Andrade-Gonzalez, Xavier

Abstract Introduction: The landscape of classical hematology has undergone substantial evolution over the last decade, marked by new diagnostic tools, nuanced therapeutic strategies, and an expanding understanding of underlying pathophysiology. This increasing complexity often presents unique diagnostic and therapeutic challenges. These challenges are further compounded by workforce shortages in classical hematology marked by educational gaps, geographic limitations, and difficulty accessing prompt specialty care. Unlike the well-established “Tumor Boards” in oncology, a comparable dedicated, widely accessible forum for classical hematology has largely been absent. This gap contributes to potential practice heterogeneity and isolation, especially across dissimilar care settings and geographic regions, impacting the consistent application of evidence-based practices and access to specialized expertise. To address this critical need, the “Blood Board” initiative was created to serve as an informal, virtual resource for discussing classical hematology topics and cases, fostering diverse perspectives, and collaborative problem-solving. Methods: The Blood Board was formally launched in April 2025, operating as a bi-monthly, hour-long virtual meeting conducted via non-recorded videoconferencing. The forum was designed with broad inclusivity in mind, welcoming all healthcare professionals involved in the care of adult patients with classical hematologic disorders, including trainees such as hematology/oncology fellows, academic and community-based hematologists, and any other interested providers. To date, dissemination of this initiative has been by word of mouth and e-mails containing a calendar invitation have been sent to anyone interested in participating (https://forms.gle/doyHNSx1SVFBJZmy8). To facilitate participation from various practice models and geographical locations, the virtual format was prioritized, eliminating barriers of travel and time commitment often associated with traditional in-person conferences. Participation is voluntary, encouraging an open and informal environment for de-identified case presentations, didactic discussions, and expert opinion sharing. Self-reported data on participant demographics (e.g., role, practice setting) and geographical representation have been collected to assess the initiative's reach and impact. Results: To date, sessions have consistently garnered engagement with a median of five participants in attendance at each session. There have been over 20 unique participants representing a broad spectrum of roles including both trainees and practicing hematologists/hematologist-oncologists and drawing from private practice, community-based hospitals, and academic medical centers. Over half of all participants were either in fellowship or within five years of completion of training. The virtual format has successfully overcome geographical limitations, attracting attendees from numerous states including Alabama, Florida, Georgia, Maryland, Minnesota, Missouri, Pennsylvania, South Dakota, and Wisconsin highlighting its broad appeal and potential for knowledge dissemination across a wide professional network. Conclusions: The Blood Board has demonstrated utility and met a previously unaddressed need. Sustained intellectual interest in Blood Board, coupled with a wide geographical and professional reach underscores a critical role for accessible, free, and multidisciplinary collaborative platforms in the evolving field of classical hematology. Similar to Tumor Boards, Blood Board promotes knowledge exchange and evidence-based care, garners expert support for challenging cases, and harmonizes clinical approaches, which we hope may ultimately improve patient care outcomes across diverse settings. Furthermore, this platform facilitates a national classical hematology community to support workforce development. Future goals include expanding dissemination efforts, measuring specific educational benefits, analyzing impacts on patient care, and increasing participant diversity, including recruitment of advanced practice providers and pharmacists.

7052 Background: Secondary central nervous system lymphoma (SCNSL) is a rare but serious complication that occurs in 2-10% of patients (pts) with diffuse large B-cell lymphoma (DLBCL). The CNS-International Prognostic Index (CNS-IPI) is a validated tool used to assess the risk of CNS relapse. However, with advancements in molecular profiling and treatments, the predictors of SCNSL, including the predictive ability of CNS-IPI combined with other molecular markers and treatment regimens, have become uncertain. This study evaluates new prognostic markers for CNS relapse in DLBCL, focusing on cell-of-origin (COO), gene rearrangements, and treatment types. Methods: DLBCL pts enrolled from 2002-2015 in the Mayo Clinic and University of Iowa Lymphoma Molecular Epidemiology Resource were prospectively followed for incidence of CNS relapse. Pts with primary CNS lymphoma or CNS involvement at diagnosis, and pts treated with high-dose methotrexate containing regimens were excluded. COO classification (GCB vs non-GCB) was determined using Hans algorithm and/or NanoString data. Multivariate Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for predictors of SCNSL. Survival analysis was performed from time of DLBCL diagnosis using Cox proportional hazards model. Results: Among 1278 newly diagnosed DLBCL pts included (median age: 63 years; 43.6% female), SCNSL occurred in 46 (3.6%) pts. Pts with SCNSL were more likely to have intermediate/high CNS-IPI scores (80.4%) than pts without SCNSL (63.9%, p=0.02). The 3 yr SCNSL incidence was 0.016 (0.008-0.0033) in low CNS-IPI vs 0.043 (0.031-0.0060) in high CNS-IPI. Those with SCNSL were significantly more likely at baseline to have non-GCB COO (56.3% vs. 36.5%, p=0.02), and advanced stage (76.1% vs. 59.9%, p=0.03) compared to those without SCNSL. The incidence of SCNSL was not influenced by MYC, BCL2, or BCL6 rearrangements, nor by treatment with R-CHOP, R-EPOCH, or other immunochemotherapy regimens. MVA showed that intermediate CNS-IPI (HR 2.32, 95% CI 1.09-4.92, p=0.029), high CNS-IPI (HR 3.59, 95% CI 1.49-8.62, p=0.004), and non-GCB COO (HR 2.19, 95% CI 1.08-4.42, p=0.0029) were independently associated with increased SCNSL risk. After a median follow-up of 12 years (IQR 9–15), overall survival (OS) was 52.1% for the cohort, with OS significantly worse for SCNSL pts (17.4% vs. 53.4%, p<0.001). Conclusions: This large cohort with extensive follow-up highlights the role of COO as an additional prognostic factor for SCNSL while reinforcing CNS-IPI’s prognostic value. Non-GCB COO pts had a higher risk of SCNSL even when adjusted by CNS-IPI risk. Despite advancements in management, SCNSL pts continue to have poor outcomes. Strategies to reduce the incidence of CNS relapse in high-risk pts require further investigation. MER CNS relapse hazard ratios (95% CI). Characteristics Hazard Ratio 95% CI P-Value CNS-IPI Group 0-1 Low Ref Ref Ref 2-3 Intermediate 2.32 1.09 - 4.92 0.029 4+ High 3.59 1.49 - 8.62 0.004 Cell of Origin* GCB Ref Ref Ref non-GCB 2.19 1.08 - 4.42 0.029 Double Hit* non-DHL Ref Ref Ref DHL 1.08 0.14 - 8.3 0.941 Not Done/Missing 1.38 0.76 - 2.51 0.297 Double Expressor* Negative Ref Ref Ref Positive 2.36 0.71 - 7.85 0.162 Not Done/Missing 1.91 0.89 - 4.08 0.097 IC Group* R-CHOP R-EPOCH Ref 1.63 Ref 0.71 - 3.74 Ref 0.249 Other IC 1.18 0.57 - 2.46 0.660 Any EN Involvement* No Ref Ref Ref Yes 0.98 0.51 - 1.88 0.946 Albumin Group* >=Normal Ref Ref Ref <Normal 1.70 0.75 - 3.85 0.201 Head & Neck Involvement* No Ref Ref Ref Yes 0.35 0.048 - 2.63 0.310 *Adjusted by CNS-IPI risk group.

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&lt;0.001) and were less likely to be managed at an academic center (ET=40% vs DT=53% vs SUR= 51%, p&lt;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 &gt; 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&lt;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&lt;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&lt;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&lt;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.

Abstract Introduction: Central nervous system (CNS) relapse is an uncommon but devastating complication occurring in about 2-10% of patients with diffuse large B-cell lymphoma (DLBCL). The CNS-IPI is a validated prognostic tool used to identify patients at high risk of CNS relapse. MYC and BCL2/BCL6 rearrangements (double-hit lymphoma- DHL) and non-GCB (NGCB) cell of origin may be at higher risk of CNS relapse. However, the ability of these molecular data to refine CNS-IPI risk prediction has not been assessed. In this study, we aimed to describe the impact of DHL and NGCB cell of origin in the incidence of CNS relapse in patients with newly diagnosed DLBCL. Methods: A retrospective review was conducted to identify patients with newly diagnosed DLBCL from 01/01/2002 - 06/30/2015 using the University of Iowa/Mayo Clinic Lymphoma Molecular Epidemiology Resource database. Patients with primary CNS lymphoma or with CNS involvement at diagnosis were excluded. All patients received chemoimmunotherapy. Clinical, biological, laboratory and radiological data were abstracted including the occurrence of CNS relapse. CNS IPI risk score categories were defined as low- (≤1) intermediate- (2-3) and high-risk (≥4) as originally described (Schmitz et al, JCO 2016). Cell of origin (COO) classification into GCB and non-GCB subtypes was performed using Hans algorithm and/or NanoString data. Characteristics between groups were compared using Kruskal-Wallis or Chi-squared test as appropriate. Survival analysis was performed using Cox proportional hazards model and the Kaplan Meier method. Statistical analysis was performed using SAS version 9.4M5 and R version 4.0.3. Results: We identified a total of 1,360 patients with newly diagnosed DLBCL. Patients had a median age of 62 years (range 18-93 years), were predominantly male (56%), had a performance status &lt;2 (84%), had stage III/IV disease (61%) and an elevated LDH (56%) at diagnosis. Fluorescent in situ hybridization data was present for 65% of patients. Among patients with available data, DHL and NGCG COO represented 2.8% and 36% of the cohort, respectively. CNS IPI high- and intermediate-risk categories were present in 12.4% and 52% of the patients, respectively. R-CHOP was the most common upfront chemotherapy (n=980, 72%). The median follow-up for the entire cohort was 84.9 months (IQR: 58.9-120.9). Overall, 50 patients (3.6%) had a CNS relapse during the study period. Lymphoma relapsed in the CNS in 3.0% (95% CI:2.1-4.4), 4.7% (95% CI: 3.4-6.3) and 4.9% (95% CI: 3.7-6.6)% of patients with CNS-IPI score of intermediate/high risk at 1, 3 and 5 years following the diagnosis of DLBCL. In patients with low risk CNS-IPI, 1.1% (95% CI: 0.4-2.5), 1.7% (95% CI: 0.9-3.4) and 1.7% (95% CI: 0.9-3.4) had a CNS relapse at 1, 3 and 5 years following DLBC diagnosis. Patients with CNS relapse were more likely to have elevated LDH (76% vs 56%, p=0.008), advanced stage (78% vs 61%, p=0.01), adrenal involvement (8% vs 2.4%, p=0.014) and have a CNS IPI intermediate risk (68% vs 51%), and high risk (16% vs 12.3%, p=0.014) at the time of initial diagnosis compared to patients without CNS relapse. Patients with CNS relapse had similar rates of DHL (4% vs 2.7%, p=0.17) and NGCB cell of origin (42% vs 36.2%, p=0.52) compared to patients without CNS relapse. In the multivariable analysis, the strongest predictors for CNS relapse were CNS IPI high risk (Hazard Ratio [HR]= 3.6, 95%CI= 1.4-9.7) and CNS IPI intermediate risk (HR= 3.15, 95%CI= 1.46-6.81). DHL and NGCB cell of origin were not associated with a significantly higher risk of CNS relapse (HR= 1.79, [95%CI=0.42-7.59] and HR= 1.26, [95%CI=0.62-2.57], respectively). Conclusion: CNS relapse was an uncommon occurrence in patients with DLBCL who were treated with chemoimmunotherapy. CNS IPI intermediate- and high-risk scores remain the strongest predictors of CNS relapse in patients with DLBCL after adjusting to biological high-risk variables including DHL and NGCB cell of origin. Larger studies are required to validate our findings due to the low incidence of CNS relapse in our cohort. Figure 1 Figure 1. Disclosures Farooq: Kite, a Gilead Company: Honoraria. Maurer: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Genentech: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Novak: Celgene/BMS: Research Funding.

PURPOSE: The benefit of routine pre-emptive screening for severe acute respiratory syndrome coronavirus 2 infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of patients with cancer who were diagnosed with COVID-19 by routine screening (RS) in comparison with those diagnosed on the basis of clinical suspicion or exposure history (nonroutine screening [NRS]). METHODS: A multisite prospective observational study was conducted at three major and five satellite campuses of the Mayo Clinic Cancer Center between March 18 and July 31, 2020. The primary outcome was COVID-19–related hospital admission. Secondary outcomes included intensive care unit admissions and all-cause mortality. RESULTS: Five thousand four hundred fifty-two patients underwent RS in the outpatient setting only, and 44 (0.81%) were diagnosed with COVID-19. RS detected 19 additional patients from the scheduled inpatient admissions for surgical or interventional procedures or inpatient chemotherapy. One hundred sixty-one patients were diagnosed with COVID-19 on the basis of NRS. COVID-19–related hospitalization rate (17.5% v 26.7%; P = .14), intensive care unit admission (1.6% v 5.6%; P = .19), and mortality (4.8% v 3.7%; P = .72) were not significantly different between the RS and NRS groups. In the multivariable analysis, age ≥ 60 years (odds ratio, 4.4; P = .023) and an absolute lymphocyte count ≤ 1.4 × 109/L (odds ratio, 9.2; P = .002) were independent predictors of COVID-19–related hospital admission. CONCLUSION: The COVID-19 positivity rate was low on the basis of RS. Comparing the hospital admission and mortality outcomes with the NRS cohort, there were no significant differences. The value of routine pre-emptive screening of asymptomatic patients with cancer for COVID-19 remains low.

7542 Background: 60-70% of patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) can be cured with R-CHOP or R-CHOP-like immunochemotherapy. However, patients ≥80 years of age were either excluded or underrepresented in modern DLBCL trials, and their outcomes are understudied. The aim of this study is to define the survival trends and risk factors for inferior survival in older adult patients with DLBCL. Methods: Patients with newly diagnosed DLBCL were identified from the National Cancer Database (2004-2017, representing the rituximab era). Clinical characteristics, treatment, and outcomes were compared between patients ages ≥ 80, 65-79, and < 65 years. The Kaplan-Meier method and Cox proportional hazards model were used for survival analysis. Results: A total of 231,756 patients with newly diagnosed DLBCL were identified; 46,250 (20%) were ≥80 years, 87,702 (38%) were 65-79 years, and 97,904 (42%) were < 65 years. Patients ≥80 years were more likely to have a higher Charlson-Deyo Comorbidity Index score (CDS) (CDS ≥2, 12% vs 11% vs 8%, p = 0.001), less likely to receive systemic chemotherapy (63% vs 83% vs 89%, p < 0.001), and more likely to receive treatment at a non-academic center (71% vs 65% vs 48%, p < 0.001), compared to patients 65-79 and < 65 years, respectively. Median overall survival (OS) was significantly worse for patients ≥80 years compared to patients 65-79 years (11.6 vs 61.0 months, p = 0.001) and patients < 65 years (11.6 vs 178.1 months, p = 0.001). During the study period, the median OS had only minimally improved for patients ≥80 years (10.6 months in 2004-2007 vs 11.5 months in 2008-2011 vs 12.3 months in 2012-2016, p = 0.006). In contrast, the OS improvement appears more meaningful in patients 65-79 years (median in months: 51 vs 61.2 vs 65.9, p = < 0.001) and patients < 65 years (median in years: 14.6 vs 11.3 vs not reached, p < 0.001) in the prespecified intervals (2004-07, 2008-11, and 2012-16). In multivariate analysis, the most substantial risk factor for worse survival in patients ≥80 years was not receiving systemic therapy (hazard ratio [HR] = 3.26, 95%CI = 3.01-3.54, p = 0.001). Other risk factors associated with worse survival included high-risk IPI score (HR = 2.16, 95%CI = 1.96-2.39, p = 0.001), CDS score ≥2 (HR = 1.56, 95%CI = 1.40-1.73, p = 0.001), male sex (HR = 1.16, 95%CI = 1.09-1.24, p = 0.001), B symptoms at diagnosis (HR = 1.16, 95%CI = 1.08-1.25, p = 0.001), and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.20, p = 0.001). Conclusions: Patients ≥ 80 years of age with DLBCL have a significantly inferior survival which has not meaningfully improved in recent years. More than 1/3 of patients ≥ 80 years did not receive systemic therapy. Older adult patients with DLBCL should be assessed for fitness for chemotherapy using validated geriatric assessment tools. Novel therapeutic strategies with favorable safety profiles are urgently needed for this expanding patient population.

6539 Background: The impact of insurance status on survival in diffuse large B‐cell lymphoma (DLBCL), the most common aggressive lymphoma, has not been evaluated after the implementation of the Affordable Care Act (ACA). The aim of this study is to compare overall survival (OS) in patients across insurance status groups and in the periods before and after the ACA. Methods: Adult patients with newly diagnosed DLBCL were identified from the National Cancer Database. The analysis was restricted to patients 64 years of age or younger as most patients 65 years or older are eligible for Medicare under the ACA. The 2004-2017 period was chosen to represent the immunochemotherapy era preceding and following the ACA. Logistic regression was used to explore associations between abstracted variables and insurance status groups. The Kaplan-Meier method and Cox proportional hazards model were used for survival analysis. Results: 93,692 adults (age < 64 years) with newly diagnosed DLBCL and known insurance status were identified (41.3% female, median age 54 years [range: 18 – 64], 81.8% White and 12.1% Black). 7,211 (7.7%) patients were uninsured, 64,744 (69.1%) had private insurance, 11,936 (12.7%) had Medicaid, and 9,801 (10.5%) had Medicare. When compared to insured patients (private insurance, Medicaid or Medicare), uninsured patients were more likely to have a median household outcome of < $38,000 [OR 1.93 (95% CI 1.79-2.07)], less likely to receive chemotherapy [OR 0.69 (0.64-0.77)], more likely to be male [OR 1.14 (1.07-1.21)], more likely to be non-White [OR 1.30 (1.20-1.40], and more likely to present with stage III or IV disease [OR 1.24 (1.16-1.32)]. Uninsured patients had an inferior OS [HR 1.21 (95% CI 1.15-1.27)] when compared to insured patients after adjustment for baseline comorbidity (Charlson-Deyo score ≥2), advanced stage, treatment with chemotherapy, and sociodemographic factors including sex, age, race, household income, facility type (academic/community), and location (urban/rural). With a median follow-up time of 14.8 years (95% CI 14.6-not reached), median OS was lower in uninsured patients [13.4 years (12.3-not reached) vs 14.8 years (14.7-not reached); p < 0.0001]. Despite the lack of major changes in DLBCL therapies, a diagnosis after the implementation of the ACA (in 2010 or later) was associated with a superior OS when compared with the outcomes of patients diagnosed in 2010 or earlier [HR 0.93 (95% CI 0.90-0.95)]. Similarly, five-year OS was superior in the insured group [HR 0.93 (95% CI 0.89-0.96)]. Conclusions: Uninsured patients with DLBCL and < 64 years old had inferior OS when compared with insured patients, and uninsured status emerged as an independent risk factor for inferior OS. Our data highlight the independent effect of insurance disparities - a potential indicator of variations in access to health care - on survival in DLBCL.

Abstract Abstract Importance: The benefit of routine screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of cancer patients diagnosed with Coronavirus Disease 2019 (COVID-19) by routine screening in comparison with those diagnosed based on clinical suspicion or exposure history (non-routine screening). Objective: To describe and compare the outcomes of cancer patients diagnosed with COVID-19 on routine screening vs. non-routine screening at a multi-site tertiary cancer center. To identify risk factors for COVID-19-related hospital admission. Design: A multi-site prospective observational study was conducted between March 18 and July 31, 2020. Setting: Three major and 5 satellite campuses of the Mayo Clinic Cancer Center. Participants: Adult patients diagnosed with active cancer within the past five years and confirmed SARS-CoV-2 infection were included. Primary Outcomes and Measures: Clinical and laboratory data were assessed as independent variables. The primary outcome was COVID-19-related hospital admission. Secondary outcomes included intensive care unit (ICU) admissions and all-cause mortality. Results: Between March 18 and July 31, 2020, 5452 patients underwent routine screening in the outpatient setting, 44 (0.81%) were diagnosed with COVID-19. Routine screening detected additional 19 patients from inpatient and pre-procedural settings; 161 patients were diagnosed with COVID-19 based on non-routine screening. The median age of the entire cohort at diagnosis was 54 years, and 95 patients (42.2%) were female. COVID-19 related-hospitalization rate (17.5% vs. 26.7%, p=0.14), ICU admission (1.6% vs. 5.6%, p=0.19), and mortality (4.8% vs. 3.7%, p=0.72) were not significantly different between routine screening and non-routine screening groups. In the multivariable analysis, age ≥ 60 years (odds ratio: 4.4, p=0.023) and an absolute lymphocyte count ≤1.4 × 109/L (odds ratio: 9.2, p=0.002) were independent predictors of COVID-19-related hospital admission. Conclusions and Relevance: The COVID-19 positivity rate was low based on routine screening. Comparing the outcome with the non-routine screening cohort, there was no significant difference. These results led to an important practice change at our cancer center. We currently follow a testing strategy based on symptoms, exposure, risk factors, and clinical judgment. Citation Format: Zhuoer Xie, Antoine N. Saliba, Jithma Abeykoon, Umair Majeed, Daniel Almquist, Julia Wiedmeier-Nutor, Evandro Bezerra, Xavier Andrade-Gonzalez, Ashley Hickman, Karl Sorenson, Sagar Rakshit, Christopher Wee, Sri Tella, Anuhya Kommalapati, Nadine Abdallah, Joshua Pritchett, Mariza De Andrade, Dipesh Uprety, Andrew Badley, Joleen Hubbard, Naseema Gangat, Carrie A. Thompson, Thomas Witzig, Robert R. McWilliams, Konstantinos Leventakos, Thorvardur R. Halfdanarson. Outcomes of COVID-19 in patients with cancer: Results of a prospective observational comparison of routine screening strategy versus testing based on clinical suspicion [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr S06-03.