokine release syndrome (CRS) and neurologic events occurred in 20% and 29% of pts, respectively. There was 1 grade 5 KTE-C19-related event of hemophagocytic lymphohistiocytosis. CAR T cells expanded within 14 days of KTE-C19 infusion, and peak expansion was associated with ongoing response at mo 3 (P=0.008). Pts who developed grade ≥3 neurological events had increased serum levels of IL-15 (P=0.0002), IL-6 (P=0.003); IL-10 (P=0.009) and IP-10 (P=0.0003). Cytokines/chemokines returned to baseline levels in most pts by day 28. Data from 93 pts with at least 1 mo of follow-up at the data cutoff will be presented. Conclusions: ZUMA-1 is the first reported multicenter trial of CAR T cell therapy in pts with refractory aggressive NHL. KTE-C19 induced a nearly 6-fold higher CR rate compared to historical outcomes in SCHOLAR-1. Efficacy strongly associated with peak CAR T levels. Central manufacturing, logistics, and AE management were successfully implemented across 22 sites, most with no prior CAR T therapy experience. Results from cohort 2 of ZUMA-1 are also presented (Abstract #998). KTE-C19 demonstrated significant clinical benefit in pts with no curative treatment options. Supported in part by funding from The Leukemia & Lymphoma Society Therapy Acceleration Program®. Drs Neelapu and Locke contributed equally to this study. Disclosures Neelapu: Kite Pharma: Membership on an entity's Board of Directors or advisory committees, Research Funding. Locke:Kite: Membership on an entity's Board of Directors or advisory committees. Miklos:pharmacyclics: Research Funding; Kite Pharma: Research Funding; Roche: Research Funding; Novartis: Research Funding. Jacobson:Kite: Membership on an entity's Board of Directors or advisory committees. Siddiqi:Pharmacyclics: Speakers Bureau; Janssen: Speakers Bureau; Seattle Genetics: Speakers Bureau. Lin:Mayo Clinic: Employment; Janssen: Research Funding. Timmerman:Bristol-Myers Squibb, Kite Pharma, Valor Biopharmaceuticals, Janssen: Research Funding; Seattle Genetics, Genmab, Celgene: Consultancy, Honoraria. Goy:COTA: Membership on an entity's Board of Directors or advisory committees; Janssen/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Research funding for clinical trials through institution, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Research funding for clinical trials through institution; Acerta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Genentech: Other: Research funding for clinical trials through institution. Smith:Abbvie: Research Funding; Celgene: Honoraria; Spectrum: Honoraria; Genentech: Honoraria. Deol:Jazz Pharmaceuticals: Consultancy. Avivi:Tel Aviv Sourasky Medical center: Consultancy, Other: consultancy to :BMS Roche. Westin:Genentech: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Spectrum: Membership on an entity's Board of Directors or advisory committees; ProNAi: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Chavez:Janssen: Speakers Bureau. Levy:Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding. Reagan:Seattle Genetics: Research Funding. Bot:Kite Pharma: Employment, Equity Ownership. Rossi:Kite Pharma: Employment, Equity Ownership. Navale:Kite Pharma: Employment, Equity Ownership. Jiang:Kite Pharma: Employment, Equity Ownership. Aycock:Kite Pharma: Employment, Equity Ownership. Elias:Kite: Employment, Equity Ownership. Wiezorek:Kite Pharma: Employment, Equity Ownership. Go:Kite Pharma: Employment, Equity Ownership.

Abstract Aims This observational study characterized cardiovascular disease (CVD) mortality risk for multiple cancer sites, with respect to the following: (i) continuous calendar year, (ii) age at diagnosis, and (iii) follow-up time after diagnosis. Methods and results The Surveillance, Epidemiology, and End Results program was used to compare the US general population to 3 234 256 US cancer survivors (1973–2012). Standardized mortality ratios (SMRs) were calculated using coded cause of death from CVDs (heart disease, hypertension, cerebrovascular disease, atherosclerosis, and aortic aneurysm/dissection). Analyses were adjusted by age, race, and sex. Among 28 cancer types, 1 228 328 patients (38.0%) died from cancer and 365 689 patients (11.3%) died from CVDs. Among CVDs, 76.3% of deaths were due to heart disease. In eight cancer sites, CVD mortality risk surpassed index-cancer mortality risk in at least one calendar year. Cardiovascular disease mortality risk was highest in survivors diagnosed at &lt;35 years of age. Further, CVD mortality risk is highest (SMR 3.93, 95% confidence interval 3.89–3.97) within the first year after cancer diagnosis, and CVD mortality risk remains elevated throughout follow-up compared to the general population. Conclusion The majority of deaths from CVD occur in patients diagnosed with breast, prostate, or bladder cancer. We observed that from the point of cancer diagnosis forward into survivorship cancer patients (all sites) are at elevated risk of dying from CVDs compared to the general US population. In endometrial cancer, the first year after diagnosis poses a very high risk of dying from CVDs, supporting early involvement of cardiologists in such patients.