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Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.

Background MBL is a precursor to chronic lymphocytic leukemia (CLL) and is subclassified into low-count (LC) MBL (absolute B-cell count<0.5x109/L) and high-count (HC) MBL (absolute B-cell count between 0.5 and 5x109/L). We previously reported that a polygenic risk score (PRS) based on a weighted average of 41 CLL-susceptibility variants was associated with risk of both MBL and CLL among a cohort of individuals from CLL families. Here we evaluate this PRS in an independent cohort of MBL and CLL individuals of European ancestry (EA), all of whom were ascertained agnostic to CLL family-history status. We also evaluate the PRS by MBL subtype (LC/HC), and in African American (AA) CLL cases and controls. Methods We genotyped 535 EA MBLs (139 HC-MBL, 396 LC-MBLs), 735 CLLs (640 EA, 95 AA), and 2,866 controls (2,631 EA, 235 AA) from the Mayo Clinic CLL Resource, Duke University, and Weill Cornell Medical College. We computed the CLL-PRS for each individual and used logistic regression to estimate odds ratios (OR) and 95% confidence intervals, adjusting for age and sex. To assess discriminatory accuracy, we computed the c-statistic. Among EA individuals, we calculated a trend test among LC-MBL, HC-MBL, and CLL risk using the P-value for heterogeneity from a polytomous logistic regression analysis. Moreover, we plotted a boxplot for the PRS among controls, LC-MBL, HC-MBL, and EA CLL, as well as for AA CLL cases and controls, and tested the statistical difference using the Kruskal Wallis test and Mann-Whitney test, respectively. Results We found a significant association of PRS with overall MBL risk (OR=1.87, P=1.1x10-28) with good discrimination (c-statistic=0.72). Significant associations were also found for LC-MBL (OR=1.75, P=7.5x10-19, c-statistic=0.72), HC-MBL (OR=2.22, P=1.4x10-17, c-statistic=0.74), and CLL of EA (OR=2.60, P=1.2x10-62, c-statistic=0.78), with a significant difference among these cohorts (Figure 1.A) and a significant positive trend across these cohorts (Pheterogeneity=8.4x10-6). Although we observed a 33% increased risk of CLL in AA (c-statistic=0.57), the PRS was borderline significant (P=0.07, Figure 1.B). Conclusion The CLL-PRS is a strong prediction-tool for risk of CLL and MBL among individuals of EA. Future studies are needed to improve the PRS for AAs including performing GWAS of AA in order to identify CLL-susceptibility SNPs that are more representative within known CLL loci and to discover novel CLL loci that are unique for AAs. Disclosures Parikh: GlaxoSmithKline: Honoraria; Janssen: Honoraria, Research Funding; Ascentage Pharma: Research Funding; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; Genentech: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Verastem Oncology: Honoraria. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Brander:Genentech: Consultancy, Honoraria, Other, Research Funding; Juno/Celgene/BMS: Other, Research Funding; MEI Pharma: Other, Research Funding; Ascentage: Other, Research Funding; ArQule: Consultancy, Other, Research Funding; NCCN: Other; Teva: Consultancy, Honoraria; Tolero: Research Funding; AstraZeneca: Consultancy, Honoraria, Other, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Other, Research Funding; Pfizer: Consultancy, Other; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Novartis: Consultancy, Other; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Tolero: Research Funding; Teva: Consultancy, Honoraria; DTRM: Other, Research Funding; BeiGene: Other, Research Funding; Novartis: Consultancy, Other; NCCN: Other; Verastem: Consultancy, Honoraria, Other, Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Research Funding; Abbvie: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees. Furman:Acerta: Consultancy; AstraZeneca: Consultancy, Research Funding; Beigene: Consultancy; Abbvie: Consultancy; Pharmacyclics: Consultancy; Sunesis: Consultancy; TG Therapeutics: Consultancy, Research Funding; Verastem: Consultancy; Incyte: Consultancy; Genentech: Consultancy; Janssen: Consultancy, Speakers Bureau; Loxo Oncology: Consultancy; Oncotarget: Consultancy. Shanafelt:Mayo Clinic: Patents & Royalties: and other intellectual property; Genentech, Pharmacyclics LLC, an AbbVie Company, AbbVie, GlaxoSmithKline, and Merck: Research Funding.

BACKGROUND: Health care settings are a primary location for workplace violence that involves clients, but risk factors for behavioral emergencies in medical settings are largely unknown. AIM: This study proposed to identify risk factors for assault and physical aggression among medically hospitalized patients who needed a behavior emergency response team. METHOD: This descriptive study, conducted at a large Midwestern academic medical center, used univariable and multivariable logistic regression to assess relationships between demographic, medical, mental health, and contextual variables with assault and physical aggression. Predictive ability was summarized using area under the receiver operating characteristic curve. RESULTS: Among patients who received a behavior emergency response ( N = 820), 86 (10%) were assaultive. Physical aggression was the most significant predictor of assault. Physical aggression was predicted by older age, male gender, and verbal threats to others. Conversely, internalizing mental health conditions of anxiety, depression, and suicidal ideation were significant for decreased risk of assault and/or physical aggression. Contextual factors, identified as wanting to smoke or leave the hospital, were significantly associated with decreased risk of both assault and physical aggression. CONCLUSION: Health care providers are encouraged to (1) consider the demonstration of physical aggression as a sign to urgently implement precautionary measures for safety, (2) avoid predicting violent situations based on particular medical or mental health conditions alone, and (3) understand that not all disruptive behavior leads to violent situations.

Abstract Chronic lymphocytic lymphoma (CLL) has one of the highest familial risks among cancers. Monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, has a higher prevalence (13%-18%) in families with 2 or more members with CLL compared with the general population (5%-12%). Although, the rate of progression to CLL for high-count MBLs (clonal B-cell count ≥500/µL) is ∼1% to 5%/y, no low-count MBLs have been reported to progress to date. We report the incidence and natural history of MBL in relatives from CLL families. In 310 CLL families, we screened 1045 relatives for MBL using highly sensitive flow cytometry and prospectively followed 449 of them. MBL incidence was directly age- and sex-adjusted to the 2010 US population. CLL cumulative incidence was estimated using Kaplan-Meier survival curves. At baseline, the prevalence of MBL was 22% (235/1045 relatives). After a median follow-up of 8.1 years among 449 relatives, 12 individuals progressed to CLL with a 5-year cumulative incidence of 1.8%. When considering just the 139 relatives with low-count MBL, the 5-year cumulative incidence increased to 5.7%. Finally, 264 had no MBL at baseline, of whom 60 individuals subsequently developed MBL (2 high-count and 58 low-count MBLs) with an age- and sex-adjusted incidence of 3.5% after a median of 6 years of follow-up. In a screening cohort of relatives from CLL families, we reported progression from normal-count to low-count MBL to high-count MBL to CLL, demonstrating that low-count MBL precedes progression to CLL. We estimated a 1.1% annual rate of progression from low-count MBL, which is in excess of that in the general population.

Introduction: In 2017, a two-dose recombinant zoster vaccine (RZV) containing glycoprotein E (gE) and a novel adjuvant was approved to prevent herpes zoster and its complications in adults ≥50 years of age in the US. There are limited data on the immunogenicity and safety of RZV in patients with chronic lymphocytic leukemia (CLL). Methods: Between 8/15/2018 and 11/4/2019, RZV-naïve patients with CLL and monoclonal B-cell lymphocytosis (MBL) seen at Mayo Clinic, Rochester, MN were enrolled in this IRB-approved study. All participants received two doses of vaccine, at 0 and 2 months after enrollment. Cell-mediated immunity was measured by dual-color interferon γ (IFN- γ) and interleukin 2 (IL-2) FLUOROSpot in peripheral blood mononuclear cells (PBMC) after depletion of ≥50% leukemic B cells. Results were reported in spot forming cells (SFC)/106 PBMC in wells stimulated with gE overlapping peptides or varicella zoster virus (VZV) inactivated cell lysate. Response rate was defined as the proportion of participants who achieved a ≥2-fold increase in IFN- γ or IL-2 SFC/106 PBMC one month after the second dose of vaccine compared with pre-vaccination baseline levels. We also determined the geometric mean fold rise (GMFR) and geometric mean count (GMC) of SFC /106 PBMC at baseline and 90 days after the first dose of vaccine. Two cohorts were enrolled: Cohort 1 consisted of individuals with previously untreated MBL and CLL; and Cohort 2 consisted of CLL participants who were on Bruton tyrosine kinase inhibitor (BTKi) therapy. We compared the response data to historic age-and sex-matched healthy controls who received RZV. Chi-square or Fisher's exact tests measured differences in response rates and Kruskall-Wallis tested for differences in continuous variables. Among CLL participants, multivariable regression analysis was used to investigate the association between day 90 SFC and prior BTKi treatment, accounting for age at RZV, age at CLL diagnosis, sex, and day 0 SFC. Results: Sixty-one participants were enrolled. Paired samples were available for 41 participants (22 in Cohort 1 and 19 [18 ibrutinib and 1 acalabrutinib] in Cohort 2), and 68 age- and sex-matched healthy controls. Baseline characteristics of MBL/CLL participants are shown in Table 1. Compared to healthy controls, participants with MBL/CLL had a significantly lower IFN-γ VZV-specific response rate (41% vs. 63%, p=0.03) and IFN-γ gE-specific response (56% vs. 96%, p<0.001); although there were no differences in IL-2 response rates to VZV (51% vs 68%, p=0.09) or gE (95% vs. 100%, p=0.14, Table 2). There were differences in GMFR between participants with MBL/CLL and controls that reached statistical significance for IFN-γ and IL-2 gE-specific responses and borderline significance for IFN-γ VZV-specific responses (Table 2). VZV-specific response rates did not significantly differ between individuals with untreated MBL/CLL and those receiving BTKi (52% vs. 28%, p=0.12 for IFN- γ; (62% vs. 39%, p=0.15 for IL-2), nor did IL-2 gE-specific responses (96% vs. 95%, p=1.0). However, untreated MBL/CLL participants had significantly higher IFN- γ gE-specific response rate compared to BTKi-treated individuals (73% vs. 37%, p=0.02, Table 2). IFN- γ and IL-2 VZV- and gE-specific SFC GMC was significantly higher at Day 90 compared to Day 0 in all vaccinees, although the magnitude of response was significantly lower in MBL/CLL participants compared to healthy controls (Table 3). Among participants with MBL/CLL, age at diagnosis, age at receipt of RZV, and sex did not predict response to vaccination after accounting for prevaccination values. No serious adverse events were noted among MBL/CLL participants while on this study. Additional analyses including determination of humoral immunogenicity and long-term follow up to ascertain risk of herpes zoster infection are ongoing at this time. Conclusion: RZV was associated with lower cell mediated immunogenicity in MBL/CLL participants compared to healthy older adults. Among individuals with CLL, BTKi treatment was associated with decreased cell mediated immunogenicity of the vaccine. Given the inferior response rates, an additional dose of RZV may be considered to boost responses and should be studied in this patient population. Disclosures Johnson: GSK: Research Funding. Ding:Abbvie: Research Funding; Astra Zeneca: Research Funding; DTRM: Research Funding; alexion: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; Octapharma: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Membership on an entity's Board of Directors or advisory committees. Kenderian:Humanigen: Consultancy, Patents & Royalties, Research Funding; Torque: Consultancy; Novartis: Patents & Royalties, Research Funding; Kite: Research Funding; Gilead: Research Funding; Juno: Research Funding; BMS: Research Funding; Tolero: Research Funding; Mettaforge: Patents & Royalties; Lentigen: Research Funding; MorphoSys: Research Funding; Sunesis: Research Funding. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Kay:Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding. Levin:GSK: Research Funding. Kennedy:ICW Ventures: Research Funding. Weinberg:GSK: Research Funding. Parikh:GlaxoSmithKline: Honoraria; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; TG Therapeutics: Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; MorphoSys: Research Funding; Verastem Oncology: Honoraria; Pharmacyclics: Honoraria, Research Funding; Genentech: Honoraria; Ascentage Pharma: Research Funding.

Background: Clinical factors including previous history of AF, heart failure, hypertension, valvular heart disease, increased age and male gender increase the risk of AF in CLL patients (Shanafelt, Leukemia and Lymphoma 2017). Treatment with Bruton tyrosine kinase inhibitors (BTKi) such as ibrutinib has also been associated with an increased risk of AF in CLL. We evaluated the role of artificial intelligence electrocardiography (AI-ECG) in predicting ibrutinib-induced AF (and, for reference, AF unrelated to ibrutinib) in patients with CLL. Methods: We identified two cohorts of CLL patients using the Mayo Clinic CLL Database. Cohort 1 included patients evaluated within 12 months of CLL diagnosis who did not ever receive ibrutinib. Cohort 2 included patients who were treated with ibrutinib. The electrocardiographic signature of AF in sinus rhythm was detected by an AI-ECG algorithm previously developed using a convolutional neural network (Attia, Lancet 2019). The baseline AI-ECG AF score (positive defined as >0.10 on a scale of 0-1 which offers best balance between sensitivity and specificity per Attia et al.) was computed based on ECGs obtained within 10 years prior to CLL diagnosis (Cohort 1) or 10 years prior to initiation of ibrutinib therapy (Cohort 2). Patients with AF at baseline, missing data, or with ECGs previously used to train the AI algorithm were excluded. Reverse Kaplan Meier diagrams were plotted for both cohorts grouped by AI-ECG positivity. Cox proportional hazards were fitted to assess the predictive ability of AI-ECG in both cohorts. Results: After screening 2,739 patients and applying exclusion criteria (126 patients had baseline AF) a total of 1,149 patients with median 4 (interquartile range [IQR] 2-9) baseline ECGs were included in the analysis (Figure 1A). Cohort 1 included 951 patients with a median follow up of 3.0 (IQR 0.6-7.0) years and positive baseline AI-ECG in 546 (57%) patients. Cohort 2 included 198 patients with a median follow up of 1.6 (IQR 0.7-3.2) years and positive baseline AI-ECG in 91 (46%) patients. In Cohort 1, the median age was 67 years (IQR 58-72), 681 (72%) of patients were men, 68% had low/intermediate risk CLL-International Prognostic Index (IPI), and 32% had high/very high-risk CLL-IPI. In Cohort 2, the median age was 69 years (IQR 62-75), 139 (70%) of patients were men, 13% had low/intermediate risk CLL-IPI, and 87% had high/very high-risk CLL-IPI. AF occurred during follow up in 164 patients (17%) in Cohort 1 and 46 patients (23%) in Cohort 2. In both Cohorts 1 and 2, a positive baseline AI-ECG significantly increased the incidence of AF during follow up (log rank <0.001) (Figure 1B and C). Hazard ratios (for positive vs. negative AI-ECG) were 33.9 (95% confidence interval [CI] 15.0-76.6) for Cohort 1 and 14.8 (95% CI 5.3-41.3) for Cohort 2. Conclusion: The addition of AI to a standard 12-lead ECG obtained during normal sinus rhythm - an inexpensive and ubiquitous test - predicts the occurrence of future AF in patients with CLL. This holds true irrespective of BTKi -based therapy and has important implications for the management of CLL patients. Disclosures Ding: Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees. Kenderian:BMS: Research Funding; Gilead: Research Funding; Novartis: Patents & Royalties, Research Funding; Mettaforge: Patents & Royalties; Juno: Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding; Sunesis: Research Funding; Tolero: Research Funding; Kite: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Torque: Consultancy. Wang:Novartis: Research Funding; Innocare: Research Funding; Incyte: Research Funding. Kay:Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Celgene: Honoraria; GlaxoSmithKline: Research Funding. Parikh:MorphoSys: Research Funding; Janssen: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; TG Therapeutics: Research Funding; Pharmacyclics: Honoraria, Research Funding.