Citations

Introduction: Atrial fibrillation (AF) adversely affect the outcomes in the patients of heart failure (HF) with reduced ejection fraction, however there are limited data exploring such an association in HF with preserved ejection fraction (HFpEF). Hypothesis: AF is associated with worse outcomes in HFpEF. Methods: We included all the patients with the primary diagnosis of HFpEF from the national inpatient sample (NIS) database (2012-2014) using ICD-9 codes. Exposure of interest was AF. Primary outcome was in-hospital mortality and secondary outcomes were rates of sudden cardiac arrest (SCA), syncope, cardiogenic shock, embolic stroke, acute myocardial infarction (AMI), acute kidney injury (AKI), passive hepatic congestion, ventricular fibrillation (V fib) and flutter, ventricular assist device (VAD), AICD, cardiac resynchronization therapy (CRT), intra-aortic balloon placement (IABP) placement and heart transplantation. Hospitalization cost was also studied. Results: Our study cohorts included 26,51,970 patients of HFpEF with AF and 37,44,101 patients of HFpEF without AF. AF cohort had more numbers of older patients and less female representation. In-hospital mortality was more in AF cohort. Similarly, the odds of SCA, cardiogenic shock, embolic stroke, passive hepatic congestion, Vfib and flutter, AICD and CRT placement were higher in AF cohort. The odds of syncope, AMI and AKI were lower in AF cohort as compared to non-AF cohort. While the odds of heart transplantation and VAD and IABP use remained comparable between the study cohorts, AF cohort incurred greater of cost of hospitalization. Conclusion: AF in HFpEF patients is associated with increased in-hospital mortality and cardiogenic shock and should be aggressively treated for improved outcomes.

Abstract The primary cause of morbidity and mortality in patients with multiple myeloma (MM) is an infection. Therefore, there is great concern about susceptibility to the outcome of COVID-19–infected patients with MM. This retrospective study describes the baseline characteristics and outcome data of COVID-19 infection in 650 patients with plasma cell disorders, collected by the International Myeloma Society to understand the initial challenges faced by myeloma patients during the COVID-19 pandemic. Analyses were performed for hospitalized MM patients. Among hospitalized patients, the median age was 69 years, and nearly all patients (96%) had MM. Approximately 36% were recently diagnosed (2019-2020), and 54% of patients were receiving first-line therapy. Thirty-three percent of patients have died, with significant geographic variability, ranging from 27% to 57% of hospitalized patients. Univariate analysis identified age, International Staging System stage 3 (ISS3), high-risk disease, renal disease, suboptimal myeloma control (active or progressive disease), and 1 or more comorbidities as risk factors for higher rates of death. Neither history of transplant, including within a year of COVID-19 diagnosis, nor other anti-MM treatments were associated with outcomes. Multivariate analysis found that only age, high-risk MM, renal disease, and suboptimal MM control remained independent predictors of adverse outcome with COVID-19 infection. The management of MM in the era of COVID-19 requires careful consideration of patient- and disease-related factors to decrease the risk of acquiring COVID-19 infection, while not compromising disease control through appropriate MM treatment. This study provides initial data to develop recommendations for the management of MM patients with COVID-19 infection.

Purpose: Acute myeloid leukemia (AML) is the most common acute leukemia in adults, affecting approximately 20,000 patients annually in the United States. AML genetic subtypes, as defined by the World Health Organization (WHO), are identified through fluorescence in situ hybridization (FISH), conventional chromosome analysis, and sequencing techniques. Mate pair sequencing (MPseq) is a next generation sequencing (NGS) technology optimized to detect genome wide structural variants and copy number alterations at high resolution. Our study goal was to investigate the prognostic value of MPseq in comparison to FISH, chromosome, and sequencing studies in the evaluation of AML patients. Methods: We performed a prospective study using blood and bone marrow samples from 105 patients with a diagnosis of AML, using MPseq, along with chromosome, FISH, and NGS or PCR studies to detect small mutations. Cytogenetic and molecular genetic results were correlated with MPseq findings. We also analyzed the MPseq data for chromoplexy, chromothripsis, and progressive complexity. Junction and copy number burden, the incidence of structural variation in the genome and the percent of the genome with aberrant copy number, were evaluated. Overall survival statistics were stratified by AML subtypes and observed anomalies. Results: Although structural variants in AML were characterized at a high resolution using MPseq when compared to conventional cytogenetic methods, risk stratification using current European Leukemia Net (ELN) guidelines was not improved by MPseq. The cohorts involving 5q and/or 7q deletions exhibited high levels of genomic complexity when compared to normal karyotype AML (NK-AML). The incidence of copy number gains, losses and junctions was greatest in 5q and 7q co-deletions (5q/7q) (16.5, 25.0, 69.3) and 5q deletions (5q) (9.8, 16.7, 31.6) subtypes compared to 7q deletions (3.4, 7.0, 6.7) and NK-AML (2.6, 4.3, 3.8) (p<0.001) subtypes. Chromoplexy, chromothripsis, and progressive structural complexity were detected in most samples with 5q deletions and 5q/7q co-deletions, but absent in samples with 7q or NK-AML. Biallelic inactivation of TP53 by sequencing mutation and/or deletion was common in the 5q/7q co-deletion (14/18 cases) and 5q deletion cohorts (7/10 cases), rare in the 7q deletion cohort (1/11 cases), and absent in the NK-AML (n=44) cohort. The median OS was significantly worse for patients with 5q/7q deletions (122.5 days) and 5q deletions (248 days) compared to NK-AML (413.5 days; p<0.001 and p=0.017, respectively) and between 5q/7q deletions and 7q deletions (370.5 days; p<0.001). No significant difference was observed between 5q/7q and 5q deletion subtypes, between NK and 7q deletion subtypes and between 5q and 7q deletion subtypes. The median OS was also significantly shorter for patients with TP53 alterations compared to patients with normal TP53 status. Patients with chromoplexy, chromothripsis and/or progressive structural complexity identified by MPseq had a significantly shorter median OS compared to patients without these features. (p<0.0001) Discussion: Risk stratifications based on current guidelines using cytogenetic and sequencing results were not adjusted due to MPseq results, which is not surprising when primary abnormalities are observed by conventional cytogenetic methods. NK-AML cases did not appear to benefit from a high resolution genomic evaluation. However, MPseq added value when structural variation required additional characterization, detecting novel rearrangements, such as a KAT6A/SORBS3 fusion. Lastly, we recognized common mischaracterizations made by conventional chromosome studies - including missed TP53 deletions in 7 cases, 5q/7q deletions misinterpreted as monosomies, cryptic NUP98 rearrangements, and unappreciated genomic complexity correlating with poor OS. These mischaracterizations challenge the use of conventional chromosome studies as a gold standard without accompanying FISH or MPseq studies. MPseq, similar to other structural methodologies such as optical mapping and long read sequencing, should be considered important complements to standard cytologic techniques given the important additional genomic information obtained. The additional structural variant characterization will be critical in paving the way for genomic discovery with the overall goal of improving prognostication for patient care. Figure Disclosures Vasmatzis: WholeGenome LLC.: Other: Owner; Mayo Clinic: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Introduction: Daratumumab (DARA) is a human IgGκ monoclonal antibody targeting CD38 with a direct on-tumor and immunomodulatory mechanism of action, and has been approved across lines of therapy for the treatment of multiple myeloma. The addition of DARA to standard-of-care (SoC) regimens, lenalidomide and dexamethasone (D-Rd) and bortezomib, melphalan, and prednisone (D-VMP), in the phase 3 MAIA and ALCYONE clinical studies reduced the risk of disease progression or death by ≥44%, nearly doubled the rate of complete response (CR) or better, and induced a >3-fold increase in MRD-negativity rates (10-5 sensitivity threshold) vs SoC alone in pts with TIE NDMM. In both MAIA and ALCYONE, MRD negativity was associated with longer progression-free survival (PFS), irrespective of trial treatments. MRD negativity provides an index of deep clinical response and may be a more robust evaluation of disease control if sustained over time. Here, we evaluate MRD negativity, including sustained MRD negativity, in pts with TIE NDMM from MAIA and ALCYONE and its association with PFS with longer follow-up. Methods: Pts with NDMM ineligible for high-dose chemotherapy with stem cell transplantation due to age (≥65 years) or unacceptable coexisting conditions in MAIA and ALCYONE were randomized (1:1) to SoC ± DARA. Pts in MAIA received 28-day cycles of Rd (R: 25 mg PO on Days 1-21; d: 40 mg PO QW) ± DARA (16 mg/kg IV QW for Cycles 1-2, Q2W for Cycles 3-6, and Q4W thereafter). ALCYONE pts received up to nine 6-week cycles of VMP (V: 1.3 mg/m2 SC twice weekly during Weeks 1, 2, 4, and 5 of Cycle 1 and QW during Weeks 1, 2, 4, and 5 of Cycles 2-9; M: 9 mg/m2 PO on Days 1-4 of Cycles 1-9; and P: 60 mg/m2 PO on Days 1-4 of Cycles 1-9) ± DARA (16 mg/kg IV QW for Cycle 1, Q3W for Cycles 2-9, and Q4W for Cycles 10+). Study treatments continued until progressive disease or unacceptable toxicity. MRD was assessed in MAIA and ALCYONE in all pts who achieved a CR or stringent CR. For ≥CR pts, additional MRD assessments occurred at 12, 18, 24, and 30 months after the first dose in ALCYONE and at 12, 18, 24, 30, 36, 48, and 60 months in MAIA. MRD was assessed via next-generation sequencing using the clonoSEQ® assay (v.2.0; Adaptive Biotechnologies, Seattle, WA) at the 10-5 sensitivity threshold. Sustained MRD negativity was defined as the maintenance of MRD negativity confirmed ≥6 or ≥12 months apart with no MRD positive test in between and was evaluated in the intention-to-treat (ITT) population. Results: A total of 737 (D-Rd, n=368; Rd, n=369) pts in MAIA and 706 (D-VMP, n=350; VMP, n=356) pts in ALCYONE were randomized; median duration of follow-up was 36.4 months in MAIA and 40.1 months in ALCYONE. In both studies, DARA-based therapy improved MRD negativity vs SoC in the ITT population (D-Rd, 28.8% vs Rd, 9.2%, P<0.0001; D-VMP, 28.3% vs VMP, 7.0%, P<0.0001). MRD-negative pts had improved PFS vs MRD-positive pts in the ITT population, regardless of treatment arm. In a pooled analysis of pts from DARA (D-Rd/D-VMP) and SoC (Rd/VMP) treatment groups, MRD-negative pts had improved PFS vs pts who were MRD positive, irrespective of treatment arm. In general, baseline characteristics were comparable among pts in DARA and control treatment groups with sustained MRD negativity. A higher proportion of pts in the ITT populations of MAIA (D-Rd, n=368; Rd, n=369) and ALCYONE (D-VMP, n=350; VMP, n=356) achieved sustained MRD negativity with DARA-based therapy vs SoC for ≥6-months (D-Rd, 14.9% vs Rd, 4.3%, P<0.0001; D-VMP, 15.7% vs VMP, 4.5%, P<0.0001) and ≥12-months (D-Rd, 10.9% vs Rd, 2.4%; P<0.0001; D-VMP, 14.0% vs VMP, 2.8%, P<0.0001). PFS was prolonged in pts with sustained MRD negativity for ≥6-months and ≥12-months vs pts without sustained MRD negativity in the pooled ITT populations, regardless of treatment arm (Figure). Conclusions: DARA-based combination regimens induce higher rates of MRD-negativity and sustained MRD negativity in pts with TIE NDMM, which are associated with deep responses and improved PFS. Disclosures San-Miguel: Amgen, BMS, Celgene, Janssen, MSD, Novartis, Takeda, Sanofi, Roche, Abbvie, GlaxoSmithKline and Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees. Avet-Loiseau:Celgene and Janssen: Research Funding; Celgene, Amgen, Bristol-Myers Squibb, Sanofi, and Janssen;: Honoraria, Speakers Bureau. Paiva:Takeda: Consultancy, Honoraria, Research Funding; SkylineDx: Consultancy; Kite: Consultancy; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Honoraria; Adaptive: Honoraria; Roche: Research Funding; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Research Funding. Kumar:Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Kite Pharma: Consultancy, Research Funding; MedImmune: Research Funding; Sanofi: Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Karyopharm: Consultancy; BMS: Consultancy, Research Funding; Carsgen: Other, Research Funding; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Tenebio: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria. Dimopoulos:Beigene: Honoraria; Amgen: Honoraria; Bristol-Myers Squibb: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Takeda: Honoraria. Facon:Celgene, Janssen, Takeda, Amgen, Roche, Karyopharm, Oncopeptides, BMS, Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Mateos:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, 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; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; Pharmamar: Consultancy; Adaptive: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; EDOMundipharma: Consultancy; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Touzeau:Sanofi: Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Amgen: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Abbvie: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; GlaxoSmithKline: Honoraria, Research Funding. Jakubowiak:AbbVie, Amgen, BMS/Celgene, GSK, Janssen, Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive, Juno: Consultancy, Honoraria. Usmani:Sanofi: Consultancy, Honoraria, Research Funding; Celgene: Other; Amgen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Abbvie: Consultancy; BMS, Celgene: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; GSK: Consultancy, Research Funding; Array Biopharma: Research Funding; Takeda: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Incyte: Research Funding; SkylineDX: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Seattle Genetics: Consultancy, Research Funding; Pharmacyclics: Research Funding; Merck: Consultancy, Research Funding. Cook:Karyopharm: Honoraria; Celgene, Janssen, Takeda: Research Funding; Amgen, Bristol-Myers Squibb, Celgene, Janssen, Takeda, Roche, Sanofi: Honoraria. Cavo:BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GlaxoSmithKline: Honoraria, Speakers Bureau; Karyopharm: Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel accomodations, Speakers Bureau; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel accomodations, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Quach:GlaxoSmithKline, Karyopharm, Amgen, Celgene, Janssen Cilag: Consultancy; GlaxoSmithKline, Karyopharm, Amgen, Celgene, Janssen Cilag: Honoraria; Amgen, Celgene, karyopharm, GSK, Janssen Cilag, Sanofi.: Membership on an entity's Board of Directors or advisory committees; Amgen, sanofi, celgene, Karyopharm, GSK: Research Funding. Ukropec:Janssen: Current Employment, Current equity holder in publicly-traded company. Ramaswami:Janssen: Current equity holder in publicly-traded company. Pei:Janssen: Current Employment, Current equity holder in publicly-traded company. Sun:Janssen: Current Employment, Current equity holder in publicly-traded company. Wang:Janssen: Current Employment. Krevvata:Janssen: Current Employment. DeAngelis:Janssen: Current Employment, Current equity holder in publicly-traded company. Heuck:Christoph Heuck: Current Employment, Current equity holder in publicly-traded company. Van Rampelbergh:Janssen: Current Employment. Kudva:Janssen: Current Employment, Current equity holder in publicly-traded company; Memorial Sloan Kettering Cancer Center: Other: non-paid consultancy. Kobos:Janssen: Current Employment, Current equity holder in publicly-traded company. Qi:Janssen: Current Employment, Current equity holder in publicly-traded company; Johnson and Johnson: Current equity holder in publicly-traded company. Bahlis:Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; BMS/Celgene and Janssen: Consultancy, Honoraria, Other: Travel, Accomodations, Research Funding; Sanofi: Consultancy, Honoraria; Karyopharm Therapeutics: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; GSK: Consultancy, Honoraria.

Background: Oncolytic virotherapy is a novel immunomodulatory therapeutic approach for relapsed refractory hematologic malignancies. The Indiana strain of Vesicular Stomatitis Virus (VSV) has been engineered to encode interferon beta (IFNβ) and sodium iodine symporter (NIS) to produce VSV-IFNβ-NIS. The virally encoded IFNβ serves as an index of viral proliferation and enhances host anti-tumor immunity. The NIS is inserted as a reporter gene into the viral genome to enable noninvasive monitoring of viral spread using PET/CT imaging. Here we present results of Arm A (patients with low tumor burden) of the Phase I clinical trial (NCT03017820) of intravenous administration of VSV-IFNβ-NIS for patients (pts) with relapsed refractory hematological malignancies: MM, AML, and TCL. Methods: This was a classical 3+3 phase I trial, starting at 5x10^9 TCID50 (50% tissue culture infectious dose) level 1 through 1.7x10^11 TCID50 dose level 4 given as a single IV dose. The primary objective was determining the maximum tolerated dose of VSV-IFNβ-NIS alone; secondary objectives include estimating the safety profile and preliminary efficacy. Correlative objectives include monitoring the pharmacodynamics of viral replication through SPECT/CT imaging with NIS gene, viremia, virus shedding and changes in the immune profile of peripheral blood leukocytes. Adverse events (AEs) are reported based on CTCAE v4; cytokine release syndrome (CRS) grading was based on Lee (Blood 2014; 124(2):188-195) criteria. Results: From April 2017, 12 pts have received IV VSV-IFNβ-NIS in Arm A: MM (7), TCL(4) and AML(1); 3 pts were treated at each dose level (DL) 1 to 4. At DL1, there were five grade 3 hematologic AEs (leukopenia [1], neutropenia [1], lymphopenia [3]) and no grade 3+ non-hematologic AEs. At DL2, three grade 3 hematologic AEs (anemia [2], lymphopenia [1]), four grade 4 hematologic AEs (neutropenia [1], leukopenia [1], lymphopenia [1] and thrombocytopenia [1]). There was 1 event of grade 2 CRS at DL2, two grade 3 non-hematologic AEs (nausea [1], dehydration [1]). At DL3, there was one grade 4 hematologic AE (neutropenia [1]), three grade 3 hematologic AEs (lymphopenia [2], leukopenia [1]), and one grade 3 non-hematologic AE (vasovagal reaction [1]). One grade 2 and grade 1 CRS by Lee criteria were observed. At DL4, there were three grade 3 hematologic AEs (lymphopenia [1], neutropenia [1], thrombocytopenia [1]), and three grade 3 non-hematologic AEs (AST increase [1], skin infection [1], soft tissue infection [1])and two grade 2 CRS. There were no dose limiting toxicities. There was one partial response (DL2) and one complete response (DL4); 3 pts had stable disease, 7 pts progressed. The MM pts receiving VSV-IFNβ-NIS at DL4 had stable disease. Six of the 7 MM pts demonstrated transient reduction in serum level of involved free light chain (LC) after VSV-IFNβ-NIS infusion, with median 20.4% reduction of the involved LC within 48 hrs. Viremia was detectable in all pts at the end of infusion, to varying levels at 30 mins, 1, 2, 4, 24, 48h or 72 hrs post infusion. No persistent viremia was observed. No infectious virus was recovered in buccal swabs or urine and neutralizing anti-VSV antibodies were present by day 29. The IFN levels were detectable within 30 minutes of infusion and peaked between 4 and 48 hrs. The pts with a diagnosis of TCL mounted higher hIFNβ levels within 48 hrs. Remarkably, the TCL pt with CR (DL4) mounted a peak hIFNβ response of 18213.3pg/ml at 48 hrs post infusion, approximately 15-fold higher than any other pt enrolled in the study; and highest neutralizing antibody titer (1:40960). Conclusion: VSV-IFNβ-NIS can be safely administered by intravenous infusion in pts with hematological malignancies, up to a dose level of 1.7 x 1011 TCID50, without dose limiting toxicities and no CRS higher than grade 2. In this small phase 1 study, pts with T-cell lymphoma demonstrated the strongest signal of response, with one confirmed partial response at DL2 and one complete response at DL4. IFNβ is an early biomarker of tumor susceptibility; the magnitude of IFNβ elevation appears to correlate with treatment response. There were no confirmed responses in pts with MM, but the 20.4% transient reduction in LC suggests that combination strategies with VSV-IFNβ-NIS may result in deeper and sustained responses in MM. Future trials investigating combination strategies with immune modulatory drugs are being planned. Disclosures Peng: Imanis: Other: Equity Ownership. Witzig:Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Incyte: Consultancy; Acerta: Research Funding; MorphSys: Consultancy; AbbVie: Consultancy; Celgene: Consultancy, Research Funding. Dispenzieri:Intellia: Research Funding; Pfizer: Research Funding; Takeda: Research Funding; Alnylam: Research Funding; Janssen: Research Funding; Celgene: Research Funding. Gertz:Celgene: Consultancy; Annexon: Consultancy; Sanofi: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Data Safety Monitoring board from Abbvie: Membership on an entity's Board of Directors or advisory committees; NCI SPORE MM: Research Funding; Johnson and Johnson: Speakers Bureau; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; Advisory Board for Proclara: Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy; Ionis/Akcea: Consultancy; Spectrum: Consultancy, Research Funding; Prothena: Consultancy; DAVA oncology: Speakers Bureau; i3Health: Consultancy; Advisory Board for Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; International Waldenstrom Foundation: Research Funding. Dingli:Rigel: Consultancy; Bristol Myers Squibb: Research Funding; Janssen: Consultancy; Alexion: Consultancy; Apellis: Consultancy; Karyopharm Therapeutics: Research Funding; Millenium: Consultancy; Sanofi-Genzyme: Consultancy. Kapoor:Cellectar: Consultancy; Celgene: Honoraria; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding. Lin:Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Legend BioTech: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Merck: Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy. Kumar:Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; Carsgen: Other, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Sanofi: Research Funding; Dr. Reddy's Laboratories: Honoraria; Adaptive Biotechnologies: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; MedImmune: Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Karyopharm: Consultancy; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Genecentrix: Consultancy; Tenebio: Other, Research Funding. Russell:Imanis: Other: Equity Ownership. OffLabel Disclosure: This describes of administration of Vesicular Stomatitis Virus Genetically Engineered to Express NIS and human interferon, in Patients with Relapsed or Refractory Hematologic malignacies

Background: Within the heterogeneous genomic landscape of multiple myeloma (MM), clonal evolution including the acquisition of risk-defining mutations and chromosomal abnormalities is a recurrent event and can be detected by fluorescence in situ hybridization (FISH). The effects of acquired abnormalities on clinical outcomes have not been well defined. We previously reported that patients who acquired 17p deletion [del(17p)] during the course of their disease had a significantly reduced overall survival (OS) by 38 months compared to patients who did not acquire del17p (Lakshman et al 2019). Similarly, while de novo gain of the long arm of chromosome 1 (1q22 gain) is a known high-risk aberration associated with significantly shorter OS and progression-free survival (PFS) in MM, the prognostic significance of acquired 1q22 gain has not been described. The primary objective of this study was to analyze factors predictive for acquired 1q22 gain and determine its impact on survival. Methods: We identified MM patients from the Mayo Clinic Dysproteinemia Database who had at least one follow up FISH performed ≥6 months from diagnosis. The clinical characteristics, concomitant cytogenetic abnormalities, first line treatments administered, and OS were compared between patients with acquired 1q22 gain and patients who did not acquire this abnormality. The Mayo Clinic IRB approved this study. Results: A total of 1041 MM patients met the inclusion criteria. Of these, 63 patients (6.1%) had acquired 1q22 gain, defined as being negative for 1q22 gain on initial FISH at diagnosis and having this abnormality detected on subsequent FISH. Median age at diagnosis was 59 years and 56% were male. Median time to acquisition of 1q22 gain was 60 months (range 8-140 months). We identified one control patient for each case who was diagnosed within one year of the case and had a subsequent FISH performed at a similar duration from diagnosis. Patients with acquired 1q22 gain had similar baseline characteristics except for a higher proportion of high-risk (HR) FISH at diagnosis [t(4;14), t(14;16), t(14;20), and del(17p13)] when compared to controls (27% HR FISH versus 6% HR FISH; P<0.01). 1q22 gain was concomitantly present with trisomies in 33 patients (54%), monosomy 13 in 24 patients (39%), t(4;14) in 8 patients (13%), and del(17p13) in 7 patients (12%). All patients received treatment prior to acquisition of 1q22 gain. Of the 63 patients, first line induction therapy consisted of proteasome inhibitor (PI) with steroid in 43%, immunomodulatory drugs (IMiD) with steroid in 40%, and PI + IMiD with steroid in 17% of patients. 54 patients (85%) received upfront stem cell transplant (SCT) (median 5.9 months to SCT), compared to 50 patients (79%) in the control group who received SCT. The median follow up of all 126 patients was 6.8 years. There was a statistically significant reduction in median OS from diagnosis in patients with acquired 1q22 gain compared to the control group (10.8 years versus 13.0 years; P = 0.02; Figure 1). Predictors of acquisition of 1q22 gain were identified using a case-control method. Age ≥70 at diagnosis and presence of HR FISH at baseline appeared to increase the risk of acquiring 1q22 gain. Conclusion: Acquisition of 1q22 gain is a relatively uncommon occurrence, but notably reduced OS by 2.2 years compared to patients who did not acquire 1q22 during the course of their disease (P = 0.02). Older age and the presence of HR FISH at diagnosis increased the risk of acquisition of 1q22 gain. The presence of high-risk translocations at baseline suggests that acquisition of 1q22 gain occurs in the context of more aggressive disease biology. Disclosures Kapoor: Cellectar: Consultancy; Celgene: Honoraria; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding. Dispenzieri:Alnylam: Research Funding; Pfizer: Research Funding; Celgene: Research Funding; Takeda: Research Funding; Intellia: Research Funding; Janssen: Research Funding. Gertz:Prothena: Consultancy; Celgene: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Advisory Board for Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Advisory Board for Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Consultancy; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding; NCI SPORE MM: Research Funding; Ionis/Akcea: Consultancy; Physicians Education Resource: Consultancy; Medscape: Consultancy, Speakers Bureau; Amgen: Consultancy; Appellis: Consultancy; Annexon: Consultancy; Spectrum: Consultancy, Research Funding; Janssen: Consultancy; Sanofi: Consultancy; Data Safety Monitoring board from Abbvie: Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy. Dingli:Apellis: Consultancy; Rigel: Consultancy; Millenium: Consultancy; Bristol Myers Squibb: Research Funding; Karyopharm Therapeutics: Research Funding; Sanofi-Genzyme: Consultancy; Alexion: Consultancy; Janssen: Consultancy. Lin:Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy; Merck: Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy; Legend BioTech: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding. Kumar:Cellectar: Other; Genecentrix: Consultancy; Dr. Reddy's Laboratories: Honoraria; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Sanofi: Research Funding; Karyopharm: Consultancy; MedImmune: Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Kite Pharma: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Adaptive Biotechnologies: Consultancy; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Merck: Consultancy, Research Funding; Carsgen: Other, Research Funding; Tenebio: Other, Research Funding.

Background: Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Despite improvements in clinical outcomes, variability in treatment response exists. Some patients experience innate resistance, while others develop resistance over the course of treatment. Identifying signatures of treatment response and resistance is essential to predicting effective treatment strategies. Given the phenotypic clonal heterogeneity observed in MM, we hypothesize that resistant and sensitive subpopulations have unique phenotypic signatures. We used mass cytometry (CyTOF) to characterize primary MM samples and correlated their phenotypic signatures with treatment response. Methods: We characterized 102 primary MM samples by CyTOF using 37 antibodies (see figure). Frozen whole BM specimens were thawed, stained with antibodies, barcoded, and analyzed on a Helios mass cytometer. Data was processed using Cytobank or Pathsetter and analyzed using t-distributed stochastic neighbor embedding (t-SNE) algorithm. T-SNE plots for CD45+ cells were created using lineage defining markers. T-SNE plots of neoplastic PCs (CD138+,CD3-,CD16-,CD19-,cytoplasmic kappa or lambda+) were created using all remaining markers. A differential analysis of each sample's clusters was performed against all other events. Z-score values, representing marker changes from the differential analysis, for each cluster were used to group similar clusters across all samples, allowing us to compare sub populations of PCs between patients (see figure). From these groups of clusters with similar changes in markers, we calculated the proportion of cells with increases or decreases in markers for each sample. Results were compared to IMWG 90-day response criteria using a random forest model. Results: We performed mass cytometry on 102 samples from 68 unique MM patients. Median age was 66 years (range 34-95) with a 1.5:1 male:female ratio. 37 (36%) had NDMM, 55 (54%) had RRMM, 2 (2%) had SMM and 8 cases (8%) had unknown status. FISH studies revealed 47 (46%) had hyperdiploidy without a recurrent primary IgH rearrangement, 31 (30%) had t(11;14), 13 (13%) had t(4;14), 5 (5%) had 14;16 or 14;20, 3 (3%) had hyperhaploidy and 3 (3%) had an undefined primary. 54 (53%) had 1q gain/amp, 22 (22%) had TP53 deletion, 22 (22%) had a MYC rearrangement and 71% had high mSMART risk. Nearly all patients received an immunomodulatory compound, a proteasome inhibitor and dexamethasone. 2 (2%) had complete response (CR), 10 (9.8%) very good partial response (VGPR), 36 (35%) had partial response (PR), 14 (14%) had minimal response (MR), 13 (13%) had stable disease (SD) and 17 (17%) had progressive disease (PD). Characterization of CD45+ cells identified a relatively conserved and reproducible map from each patient. Notable differences included an ~2-fold reduction in the percentage of total B cells (CD45+,CD3-,CD19+)/total CD45+ cells in cases with high risk mSMART, RRMM and TP53 deletion compared to cases with standard risk, NDMM or normal TP53 status. Similar B-cell reductions were found in RRMM vs. NDMM samples from matched-patients. In contrast to the conserved t-SNE structure of the CD45+ population, the neoplastic PC population displayed dramatically reduced t-SNE structural consistency. Heterogeneity between patient samples and between the same patient samples over time was observed. We focused our analysis on 22 patients with serial samples. The proportion of each t-SNE group present in each patient sample was calculated and then correlated to the 90-day response metric. We identified a phenotypic group whose proportion increased in relation to disease progression (R2=0.85). This group had elevated BCL-2, pSTAT3, CD56 levels and reduced CD45 and CD81 levels. Evaluating individual markers, we found an increase in the proportion of cells with elevated BCL-2, pS6, Ki67, and IKZF1 levels in relation to disease progression. We also observed an increase in the proportion of cells with reduced pCREB levels and a reduction in the proportion of cells with reduced CD147 levels in relation to disease progression. Conclusions: We have utilized CyTOF to characterize phenotypic and functional markers in MM cells on a single cell level and can identify unique subgroups that change in relation to disease progression. Future analysis will correlate phenotypic signatures with ISS stage, mSMART risk, cytogenetic abnormality and type of treatment. Figure 1 Disclosures Kumar: Kite Pharma: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; Merck: Consultancy, Research Funding; Carsgen: Other, Research Funding; Cellectar: Other; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Dr. Reddy's Laboratories: Honoraria.

Abstract Background: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers. Methods: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data. Results: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10−7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation. Conclusions: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis. Impact: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

Background: Assessment of heterogeneity in meta-analyses is critical to ensure the consistency of pooled results. Therefore, we sought to assess the evaluation and reporting of heterogeneity in heart failure (HF) meta-analyses. Methods: Study level meta-analyses pertaining to HF were selected from January 2009 to July 2019, published in 11 high impact factor journals. We tabulated the overall proportion of the meta-analyses reporting statistical heterogeneity and specific metrics and methods employed to quantify and explore heterogeneity. Results: Of 126 HF meta-analyses (612 outcomes), heterogeneity was reported for 422 outcomes (68.9 %) in 108 meta-analyses. Out of the 422 outcomes reporting statistical heterogeneity, 137 outcomes (32.5%) had no observable heterogeneity: ( I 2 =0%), 40 outcomes (9.5%) had low heterogeneity ( I 2 <25%), 76 outcomes (18%) had moderate heterogeneity ( I 2 =25%–50%), and 169 outcomes (40%) had high heterogeneity ( I 2 >50%). Reporting of statistical heterogeneity was not significantly associated with year of publication, funding source, disclosure information, or the type of studies pooled. Sensitivity analysis (n=68) was the most common statistical technique employed to evaluate the source of heterogeneity followed by subgroup analyses (n=59) and meta-regression (n=40). Conclusions: Despite being an essential component of meta-analyses, heterogeneity was not reported for nearly 30% of outcomes and variably handled in contemporary HF meta-analyses. As meta-analyses increase across HF science, interpreting and handling of heterogeneity should be standardized.