Citations

Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.

Background Previous reports have described a leveling off of mortality from premature coronary artery disease (CAD). In recent years, the prevalence of cardiovascular risk factors has increased in rural communities and young adults. Methods and Results We extracted CAD mortality rates from the Centers for Disease Control and Prevention Wide‐Ranging Online Data for Epidemiologic Research (CDC WONDER) database from 1999 to 2017, focusing on mortality from premature CAD (defined as <65 years of age in women) and urban–rural differences. Variations in mortality rates over time, assessed with Joinpoint regression modeling, are expressed as estimated annual percentage change (95% CI) and stratified by urbanization, sex, age, and race. Age‐adjusted mortality rates decreased for women and men. Stratification by urbanization revealed that premature CAD mortality is stagnating among women in rural areas. However, this stagnation conceals a statistically significant increase in CAD mortality rates since 2009 in women aged 55 to 64 years (estimated annual percentage change: +1.4%; 95% CI, +0.3% to +2.5%) and since 1999 in women aged 45 to 54 years (estimated annual percentage change: +0.6%; 95% CI, +0.2% to 1.0%). Since 1999, mortality has been stagnating in the youngest group (aged 35–44 years; estimated annual percentage change: +0.2%; 95% CI, −0.4% to +0.8%). Stratification by race indicated an increase in mortality rates among white rural women. Premature CAD mortality remains consistently higher in the rural versus urban United States, regardless of sex, race, and age group. Conclusions Premature CAD mortality rates have declined over time. However, stratification by sex and urbanization reveals disparities that would otherwise remain concealed: CAD mortality rates have increased among women from rural areas since at least 2009.

Background: Atrial fibrillation (AF) adversely impacts health-related quality of life (hrQoL). While some patients demonstrate improvements in hrQoL, the factors associated with large improvements in hrQoL are not well described. Methods: We assessed factors associated with a 1-year increase in the Atrial Fibrillation Effect on Quality-of-Life score of 1 SD (≥18 points; 3× clinically important difference), among outpatients in the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation I registry. Results: Overall, 28% (181/636) of patients had such a hrQoL improvement. Compared with patients not showing large hrQoL improvement, they were of similar age (median 73 versus 74, P =0.3), equally likely to be female (44% versus 48%, P =0.3), but more likely to have newly diagnosed AF at baseline (18% versus 8%; P =0.0004), prior antiarrhythmic drug use (52% versus 40%, P =0.005), baseline antiarrhythmic drug use (34.8% versus 26.8%, P =0.045), and more likely to undergo AF-related procedures during follow-up (AF ablation: 6.6% versus 2.0%, P =0.003; cardioversion: 12.2% versus 5.9%, P =0.008). In multivariable analysis, a history of alcohol abuse (adjusted OR, 2.41; P =0.01) and increased baseline diastolic blood pressure (adjusted OR, 1.23 per 10-point increase and >65 mm Hg; P =0.04) were associated with large improvements in hrQoL at 1 year, whereas patients with prior stroke/transient ischemic attack, chronic obstructive pulmonary disease, and peripheral arterial disease were less likely to improve ( P <0.05 for each). Conclusions: In this national registry of patients with AF, potentially treatable AF risk factors are associated with large hrQoL improvement, whereas less reversible conditions appeared negatively associated with hrQoL improvement. Understanding which patients are most likely to have large hrQoL improvement may facilitate targeting interventions for high-value care that optimizes patient-reported outcomes in AF. Registration: URL: http://www.clinicaltrials.gov . Unique identifier: NCT01165710.

10503 Background: Gender bias can be reinforced through the use of gender-subordinating language and differences in the forms of address. We examined how professional titles were used during speakers’ introductions at the American Society of Clinical Oncology (ASCO) Annual Meeting. Methods: A retrospective observational study of video-archived speaker introductions at the 2017 and 2018 ASCO annual meetings was conducted. Data were extracted by mixed-gender coders. Professional address was defined as professional title followed by full name or last name. Multivariable logistic regressions were used to identify factors associated with the form of address. Results: 2511 videos were reviewed and 812 met inclusion criteria. Regarding speakers’ characteristics, 530 (65%) were non-Hispanic white (NHW), 743 (92%) held a MD or MD-PhD degree, and 484 (60%) were an associate or full professor. Female speakers were less likely to receive a professional address compared to male speakers (61% vs. 81%, p < 0.001). Female speakers were more likely to be introduced by first name only (17% vs. 3%, p < 0.001). Males were less likely to use a professional address when introducing female speakers compared to male speakers (53% vs. 80%, p < 0.01). No gender differences in professional address were observed for female introducers (p = 0.13). Male introducers were more likely to address female speakers by first name only compared to female introducers (24% vs. 7%, p < 0.01). In a multivariable regression including gender, race, degree, and academic rank, male speakers were more likely to receive a professional address compared to female speakers (OR: 2.67, 95%CI: 1.81-3.94, p < 0.01). Black speakers of both genders were less likely to receive a professional address compared to NHW (OR: 0.10, 95%CI: 0.01-0.53, p < 0.01). Female gender was a predictor for a non-professional form of address (first name only) (OR: 9.50, 95%CI: 4.38-20.62, p < 0.01). Conclusions: When introduced by men, female speakers were less likely to receive a professional address and more likely to be introduced by first name only compared to male speakers. Selective use of forms of address may strengthen gender bias; more research is needed to explore the causes of this disparity and its influence.

8039 Background: Treatment of MM has evolved significantly over the past decade, with increasing use of multi-drug combinations for initial therapy. In addition, supportive care approaches have also improved. We examined how these improvements have translated to survival outcomes in patients with newly diagnosed MM. Methods: Patients (n=3449) with a diagnosis of MM made between 2004 and 2017 and seen at Mayo Clinic within six months of the diagnosis, were included in this analysis. Patients were divided into three groups based on the year of diagnosis; group 1- 2004-07 (n=831), group 2-2008-12 (n=1161), and group 3-2013-17 (n=1457). Survival of the groups were estimated using Kaplan-Meier method, and compared using log rank test. Results: The median age was 64 years (22 to 96); 60% were male and 40% were female. 14% were >75 years, 33% were aged 65-75 and 53% were <65 years. The median overall survival for the whole cohort was 5.7 years (95%CI; 5.4, 6.3). The median OS for the groups 1, 2 and 3 were 3.9, 6.3 and NR, respectively; p<0.001. The 4-year survival estimates were 50%, 62%, and 75%, respectively. We then explored the improvements in patient subgroups. While all patients experienced improvements in OS over time, improvement in group 3 was most prominent for those >75 years. In patients <65 years, the 4-yr OS for groups 1, 2 and 3 were 57, 71, and 79% respectively. In patients 65-75 years of age, the 4-yr OS for groups 1, 2 and 3 were 48, 60, and 75% respectively. In patients >75 years, the 4-yr OS for groups 1, 2 and 3 were 24, 35, and 56% respectively. While patients with high-risk disease did not see as much benefit in the earlier period, substantial progress was seen in the last group. The 3-yr OS for patients with high-risk cytogenetics were 52, 55, and 73% for groups 1, 2 and 3 compared to 67, 75, and 85% for standard-risk cytogenetics respectively. 2067 patients were staged according to the International Staging System and the median OS for stages 1, 2 and 3 were 6.5, 4.6 and 2.4 in group 1; 9.2, 6.6 and 3.5 in group 2 and NR for any of the stages in group 3. Conclusions: The results confirm continued improvement in survival of newly diagnosed multiple myeloma patients, including elderly and high-risk MM.

AbstractThe diagnosis of primary plasma cell leukemia (pPCL) has been made by quantifying circulating plasma cells (cPCs) morphologically on a peripheral blood (PB) smear. However, this technique is not sufficiently sensitive. Multiparametric flow cytometry (MFC) provides a readily available and highly sensitive method to identify and quantify cPCs that could complement PB smear assessment. However, an optimal quantitative cutoff for cPCs by MFC to identify pPCL has not been established. Thus, a total of 591 patients newly diagnosed multiple myeloma (NDMM) patients who had their PB samples evaluated morphologically by PB smear, and immunophenotypically by MFC prior to beginning therapy were evaluated. The presence of ≥200 cPCs/μL by MFC (N = 25 or 5% of the total population) was chosen to identify patients with ≥5% cPCs by PB smear with a specificity of 99% and a sensitivity of 77%. For patients with ≥200 cPCs/μL by MFC compared to the remainder of the cohort, the median Time to next therapy (TTNT) was 18 vs 30 months and the median OS was 38 vs 70 months respectively. Thus, MFC assessment of PB can be utilized in conjunction with the morphological assessment of a PB smear to aid in improving the identification of pPCL among NDMM patients.

AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case‐only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER– breast cancer. We further identified 30 TWAS‐significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast‐cancer gene in three of six regions harboring multiple TWAS‐significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

Background: Invasive hemodynamic evaluation through right heart catheterization plays an essential role in the diagnosis, categorization, and risk stratification of patients with pulmonary hypertension. Methods: Subjects enrolled in the PVDOMICS (Redefining Pulmonary Hypertension through Pulmonary Vascular Disease Phenomics) program undergo an extensive invasive hemodynamic evaluation that includes repeated measurements at rest and during several provocative physiological challenges. It is a National Institutes of Health/National Heart, Lung, and Blood Institute initiative to reclassify pulmonary hypertension groups based on clustered phenotypic and phenomic characteristics. At a subset of centers, participants also undergo an invasive cardiopulmonary exercise test to assess changes in hemodynamics and gas exchange during exercise. Conclusions: When coupled with other physiological testing and blood -omic analyses involved in the PVDOMICS study, the comprehensive right heart catheterization protocol described here holds promise to clarify the diagnosis and clustering of pulmonary hypertension patients into cohorts beyond the traditional 5 World Symposium on Pulmonary Hypertension groups. This article will describe the methods applied for invasive hemodynamic characterization in the PVDOMICS program. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02980887.

Background: The current commonly used regimens in newly diagnosed multiple myeloma (NDMM) utilize steroids (dexamethasone or prednisone) in various combinations: bortezomib, lenalidomide and dexamethasone (VRd), bortezomib, thalidomide and dexamethasone (VTd), daratumumab, lenalidomide and dexamethasone (DRd) and daratumumab, bortezomib, melphalan and prednisone (DVMP). However, steroid therapy may be associated with various adverse effects, including, but not limited to mood changes, insomnia, hypertension, hyperglycemia, osteoporosis, adrenal suppression, muscle weakness, and increased risk of opportunistic infections. A recent trial demonstrated improved tolerability of a regimen (Rd-R) involving an abbreviated course of dexamethasone, without compromising the efficacy in patients with intermediate-fit NDMM (Lorocca A., et al., ASH 2018). We designed a phase 2 clinical trial to examine the safety and efficacy of daratumumab, an anti-CD38 monoclonal antibody in combination with an all-oral regimen of ixazomib, a proteasome inhibitor, lenalidomide, an immunomodulatory drug, and modified dose dexamethasone (IRd). Patients and Methods: NDMM patients with measurable disease and adequate organ function were enrolled, irrespective of their transplant eligibility. The primary objective was to determine the rate of complete response (CR) to daratumumab-IRd. Treatment consisted of daratumumab, 16 mg/kg, weekly for two cycles, every other week during cycles 3-6 and then every 4 weeks, ixazomib, 4 mg days 1, 8, 15, lenalidomide, 25 mg days 1-21, and dexamethasone upto 40 mg intravenously weekly for no more than two cycles, followed by use only as a prophylactic premedication for daratumumab-associated infusion reactions. Myeloma risk stratification was assessed by cytoplasmic immunoglobulin fluorescence in-situ hybridization (cIg FISH) analysis. Results: Overall, 40 patients were accrued, with data available on all patients for analysis at the cutoff date of July 19, 2019. The median age at enrollment was 64.5 (33-81) years; 37.5% were female. Eight (20%) patients were high risk by FISH. The median number of cycles was 6 (2-11) and the median follow up was 6.1 (2-11.7) months. Among 40 patients who had received at least 2 cycles of therapy, responses were attained rapidly; at the end of cycle 2, 88% patients achieved at least a partial response and 33% at least a very good partial response (VGPR) that improved to 52% at the end of 4 cycles among 29 patients who had completed at least 4 cycles. The overall best confirmed response rate among all 40 patients (Figure 1) was 95%, including 10% stringent CR, 5% CR and 23% near CR (13% VGPR excluding nCR). Stem cell collection was completed in 17 patients so far, all of whom required filgrastim and plerixafor. The median CD34+ cell count was 7.7 (range 2.9-11.6) million/kg . All patients were alive and 39 (97.5%) patients were progression-free at last follow up. Four (10%) patients proceeded to autologous stem cell transplantation off study, per patient and/or investigator discretion (1 in CR, 2 in PR, 1 with progressive disease). Overall, 224 cycles have been administered across the study, with dose reduction/ hold required in a subset of patients; ixazomib (10%), lenalidomide (20%), daratumumab (0%) and dexamethasone (13%); the most frequent reasons for dose adjustment were skin rash and hematologic toxicities. A grade 3 or higher adverse event, at least possibly attributed to the study drugs, was observed in 40% of patients; hematologic in 30% (lymphopenia 25%, neutropenia 15%, thrombocytopenia 5% and anemia 2.5%) and non-hematologic in 18% of patients (hyperglycemia 8%, diarrhea 5%, infections 5%, ileus 2.5%, maculopapular rash 2.5%, and fatigue 2.5%). Updated results with additional 6 months of follow up and minimal residual disease assessment related data will be presented at the meeting. Conclusion: Our early results suggest that the combination of daratumumab, ixazomib, lenalidomide and modified dose dexamethasone is well-tolerated, with excellent activity, and does not adversely impact stem-cell mobilization in patients with NDMM. Disclosures Kapoor: Takeda: Honoraria, Research Funding; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria; Janssen: Research Funding; Glaxo Smith Kline: Research Funding; Cellectar: Consultancy. Gertz:Spectrum: Honoraria, Research Funding; Janssen: Honoraria; Celgene: Honoraria; Prothena: Honoraria; Alnylam: Honoraria; Ionis: Honoraria. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Leung:Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding. Dispenzieri:Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Akcea: Consultancy; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Lacy:Celgene: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. OffLabel Disclosure: Daratumumab in combination with Ixazomib, lenalidomide and dexamethasone for the management of newly diagnosed multiple myeloma

MDJ and MTJ contributed equally; FLL and AG contributed equally. Introduction Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are autologous anti-CD19 CAR T-cell therapies approved for the treatment of adults with relapsed or refractory large B-cell lymphoma (LBCL) who have failed at least two lines of systemic therapy. In the ZUMA-1 trial that led to axi-cel approval, the median time between apheresis and delivery of CAR T cells to the treating facility was 17 days (Neelapu, Locke et al. NEJM 2018). Bridging therapy, defined as lymphoma therapy given between apheresis and the start of lymphodepleting chemotherapy, was not permitted on ZUMA-1. By contrast, the pivotal JULIET trial for tisa-cel (Schuster et al. NEJM 2019) had a median time from enrollment to infusion of 54 days and 92% of patients received bridging therapy. Whether bridging therapy affects lymphoma CAR T outcomes is unknown. Here we evaluate patients receiving bridging therapy for axi-cel in a large multicenter cohort. Methods and Results The US Lymphoma CAR T Consortium includes seventeen US academic centers that contribute data from lymphoma patients treated with standard of care (SOC) CAR T-cell therapy independently of manufacturers. As of 8/31/2018, 300 patients were apheresed with intent to manufacture SOC axi-cel for LBCL. Of the 23 patients that underwent apheresis for axi-cel and did not receive it, 20 had lymphoma progression or death that precluded CAR T infusion, of which 16 received bridging therapy. In this study, we analyze the modified intent-to-treat (mITT) population of 276 patients receiving CAR T infusion with a median follow up of 9 months. In this group, 146 (53%) patients received bridging therapy while 130 (47%) patients received no bridging therapy. Bridging therapy consisted of steroids alone (n = 35, 24%), chemotherapy (n = 73, 50%), radiation (n = 24, 16%), or targeted therapies (n = 14, 10%). At baseline, a higher proportion of patients in the bridging therapy group had an ECOG score of 2 - 4 vs. 0 - 1 (bridging 24.8%, no bridging 6.1%, p &lt;0.001), IPI score of 3 -5 vs. 0 - 2 (bridging 67.6%, no bridging 34.3%, p &lt;0.001), bulky disease greater than 10 cm (28.2% vs. 13.0%, p = 0.002), immunohistochemical double expression of MYC and BCL2 (42.5% vs. 24.6%, p =0.004) and would not have met all the eligibility criteria for ZUMA-1 for reasons other than bridging therapy (48.6% vs. 30.0%, p = 0.002). After axi-cel infusion, patients in the bridging group had similar rates of severe (grade 3 or higher) cytokine release syndrome (CRS) (8.2% vs. 5.3%, p = 0.34) and ICANS (35.2% vs. 28.2%, p = 0.25). However, the rate of ICU admission was higher in the bridging group (41.4% vs. 22.9%, p = 0.001) as was median length of hospital stay (15 vs. 14 days, p = 0.02). While data on cytopenias was not collected, use of G-CSF after CAR T therapy was higher in the bridging group (48.2% vs. 32.1%, p = 0.006). In terms of outcomes, in multivariate analysis correcting for confounding features, there was no statistically significant difference in overall response rate (p = 0.2), complete response rate (p = 0.19), and progression free survival (p = 0.3) between bridging and no bridging groups; but bridging therapy was associated with significantly poorer overall survival (p = 0.001) and lymphoma specific survival (p = 0.019) (figure 1.). Both death due to lymphoma (33.1% vs. 13.0%) and death due to treatment-related mortality (TRM) (6.9% vs. 1.5%) were higher in the bridging group (p &lt;0.001). Conclusions Lymphoma patients receiving bridging therapy had poorer prognostic factors at baseline and after axi-cel infusion experienced decreased lymphoma-specific and overall survival compared with patients with no bridging. This inferior outcome raises the possibility that bridging therapy may identify a sub-group of lymphoma patients with a different biology, or alternatively, bridging therapy may have an effect on the host or the tumor microenvironment that may impact CAR-T efficacy. With or without bridging, 7% of patients in our series did not receive axi-cel due to lymphoma progression and/or death. In addition, there may have been patients where bridging prevented progression or death prior to axi-cel infusion. Prospective evaluation of different bridging strategies is warranted to determine if any can improve outcomes after axi-cel, and/or if they should be utilized only for patients requiring emergent disease control during the manufacture period. Disclosures Jain: Kite/Gilead: Consultancy. Nastoupil:Bayer: Honoraria; Celgene: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; TG Therapeutics: Honoraria, Research Funding; Spectrum: Honoraria. Lin:Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BlueBird Bio: Research Funding; Kite/Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Sorrento: Membership on an entity's Board of Directors or advisory committees. Lunning:Curis: Research Funding; Janssen Scientific Affairs, LLC: Consultancy, Research Funding; Juno Therapeutics: Consultancy, Research Funding; MiRagen: Research Funding; TG Therapeutics: Consultancy, Research Funding; AbbVie: Consultancy; Bayer: Consultancy; DAVA: Consultancy; Gilead Sciences, Inc.: Consultancy; Kite: Consultancy; Novartis: Consultancy; OncLive: Consultancy; Portola: Consultancy; Seattle Genetics: Consultancy; Spectrum: Consultancy; VANIUM: Consultancy; Verastem: Consultancy. Reagan:Kite, A Gilead Company: Consultancy; Curis: Consultancy; Seattle Genetics: Research Funding. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. McGuirk:Juno Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Fresenius Biotech: Research Funding; Astellas: Research Funding; Bellicum Pharmaceuticals: Research Funding; Kite Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Gamida Cell: Research Funding; Pluristem Ltd: Research Funding; ArticulateScience LLC: Other: Assistance with manuscript preparation. Deol:Novartis: Other: Advisory board; Kite: Other: Advisory board; Agios: Other: Advisory board. Goy:University of Nebraska: Research Funding; Hakensackumc: Research Funding; Astrazenca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Hackensack University Medical Center, RCCA: Employment; Genentech: Other: Grants outside of the submitted work, Research Funding; Acerta: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work, Research Funding; Pharmacyclics/Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work; Takeda: Other: Grants outside of the submitted work; COTA: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: leadership role for profit healthcare company. Hill:Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite: Consultancy, Honoraria; Celegene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Takeda: Research Funding; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Honoraria; Genentech: Consultancy, Research Funding; TG therapeutics: Research Funding; Amgen: Research Funding. Munoz:Kyowa: Consultancy, Honoraria, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene/Juno: Consultancy, Research Funding; Genentech: Consultancy, Research Funding, Speakers Bureau; Kite/Gilead: Consultancy, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy; Alexion: Consultancy; Pfizer: Consultancy; Fosunkite: Speakers Bureau; AstraZeneca: Speakers Bureau; Portola: Research Funding; Incyte: Research Funding; Pharmacyclics /Janssen: Consultancy, Research Funding, Speakers Bureau; Bayer: Consultancy, Speakers Bureau; Merck: Consultancy. Chavez:Janssen Pharmaceuticals, Inc.: Speakers Bureau; Kite Pharmaceuticals, Inc.: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Genentech: Speakers Bureau. Vose:Acerta Pharma: Honoraria, Other: Grants, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Celgene Corporation: Research Funding; Incyte Corporation: Research Funding; Kite Pharma: Honoraria, Other: Grants, Research Funding; Novartis: Research Funding; Seattle Genetics: Research Funding; AbbVie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Legend Pharmaceuticals: Honoraria. Miklos:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Kite-Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; AlloGene: Membership on an entity's Board of Directors or advisory committees; Precision Bioscience: Membership on an entity's Board of Directors or advisory committees; Miltenyi Biotech: Membership on an entity's Board of Directors or advisory committees; Becton Dickinson: Research Funding; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Neelapu:Cellectis: Research Funding; Allogene: Consultancy; Incyte: Consultancy; BMS: Research Funding; Novartis: Consultancy; Karus: Research Funding; Celgene: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Merck: Consultancy, Research Funding; Acerta: Research Funding; Poseida: Research Funding; Unum Therapeutics: Consultancy, Research Funding; Pfizer: Consultancy; Precision Biosciences: Consultancy; Cell Medica: Consultancy. Bennani:Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Adicet Bio: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board. Andreadis:Pharmacyclics: Research Funding; Novartis: Research Funding; Roche: Equity Ownership; Celgene: Research Funding; Juno: Research Funding; Jazz Pharmaceuticals: Consultancy; Genentech: Consultancy, Employment; Merck: Research Funding; Gilead: Consultancy; Kite: Consultancy. Sehgal:Juno/Celgene: Research Funding; Merck: Research Funding; Kite/Gilead: Research Funding. Locke:Novartis: Other: Scientific Advisor; Kite: Other: Scientific Advisor; Cellular BioMedicine Group Inc.: Consultancy.