Wang, Jacqueline F

Background Mantle cell lymphoma (MCL) is more common in males, with a male to female ratio of approximately 3:1. However, it is unclear whether disease characteristics and survival outcomes differ by sex. Additionally, the impact of race and ethnicity in MCL is understudied due to underrepresentation of minorities in clinical studies and trials. In this study, we utilized the National Cancer Database (NCDB) to examine patient and disease characteristics and survival outcomes of MCL by sex, race and ethnicity. Methods Patients diagnosed with MCL in 2004-2020 were identified from NCDB. Patient and disease characteristics were compared between groups using Chi-squared tests. Overall survival (OS) analysis was performed using the Kaplan-Meier method and the Cox proportional hazards model. Results A total of 35,135 patients was included. The median follow-up was 83.0 months (95% CI 81.9-84.1). 24,980 (71.1%) patients were male and 10,155 (28.9%) were female. Females had more elderly (age ≥65: 65.9% vs 59.0%; p<0.001), lower Charlson-Deyo Score (CDS ≥2: 7.0% vs 8.6%; p<0.001) and fewer stage III-IV diagnoses (80.5% vs 82.6%; p<0.001). Females were less likely to receive upfront treatment (80.7% vs 85.3%; p<0.001), treatment at academic centers (36.8% vs 40.9%; p<0.001) or transplant (11.0% vs 13.7%; p<0.001). Compared to males, females were more likely to live in metropolitan areas (83.7% vs 81.6%; p<0.001), had similar incomes, had more Medicare (60.4% vs 53.1%) and fewer private insurance (33.2% vs 38.6%; p<0.001). Female patients had better OS, with a median OS of 76.0 (95% CI 72.4-79.6) vs 67.8 months (95% CI 65.9-69.6; p<0.001). In the multivariable Cox model adjusting for age, race, ethnicity, CDS, stage, upfront treatment (vs surveillance or no treatment), facility (academic vs nonacademic), transplant, residence (metropolitan vs urban or rural) and insurance, female sex remained associated with superior OS (HR 0.84, 95% CI 0.793-0.890; p<0.001). For race, 32,399 (92.2%) patients were White, 1,465 (4.2%) were Black, 942 (2.7%) were others and 329 (0.9%) were unknown. Compared to White patients, Black patients had fewer elderly (52.9% vs 61.7%; p<0.001), higher CDS (≥2: 11.4% vs 7.9%; p<0.001), similar stage III-IV (80.7% vs 82.1%; p=0.276) and upfront treatment (84.7% vs 84.0%; p=0.074), were more likely to be treated at academic centers (50.6% vs 38.9%; p<0.001), were less likely to receive transplant (11.1% vs 13.0%; p=0.042), were more likely to live in metropolitan areas (91.0% vs 81.5%; p<0.001), had lower income, had more uninsured (4.4% vs 2.0%) and Medicaid (9.8% vs 3.7%) and fewer Medicare (47.4% vs 56.0%; p<0.001). Black and White patients had similar OS, with a median of 67.9 (95% CI 57.5-78.2) vs 69.6 months (95% CI 67.8 vs 71.3; p=0.697). In the multivariable Cox model, Black vs White race was not associated with OS (HR 1.086, 95% CI 0.952-1.240; p=0.219). For ethnicity, 31,717 (90.3%) patients were non-Hispanic, 1,894 (5.4%) were Hispanic and 1,534 (4.3%) were unknown. Hispanics had fewer elderly (51.3% vs 61.5%; p<0.001), lower CDS (≥2: 6.4% vs 8.2%; p=0.018), similar stage III-IV (81.2% vs 82.1%; p=0.467), were more likely to receive upfront treatment (86.0% vs 83.9%; p<0.001), more likely to be treated at academic centers (49.9% vs 39.5%; p<0.001), equally likely to receive transplant (13.0% vs 13.1%; p=0.943), more likely to live in metropolitan areas (95.4% vs 81.6%; p<0.001), had lower income, had more uninsured (9.6% vs 1.8%) and Medicaid (15.2% vs 9.6%) and fewer Medicare (40.3% vs 55.9%; p<0.001). Hispanics had better OS, with a median OS of 84.4 (95% CI 71.7-97.1) vs 70.5 months (95% CI 68.7-72.2; p<0.001). However, in the multivariable Cox model, Hispanic ethnicity was not associated with OS (HR 0.952, 95% CI 0.839-1.080; p=0.448). Conclusion This study is the first of its scale to demonstrate that female MCL patients have superior survival outcomes compared to males. It also demonstrated that neither Black race nor Hispanic ethnicity appears to be associated with worse survival outcomes. Despite its size, the NCDB is limited in the treatment information and disease characteristics it provides, including MIPI score, Ki-67, blastoid/pleomorphic morphology, and TP53 status. Thus, additional real-world dataset studies are needed to gain a deeper understanding of potential differences in sex, race and ethnicity with regards to disease characteristics and survival outcomes.

Background The therapeutic landscape of mantle cell lymphoma (MCL) has evolved significantly since the early 2000s. We previously reported that a shift in frontline immunochemotherapy choices from 2002−2009 to 2010−2015 was associated with improved event-free survival and overall survival (OS) in both younger (age ≤65) and older (age >65) patients in a prospective Mayo Clinic/University of Iowa cohort (Castellino, Blood Advances, 2022). In addition, we recently reported that changes in treatment for relapsed/refractory (R/R) disease over the last two decades were associated with steady improvements in OS for R/R MCL (Tawfiq, ASH, 2022). In this study, we utilized data from the National Cancer Database (NCDB) to investigate the impact of therapy in the modern era on survival outcomes of patients with MCL in the US since 2004. Methods The NCDB is an outcomes database of more than 1,500 hospital-based cancer registries covering approximately 70% of newly diagnosed cancers in the US. The latest version of NCDB was used to identify patients with newly diagnosed MCL from 2004 to 2020. Three treatment eras were defined as Era 1 (2004−2009), Era 2 (2010−2014), and Era 3 (2015−2020), based on immunochemotherapy changes (from Era 1 to Era 2) and increased access to novel agents such as Bruton's tyrosine kinase (BTK) inhibitors (from Era 2 to Era 3). Overall survival between different eras was stratified and analyzed based on 3 age groups: <65, 65−79, and ≥80 years. Survival analysis was performed using the Kaplan-Meier method and all Cox proportional hazards models were adjusted for age and sex. Results A total of 32,746 patients with newly diagnosed MCL was identified, 12,847 (39.2%) patients were age <65, 14,297 (43.7%) age 65−79, and 5,602 (17.1%) age ≥80. For every age group, the distributions in sex, stage, Charlson-Deyo score, and treatment facility type (academic vs non-academic) were largely similar between eras. As expected, the median OS was significantly worse for patients age ≥80 years (19.8 months) compared to patients age 65-79 years (60.9 months) and patients age <65 years (143.9 months; p <0.001). In patients age <65 years, OS in Era 2 was improved when compared to Era 1, with a 5-year OS rate of 72.1% vs 67.6% (logrank p<0.001) (Figure 1) and an age- and sex-adjusted hazard ratio (HR) of 0.83 (95% CI 0.78−0.89, p<0.001). OS in Era 3 was similar to that in Era 2, with a 5-year OS rate of 72.2% vs 72.1% (logrank p=0.61) and an age- and sex-adjusted HR of 0.97 (95% CI 0.89−1.06, p=0.51). In patients age 65-79 years, similar trends in OS changes between eras were observed. The 5-year OS rate in Era 1, Era 2, and Era 3 was 43.3%, 52.8%, and 54.9%, respectively (p<0.001 comparing Era 2 vs Era 1, and p=0.05 comparing Era 3 vs Era 2). Age- and sex-adjusted HR was 0.83 (95% CI 0.78−0.87, p<0.001) comparing Era 2 vs Era 1 and 0.95 (95% CI 0.89−1.01, p=0.07) comparing Era 3 vs Era 2. In patients age ≥80 years, the improvement in OS from Era 1 to Era 2 was less prominent than other age groups. The 5-year OS rate was 22.9% vs 19.5% (p=0.03), and age- and sex-adjusted HR was 0.88 (95% CI 0.81−0.94, p<0.001). OS in Era 3 vs Era 2 was similar, with a 5-year OS rate of 23.1% vs 22.9% (logrank p=0.79) and an age- and sex-adjusted HR of 0.98 (95% CI 0.91−1.06, p=0.67). Conclusions This large NCDB study demonstrated that survival of patients with newly diagnosed MCL has improved over the last two decades. The results are consistent with our Mayo Clinic/University of Iowa prospective cohort study and confirm an improved OS after 2010, which was most likely driven by both frontline immunochemotherapy changes and better access to novel agents for R/R disease. The 5-year OS in patients diagnosed in 2015−2020 did not appear to be superior to those in 2010−2014, despite presumably having better access to novel agents such as BTK inhibitors in the R/R setting. This lack of a difference in OS may be partially due to short follow-up, but may also suggest that further improvement in frontline therapies by incorporating novel agents may be required to improve OS. The OS of patients age ≥80 years is limited and has only minimally improved after 2010, highlighting a significant unmet need for therapies with improved tolerability and efficacy in this age group.

Background Limited stage (stage I−II) disease is an uncommon presentation of mantle cell lymphoma (MCL), and its optimal management remains controversial. A British Columbia study (Leitch, Ann Oncol, 2003) and a Surveillance, Epidemiology and End Results (SEER) study (Murthy, Clin Lymphoma Myeloma Leuk, 2014) suggested the importance of radiotherapy (RT). A National Cancer Database (NCDB) study in the pre-BTK inhibitor (BTKi) era showed that chemotherapy (Chemo) plus RT (Chemo+RT) improved overall survival (OS) compared to Chemo or RT alone (Gill, Int J Radiat Oncol Biol Phys, 2015). However, a retrospective International Lymphoma Radiation Oncology Group study (Dabaja, Ann Oncol, 2017) reported comparable OS with Chemo+RT vs Chemo or RT alone. The potential differences in OS with different frontline therapy modalities may be modified by subsequent therapies for relapsed or refractory (R/R) disease. In this study, we used the NCDB data to analyze frontline treatment patterns and outcomes of limited stage MCL in the pre-BTKi vs BTKi eras. Methods The NCDB was queried to identify patients with stage I−II MCL diagnosed in 2004−2020. Because the first BTKi ibrutinib was approved by the FDA for R/R MCL in November 2013, we defined 2004−2013 as pre-BTKi era and 2014−2020 as BTKi era. Frontline treatment modalities were categorized to RT alone, Chemo alone, or Chemo+RT. Survival analysis was performed using the Kaplan-Meier method and the Cox proportional hazards model. Results Among 32,746 patients with newly diagnosed MCL in 2004−2020, 3,836 (11.7%) presented with stage I−II disease, with 2,428 having treatment information available. For patients diagnosed in the pre-BTKi era (n = 2005), 195 (9.7%) received RT alone, 1472 (73.4%) received Chemo alone, and 338 (16.9%) received Chemo+RT. For patients diagnosed in the BTKi era (n = 423), 45 (10.6%) received RT alone, 320 (75.7%) received Chemo alone, and 58 (13.7%) received Chemo+RT. Patients in the RT alone group were older (P <0.001) and had more females (P <0.001), but there were no statistically significant differences in Charlson-Deyo score, international prognostic index, or treating facility type (academic vs non-academic). For patients with stage I disease, in the pre-BTKi era, patients treated with Chemo+RT (n = 182) had improved OS compared to those treated with RT alone (n = 141) or Chemo alone (n = 647), with a 5-year OS of 75.4% vs 71.8% vs 61.4% (P <0.001). Similar results were observed in the BTKi-era, with a 5-year OS of 83.6% vs 69.5% vs 62.9% (P <0.001) for patients treated with Chemo+RT (n = 31), RT alone (n = 30), or Chemo alone (n = 130), respectively. After adjusting for age and sex, Chemo+RT was associated with improved OS compared to Chemo alone (pre-BTKi era, HR = 0.59, 95% CI = 0.46−0.75, P <0.001; BTKi era, HR = 0.34, 95% CI = 0.14−0.79, P = 0.013), but not compared to RT alone (pre-BTKi era, HR = 0.62, 95% CI = 0.23−1.68, P = 0.343; BTKi era, HR = 0.77, 95% CI = 0.56−1.05, P = 0.10) (Figure 1A). For patients with stage II disease, in the pre-BTKi era, patients treated with Chemo+RT (n = 156) had improved OS compared to those treated with RT alone (n = 54) or Chemo alone (n = 825), with a 5-year OS of 70.0% vs 47.3% vs 58.4% (P <0.001). In the BTKi-era, patients treated with Chemo+RT (n = 27) or RT alone (n = 15) both had improved OS compared to those treated with Chemo alone (n = 190), with a 5-year OS of 77.2% vs 78.8% vs 61.9% (P = 0.004) . After adjusting for age and sex, Chemo+RT was associated with improved OS compared to Chemo alone in BTKi era only (pre-BTKi era, HR = 0.85, 95% CI = 0.67−1.07, P = 0.165; BTKi era, HR = 0.49, 95% CI = 0.25−0.95, P = 0.034), but not compared to RT alone (pre-BTKi era, HR = 0.78, 95% CI = 0.53−1.13, P = 0.185; BTKi era, HR = 0.97, 95% CI = 0.35−2.67, P = 0.952) (Figure 1B). Conclusions Treatment pattern for limited stage MCL has not changed significantly from the pre-BTKi era to the BTKi era, with approximately 15% of patients receiving Chemo+RT, 10% RT alone and 75% Chemo alone. In the BTKi era, Chemo+RT appeared to be associated with better OS compared to Chemo alone but not compared to RT alone. Interpretation of the results requires caution because (1) patient numbers are small in Chemo+RT and RT alone groups, (2) longer follow-up may be needed, and (3) selection of therapy was not random, eg, patients receiving Chemo alone may have had higher disease burden such as bulky disease and/or non-contiguous stage II involvement but these details are not available in NCDB.

Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) have dramatically changed the landscape of cancer therapy. Both remarkable and durable responses have been observed in patients with melanoma, non-small-cell lung cancer (NSCLC), and other malignancies. However, the PD-1/PD-L1 blockade has demonstrated meaningful clinical responses and benefits in only a subset of patients. In addition, several severe and life-threatening adverse events were observed in these patients. Therefore, the identification of predictive biomarkers is urgently needed to select patients who are more likely to benefit from ICI therapy. PD-L1 expression level is the most commonly used biomarker in clinical practice for PD-1/PD-L1 inhibitors. However, negative PD-L1 expression cannot reliably exclude a response to a PD-1/PD-L1 blockade. Other factors, such as tumor microenvironment and other tumor genomic signatures, appear to impact the response to ICIs. In this review, we examine emerging data for novel biomarkers that may have a predictive value for optimizing the benefit from anti-PD-1/PD-L1 immunotherapy.

Abstract Introduction: The clinical presentation of mantle cell lymphoma (MCL) is heterogeneous, ranging from indolent disease that does not require immediate treatment to rapidly progressive disease that responds poorly to aggressive immunochemotherapy. Several small retrospective studies suggested that active surveillance or delayed treatment is feasible in select patients with an indolent disease course. A shorter diagnosis-to-treatment interval (DTI) is a strong predictor for inferior survival in patients with aggressive non-Hodgkin lymphomas such as diffuse large B-cell lymphoma (DLBCL) and peripheral T-cell lymphoma (PTCL), which likely correlates with an aggressive disease biology and clinical presentation. However, the impact of DTI on survival has not been well studied in patients with MCL. In this study, we utilized the National Cancer Database to investigate the feasibility of active surveillance or delayed treatment in patients with MCL and to explore the potential impact of DTI on survival of MCL patients requiring treatment. Methods: The National Cancer Database was used to identify adult patients (≥ 18 years) with MCL diagnosed from 2004 through 2017. Patients were divided into three groups based on their time to initial treatment: early treatment [ET] (treatment started within 3 months), delayed treatment [DT] (treatment started after 3 months) and surveillance [SUR] (watch and wait without treatment). Baseline clinical characteristics were compared between the 3 groups using Chi-square test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. Statistical analysis was performed using SPSS version 25. Results: A total of 27,867 patients with newly diagnosed MCL were included in this study, 995 patients (4%) in the SUR group, 21,425 patients (77%) in the ET group, and 1,052 patients (4%) in the DT group. There were no major differences in the baseline characteristics among the three groups including demographics (age, sex, ethnicity), socioeconomic factors (annual income, insurance status, place of residence), comorbidity scores, and Ann Arbor stage. Compared to patients in the SUR and DT groups, patients in the ET group were more likely to have B symptoms (ET=27% vs DT=16% vs SUR=8%, p<0.001) and were less likely to be managed at an academic center (ET=40% vs DT=53% vs SUR= 51%, p<0.001) (Table 1). The median follow-up was 38.2 months for the entire cohort. There was a significant difference in OS among the three groups, with a median OS of 107 months (95% CI = 92.7-122.7) in the SUR group, 84.9 months (95% CI = 74.2-95.7) in the DT group, and 68.9 months (95% CI = 66.8-70.9) in the ET group (p=0.001) (Figure 1A). Among patients in the DT group, a shorter DTI was associated with a trend of inferior OS, with a median OS of 82 months (95% CI = 73.2-90.8) for those with a DTI of 3-12 months and not reached (95% CI not calculable) for those with a DTI > 12 months (p=0.095) (Figure 1B). Among the ET group, a shorter DTI was associated with shorter OS, with a median OS of 53 months (95% CI = 50.2-55.8) for patients with a DTI ≤ 15 days, 71.7 months (95% CI = 67.1-76.3) for those with a DTI of 16-30 days, and 87.1 months (95% CI= 82.8-91.4) for patients with a DTI of 31-90 days (p<0.001) (Figure 1C) Conclusion: In this retrospective analysis, patients with newly diagnosed MCL who were managed with an active surveillance strategy or delayed therapy had a better survival compared to patients who underwent early therapy. These findings validate the feasibility of delayed therapy in select patients with MCL. In patients who undergo early therapy, a shorter DTI is associated with an inferior survival outcome, mirroring findings in DLBCL and PTCL. A shorter DTI is likely associated with more aggressive disease biology and clinical presentation. Figure 1 Figure 1. Disclosures Munoz: Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Portola: Research Funding; Merck: Research Funding; Celgene: Research Funding; Gilead/Kite Pharma: Research Funding; Bayer: Research Funding; Physicians' Education Resource: Honoraria; Seattle Genetics: Honoraria; Targeted Oncology: Honoraria; OncView: Honoraria; Kyowa: Honoraria. Paludo: Karyopharm: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Nowakowski: Nanostrings: Research Funding; Blueprint Medicines: Consultancy; TG Therapeutics: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Zai Labolatory: Consultancy; Daiichi Sankyo: Consultancy; Bantham Pharmaceutical: Consultancy; Karyopharm Therapeutics: Consultancy; Curis: Consultancy; Selvita: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; MorphoSys: Consultancy. Wang: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; Genentech: Research Funding; Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; InnoCare: Research Funding.

Abstract Introduction: Mantle cell lymphoma (MCL) is an uncommon hematological malignancy with an estimated incidence of 1 per 100,000 persons per year in the United States and represents only about 5% of all non-Hodgkin lymphomas. Several studies have shown that treatment at academic centers and a higher hospital case volume are associated with improved outcomes for uncommon hematological malignancies, probably due to increased provider expertise and access to novel therapies. Treatment of MCL can be complex given the heterogenous nature of the disease and a frequent need for autologous stem cell transplantation in eligible patients. However, the impact of treatment at an academic center and facility patient volume on the survival of patients with MCL has not been well studied in large cohorts. In this study, we utilized the National Cancer Database (NCDB) to investigate the impact of treatment at an academic center and treatment facility volume on the overall survival (OS) of patients with MCL. Methods: The NCDB was used to identify adult patients (≥ 18 years) with newly diagnosed MCL from 2004 through 2017. For facility patient volume analysis, patients were divided into groups based on the average number of new MCL patients seen annually: Tercile 1 [T1] (1-3 patients/year), Tercile 2 [T2] (4-5 patients/year) and Tercile 3 [T3] (≥6 patients/year). Treating centers were divided into Academic and Non-academic using the NCDB definitions. Academic centers were defined as centers that accessions more than 500 newly diagnosed cancer cases per year, participate in postgraduate medical education in at least four program areas including internal medicine and surgery and participates in cancer-related clinical trials. The primary endpoint was overall survival (OS). Survival analysis was performed using the Kaplan-Meier method and Cox hazards proportional model. Statistical analysis was performed using SPSS version 25. Results: We identified 22,752 patients with MCL during the study period. 9,484 (42%) patients were treated at academic centers and 13,070 (57%) were treated at non-academic centers. In terms of facility patient volume 10,948 patients (48%) were in the T1 group, 4,637 (20%) were in the T2 group and 7,166 (31%) were in the T3 group. No significant differences were found in baseline demographics (age, gender, race/ethnicity, comorbidity scores), socioeconomical variables (insurance type, median income, area of residence) and disease-related factors (B-symptoms, Ann Arbor stage) between patients treated academic vs nonacademic centers, or between patients in T1 vs T2 vs T3 groups. Notably, compared to lower volume facilities, T3 facilities were more likely to be academic centers (T3: 81% vs T2: 42% vs T1: 16%, p<0.001) . After a median follow-up of 3.4 years, the median overall survival (OS) was 5.6 years for the entire cohort. The median OS was inferior for patients treated at lower volume facilities (4.1 years for T1, 5.1 years for T2 and 9.0 years for T3, p<0.001) (Figure 1A). Similarly, the median OS was shorter for patients treated at non-academic centers vs academic centers (4.3 years vs 7.5 years respectively, p<0.001) (Figure 1B). In a multivariate analysis, treatment at a lower patient volume facility (Hazard ratio [HR] Q1= 1.26 [95%CI = 1.18-1.34]) and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.12) were both independent prognostic factors of inferior OS, after adjusting for demographics (age, gender, ethnicity, area of residence) and socioeconomic variables (income and insurance status). Conclusion: Patients with MCL treated at academic and higher volume facilities had a higher OS compared to patients treated at non-academic and lower volume facilities.. Additional research is needed to fully understand the mechanisms behind these differences. Patients with MCL may benefit from an early referral to academic and high-volume centers. Figure 1 Figure 1. Disclosures Munoz: Merck: Research Funding; Portola: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Physicians' Education Resource: Honoraria; Gilead/Kite Pharma: Research Funding; Kyowa: Honoraria; Bayer: Research Funding; Seattle Genetics: Honoraria; OncView: Honoraria; Targeted Oncology: Honoraria. Paludo: Karyopharm: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Nowakowski: Daiichi Sankyo: Consultancy; Zai Labolatory: Consultancy; TG Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; Selvita: Consultancy; Curis: Consultancy; Karyopharm Therapeutics: Consultancy; Bantham Pharmaceutical: Consultancy. Wang: Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.