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Abstract Background Exercise intolerance is the cardinal manifestation of heart failure (HF), yet its underlying mechanism(s) remain poorly understood. Measures of ventricular function such as ejection fraction often have no relationship with exercise capacity in HF. 2D-STE has proved to be more reliable method to evaluate myocardial mechanical function. Our hypothesis is Resistance exercise training (Resist-HFpEF) will improve exercise tolerance and exertional symptoms in patients with HFpEF. Purpose The study aims to evaluate myocardial mechanical function by strain imaging during resistance exercise training in Heart Failure patients with Preserved Ejection Fraction (HFpEF) Methods This is a single centre prospective pilot study. 24 HFpEF patients were enrolled and their baseline comorbidities were recorded. Baseline 2D-STE imaging was performed at rest and during exercise (Visit 1). Patients were then randomized to either a novel resistance exercise training program for 36 sessions (3 sessions per week for 12 weeks) or a standard of care control group. 2D STE imaging was then repeated at follow-up (Visit 2). Peak Global Longitudinal strain (GLS), systolic strain rate (SSR), early diastolic strain rate (SRe) were measured offline. Results Mean age of the Rehab cohort was 68.57±10.52 years and the control cohort was 68.1±6.47 years. 23 study subjects were male (96%). 17% of the study subjects were hypertensive, 63% were diabetic, 42% had NYHA I, 46% had NYHA II and 13% had NYHA III of heart failure. The mean LV ejection fraction in the control and Rehab group after the training program at rest was 61.88±2.26% & 54.74±2.07% (p-value 0.04) and during exercise was 59.90±2.05% and 53.13±2.05% (p-value 0.04). The peak GLS was −18.80±4.29% and −17.7±2.21% in controls during rest and exercise at Visit 1 (p-value 0.27) while the rehab cohort had −18.71±4.7% and −20.82±2.4% respectively (p-value 0.0268). The peak GLS was −17.40±3.05% and −17.96±2.65% in controls during rest and exercise at Visit 2 (p-value 0.3430) while rehab cohort had −17.97±6.21% and −16.57±3.82% respectively (p-value 0.67). (Figure 1a and 1b shows GLS, SRS, SRe at Baseline and after exercise program respectively) Conclusion This pilot study suggest systolic and diastolic reserve can be measured reliably during low grade exercise. These results could reflect improvement in clinical status and exercise tolerance. Figure 1. Comparison of peak GLS, SSR, SRe amongst HFpEF patients at rest and during exercise before and after the rehab training program. Funding Acknowledgement Type of funding source: None

Abstract Background The left ventricle (LV) in obese patients undergoes different patterns of remodeling in order to normalize wall stress. However, little is known about how LV volume indices, LV global longitudinal strain and right ventricular free wall strain (GLS) vary according to the pattern of LV remodeling. Aim To define the echocardiographic reference values of LV volumes and biventricular GLS across the different LV remodeling patterns in obese patients with a preserved ejection fraction. Methods 2393 adult obese patients (1428 females, 965 males) with a normal ejection fraction who underwent echocardiography from January 2008 to December 2018 were selected. They were categorized according to 4 cardiac remodeling groups defined by LV mass index (102g/m2 in males, 88g/m2 in females) and relative ventricular wall thickness (0.42): normal geometry (NG), eccentric hypertrophy (EH), concentric remodeling (CR) and concentric hypertrophy (CH). Obese subjects were further categorized by BMI class (30–35, 35–40, >40 kg/m2). Obese subjects were gender matched to controls with a normal BMI (18.5–25 kg/m2) and normal cardiac geometry. Mean ± SD, One-way Anova and Tukey- Kramer HSD were applied. P<0.05 is considered significant. Results The mean age of controls and obese patients' were 50±16 and 57±13.6 years respectively (P<0.0001). LV GLS for controls compared to obese subjects with NG, EH, CR and CH was −21.1±2 vs. −20.2±1.9, −19.6±2.8, −18.5±2.9, −17.5±3.4 respectively (p<0.0001 for all), and for RV GLS it was −27.9±4 vs −26.7±3.9, −25.1±5, −23.5±5.5, −24.1±5.2 respectively (p<0.01 for all, except for NG where p=0.2). The distribution of LV indices according to cardiac remodeling subtypes is shown in the figure. Indexed end diastolic and end systolic volumes were smaller in NG, CH and CR compared to controls (p<0.001 for each respectively). LV GLS and ejection fraction were higher in females, while indexed LV volumes were higher in males within each remodeling category (P<0.0001). No significant difference in LV GLS or indexed LV volume was seen across BMI categories within each remodeling pattern (P>0.05). Obese subjects with CH had the highest incidence of the cardiovascular risk factors hyperlipidemia, hypertension and history of myocardial infarction or stroke, compared to those with other remodeling patterns (p<0.0001 for each, vs. NG, EH and CR). Conclusion To our knowledge, this is the largest study to define LV volumes and left and right ventricular GLS according to LV remodeling pattern and BMI category. The Lowest GLS was noted in CH. Ejection fraction was similar across the LV remodeling patterns. There were no differences in GLS and LV indexed volumes across BMI categories within each remodeling group. These results can be applied as a reference values for the obese population with a normal LV ejection fraction. Funding Acknowledgement Type of funding source: None

Background: Axi-cel is an autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy approved for the treatment of patients (pts) with relapsed/refractory large B cell lymphoma (LBCL) with ≥ 2 prior systemic therapies. ZUMA-1 is the multicenter, single-arm, registrational Phase 1/2 study of axi-cel in pts with refractory LBCL. In a 2-year analysis of ZUMA-1 (median follow-up, 27.1 months; N=101), axi-cel demonstrated objective response, complete response (CR), and ongoing response rates of 83%, 58%, and 39%, respectively (Locke et al. Lancet Oncol. 2019). Here, we present additional survival follow up and recovery of normal, polyclonal B cells from ongoing responders in ZUMA-1. Methods: Eligible pts with refractory large B cell lymphoma (diffuse large B cell lymphoma, primary mediastinal B cell lymphoma, transformed follicular lymphoma) underwent leukapheresis at enrollment and subsequently received low-dose conditioning chemotherapy (fludarabine and cyclophosphamide) followed by a target dose of 2 × 106 anti-CD19 CAR T cells/kg (Neelapu et al. NEJM. 2017; NCT02348216). The primary endpoint was objective response rate (ORR), and the first response assessment was 4 weeks post infusion. Response assessments were performed per protocol up to 24 months or disease progression, whichever occurred first. For pts in ongoing response beyond Month 24, response assessments continued per institutional standard-of-care (SOC). Blood levels of CAR T cells were quantified using polymerase chain reaction and B cells were characterized using flow cytometry in pts with ongoing responses and evaluable samples. Results: A total of 111 pts were enrolled, and axi-cel was administered to 101 pts. As previously reported in the ZUMA-1 2-year analysis, among pts who received axi-cel, the median time from axi-cel infusion to both objective response and CR was 1.0 month (range, 1 - 12 months; Locke et al. Lancet Oncol 2019). When the entire enrolled population (N = 111) was included on an intent-to-treat basis, the median manufacturing time was 17 days (range, 14 - 51; n = 110 as manufacturing was not feasible for 1 pt). Additionally, among the 111 pts, the median time from enrollment/leukapheresis to objective response and CR was 1.7 months (range, 0.7 - 12.9) and 1.9 months (range, 0.7 - 13.3), respectively. Responses have been durable, and with a minimum of 3 years of follow-up (median, 39.1 months), the median overall survival (OS) was 25.8 months, and the 3-year OS rate was 47%. Importantly, no axi-cel-related secondary malignancies have been reported. As previously reported, pts in ongoing response after 2 years had significantly greater peak CAR T cell expansion in blood 7 - 14 days after axi-cel infusion than did those with relapse (P = 0.014) or no response (P = 0.0003; Locke et al. Lancet Oncol 2019). Blood samples from 22 pts in ongoing response (per institutional SOC) at ≥ 3 years were available for analysis of CAR T cells and evaluation of B cell recovery. All evaluable pts had detectable B cells in blood at 3 years post axi-cel. Notably, 91% of pts in ongoing response at 3-year follow-up demonstrated recovery of polyclonal B cells measured by presence of both kappa and lambda light chains on non-malignant CD19+CD20+ B cells. The median kappa-lambda ratio of 1.6 and relative levels of key B cell subsets, including memory and naive B cell immunophenotypes, suggested reconstitution of B cell repertoire, consistent with published data from healthy individuals (Deneys et al. J Immunol Methods 2001; Scott et al. J Clin Pathol 2018). Additionally, 15/22 (68%) had both minimal levels of detectable CAR gene-marked cells and detectable polyclonal B cells in blood. Altogether, these findings support the hypothesis that persistence of functional CAR T cells is not necessary for durable remissions of LBCL. Overall survival and translational findings with ≥ 4 years of follow-up will be presented. Conclusions: Axi-cel produced rapid responses and longterm disease control in pts with refractory LBCL. Most responses occurred by the first assessment, and the brief time elapsed between enrollment and response supports both the speed and success of manufacturing. Furthermore, axi-cel-treated pts with ongoing responses at ≥ 3 years showed evidence of restoration of a normal B cell compartment and clearance of functional CAR T cells, a critical component of the long-term safety of CD19-directed CAR T cell therapies. Disclosures Locke: Kite, a Gilead Company: Consultancy, Research Funding; Wugen: Consultancy; Calibr: Consultancy; Allogene: Consultancy; Cellular Biomedicine Group: Other: Consultancy with grant options; GammaDelta Therapeutics: Consultancy; Celgene/Bristol-Myers Squibb: Consultancy; Novartis: Consultancy. Ghobadi:Celegene: Consultancy; Wugen: Consultancy; Atara: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding, Speakers Bureau; EUSA: Consultancy. Miklos:Novartis: Consultancy, Other: Travel support, Research Funding; Allogene Therapeutics Inc.: Research Funding; Juno-Celgene-Bristol-Myers Squibb: Consultancy, Other: Travel support, Research Funding; Janssen: Consultancy, Other: Travel support; Miltenyi Biotec: Research Funding; Pharmacyclics: Consultancy, Other: Travel support, Patents & Royalties, Research Funding; Adaptive Biotech: Consultancy, Other: Travel support, Research Funding; Kite-Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Research Funding. Oluwole:Pfizer: Consultancy; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Spectrum Pharmaceuticals: Consultancy; Bayer: Consultancy. Lin:Legend BioTech: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Merck: Research Funding; Vineti: Consultancy. Hill:Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding. Timmerman:Merck: Research Funding; Corvus: Current equity holder in publicly-traded company; Marker Therapeutics: Current equity holder in publicly-traded company; Immune Design: Honoraria; Celldex Therapeutics: Consultancy; BMS: Other: Travel support, Research Funding; Spectrum Pharmaceuticals: Research Funding; Kite, a Gilead Company: Consultancy, Other: Travel support, Research Funding; Bluebird Bio: Current equity holder in publicly-traded company; Genmab: Current equity holder in publicly-traded company; Valor: Research Funding. Deol:Novartis: Consultancy; Kite, a Gilead Company: Consultancy. Reagan:Kite, a Gilead Company: Consultancy; Curis: Consultancy; Seattle Genetics: Research Funding. Stiff:Kite, a Gilead Company: Research Funding; Delta-Fly: Research Funding; Gamida Cell: Research Funding; Atara: Research Funding; Unum: Research Funding; Macrogenics: Research Funding; Amgen: Research Funding. Flinn:Celgene: Research Funding; TG Therapeutics: Consultancy, Research Funding; Trillium Therapeutics: Research Funding; Curio Science: Consultancy; Merck: Research Funding; Constellation Pharmaceuticals: Research Funding; BeiGene: Consultancy, Research Funding; ArQule: Research Funding; Kite Pharma: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Pfizer: Research Funding; Calithera Biosciences: Research Funding; Agios: Research Funding; Karyopharm Therapeutics: Research Funding; AstraZeneca: Consultancy, Research Funding; Forty Seven: Research Funding; Forma Therapeutics: Research Funding; Loxo: Research Funding; F. Hoffmann-La Roche: Research Funding; Incyte: Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; Novartis: Research Funding; MorphoSys: Consultancy, Research Funding; Rhizen Pharmaceuticals: Research Funding; Yingli Pharmaceuticals ≠: Consultancy, Research Funding; Johnson & Johnson: Other; Roche: Consultancy, Research Funding; Vincera Pharma: Consultancy; Gilead Sciences: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Iksuda Therapeutics: Consultancy; Great Point Partners: Consultancy; Curis: Research Funding; Nurix Therapeutics: Consultancy; Portola Pharmaceuticals: Research Funding; Acerta Pharma: Research Funding; Juno Therapeutics: Consultancy, Research Funding; Unum Therapeutics: Consultancy, Research Funding; Infinity Pharmaceuticals: Research Funding; IGM Biosciences: Research Funding; Takeda: Consultancy, Research Funding; Teva: Research Funding; Seattle Genetics: Consultancy, Research Funding; Triphase Research & Development Corp.: Research Funding; Verastem: Consultancy, Research Funding. Farooq:Kite, a Gilead Company: Honoraria. Goy:COTA: Consultancy, Current equity holder in publicly-traded company, Other: leadership role; Karyopharm: Research Funding; Genentech/Roche: Research Funding; Constellation: Research Funding; Bayer: Research Funding; CALBG: Research Funding; PracticeUpdate Oncology: Consultancy; RCCA/OMI: Current Employment; Acerta: Consultancy, Honoraria, Other: leadership role, Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: leadership role, Research Funding; Celgene: Honoraria, Research Funding; Hackensack UMC and University of Nebraska: Research Funding; Infinity: Research Funding; Xcenda: Consultancy; Infinity Verastem: Research Funding; Morphosys: Research Funding; AbbVie: Research Funding; MD Anderson: Research Funding; Regional Cancer Care Associates/OMI: Current Employment; Kite, a Gilead Company: Consultancy, Current equity holder in publicly-traded company, Honoraria, Other: leadership role, Research Funding; Janssen: Consultancy, Honoraria, Other: leadership role, Research Funding. McSweeney:Kite, a Gilead Company: Consultancy, Honoraria, Research Funding, Speakers Bureau; Colorado Blood Cancer Institute: Current Employment; Fred Hutchinson: Patents & Royalties. Munoz:Juno/Celgene/BMS: Consultancy, Research Funding, Speakers Bureau; Alexion: Consultancy; Beigene: Consultancy, Speakers Bureau; Fosunkite: Consultancy; Innovent: Consultancy; Acrotech/Aurobindo: Speakers Bureau; Kyowa: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau; Pfizer: Consultancy; Verastem: Speakers Bureau; AstraZeneca: Speakers Bureau; Genentech/Roche: Research Funding, Speakers Bureau; AbbVie: Consultancy, Speakers Bureau; Merck: Research Funding; Portola: Research Funding; Incyte: Research Funding; Millenium: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Bayer: Consultancy, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau. Siddiqi:Oncternal: Research Funding; TG Therapeutics: Research Funding; Janssen: Speakers Bureau; Seattle Genetics: Speakers Bureau; AstraZeneca: Consultancy, Research Funding, Speakers Bureau; BeiGene: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Celgene: Consultancy, Research Funding; Juno: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding. Rossi:Gilead Sciences: Current equity holder in publicly-traded company; Kite, a Gilead Company: Current Employment. Bot:Kite, a Gilead Company: Current Employment; Gilead Sciences: Consultancy, Current equity holder in publicly-traded company, Other: Travel support . Zheng:Kite, a Gilead Company: Current Employment; Gilead Sciences: Current equity holder in publicly-traded company. Vezan:Merck: Current equity holder in publicly-traded company; Abbvie: Current equity holder in publicly-traded company; Kite, a Gilead Company: Current Employment, Honoraria, Other: Travel support. Bashir:Kite, a Gilead Company: Current Employment; OmniacPharmConsult Ltd: Current Employment, Current equity holder in private company. Kim:Kite, a Gilead Company: Current Employment; Gilead Sciences: Current equity holder in publicly-traded company. Chu:Kite, a Gilead Company: Current Employment; Gilead Sciences: Current equity holder in publicly-traded company; Amgen: Current equity holder in publicly-traded company; Celgene: Current equity holder in publicly-traded company. Neelapu:N/A: Other; Takeda Pharmaceuticals: Patents & Royalties; Acerta: Research Funding; Karus Therapeutics: Research Funding; Cellectis: Research Funding; Poseida: Research Funding; Unum Therapeutics: Other, Research Funding; Calibr: Other; Kite, a Gilead Company: Other: personal fees, Research Funding; Bristol-Myers Squibb: Other: personal fees, Research Funding; Merck: Other: personal fees, Research Funding; Novartis: Other: personal fees; Celgene: Other: personal fees, Research Funding; Pfizer: Other: personal fees; Allogene Therapeutics: Other: personal fees, Research Funding; Adicet Bio: Other; Legend Biotech: Other; Precision Biosciences: Other: personal fees, Research Funding; Incyte: Other: personal fees; Cell Medica/Kuur: Other: personal fees.

e14018 Background: Cancer survival rates are improving. Therefore, management of cardiovascular complications has now become a crucial clinical concern. Cardio-oncology is the sub-specialty that assists in the overall management of cancer patients in a multi-disciplinary manner. Mayo Clinic cardio-oncology practice was initiated to work closely with our oncology colleagues for early detection of cardiovascular complications in response to cancer-therapy. Majority of the patients visit our cardio-oncology clinic once, we thought it is important to study the group of patients that visited frequently due to cardiovascular complications. Aim: To evaluate the most common cardiovascular complication in patients with 2 or more visits to our cardio-oncology clinic. Methods: From 2012-2017, there were > 2500 patients visits to our clinic, with 24 patients having 2 or more visits. Data including patients’ demographics, ethnicity, chemotherapeutic medications, primary cancer type, cardiovascular risk factors, echocardiography and clinical outcomes were collected. Cardiotoxicity was defined as the decrease in left ventricular ejection fraction (LVEF) of > 10% to a value of < 53%. Heart failure was diagnosed based on Framingham’s criteria or by a cardiologist. Results: There were 19 women (80%) and 5 men (20%). Median age at the time of diagnosis was 56 years [19-76]. The most common malignancy was breast cancer (70%), followed by B-cell lymphoma (12%) and acute myeloid leukemia (8%). Thirty percent had > 2 risk factors for cardiovascular disease. 75% of the patients had an LVEF of < 53, of these 67% developed heart failure with 58% preserved and 42% reduced ejection fraction. Those with heart failure had received a mean anthracycline dose of 305 ± 91.8mg/m2. With initiation of ACEI, B-Blockers, and diuretics (GDMT) 79% showed recovery of LVEF to ≥53 during the follow up. Conclusions: In our experience, most patients who were seen at least twice in the cardio-oncology clinic for heart failure had received a dose of > 300mg/m2 anthracycline. With GDMT over 75% of the patients recovered. Care in the cardio-oncology clinic plays a key role in optimizing these clinical outcomes.

PURPOSE: Gender disparity persists in academic medicine. Female faculty are underrepresented in leadership positions and have lower research output. We studied gender differences in faculty rank and departmental leadership and contributing factors among academic hematologists and oncologists in the United States. METHODS: For clinical faculty at 146 hematology or oncology fellowship programs listed in the Fellowship and Residency Electronic Interactive Database, we collected data on demographics, academic rank, and research output using the Doximity and Scopus databases. We compared unadjusted characteristics of men and women by using 2-sided t tests and χ2 tests where appropriate. To predict probability of full professorship or leadership position among men versus women, we performed multivariable logistic regression analysis adjusted for clinical experience in years, number of publications, h-index, clinical trial investigator status, National Institutes of Health funding, and workplace ranking (top 20 v not). RESULTS: Two thousand one hundred sixty academic hematologists and oncologists were included. Women composed 21.9% (n = 142) of full professors, 35.7% (n = 169) of associate professors, and 45.4% (n = 415) of assistant professors. Thirty percent (n = 70) of departmental leaders were women. Female faculty, compared with male faculty, had a lower mean h-index (12.1 v 20.9, respectively; P < .001) and fewer years of professional experience since fellowship (10 v 16 years, respectively; P < .001). After adjusting for duration of clinical experience, academic productivity, and workplace ranking, the odds of obtaining professorship (odds ratio [OR], 1.05; 95% CI, 0.71 to 1.57; P = .85) or divisional leadership (OR, 0.57; 95% CI, 0.20 to 1.58; P = .28) for female physicians were not different compared with male physicians. CONCLUSION: Gender disparity exists in senior ranks of academic hematology and oncology; however, gender is not a significant predictor in achieving professorship or department leadership position.

Introduction: We have previously reported underrepresentation of female faculty at senior academic ranks in hematology/oncology (H/O). In this analysis we aimed to investigate the influence of sex in attaining leadership positions amongst academic hematologists/oncologists in United States. Methods: Faculty members were identified at 146 H/O fellowship programs listed on fellowship and residency electronic interactive database (FREIDA.) Data was collected on demographics, academic rank and research output using Doximity and Scopus databases. We compared the unadjusted characteristics of men and women by using two-sided t-tests and χ2 tests where appropriate. In primary analysis, logistic regression models were used to evaluate sex differences on probability of having full professorship (versus assistant and associate professorship) and of achieving leadership positions including division chief, Program Director (PD) and Associate Program Director (APD). Adjusted models included the following variables: clinical experience in years, number of publications, h-index, appointment at top 20 hospital, clinical trial investigator status and National Institutes of Health funding. Stratified analysis was performed adjusting for duration of clinical experience (≤15 vs >15 years) Results: Fewer women were full Professors (21.9% vs 78.1%), division chiefs (16.7% vs 83.3%), and PDs (30.5% vs 69.5) but the number was similar for Associate Program Directors (47.1% vs 52.9%). In a univariate unadjusted model, women were less likely to be full professors compared to men (OR 0.39; 95% confidence interval [CI], 0.31-0.48; P<.001). However, in the multivariable adjusted model no statistically significant sex difference in full professorship was found (OR 1.05; 95% CI 0.71, 1.57; P=.85; Table). The likelihood of full professorship was positively associated with clinical experience in years, number of first/last author publications, h-index, and being a primary investigator on at least one clinical trial.In a univariate unadjusted model, women were less likely to be division chiefs as compared to males (OR 0.35; 95% CI, 0.16, 0.80; P=.01). However, in the multivariable adjusted model, there was no statistically significant sex difference in achieving the position of division chief (OR 0.57; 95% CI 0.20, 1.58; P=.28; Table). No significant difference was found between females and males for being program directors or associate program directors in both univariate and multivariate analysis. Similarly, a stratified analysis adjusting for duration of clinical experience (≤15 vs >15 years) found no significant sex differences in attaining leadership position (Table) Conclusion: We found that women are underrepresented at higher academic ranks and in leadership positions in hematology/oncology, but that sex is not a significant negative predictor to women obtaining leadership positions after correcting for traditional predictors of academic success. However, "non-traditional" and therefore less measurable and analyzable factors such as networking, mentorship, sponsorship, gender bias, balancing work and home responsibilities and many others may contribute and should be further investigated. Disclosures No relevant conflicts of interest to declare.

Abstract Background : TET2, located on chromosome 4q24, is frequently mutated in the majority of patients with chronic myelomonocytic leukemia (CMML). TET2 has 11 exons, and variations, especially in exon 3 have been described as a part of age related clonal hematopoiesis (Jaiswal NEJM 2015). In CMML, somatic TET2 mutations in the absence of ASXL1 mutations (ASXL1wt/TET2mt) were previously shown to impart a favorable outcome on survival (Patnaik BCJ 2015). In the current larger CMML patient cohort, we describe the number and type of TET2 mutations and examine their phenotypic and prognostic effects. Methods : 261 patients with CMML were included in the study. All patients had bone marrow (BM) biopsies and cytogenetics performed at diagnosis. Targeted capture assays were carried out on BM DNA specimens obtained at diagnosis for the following genes; TET2, DNMT3A, IDH1, IDH2, ASXL1, EZH2, SUZ12, SRSF2, SF3B1, ZRSR2, U2AF1, PTPN11, Tp53, SH2B3, RUNX1, CBL, NRAS, KRAS, JAK2, CSF3R, FLT3, KIT, CALR, MPL, NPM1, CEBPA, IKZF, and SETBP1. TET2 (NM_001127208.2) coverage extended from exons 3-11, with frame shift, non-sense, and missense variations considered pathogenic. Previously annotated single nucleotide polymorphisms (http//www.hapmap.org) were considered non-pathogenic. Results: Among the 261 study patients, 65% were males and median age was 70 years (range, 28-91). 154 (59%), 64 (25%) and 43 (16%) patients were classified as CMML-0, 1 and 2, respectively. At a median follow-up of 23 months, 174 (67%) deaths and 37 (14%) leukemic transformations were documented. Mutational frequencies ≥4% were encountered in; ASXL1 45%, TET2 42%, SRSF2 40%, NRAS 14%, SETBP1 13%, CBL 10%, JAK2 7%, RUNX1 6%, DNMT3A 6%, U2AF1 6%, SF3B1 5%, ZRSR2 4%, Tp53 4% and IDH2 4%. i) TET2 mutations type, number and phenotypic correlates: TET2 mutations were seen in 109 (42%) patients; these included frameshift 33 (30%), nonsense 29 (27%) and missense 11 (10%) variants whereas 36 (33%) had more than one type of mutation. Overall, 57 (52%) patients had more than 1 TET2 mutations: 52 (48%) patients had 1, 46 (42%) 2 and 11 (10%) ≥3 TET2 mutations. Among all 109 TET2 mutated patients, 65% were male, and median age was 71 years with no significant difference in age and gender distribution between mutated and un-mutated cases, or type of TET2 mutations; however, older patients were more likely to carry multiple TET2 mutations (p=0.01). Compared to their un-mutated counterparts, TET2 mutated cases were less likely to have a low hemoglobin (p<0.001), circulating immature myeloid cells (IMC) (p=0.001), peripheral blood (PB) (p=0.009) and BM blasts (p=0.009), and have high-risk stratification per Mayo Molecular Model (p<0.001); these differences were not affected by the type or number of TET2 mutations. TET2 mutated cases were more likely to have a higher frequency of SRSF2 (p=0.004) and lower frequency of ASXL1 (p=0.03), Tp53 (p=0.04) and IDH1/2 mutations (p<0.001); these associations were also not affected by the type or number of TET2 mutations. ii) Impact on survival: Median survival for the entire cohort (n=261) was 24 months. In univariate analysis, survival was superior in TET2 mutated (median 33 months) versus wild-type (median 21 months) patients [p=0.03; HR 1.3 95% CI 1.12-1.86] (figure 1A). This survival difference remained significant after adjustment for age (p=0.04), leukocyte count (p=0.017), absolute monocyte count (p=0.02), absolute lymphocyte count (p=0.02), platelet count (p=0.015), circulating IMC (p=0.03), DNMT3A (p=0.02) and ASXL1 (p=0.045) mutations; however, significance was lost after adjustment for abnormal karyotype (p=0.32) and the Mayo Molecular Model (p=0.0003). These observations were not affected by the type or number of TET2 mutations. Finally, our previous observation regarding the survival advantage of ASXL1wt/TET2mt versus other genotypes was most apparent for patients with multiple TET2 mutations (p=0.02) (figure 1B). Conclusions: TET2 mutations are common in CMML and constitute approximately equal proportions of frameshift and non-sense mutations, while missense mutations are less frequent. Majority of TET2 mutated CMML cases harbor more than one mutant variant. Regardless, the relevance of type and number of TET2 mutations in CMML was limited to an association between older age and number of mutations and the latter with possibly improved survival in the absence of clonal ASXL1 mutations. Disclosures No relevant conflicts of interest to declare.