Citations

Sudden death syndrome (SDS) foliar symptoms consist of foliar chlorosis, foliar necrosis, leaf marginal curling, and premature defoliation, but resistance screening has been evaluated mostly based on the overall SDS foliar severity rather than on a specific foliar symptom. This study generated an F2 population derived from crossing the susceptible variety Sloan and the resistant germplasm line PI 243518, which exhibits resistance to both foliar chlorosis and necrosis. A total of 400 F2 lines were evaluated for foliar chlorosis, foliar necrosis, and overall SDS foliar symptoms, separately. Genotyping-by-sequencing was applied to obtain single nucleotide polymorphisms (SNPs) in the F2 population, and linkage mapping using 135 F2 lines with 969 high-quality SNPs identified a locus on chromosome 13 for foliar necrosis and SDS foliar symptoms. The locus partially overlaps with loci previously reported for SDS on chromosome 13, which is the third time the region from 15.98 to 21.00 Mbp has been reproduced independently and therefore qualifies this locus for a new nomenclature proposed as Rfv13-02. In summary, this study generated a new biparental population that enables not only the discovery of a locus for foliar necrosis and SDS foliar symptoms on chromosome 13 but also the potential for advanced exploration of SDS foliar resistance derived from the germplasm line PI 243518.

Background: Progress in genome technology allows analysis of previously completed trials to identify subgroups potentially benefiting from therapy. Enzastaurin is a potent inhibitor of protein kinase C beta (PKC-β) and suppresses the phosphoinositide 3-kinase (PI3K)/AKT pathway. The safety and efficacy of Enzastaurin has been tested in more than 60 clinical trials including 2 major studies in DLBCL: (1) PRELUDE (A phase III maintenance trial of Enzastaurin vs Placebo, N=758) (Crump, 2016), and (2) S028 (A randomized phase II study of Enzastaurin/R-CHOP vs R-CHOP in frontline intermediate/high-risk DLBCL, N=101) (Hainsworth, 2016). DNA samples extracted from blood of patients from PRELUDE were retrospectively genotyped using whole genome SNP arrays. From the genome wide screening a novel genetic biomarker, DGM1, was identified showing high correlation with response to Enzastaurin treatment (Luo, ASH 2018). Importantly, these findings were replicated in the phase II S028 study. In the S028 study the hazard ratio (HR) for OS in high-risk (IPI ≥ 3) DGM1 positive (+) patients who received Enzastaurin/R-CHOP was 0.28 (0.1-0.81) when compared to subjects who received R-CHOP, a benefit favoring Enzastaurin (p=0.018). These data suggest that addition of Enzastaurin to R-CHOP may significantly improve outcome in frontline high-risk DGM1 (+) DLBCL. The ENGINE study was initiated to validate this finding in a prospective study. Study Design and Methods: Adult patients must have untreated CD20+ DLBCL, IPI ≥ 3. Patients are randomized 1:1 to Enzastaurin/R-CHOP or Placebo/R-CHOP for 6 cycles during combination phase. Each subject's treatment assignment will be unblinded after response assessment at the end of the combination phase. Subjects randomized to the investigational arm who have a complete or partial response will have the option to continue in the single agent phase to receive Enzastaurin for up to 2 additional years. The study intends to enroll approximately 235 patients with primary endpoint of OS in DGM1 (+) patients. The study is ongoing with 57 sites open in the US and China. As of 20 July 2019, 128 patients have been randomized. Clinical trial information: NCT03263026. Disclosures Nowakowski: Genentech, Inc.: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Brody:Acerta Pharma: Research Funding; Merck: Research Funding; Celldex Therapeutics: Research Funding; Genentech: Research Funding; Oncovir, Inc.: Research Funding; BMS: Research Funding; Kite Pharma: Research Funding. Lue:Kymera Therapeutics: Honoraria; Astex Pharmaceuticals: Honoraria. Luo:Denovo Biopharma LLC: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Zhang:Denovo Biopharma LLC: Employment. Han:Denovo Biopharma LLC: Employment. Jivani:TRACON Pharmaceuticals, Inc.: Other: Stock; Denovo Biopharma LLC: Employment. Liu:Denovo Biopharma LLC: Employment. Li:Denovo Biopharma LLC: Employment. Sun:Novartis: Other: Stock; Denovo Biopharma LLC: Employment.

Background: Multiple myeloma (MM) is a heterogeneous disease with varying survival outcomes depending on the presence of certain genetic abnormalities. Common abnormalities include trisomies, translocations involving the chromosome 14, and amplifications or deletions of chromosomes 1, 13, and 17. t(11;14), occurring in 15% of patients with myeloma, had been considered a standard risk abnormality, but recent data suggest inferior outcome. This is important as new therapeutic options such as the BCL-2 inhibitor venetoclax has been shown to be particularly effective in t(11;14) patients. Methods: This was a multicenter study to identify the outcomes of patients with t(11;14), using a retrospectively assembled cohort. Patients with MM diagnosed between 2005 and 2015 with t(11;14) identified on FISH performed within six months of diagnosis, and with treatment details available and if alive, a minimum of 12 months of follow up, were enrolled. Results: The current analysis includes 1216 patients; median age of 62.56 years; 58.7% male. The median follow-up from diagnosis for the entire cohort was 51.9 months; 69.1% of the patients were alive at the last follow up. ISS stage distribution included: Stage I (35.7%), Stage II (34.0%) and Stage III (15.1%), data was missing for the rest. The distribution of concurrent FISH abnormalities included: trisomies (3.5%), del 13q (13.3%), 1q amp (8.8%), and del 17p or monosomy 17 (5.8%). Initial regimen included: 27.2% had an immunomodulatory (IMiD), 45.9% had a proteasome inhibitor (PI), 17.7% had both, and 9.0% had no novel agent. The drug classes by line of therapy are shown in Table 1. An early stem cell transplant (defined as within 12 months of start of first line treatment) was used in 49.4% of patients. The median time to next treatment (TTNT) after starting initial treatment was 26.6 (95% CI: 23.9 to 29.2) months. The median overall survival (OS) from diagnosis for the entire cohort was 95.1 (95% CI: 85.9 to 105.9) months; 4-year estimates for those diagnosed from January 2005 to December 2009, and from January 2010 to December 2014 were 77.5% and 78.6%, respectively. The median OS for those with any one high risk FISH lesion (del 17p/ 1q amp) was 67.5 (55.2, 97.1) versus 101.7 (89.7, 107.3) months. Patients with early SCT (within 12 months of diagnosis) had better OS: 108.3 (103.8, 133.0) vs. 69.8 (61.5, 80.3) months. Conclusion: Patients with t(11;14) without high risk FISH abnormalities have an excellent survival. Patients receiving a PI + IMiD combination and those receiving autologous SCT as part of initial therapy had best survival. Though numbers are limited, patients in the later lines receiving newer drugs such as venetoclax and daratumumab had high response rates and durable responses. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Bittrich:Celgene: Other: Travel Funding, Research Funding; Else Kröner Fresenius Foundation: Research Funding; Otsuka Pharmaceuticals Europe: Other: N/A; SANOFI Aventis: Membership on an entity's Board of Directors or advisory committees, N/A, Research Funding; University Hospital Wuerzburg: Employment; Bristol Myers Squibb: Research Funding; Pfizer: Other: Travel Funding; AMGEN: Other: Travel Funding; JAZZ Pharmaceuticals: Other: Travel Funding; Wilhelm Sander Foundation: Research Funding; German Research Foundation (DFG): Other: N/A; University of Würzburg: Other: N/A. Goldschmidt:Mundipharma: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; John-Hopkins University: Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Molecular Partners: Research Funding; John-Hopkins University: Research Funding; Amgen: Consultancy, Research Funding; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Chugai: Honoraria, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Reece:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Otsuka: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; BMS: Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Merck: Research Funding. Mateos:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmamar: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Adaptive: Honoraria; EDO: Membership on an entity's Board of Directors or advisory committees. Ludwig:Celgene: Speakers Bureau; Amgen: Research Funding, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; PharmaMar: Consultancy; Janssen: Speakers Bureau; BMS: Speakers Bureau. Mangiacavalli:celgene: Consultancy; Amgen: Consultancy; Janssen cilag: Consultancy. Dimopoulos:Sanofi Oncology: Research Funding. Kastritis:Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Honoraria; Prothena: Honoraria; Genesis: Honoraria. Yee:Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Karyopharm: Consultancy; Adaptive: Consultancy. Raje:Amgen Inc.: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene Corporation: Consultancy; Takeda: Consultancy; Janssen: Consultancy; Merck: Consultancy. Rosta:Cornerstone Research Group: Employment. Haltner:Cornerstone Research Group: Employment. Cameron:Cornerstone Research Group: Employment, Equity Ownership. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy.

Background: With the introduction of the Oncology Care Model, which aims to provide higher quality, more coordinated oncology care at the same or lower cost to Medicare, physician practices are increasingly being held accountable for performance and financial measures.1 Urgent care (emergency room [ER] visits and hospitalizations) is known to contribute a significant proportion of healthcare costs, and while there has been a focus on drug costs, little is known about the utilization or economic impact of urgent care among multiple myeloma (MM) patients. Objective: To identify the primary reasons and economic burden of ER visits and hospitalizations among newly diagnosed MM (NDMM) patients during first line of therapy (LOT1). Methods: Patients from the Optum Clinformatics Extended DataMart - Date of Death database (Optum DOD) with ≥1 medical or pharmacy claim for an NCCN listed MM treatment from 7/1/11 to 3/31/2018 (index date) were included if they had diagnosis code for MM (ICD9/10 code 203/C90) and ≥6-months continuous enrollment without MM treatment prior to index date. Patients were followed until death, end of eligibility, or end of study (9/30/2018), whichever occurred first. The top 20 most common primary diagnoses (PDx) for ER visits and for hospitalizations were identified separately for patients who received stem cell transplant (SCT) or not (nSCT) during LOT1. Total costs and per-patient-per-month (PPPM) costs of ER visits and hospitalizations for each PDx were reported. Results: Total 3,099 patients met inclusion criteria, including 2,761 (89.1%) nSCT and 338 (10.9%) SCT patients. nSCT patients were older than SCT patients (median nSCT=72 years vs SCT=62 years, p < 0.0001). The most common treatment regimens among nSCT were bortezomib in combination with dexamethasone (dex) (34%) followed by lenalidomide in combination with dex (27.4%). The most common treatment regimen among SCT were bortezomib in combination with lenalidomide and dex (46%) followed by bortezomib in combination with chemotherapy (18.3%). Median duration of LOT1 was 5.1 months among all patients, with median 11.2 months among SCT vs 4.5 months among nSCT, p < 0.0001). Among all patients, (Table 1) there was a total of 1,645 ER visits and 3,385 hospitalizations with a total cost of $3,729,208 and $56,751,758, respectively, and a PPPM cost of $146 and $2,220 for ER visits and hospitalizations, respectively. Among nSCT, there were 1,339 (81% of the total) ER visits and 2,776 (82% of the total) hospitalizations with a total cost of $3,063,727 and $39,763,095, respectively, and a PPPM cost of $151 and $1,955 for ER visits and hospitalizations, respectively. Among SCT, there were 306 (19% of the total) ER visits and 609 (18% of the total) hospitalizations, with a total cost of $665,481 and $16,988,663, respectively, and a PPPM cost of $127 and $3,253 for ER visits and hospitalizations, respectively. The most common PDx for hospitalizations among the entire cohort was 'MM, without mention of having achieved remission' with 769 hospitalizations for a total cost of $19,299,593 and a PPPM cost of $7,112. This same PDx was the most common among nSCT with 449 (58%) hospitalizations for a total cost of $7,551,282 and a PPPM cost of $9,292; as well as for SCT with 320 (42%) hospitalizations for a total cost of $11,748,760 and a PPPM cost of $4,435. This was also the most common PDx for ER visits among nSCT and SCT. For hospitalizations, the second most common PDx among the entire cohort and among nSCT was 'shortness of breath' while for SCT it was 'administration of antineoplastic chemotherapy'. The second most common PDx overall and for nSCT for ER visits was 'chest pain' and for SCT patients was 'musculoskeletal dysfunction (Segmental and somatic dysfunction of cervical region)'. Conclusions: In this analysis, NDMM patients experienced frequent ER visits and hospitalizations over a median of ~5 months for a total cost of more than $60 million, with most of the cost among nSCT patients. The most common PDx was related to MM. Even though some hospitalizations for the SCT patients may have been for the SCT procedure itself, the economic burden of urgent care for MM patients remains very high. Additional research to assess the impact of newer treatments on urgent care visits among NDMM, identify patients at high risk of urgent care visits may provide insight into the potential for developing cost-saving interventions to reduce the utilization of urgent care. Disclosures Maiese: Janssen: Employment, Equity Ownership. Zhu:Janssen Scientific Affairs, LLC: Other: contractor for Janssen. Wu:Janssen: Employment, Equity Ownership. Chen:Janssen: Employment, Equity Ownership. Ailawadhi:Janssen: Consultancy, Research Funding; Takeda: Consultancy; Celgene: Consultancy; Cellectar: Research Funding; Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding.

Chronic myelomonocytic leukemia (CMML) belongs to the group of "mixed myelodysplastic/myeloproliferative neoplasm". There is currently no effective chemotherapy to treat CMML. Approximately 30% of CMML cases evolve to acute myeloid leukemia (AML) soon after their initial diagnosis, contributing to the poor prognosis of CMML patients. Ras pathway genes are frequently mutated in CMML patients (NRAS:11%; CBL:10%; KRAS:8%). Our whole exome sequencing of CMML patient samples and targeted sequencing of CMML patient-derived myeloid colonies showed that oncogenic NRAS could serve as an initiating or progression mutation. Consistent with human genetics, all the oncogenic Nras mice we and others characterized (NrasG12D/+, NrasG12D/G12D, and NrasQ61R/+), develop myeloproliferative (MP)-CMML like phenotypes.However, oncogenic NRAS alone is insufficient to drive CMML progression or transformation to AML. Additional sex combs-like 1 (ASXL1) is a human homolog of fly Asx. Mutations in ASXL1are predominantly nonsense and frequently identified in all myeloid malignancies (e.g. 40% in CMML). ASXL1mutations predict inferior overall survival in univariable analysis in multiple large CMML cohort studies and are significantly associated with NRASmutations in CMML patients. Therefore, we hypothesize that loss of Asxl1 cooperates with oncogenic Nrasto drive CMML progression. To test our hypothesis, we used Vav-Cre to drive oncogenic Nras expression and/or Asxl1 deletion in hematopoietic system. We refer these mice as NrasG12D/+, Asxl1-/-and NrasG12D/+;Asxl1-/-(NA) mice. Consistent with the literature, Asxl1-/-mice are normal at 6-week old. NrasG12D/+ mice showed mild CMML-like phenotypes, including enlarged spleen, increased white blood cell and monocyte counts, and expanded LSKs (Lin-Sca-1+c-Kit+) and myeloid progenitors (MPs). Compared to NrasG12D/+ mice, NA mice showed more sever phenotypes that were associated with hyperactivated ERK signaling in MPs at both basal level and upon GM-CSF stimulation. Our new NrasG12D/+mice developed a fully penetrant MP-CMML with the median survival of ~430 days. Loss of Asxl1 significantly shortened the median survival of NrasG12D/+ mice to 220 days. Half of NA mice displayed CMML transformation to AML. The transformed AML was serially transplantable to recipients. To investigate the mechanism(s) underlying NA-driven CMML/AML, we performed RNA-Seq analysis in Lin- c-Kit+BM cells from age-matched control, Asxl1-/-, NrasG12D/+, and moribund NA mice with CMML or AML phenotypes. We found 844 differentially expressed genes (DEG) in AML cells compared to control cells (FDR<0.05 and fold change>2). Among these genes, 341 are common between NA and published Nf1+/-;Asxl1+/-AML cells, including AP-1 complex genes (Jun, Junb, Jund and Fosb) that are crucial for malignant propagation in several AML subtypes. The remaining 503 genes are unique in NA AML, including Flt3, one of the mostly mutated genes in AML. These data suggest that our NA AML model demonstrates some distinct mechanisms from Nf1+/-;Asxl1+/-model. In-depth analysis of RNA-seq data is still ongoing. We also examined multiple histone marks using Western blot in BM cells of 6-week old mice. Our data showed that levels of H3K4me3, H3K4me1, and H3K27me3 were downregulated in Asxl1-/-cells, whereas H3K27Ac level was comparable to that of control cells. In contrast, H3K27Ac level was upregulated in NrasG12D/+and NA cells, while levels of H3K4me3, H3K4me1, and H3K27me3 were indistinguishable from those of control cells. BRD4 is a member of the BET family of bromodomain-containing proteins that binds to acetylated histones to activate gene transcription. Thus, we hypothesized that Ras/MEK/ERK signaling and BRD/BET are potential targets to inhibit AML progression. To test this hypothesis, we treated the AML cells and BMT recipients using trametinib (a MEK inhibitor approved by FDA to treat melanoma) and GSK525762 (a pan BET inhibitor under clinical development). We demonstrated thatcombined treatment significantly inhibited the growth of leukemia cells in vitro and prolonged the survival of recipients in vivo. It was more effective than single agent alone. Overall, we established a novel CMML/AML mouse model, which represents a significant group of CMML patients with poor prognosis. Our study provides a strong rational to treat these patients with combined MEK and BET inhibition. Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Abstract BACKGROUND A major impediment to improving neuro-oncology outcomes is poor clinical trial accrual. METHODS We convened a multi-stakeholder group including Society for Neuro-Oncology, Response Assessment in Neuro-Oncology, patient advocacy groups, clinical trial cooperative groups, and other partners to determine how we can improve trial accrual. RESULTS We described selected factors contributing to poor trial accrual and possible solutions. We focused on patient and community factors, disparities, physician and provider factors, clinical trial factors, and site and organizational factors CONCLUSIONS We will implement strategies with the intent to double accrual to neuro-oncology trials over the next 5 years.

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.