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Background. BL is a rare, high-grade B-cell lymphoma that is often studied in trials with small sample sizes. Historical definitions of "low-risk BL" vary between studies, use arbitrary cutoffs for lactate dehydrogenase (LDH), and identify a small favorable group, leaving >80-90% of patients (pts) in an undifferentiated "high-risk" category. A validated prognostic index will help compare study cohorts and better define good-prognosis pts for whom reduced treatment would be appropriate vs a poor-prognosis group in need of new approaches. Herein, we constructed and validated a simplified prognostic model for BL applicable to diverse clinical settings across the world. Methods. We derived the BL-IPI from a large real-world evidence cohort of US adults treated for BL in 2009-2018 (Evens A, Blood 2020). Progression-free survival (PFS) from diagnosis until BL recurrence, progression, death, or censoring was the primary outcome. We first determined the best prognostic cutoffs for age, LDH (normalized to local upper limit normal, ULN), hemoglobin (Hgb), and albumin. Independent risk factors were ascertained by forward stepwise selection into Cox regression from candidate variables: age, sex, HIV+ status, ECOG performance status (PS) ≥2, advanced stage (3/4), involvement of >1 extranodal site, bone marrow, central nervous system (CNS), values of LDH, Hgb, and albumin. Derivation models used multiple imputation to mitigate bias from missing data and reported hazard ratios (HR) with 95% confidence interval (CI). BL-IPI groups, defined by inspection of survival curves, were compared using log-rank test for trend. We validated performance of the BL-IPI in an external retrospective dataset of BL pts treated contemporaneously in centers from the United Kingdom, Scandinavia, Canada, and Australia. Results. Characteristics of pts in the derivation (N= 633) and validation (N=457) cohorts are shown in the Table. Age ≥40 years (yr), LDH >3xULN, Hgb 3xULN, low Hgb, and low albumin were associated with inferior PFS in univariate tests. In the multivariable model age ≥40 yr, LDH >3xULN, PS ≥2, and CNS involvement were selected as 4 independent prognostic factors; adding stage did not enhance the model. The model was simplified to 3 groups with 0 (low risk; 18% of pts), 1 (intermediate risk; 36% of pts; HR=3.14; 95%CI, 1.61-6.14), or 2-4 factors (high risk; 46% of pts; HR=6.52; 95%CI, 3.48-12.20; Fig A) with 3 yr PFS of 92%, 72%, and 53%, respectively (P Among pts with stage III/IV (historically classified as "high-risk" and constituting 78% of all pts in the cohort), the BL-IPI further discriminated subgroups with 3 yr PFS of 87%, 71%, and 52%, respectively (P In the international validation cohort, fewer pts had CNS involvement; most received CODOX-M/IVAC+R; and PFS/OS estimates at 3 yr were higher. BL-IPI categories were of similar size (low-risk 15%, intermediate-risk 35%, high-risk 50%), and provided similar risk discrimination (Harrell's C=.65 in both datasets). PFS at 3 yr was 96%, 82%, and 63%, respectively (P Conclusions. BL-IPI is a novel prognostic index specific to BL, which was validated to allow for simplified stratification and comparison of risk distribution in geographically diverse cohorts. The index identified a low-risk group with PFS >90-95%, which could be targeted with future strategies for treatment de-escalation. Conversely, only about 55-60% of pts in the high-risk group achieved cure with currently available immunochemotherapy. Disclosures Olszewski: Spectrum Pharmaceuticals: Research Funding; Genentech, Inc.: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding. Jakobsen:Takeda: Honoraria. Collins:ADC Therapeutics: Consultancy, Honoraria; Celleron: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Speakers Bureau; Amgen: Research Funding; BeiGene: Consultancy; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding; Taekda: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; Roche: Consultancy, Honoraria, Other: travel, accommodations, expenses , Speakers Bureau; Pfizer: Honoraria; Celgene: Research Funding. Cwynarski:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau. Bachanova:Incyte: Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; FATE: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding. Danilov:Abbvie: Consultancy; BeiGene: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Gilead Sciences: Research Funding; Takeda Oncology: Research Funding; Pharmacyclics: Consultancy; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Astra Zeneca: Consultancy, Research Funding; Verastem Oncology: Consultancy, Research Funding; Karyopharm: Consultancy; Aptose Biosciences: Research Funding; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy. Diefenbach:Trillium: Research Funding; Millenium/Takeda: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; Denovo: Research Funding. Epperla:Pharmacyclics: Honoraria; Verastem Oncology: Speakers Bureau. Farooq:Kite, a Gilead Company: Honoraria. Feldman:Pfizer: Research Funding; Portola: Research Funding; Janssen: Speakers Bureau; AstraZeneca: Consultancy; Cell Medica: Research Funding; Seattle Genetics, Inc.: Consultancy, Honoraria, Other: Travel expenses, Research Funding, Speakers Bureau; Viracta: Research Funding; Trillium: Research Funding; Rhizen: Research Funding; Corvus: Research Funding; BMS: Consultancy, Honoraria, Research Funding; Kite: Honoraria, Other: Travel expenses, Speakers Bureau; Celgene: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses; Amgen: Research Funding; Pharmacyclics: Honoraria, Other, Speakers Bureau; Abbvie: Honoraria; Bayer: Consultancy, Honoraria; Eisai: Research Funding; Kyowa Kirin: Consultancy, Research Funding. Gerrie:AbbVie: Consultancy, Honoraria, Research Funding; Astrazeneca: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Sandoz: Consultancy. Jagadeesh:Regeneron: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Debiopharm Group: Research Funding; MEI Pharma: Research Funding; Verastem: Membership on an entity's Board of Directors or advisory committees. Kamdar:BMS: Consultancy; Abbvie: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Pharmacyclics: Consultancy; Seattle Genetics: Speakers Bureau. Karmali:Takeda: Research Funding; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; Karyopharm: Honoraria; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Khan:Seattle Genetics: Research Funding; Janssen: Honoraria; Pharmacyclics: Honoraria; Bristol Myers Squibb: Research Funding; Celgene: Research Funding. Klein:Takeda: Membership on an entity's Board of Directors or advisory committees. Lossos:Verastem: Consultancy, Honoraria; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy, Other; Janssen Biotech: Honoraria; NCI: Research Funding; Janssen Scientific: Consultancy, Other. Lunning:ADC Therapeutics: Consultancy; Legend: Consultancy; Acrotech: Consultancy; AstraZeneca: Consultancy, Honoraria; Aeratech: Consultancy, Honoraria; Beigene: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; TG Therapeutics: Research Funding; Novartis: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Curis: Research Funding; Bristol Meyers Squibb: Consultancy, Honoraria, Research Funding. Martin:I-MAB: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; Karyopharm: Consultancy, Research Funding; Janssen: Consultancy; Sandoz: Consultancy; Bayer: Consultancy; Beigene: Consultancy; Cellectar: Consultancy; Incyte: Consultancy; Kite: Consultancy; Morphosys: Consultancy; Regeneron: Consultancy. Martinex-Calle:Abbvie: Other: Travel grant. Naik:Celgene: Other: advisory board; Sanofi: Other: advisory board. Palmisiano:Genentech: Research Funding; AbbVie: Research Funding. Phillips:Beigene: Honoraria; Roche: Research Funding. Phillips:Seattle Genetics: Consultancy; Incyte: Consultancy, Other: travel expenses; AstraZeneca: Consultancy; Karyopharm: Consultancy; Beigene: Consultancy; Bayer: Consultancy, Research Funding; BMS: Consultancy; Pharmacyclics: Consultancy; Abbvie: Consultancy, Research Funding; Cardinal Health: Consultancy. Portell:Roche/Genentech: Consultancy, Research Funding; Infinity: Research Funding; Bayer: Consultancy; Amgen: Consultancy; TG Therapeutics: Research Funding; AbbVie: Research Funding; Pharmacyclics: Consultancy; Janssen: Consultancy; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding; Xencor: Research Funding; BeiGene: Consultancy, Research Funding. Reddy:Celgene: Consultancy; BMS: Consultancy, Research Funding; Genentech: Research Funding; Abbvie: Consultancy; KITE Pharma: Consultancy. Yazdy:Abbvie: Consultancy; Genentech: Research Funding; Octapharma: Consultancy; Bayer: Honoraria. Smith:Bristol Meyers Squibb: Research Funding; Ayala: Research Funding; Seattle Genetics: Research Funding; Portola: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Incyte: Research Funding; Ignyta: Research Funding; Genentech: Research Funding; De Novo Biopharma: Research Funding; AstraZeneca: Consultancy; Millenium/Takeda: Consultancy; Beigene: Consultancy; Bayer: Research Funding; AstraZeneca: Research Funding; Acerta Pharma BV: Research Funding; Karyopharm: Consultancy. Cheah:Celgene, F. Hoffmann-La Roche, Abbvie, MSD: Research Funding; Celgene, F. Hoffmann-La Roche, MSD, Janssen, Gilead, Ascentage Pharma, Acerta, Loxo Oncology, TG therapeutics: Honoraria. El-Galaly:F. Hoffmann-La Roche: Current Employment, Other: Support of parent study and funding of editorial support. Evens:Research To Practice: Honoraria, Speakers Bureau; Mylteni: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria.

Introduction: Treatment of older patients (pts) with PCNSL is challenging due to the prevalence of comorbidities, frailty, and complexities with delivery of chemotherapy (CT). The optimal induction CT and consolidation regimens for older PCNSL pts is unknown. Moreover, there are few large scale prognostication studies available, including analysis of geriatric assessments (GA). We analyzed detailed characteristics, treatment patterns and outcomes with prognostication across 17 academic centers. Methods: We conducted a large, RW retrospective study of newly diagnosed PCNSL pts (1/2008-1/2019) ages ≥ 60 years (yrs). Survival rates were estimated by Kaplan-Meier with differences assessed by log rank test. We detailed Cumulative Index Rating Scale-Geriatric (CIRS-G) scores & other GAs. Univariate associations were derived via Cox model with variables p Results: Among 491 initial cases, n=450 cases were verified for diagnosis & follow-up. Clinical features included: median age 71 yrs (60-88); male 47%; elevated LDH 30%; creatinine clearance 1 site). Cerebral involvement predominated in 75% with deep structure involvement in 20% & cerebellum in 5%. CSF involvement was documented in 13% of pts (unchecked in 26%). For GA at diagnosis, the median CIRS-G score was 6 (range 0-27) and impaired self-care activities of daily living (ADLs) were noted in 36%. Furthermore, geriatric syndrome (ie, dementia, delirium, depression, and/or falls) was present in 45% of pts. Induction therapy included CT in 91% of pts (of whom 82% had rituximab (Rtx)) and radiation therapy (RT) in 8%. The most common chemotherapy regimens were: high-dose methotrexate (HD MTX) or HD MTX with Rtx (MR) in 38%; HD MTX/procarbazine/vincristine (MPV) +/- Rtx 30%; HD MTX/temozolomide/Rtx (MTR) 22%; Rtx alone 2%; and HD MTX/cytarabine/thiotepa/Rtx (MATRIX) in 2%. Median MTX dosing for all pts was 3.5 g/m2 (range 1-8 g/m2), and by 3 most common regimens (all g/m2): MTR 5.1; MR 5.4; MPV 3.1 (P With 42 month median follow-up (1-125), 3-yr PFS & OS for all pts were 38% & 52%, respectively (Fig 1A/1B). On MVA, factors associated with inferior PFS were: advancing age (continuous HR 1.05, P Conclusions: Older pts with PCNSL have suboptimal outcomes, with 2/3 progressing in the first several years. GA is an important prognostic tool, and could be used to stratify pts in future investigations. In addition, use of Rtx, increasing MTX dose, and the MTR regimen were associated with improved outcomes. Disclosures Reddy: Genentech: Research Funding; Abbvie: Consultancy; KITE Pharma: Consultancy; Celgene: Consultancy; BMS: Consultancy, Research Funding. Bachanova:Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; FATE: Research Funding; BMS: Research Funding; Incyte: Research Funding. Bond:Seattle Genetics: Honoraria. Goldlust:COTA: Other; BMS: Membership on an entity's Board of Directors or advisory committees, Other: travel; Tocagen: Membership on an entity's Board of Directors or advisory committees, Other: travel; Novocure: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: travel, Research Funding, Speakers Bureau; Boston Biomedical: Consultancy; Cortice Bio: Consultancy, Other: travel; WEX: Consultancy, Other: travel. Spurgeon:Beigene: Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Consultancy; Bristol-Myers Squibb: Research Funding; Genentech: Research Funding; Gilead: Research Funding; Acerta: Research Funding; AstraZeneca: Research Funding; Genmab: Research Funding; VelosBio: Consultancy, Research Funding; Cardinal Health: Honoraria; Verastem: Research Funding. Epperla:Verastem Oncology: Speakers Bureau; Pharmacyclics: Honoraria. Karmali:Takeda: Research Funding; Karyopharm: Honoraria; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Naik:Celgene: Other: advisory board; Sanofi: Other: advisory board. Martin:Celgene: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Teneobio: Consultancy; Beigene: Consultancy; Cellectar: Consultancy; Incyte: Consultancy; Kite: Consultancy; Morphosys: Consultancy; Karyopharm: Consultancy, Research Funding; Regeneron: Consultancy. Smith:Genentech/Roche: Consultancy, Other: Support of parent study and funding of editorial support, Research Funding; TG Therapeutics: Consultancy, Research Funding; FortySeven: Research Funding; Karyopharm: Consultancy, Research Funding; Pharmacyclics: Research Funding; BMS: Consultancy; Janssen: Consultancy; Celgene: Consultancy, Research Funding; Acerta: Research Funding. Rubenstein:Kymera: Research Funding. Kahl:ADC Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy; Celgene Corporation: Consultancy; AstraZeneca Pharmaceuticals LP: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy; Pharmacyclics LLC: Consultancy; Roche Laboratories Inc: Consultancy; BeiGene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy, Research Funding. Evens:Merck: Consultancy, Honoraria, Research Funding; Research To Practice: Honoraria, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Mylteni: Consultancy, Honoraria.

Background Primary CNS lymphoma (PCNSL) is a rare non-Hodgkin lymphoma that is often associated with immunosuppressed states. The Epstein-Barr Virus (EBV) may play a role in tumor pathogenicity in some cases. The objective of this study was to examine the patient characteristics, tumor pathology, and survival outcomes associated with EBV tumor status in patients with PCNSL. Methods This was a retrospective subset analysis from 17 academic medical centers that included 439 patients of ages 60 years and above with PCNSL (David K et al. ASH 2020). The associations between EBV status and clinical or demographical variables were tested by Fisher's exact test, Wilcoxon rank-sum test, or CMH trend test. Kaplan-Meier estimator was used to estimate survival probability. Survival difference between groups was tested by log-rank test for statistical significance. Confidence interval of survival rate was calculated using Greenwood's formula. Results A total of 247 patients with available EBV status were included in this analysis. Median age was 71 (range 60-84) and 44.5% were male. Notably, none of the patients were HIV-positive. Twenty-five patients (10.1%) had EBV positive tumors as detected by EBER (EBV-encoded RNA) in-situ hybridization or LMP1 immunohistochemistry (IHC), 17 of which were solid organ transplant (SOT)-related post-transplant lymphoproliferative disorders (PTLD) and 8 of which were not PTLD. All EBV-positive non-PTLDs were diffuse large B-cell lymphoma. Three (15%) SOT-related PTLDs were EBV-negative. Patient characteristics analyzed included age at diagnosis, sex, ECOG performance status, history of prior or concurrent malignancies, history of solid organ transplant or autoimmune disease, history of allogeneic stem cell transplant, and immunosuppressive treatment. Of these, only a history of solid organ transplant or autoimmune disease (P Tumor characteristics analyzed included expression of C-MYC, BCL2, CD5, cell of origin markers (BCL6, MUM1, CD10), and CD20 through IHC, C-MYC and BCL2 translocation through FISH, histology, and involvement of brain parenchyma, CSF, spinal cord, and eyes. EBV-positive tumors were associated with low C-MYC (p=0.047) and BCL6 (p=0.0006) expression on immunohistochemistry, but not other factors. There were no significant differences in tumor characteristics between those with EBV-positive PTLD and EBV-positive non-PTLD. Among patients with PTLD, 30% (n=6) did not receive primary chemotherapy, and the most common treatment regimens were high-dose methotrexate (HD-MTX) with or without rituximab (n=5) and rituximab alone (n=3). However, there was no significant difference in outcomes among PTLD patients who received chemotherapy or those who did not. Among EBV-positive non-PTLD patients, only 12.5% (n=1) did not receive primary chemotherapy and the most common treatment regimens were methotrexate/rituximab/temozolomide (n=3) and HD-MTX with or without rituximab (n=3). There was no difference in overall or progression free survival between patients with EBV-positive and EBV-negative tumors, or in outcomes among SOT-related PTLD patients regardless of EBV status. However, patients with EBV-positive non-PTLD PCNSL had better overall survival compared to patients with EBV-positive PTLD and EBV-negative tumors (p=0.033, Figure). Conclusions In this large observational study of older patients with PCNSL, the incidence of EBV positive tumors was overall low and was most commonly associated with SOT-related PTLD. Mycophenolate mofetil was the most common immunosuppressive medication. In those without PTLD, there were no patient or tumor factors that were associated with EBV status. Unexpectedly, non-PTLD EBV-positive PCNSL had superior outcomes to EBV-positive PTLD and EBV-negative PCNSL. Future studies of EBV-positive non-PTLDs are warranted to further evaluate the potential impact of EBV latency and the immune response on the tumor microenvironment. Disclosures Reddy: BMS: Consultancy, Research Funding; Celgene: Consultancy; Abbvie: Consultancy; Genentech: Research Funding; KITE Pharma: Consultancy. Bachanova:Karyopharma: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; FATE: Research Funding; Incyte: Research Funding; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding. Bond:Seattle Genetics: Honoraria. Goldlust:Tocagen: Membership on an entity's Board of Directors or advisory committees, Other: travel; BMS: Membership on an entity's Board of Directors or advisory committees, Other: travel; WEX: Consultancy, Other: travel; Cortice Bio: Consultancy, Other: travel; Boston Biomedical: Consultancy; COTA: Other; Novocure: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: travel, Research Funding, Speakers Bureau. Spurgeon:Gilead: Research Funding; Genentech: Research Funding; Cardinal Health: Honoraria; Bristol-Myers Squibb: Research Funding; VelosBio: Consultancy, Research Funding; Acerta: Research Funding; Janssen: Consultancy, Research Funding; AstraZeneca: Research Funding; Pharmacyclics: Consultancy; Beigene: Research Funding; Verastem: Research Funding; Genmab: Research Funding. Epperla:Verastem Oncology: Speakers Bureau; Pharmacyclics: Honoraria. Karmali:AstraZeneca: Speakers Bureau; Karyopharm: Honoraria; Takeda: Research Funding; BeiGene: Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau. Naik:Celgene: Other: advisory board; Sanofi: Other: advisory board. Smith:Janssen: Consultancy; Celgene: Consultancy, Research Funding; Karyopharm: Consultancy, Research Funding; BMS: Consultancy; FortySeven: Research Funding; Pharmacyclics: Research Funding; Acerta: Research Funding; Genentech/Roche: Consultancy, Other: Support of parent study and funding of editorial support, Research Funding; TG Therapeutics: Consultancy, Research Funding. Rubenstein:Kymera: Research Funding. Kahl:BeiGene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca Pharmaceuticals LP: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Consultancy; AbbVie: Consultancy; Genentech: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche Laboratories Inc: Consultancy; Pharmacyclics LLC: Consultancy. Evens:Abbvie: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; MorphoSys: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria, Research Funding; Mylteni: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding. Martin:Kite: Consultancy; Morphosys: Consultancy; Regeneron: Consultancy; Incyte: Consultancy; Cellectar: Consultancy; Beigene: Consultancy; Bayer: Consultancy; I-MAB: Consultancy; Sandoz: Consultancy; Janssen: Consultancy; Karyopharm: Consultancy, Research Funding; Teneobio: Consultancy; Celgene: Consultancy.

We examined adults with untreated Burkitt lymphoma (BL) from 2009 to 2018 across 30 US cancer centers. Factors associated with progression-free survival (PFS) and overall survival (OS) were evaluated in univariate and multivariate Cox models. Among 641 BL patients, baseline features included the following: median age, 47 years; HIV+, 22%; Eastern Cooperative Oncology Group (ECOG) performance status (PS) 2 to 4, 23%; >1 extranodal site, 43%; advanced stage, 78%; and central nervous system (CNS) involvement, 19%. Treatment-related mortality was 10%, with most common causes being sepsis, gastrointestinal bleed/perforation, and respiratory failure. With 45-month median follow-up, 3-year PFS and OS rates were 64% and 70%, respectively, without differences by HIV status. Survival was better for patients who received rituximab vs not (3-year PFS, 67% vs 38%; OS, 72% vs 44%; P < .001) and without difference based on setting of administration (ie, inpatient vs outpatient). Outcomes were also improved at an academic vs community cancer center (3-year PFS, 67% vs 46%, P = .006; OS, 72% vs 53%, P = .01). In multivariate models, age ≥ 40 years (PFS, hazard ratio [HR] = 1.70, P = .001; OS, HR = 2.09, P < .001), ECOG PS 2 to 4 (PFS, HR = 1.60, P < .001; OS, HR = 1.74, P = .003), lactate dehydrogenase > 3× normal (PFS, HR = 1.83, P < .001; OS, HR = 1.63, P = .009), and CNS involvement (PFS, HR = 1.52, P = .017; OS, HR = 1.67, P = .014) predicted inferior survival. Furthermore, survival varied based on number of factors present (0, 1, 2 to 4 factors) yielding 3-year PFS rates of 91%, 73%, and 50%, respectively; and 3-year OS rates of 95%, 77%, and 56%, respectively. Collectively, outcomes for adult BL in this real-world analysis appeared more modest compared with results of clinical trials and smaller series. In addition, clinical prognostic factors at diagnosis identified patients with divergent survival rates.

Background: Early reports suggested that cancer patients have a 1.7-fold increased risk of contracting SARS-CoV-2 and higher rates of severe events and mortality compared with general population. Patients with hematologic malignancies may have worse COVID-19 outcomes, due to an impaired humoral immune response from their underlying malignancy and concurrent anticancer therapy. In this multi-center, retrospective, observational study, we evaluate the associations of COVID-19 outcomes with patient and lymphoma disease characteristics. Methods: EMRs at 10 study centers across the USA identified 519 patients with a diagnosis of lymphoma, CLL, or other lymphoid malignancies, who had a documented positive result of SARS-CoV-2 PCR or nucleocapsid antibody testing. Descriptive statistics were used to summarize the demographic and clinical characteristics. Logistic regression was used to evaluate the associations of individual characteristics with COVID-19 outcomes, adjusted for center (NYU vs. other). The interactions between each of the variables was also included in these models; since the interactions were generally small and non-significant, only the main effect of center was included. Two-sided p-values ≤0.05 were considered significant; there were no adjustments for multiple variables or endpoints. Each analysis was based on complete data for that analysis. Results: Tables 1 and 2 provide demographic and clinical characteristics, respectively, of the 519 patients. The mean age was 61.9 years, with 296 (57%) male and 374 (72%) white patients. NYU had the largest cohort (318 patients), with the remaining centers contributing a range of 3 to 69 patients (median 14). Logistic regression models for the association of each COVID-19 outcome with individual clinical and patient characteristics included adjustments for the center (NYU/other). While center effects were statistically significant, center by covariate interaction effects were not and are not included in the final models. The odds ratio (OR) estimates and p-values for each patient and CLL/lymphoma clinical variable are shown in Tables 3 and 4, respectively. The risks of experiencing a severe event, death, and hospital admission increased with age; for each 10 years of age increase, the ORs were 1.58 for experiencing severe events, 1.78 for death, and 1.65 for hospital admission. The risks of poor outcome were higher in males than in females (OR 1.93 for severe events, 1.85 for death, and 1.47 for hospital admission). Patients with Charlson Comorbidity Index (CCI) >5 had a higher risk of severe events (OR 2.46), mortality (3.30) and hospital admission (2.73) compared to patients with CCI ≤5. Compared to patients with HL, patients with aggressive NHL had a higher risk of severe events (OR 4.05), mortality (4.68) and hospital admission (4.65). Patients with CLL similarly had a higher risk of severe events (OR 4.64), mortality (4.65) and hospital admission (5.93) compared to HL patients. Patients with indolent NHL had a higher risk of hospital admission (OR 3.95) but not a higher risk of mortality compared to HL. Patients in remission at the time of COVID-19 diagnosis had a lower risk of severe events (OR 0.42), mortality (0.36) and hospital admission (0.40) relative to those who were not in remission. Patients who received cytotoxic chemotherapy within 28 days of their COVID-19 diagnosis had a higher risk of severe events (OR 2.54), mortality (2.79), and hospital admission (2.31). Patients who received an anti-CD20 monoclonal antibody within 6 months of COVID-19 diagnosis had a higher risk of severe events (OR 2.60), mortality (2.17) and hospital admission (3.28). Conclusions: In addition to demographic and comorbidity risk factors identified in previous studies, our study shows that patients with aggressive NHL and CLL, or patients who have received recent cytotoxic chemotherapy or anti-CD20 mAB, may be at risk for poor COVID-19 outcome. The difference in risk between NHL and HL patients is likely associated with young age of HL patients but may also be related to differences in underlying innate and adaptive immune defects. Patients at high risk for poor outcome should be a priority for studies of monoclonal antibody prophylaxis. If defects in humoral immunity are at the root of poor outcome, this may be compounded by poor response to vaccination. Multivariate analysis of this data will be completed in advance of the meeting. Figure 1 Figure 1. Disclosures Olszewski: Celldex Therapeutics: Research Funding; PrecisionBio: Research Funding; TG Therapeutics: Research Funding; Acrotech Pharma: Research Funding; Genentech, Inc.: Research Funding; Genmab: Research Funding. Barta: Daiichi Sankyo: Honoraria; Seagen: Honoraria; Acrotech: Honoraria; Kyowa Kirin: Honoraria. Hernandez-Ilizaliturri: AbbVie: Other: Advisory Boards; Incyte: Other: Advisory Boards; Celgene: Other: Advisory Boards; BMS: Other: Advisory Boards; Pharmacyclics: Other: Advisory Boards; Amgen: Other: Advisory Boards; Kite: Other: Advisory Boards; Gilead: Other: Advisory Boards; Epyzime: Other: Advisory Boards. Leslie: Janssen: Consultancy, Speakers Bureau; Merck: Consultancy; Abbvie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria, Speakers Bureau; PCYC/Janssen: Consultancy, Honoraria, Speakers Bureau; Seagen: Consultancy, Honoraria, Speakers Bureau; TG Therapeutics: Consultancy, Honoraria, Speakers Bureau; Celgene/BMS: Consultancy, Honoraria, Speakers Bureau; Kite, a Gilead Company: Consultancy, Honoraria, Speakers Bureau; ADC Therapeutics: Consultancy; BeiGene: Consultancy, Honoraria, Speakers Bureau; Karyopharm Therapeutics: Honoraria, Speakers Bureau; AstraZeneca: Consultancy, Honoraria, Speakers Bureau; Pharmacyclics: Consultancy, Honoraria, Speakers Bureau. Diefenbach: Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Consultancy, Honoraria, Research Funding; Morphosys: Consultancy, Honoraria, Research Funding; Genentech, Inc./ F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Perlmutter Cancer Center at NYU Langone Health: Current Employment; Incyte: Research Funding; AbbVie: Research Funding; Trillium: Research Funding; IGM Biosciences: Research Funding; IMab: Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Gilead: Current equity holder in publicly-traded company; MEI: Consultancy, Research Funding; Celgene: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding.

Background: The term HGBL, NOS was introduced by the World Health Organization (WHO) in 2016 for aggressive B-cell lymphomas with Burkitt lymphoma-like (BLL) or blastoid cytomorphology that lack double-hit genetics and do not meet criteria for other entities. Diagnostic patterns and prognosis of these rare tumors are not well understood. We examined the characteristics and outcomes of patients (pts) with HGBL, NOS diagnosed in 17 academic centers across the United States. Methods: We collected retrospective data on HGBL, NOS cases diagnosed by academic hematopathologists in 2017-2021; 8 centers performed a local review by lymphoma pathology experts to confirm fulfillment of the WHO criteria; pathology reports were reviewed centrally. We excluded pts not tested for MYC rearrangement (MYC-R), any double/triple-hit, diffuse large B-cell, or lymphoblastic lymphomas. Immunohistochemistry (IHC) and cytogenetic tests were done locally. Outcomes included rates of complete response (CR), progression-free (PFS) and overall survival (OS) estimated with 95% confidence intervals (CI). Results: Among 126 pts with HGBL, NOS, median age was 64 years (range 18-91), 67% were male, and 3 were HIV+. Advanced stage was present in 68%, poor performance status (PS, ECOG ≥2) in 21%, high serum lactate dehydrogenase (LDH) in 68%, extranodal (EN) sites in 79%, central nervous system (CNS) involvement in 6%, and International Prognostic Index (IPI) ≥ 3 in 55%. Cytomorphology was reported as BLL in 59 (47%) cases, blastoid in 28 (22%), and unspecified in 39 (31%). By IHC, 83% had germinal center B-cell (GCB) phenotype. Using cases with available data, CD10 was expressed in 79%, BCL6 in 81%, MUM1/IRF4 in 48%, MYC in 73%, BCL2 in 55% (dual MYC/BCL2 expressor [DEL]: 37%), CD5 in 13%, and median Ki-67 was 95%. MYC-R (single-hit) was detected in 27% (Fig A), MYC extra copies (EC) in 9%, BCL2-R in 13%, and BCL6-R in 10%. MYC-EC were present in 16% of cases with BCL2-R or BCL6-R, and BCL2/BCL6-EC in 12% of those with MYC-R. Blastoid tumors were more likely than BLL to involve >1 EN site or to have BCL2-R (Fig B). 9 cases were assessed by next generation sequencing and 5 (56%) had a TP53 mutation. Cases which underwent confirmatory pathology review (N=74) did not differ from others clinically but more often had a well-defined HGBL morphology (77% vs 58%, P=.031) and less often MYC-R (20% vs. 37%, P=.004). The most common first-line regimens (among treated pts, N=121) were DA-EPOCH-R (50%) and RCHOP (35%), with few pts receiving HyperCVAD/MA (5%) or CODOX-M±IVAC (2%); 97% received rituximab, and 44% CNS prophylaxis. Pts selected for DA-EPOCH-R vs. RCHOP were younger (median 61 vs. 68 years, P=.006), more often had stage 3/4 (P=.04), BLL morphology (56% vs. 29%, P=.009) or MYC-R (31% vs. 14%, P=.06). CR was attained in 62% of pts, whereas 20% had progressive disease. The most frequent salvage regimens (± rituximab) included ICE (N=12), DHAP (N=6), and GemOx (N=5). 3 pts underwent autologous, and 3 allogeneic transplant (2/3 subsequently relapsed). 13 received chimeric antigen receptor (CAR) T-cells, with response noted in 7 (54%) and CR in 4 (31%); HGBL relapsed in 3/7 (43%) responders. With median follow-up of 2.7 years, 39% of pts relapsed, and 33% died. Of 49 observed relapses, 13 (27%) involved the CNS. PFS estimate at 2 years was 51% (95% CI, 42-60%) and OS was 68% (95% CI, 58-76%; Fig C). PFS and OS were not significantly associated with age or PS, but stage and LDH were prognostic (Fig D-G). Furthermore, PFS did not differ by BLL/blastoid morphology, MYC-R status or DEL status, but non-GCB tumors had somewhat worse PFS (Fig H-J). We observed no significant PFS (or OS) difference between pts selected for RCHOP vs. DA-EPOCH-R (P=.83 for PFS, Fig K; P=.55 for OS) in aggregate or in any subset, except for de novo tumors with BLL morphology (N=41), where DA-EPOCH-R showed a superior 2-year PFS (73% vs 38% for RCHOP, P=.027; stratified by IPI: P=.040, Fig L). Conclusions: HGBL, NOS, as diagnosed in current academic practice, is highly heterogeneous, highlighting the need to classify high-grade lymphomas using molecular rather than morphologic features. Considering poor survival in all age groups (except for few pts with early stage and normal LDH), lack of prognostic significance of MYC-R, DEL status, or cytomorphology, HGBL, NOS needs prospective trials to delineate prognostic biomarkers, the role of intensified chemotherapy, and novel therapeutic approaches. Figure 1 Figure 1. Disclosures Landsburg: Triphase: Research Funding; Takeda: Research Funding; Curis: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Other: DSMB member; ADCT: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees. Hughes: Acerta Pharma: Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genzyme: Consultancy; Janssen: Consultancy; Karyopharm: Membership on an entity's Board of Directors or advisory committees. Sandoval-Sus: SeaGen, Janssen, MassiveBio, TG: Other: Advisory Board; BMS: Other: Advisory Board, Speakers Bureau. Kothari: Incyte pharmaceuticals: Consultancy, Honoraria; Karyopharm pharmaceuticals: Consultancy, Honoraria. Torka: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Smith: Acerta Pharma BV: Research Funding; ADC Therapeutics: Consultancy; AstraZeneca: Consultancy, Research Funding; De Novo Biopharma: Research Funding; Ignyta (spouse): Research Funding; Beigene: Consultancy, Research Funding; Portola Pharmaceuticals: Research Funding; Incyte: Consultancy; Incyte Corporation: Research Funding; Karyopharm: Consultancy; KITE pharm: Consultancy; Merck Sharp & Dohme Corp: Research Funding; Ayala (spouse): Research Funding; Bayer: Research Funding; Genentech: Research Funding; Bristol Myers Squibb (spouse): Research Funding; Millenium/Takeda: Consultancy. Epperla: Genzyme: Honoraria; Karyopharm: Other: Ad Board; Beigene: Speakers Bureau; Verastem: Speakers Bureau. Bond: Kite/Gilead: Honoraria. Naik: Sanofi: Other: Virtual Advisory Board Member ; Takeda: Other: Virtual Advisory Board Member ; Kite: Other: Virtual Advisory Board Member. Kamdar: ADC Therapeutics: Consultancy; AbbVie: Consultancy; KaryoPharm: Consultancy; Kite: Consultancy; Adaptive Biotechnologies: Consultancy; AstraZeneca: Consultancy; Celgene (BMS): Consultancy; TG Therapeutics: Research Funding; Genentech: Research Funding; Genetech: Other; Celgene: Other; SeaGen: Speakers Bureau. Haverkos: Viracta Therapeutics: Consultancy. Karmali: BMS/Celgene/Juno: Consultancy, Research Funding; Takeda: Research Funding; Roche: Consultancy; Epizyme: Consultancy; Janssen/Pharmacyclics: Consultancy; EUSA: Consultancy; Genentech: Consultancy; Karyopharm: Consultancy; AstraZeneca: Speakers Bureau; Kite, a Gilead Company: Consultancy, Research Funding, Speakers Bureau; Morphosys: Consultancy, Speakers Bureau; BeiGene: Consultancy, Speakers Bureau. Vose: Kite, a Gilead Company: Honoraria, Research Funding. Olszewski: PrecisionBio: Research Funding; Celldex Therapeutics: Research Funding; TG Therapeutics: Research Funding; Acrotech Pharma: Research Funding; Genentech, Inc.: Research Funding; Genmab: Research Funding.

Background: Recent studies reported low rates of seroconversion response to COVID-19 vaccination in patients (pts) with hematologic malignancies (HMs). Vaccine choice among the 3 FDA-authorized products (BNT162b2/Pfizer-BioNTech, mRNA-1273/Moderna, or Ad26.COV2.S/J&J), prior therapy, and disease-specific factors may affect seroconversion. Addressing these factors may improve seroconversion rates and identify pts at risk of severe COVID-19 infection despite vaccination. Methods: We conducted a retrospective study of adults with HMs vaccinated in our center between 2/2021 and 7/2021, excluding pts with prior COVID-19 infection. Seroconversion was assessed by the qualitative SARS-CoV-2 Total Antibody Test (IgG/IgM against Receptor Binding Domain [RBD], Wondfo USA, Willowbrook, IL). A subset of samples was tested by the semi-quantitative Abbott AdviseDx SARS-CoV-2 IgG II assay (IgG against RBD). For univariate associations (UVA) we used Fisher's exact test for categorical variables, and fractional polynomial fits for continuous variables to examine non-linearity. Multivariable analysis (MVA) used a robust Poisson model reporting risk ratio (RR) with 95% confidence intervals (CI). Results: Among 239 eligible pts, median age was 70 (range, 28-94), and 112 (47%) were female. HMs included aggressive B-cell lymphomas (n=74, 31%), indolent B-cell lymphomas (n=52, 22%), chronic lymphocytic leukemia (CLL, n=30, 13%), other lymphomas (n-19, 8%), plasma cell neoplasms (n=43, 18%), and myeloid cancers (n=21, 9%); 140 pts (59%) received BNT162b2/Pfizer, 74 (31%) mRNA-1273/Moderna, and 23 (10%) Ad26.COV2.S/J&J vaccines (2 pts had undetermined vaccine type). HM was active in 100 pts (42%), whereas 108 (45%) pts were in remission after treatment, and 31 (13%) on watchful waiting (WW, never treated); 141 (59%) had a prior exposure to an anti-B-cell monoclonal antibody, and 22 (9%) prior stem cell transplantation. Overall, 99 pts (41%; binomial 95% CI, 35-48%) showed post-vaccination seroconversion upon testing at median 10 weeks from first vaccine. Seroconversion was significantly less frequent among pts with lymphomas compared with plasma cell or myeloid neoplasms (overall P=.020; Fig A). It was also less frequent after prior anti-B-cell antibody exposure (29% vs 59%, P In a MVA (Fig. J), vaccination with mRNA-1273 remained significantly associated with higher rate of seroconversion compared with BNT162b2 (RR=0.59; 95%CI, 0.44-0.79) or Ad26.COV2.S (RR=0.35; 95%CI, 0.16-0.77). Higher seroconversion rate was also associated with remission (RR=1.98; 95%CI, 1.42-2.76) or WW status (RR=1.72; 95%CI 1.02-2.89) compared with active disease, and higher lymphocyte count. Exposure to anti-B-cell antibodies remained associated with lack of seroconversion (RR=0.66; 95%CI, 0.44-0.99). Seroconversion was borderline less frequent in CLL than lymphomas, and higher with plasma cell or myeloid disorders. Results were similar in the subset of pts (n=191) with prior treatment, adjusting for time from last chemotherapy(data not shown). The anti-COVID-19 IgG titers on semiquantitative test (n=47, all after mRNA-based vaccines) were also lower in pts with active disease compared with those in remission (P=.065) or under WW (P=.028), and in those with prior anti-B-cell antibody (P=.0095). Conclusions: Pts with HMs demonstrate overall low rates of seroconversion after vaccination against COVID-19, particularly when they have active disease or are on/after B-cell depleting monoclonal antibody therapy. The mRNA vaccines (particularly mRNA-1273) appear to have elicited superior responses compared with the adenovirus-based product. Pts with active HMs or those within 2 years of last therapy should be particularly aware of the risk of infection despite vaccines and should be considered for strategies to enhance anti-COVID-19 immunity regardless of age. Figure 1 Figure 1. Disclosures Olszewski: TG Therapeutics: Research Funding; PrecisionBio: Research Funding; Celldex Therapeutics: Research Funding; Acrotech Pharma: Research Funding; Genentech, Inc.: Research Funding; Genmab: Research Funding.

Introduction: Central nervous system (CNS) relapse in diffuse large B-cell lymphoma (DLBCL) is uncommon but is associated with poor outcomes. In selected high risk patients (pts), high dose methotrexate (HDMTX) is often used as CNS prophylaxis with frontline (1L) DLBCL therapy despite uncertain efficacy, optimum dose and timing of delivery. A recent UK study (Wilson et al 2020) showed that intercalated HDMTX (i-HDMTX) was associated with increased toxicity and R-CHOP delays compared to end of treatment (EOT) delivery. Although hypothesis generating, the study size was insufficient to determine whether EOT was non-inferior in terms of CNS relapse risk. Methods: We conducted an international, multicentre retrospective analysis of consecutive DLBCL or high grade BCL pts between 2007-20 from 47 centers in Europe, Australia and N. America. Pts were included if they received R-CHOP or R-CHOP-like 1L therapy with curative intent as well as HDMTX CNS prophylaxis (≥1 cycle). Concurrent intrathecal (IT) prophylaxis was permitted. Pts with known CNS involvement at baseline and those treated with more intensive protocols (e.g. R-DA-EPOCH) were excluded. i-HDMTX was defined as any pt receiving a HD-MTX cycle before the final R-CHOP cycle. CNS relapse events were excluded if occurring after first systemic lymphoma relapse/progression. Time to event endpoints were measured from diagnosis to first event or censor and analysed using Kaplan-Meier and Cox regression methods. Time to CNS relapse was analysed using competing risk Fine and Gray method (for death and non-synchronous systemic relapse). To mitigate for possible immortality bias in the EOT arm, a landmark analysis for pts alive and free from progression at 6 months was conducted. We aimed to exclude a 5% difference in 2-year (y) CNS relapse rates. Results: 1,384 pts were analysed. 750 received i-HDMTX and 634 received EOT HDMTX. Key baseline characteristics are summarised in Table 1. Median follow up was 37.9 months. 44.2% had high CNS IPI (4-6) with no significant difference between i-HDMTX and EOT groups (45.1% vs 43.1%, p=0.087). ≥2 cycles of HDMTX were used in 86.6% with no difference between groups (85.6% vs 87.9%, p=0.22). Concurrent IT prophylaxis use was higher for EOT pts (55.6% vs 38.1% p 78 CNS relapses (42 i-HDMTX, 36 EOT) were observed: parenchymal in 41 (53%), parenchymal and leptomeningeal in 16 (21%) and isolated leptomeningeal in 21 (27%). There was no significant difference in 2y CNS relapse rates between i-HDMTX and EOT in all pts: 5.2% vs 3.9%, adjusted hazard ratio (HR) 0.92 (95% CI 0.58-1.47), p=0.74, 2y difference -0.2% (-2.0-2.5) or landmark analysis: 2.8% vs 4.1%, HR: 0.93 (0.56-1.55), p=0.79, 2y difference: -0.3% (-1.8-2.2%) (Fig 1a/b). Exploratory analyses focusing on pts with isolated CNS relapse (n=57) demonstrated similar results (2y rates 3.6% vs 3.0%, p=0.99). On multivariable analysis (MVA) of risk factors for CNS relapse, renal/adrenal involvement was the only variable associated with increased CNS relapse risk (adjusted HR 1.74 (1.03-2.92), p=0.038). Notably, IT prophylaxis was not associated with reduction in CNS relapse. In 600 high CNS IPI (4-6) pts, there was no difference in CNS relapse risk between i-HD-MTX and EOT (3y rates 9.4% vs 8.6%, HR 0.92 (95% CI 0.52-1.62)). In a composite high risk group including CNS IPI 4-6 and/or any of the following: ≥3 extranodal sites, renal, adrenal, testicular or breast involvement (n=885) there was no difference in 3y CNS relapse rates between groups (i-HDMTX 7.6% vs EOT 7.4%, HR 0.94 (0.58-1.53)). Progression-free survival (PFS) and overall survival (OS) in the i-HDMTX and EOT groups were as follows: 3y PFS 70.7% vs 76.7% (p=0.098), 3y OS 79.9% vs 87.0% (p=0.0016). However, there were no PFS/OS differences between groups on landmark analysis (n=1259) (Fig 1c). On analysis of pts experiencing ≥1 R-CHOP delay of ≥7 days, use of i-HDMTX was the only factor on MVA associated with increased delays (p Discussion: We found no evidence that EOT delivery increases CNS relapse risk when compared to i-HDMTX in this large analysis of pts treated with 1L R-CHOP. Delays to R-CHOP cycles were increased with i-HDMTX. Findings in a high risk subgroup were unchanged and rates of CNS relapse in this HDMTX treated group were similar to published comparable high risk cohorts receiving infrequent CNS prophylaxis. Where HDMTX prophylaxis is used, delivery could be deferred until R-CHOP completion. Figure 1 Figure 1. Disclosures Wilson: Takeda: Other: Conference fees; Janssen: Other: Conference fees; Abbvie: Honoraria. Eyre: Janssen: Honoraria; Secura Bio: Consultancy, Honoraria; Gilead/KITE: Honoraria, Other: Travel support for conferences, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Other: Travel to conferences; AstraZeneca: Honoraria, Research Funding; Loxo Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy; Beigene: Honoraria, Research Funding. Ahearne: Pfizer: Research Funding; Takeda: Honoraria; Roche: Honoraria. Schorb: Roche: Research Funding; Riemser Pharma GmbH: Honoraria, Research Funding; AbbVie: Research Funding. Ku: Antegene: Consultancy; Roche: Consultancy; Genor Biopharma: Consultancy. Narkhede: Genentech/Roche: Research Funding; Gilead: Research Funding; Genmab: Other: Medical writing support, Research Funding; TG Therapeautics: Research Funding. Lewis: AstraZeneca: Consultancy, Honoraria; Janssen: Honoraria, Patents & Royalties; Novartis: Patents & Royalties; Roche: Consultancy, Honoraria. Øvlisen: Abbvie: Other: Travel expenses. Santarsiere: Janssen: Honoraria. Shah: Abbvie, Janssen and Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Roulin: Janssen: Other: Travel and meetings. Manos: Bristol-Myers Squibb: Other: Travel and meetings. Hamad: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Lopez-Garcia: Roche: Other: Speaker Honoraria, Travel and accommodation grants; Janssen: Other: Speaker Honoraria, Advisor, Travel and accommodation grants, Research Funding; Abbvie: Other: Speaker Honoraria, Advisor, Travel and accommodation grants; Celgene: Other: Speaker Honoraria; Fresenius: Other: Speaker Honoraria; Novonordisk: Other: Speaker Honoraria. El-Galaly: ROCHE Ltd: Ended employment in the past 24 months; Abbvie: Other: Speakers fee. Cheah: Beigene: Consultancy, Honoraria, Other: advisory; AbbVie: Research Funding; Celgene: Research Funding; AstraZeneca: Consultancy, Honoraria, Other: advisory; Loxo/Lilly: Consultancy, Honoraria, Other: advisory; TG Therapeutics: Consultancy, Honoraria, Other: advisory; Roche: Consultancy, Honoraria, Other: advisory and travel expenses, Research Funding; Janssen: Consultancy, Honoraria, Other: advisory; MSD: Consultancy, Honoraria, Other: advisory, Research Funding; Gilead: Consultancy, Honoraria, Other: advisory; Ascentage pharma: Consultancy, Honoraria, Other: advisory. Ferreri: Gilead, Novartis, Juno, PletixaPharm, Roche, Incyte: Membership on an entity's Board of Directors or advisory committees; BMS, Beigene, Pharmacyclics, Hutchison Medipharma, Amgen, Genmab, ADC Therapeutics, Gilead, Novartis, Pfizer: Research Funding. Fox: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Other: speaker fees. Cwynarski: Gilead: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Atara: Consultancy; Celgene: Consultancy; Takeda: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Kite, a Gilead Company: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Janssen: Consultancy, Other: travel to scientific conferences; Roche: Consultancy, Other: travel to scientific conferences, Speakers Bureau; BMS/Celgene: Other: travel to scientific conferences. McKay: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Other: Travel Support; KITE: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Janssen: Honoraria, Other: Travel Support; Beigene: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Background: CNS invasion in aggressive lymphoma (including diffuse large B-cell [DLBCL], double-hit [DHL], Burkitt [BL] and plasmablastic lymphoma [PBL]) remains a major diagnostic and therapeutic challenge. Parenchymal CNS invasion requires a brain biopsy to diagnose, and conventional CSF evaluations lack sensitivity. Furthermore, determining which patients (pts) without CNS disease at diagnosis might be at high risk of future CNS relapse, and thus would most benefit from aggressive CNS prophylaxis, is difficult. Predictors that use clinical variables (e.g. the CNS-IPI) can select a high-risk group with ~10% risk of CNS relapse, but ~50% of events occur in low/intermediate-risk groups. We evaluated a novel CSF assay that detects clonotype-specific cell-free DNA (cfDNA) by next-generation sequencing (NGS) combined with multiplex PCR (Chang T et al, BMC Cancer 2020). This assay can identify minimal residual disease (MRD) in the plasma of pts with DLCBL and can predict systemic relapse (Roschewski et al, Lancet Oncol 2015). Methods: To examine the diagnostic sensitivity of the NGS-MRD assay in the CSF, we prospectively collected CSF from 6 pts with lymphomas who had known parenchymal or leptomeningeal CNS invasion. We also tested stored DNA from historical CSF samples of 8 pts with CNS lymphoma who tested negative by conventional IGH-PCR. We then enrolled 19 pts with newly diagnosed high-risk lymphomas without CNS invasion to examine the prognostic value of the NGS-MRD assay in the CSF collected at diagnosis. In the two-step assay, tumor-specific clonotype sequences were first identified from genomic DNA in primary tumor specimens using NGS of rearranged IGK, IGH (VJ or DJ), and IGL loci. Tumor-specific clonotypes were then selected for tracking in the CSF (split into acellular CSF fluid and cell pellet) and in paired plasma samples. The assay quantitated copy numbers of each tracked sequence per mL of acellular CSF or per 106 cells in the pellet, as well as tumor clonotype frequency (%) per all B-cells. Results: The NGS-MRD detected tumor-specific clonotype in 100% of CSF samples from pts with known CNS lymphoma (n=6; Fig A), including pts with isolated parenchymal-only CNS disease that was not detected by CSF cytology, flow cytometry, or IGH-PCR. For pts with CNS parenchymal disease, median cfDNA copy count in the CSF fluid was 2 /mL (range, 0.4-929) and median clonotype frequency was 9.0% (range, 0.03-68.9%). For pts with leptomeningeal disease, these values were 2233 /mL (1.2-5620) and 37% (1.7-98.4%), respectively (Fig. B). Furthermore, the NGS-MRD assay detected tumor clonotypes in DNA isolates from 6 historical CSF samples (collected in 2014-2018) of pts with known CNS disease, in which lymphoma was not detectable by conventional IGH-PCR (2 additional historical samples failed DNA quality check). Among 19 newly diagnosed pts with no known CNS invasion (median age 57, 42% women, median LDH 352 IU/L), 12 (63%) had DLBCL with high risk of CNS relapse by virtue of high CNS-IPI or epidural tumor, and 7 had other high-risk histologies (2 DHL, 2 BL, HIV+ PBL, and 2 lymphoblastic leukemias). The CSF NGS-MRD assay was positive for tumor-specific clonotype in 8 pts (42%). Median cfDNA copy count in the CSF fluid was 3 /mL (range, 0.9-15.6) and median clonotype frequency was 18.6% (range, 1.4-28.5%). We observed no significant correlation between the amount of cfDNA and number of red cells /mL of CSF (a measure of potential CSF contamination by blood during collection procedure; Fig. C) or between the amount of cfDNA in plasma and in CSF (Fig. D). With median follow-up of 11 months, no pts with negative baseline NGS-MRD assay in CSF relapsed, whereas 2 of 10 with a positive assay had a CNS relapse (12-month incidence, 29%; Fig. E), despite both pts having negative CNS imaging, CSF cytology, flow cytometry, and IGH-PCR at diagnosis. Conclusions: In this proof-of-concept study, the NGS-MRD CSF assay showed 100% sensitivity for diagnosing intraparenchymal CNS invasion in aggressive lymphomas that were not detected in the CSF using conventional methods. In a prospective cohort of newly diagnosed pts without baseline CNS invasion but at high clinical risk for CNS relapse, the assay identified lymphoma-derived cfDNA in 42% of cases. The 100% sensitivity and 100% negative predictive value of the assay for subsequent CNS relapse warrants further prospective evaluation as a potential tool for personalizing CNS-directed prophylaxis. Disclosures Olszewski: Spectrum Pharmaceuticals: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding; Genentech, Inc.: Research Funding. Mullins:Adaptive Biotechnologies: Current Employment, Other: shareholder.

Introduction: There are few data about prognostication and outcomes in patients (pts) with HIV-BL treated in the cART era. Optimal treatment strategies to minimize treatment-related mortality (TRM) remain unclear and current recommendations are based on small studies. We conducted a multicenter international analysis to identify prognostic factors and outcomes in pts with HIV-BL treated in the cART era. Methods: This retrospective analysis included a subcohort from a recent study across 30 US sites (Evens et al. Blood 2020) augmented by data from 5 UK centers treated 2009-2018. Progression-free (PFS) and overall survival (OS) were estimated by Kaplan-Meier & differences assessed by log-rank test. Univariate (UVA) associations were derived via Cox model and multivariable (MVA) models were constructed by forward selection of significant variables with P Results: 249 (US: 140 & UK: 109) pts with newly diagnosed HIV-BL were included. Clinical features included median age 43 (IQR 35-50 years [yrs]); male sex: 84%; ECOG PS: 2-4: 48%; elevated LDH: 85% (> 3x upper limit of normal (ULN) 49% & >5xULN 39%); >1 extranodal (EN) site: 60%; any CNS involvement (CNSinv) 25%; and +bone marrow (BM) 46%. MYC rearrangement was reported in 93% of pts with t(8;14) in 49%, break-apart probe in 41% and MYC-light chain in 3%; the rest had classical BL with negative MYC testing (4%) or missing result (3%) (otherwise classical BL). Median CD4 count was 217 (IQR 90-392 cells/µL) with 68% pts having CD4>100 cells/µL. At BL diagnosis, HIV viral load was detectable in 55%; 39% of pts were on cART. Baseline features were similar between the US & UK cohorts with significant differences only in ECOG PS 2-4 (32% vs 65%; P Tx regimens included: CODOX-M/IVAC (Magrath) 60%, DA-EPOCH 25%, HyperCVAD/MA 13%, & other 1%; most pts (87%) received rituximab (R). Similar regimens were used in pts with baseline CNSinv: Magrath 64%, DA-EPOCH 24% & HyperCVAD 12%. In the US, pts most frequently received DA-EPOCH (42%) followed by Magrath (32%) & HyperCVAD/MA (24%), whereas in the UK, 96% received Magrath. R was more frequently given in the US (94% vs 79%, P With median follow-up of 4.5 yrs, 3-yr PFS & OS were 61% & 66%, respectively, and nearly identical in both countries (Fig A). Pts with CD4>100 cells/µL had better 3-yr PFS (Fig B) & OS (68% vs 57% P=0.03). We observed significantly worse outcomes in pts with baseline CNSinv (3-yr PFS 36% vs 69%, P The incidence of CNS recurrence at 3 yr across all Tx was 11%. Higher incidence was observed with DA-EPOCH (P=0.032 vs other regimens; Fig F) with no difference according to CD4 count. Variables associated with PFS & OS on UVA included: ECOG PS 2-4, >1 EN, +BM, baseline CNSinv, LDH>ULN, CD4 5xULN (HR 2.09, P1 EN sites (HR 1.58 P=0.043). The same variables were significant on MVA for OS. Adjusting for all of the prognostic variables, Tx with Magrath was associated with longer PFS (adjusted HR, 0.45, P=0.005). Conclusions: These data represent the largest analysis of HIV-BL to date. There were favorable tolerance and outcomes with intensive R-containing regimens with Magrath being associated with lower TRM and the highest PFS. In addition, prognostic factors for pt outcomes were associated with lymphoma characteristics rather than with HIV-related features. Pts with baseline CNSinv represent a high-risk group with unmet therapeutic needs. Disclosures Alderuccio: Oncinfo: Honoraria; Puma Biotechnology: Other: Family member; ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees; OncLive: Honoraria; Inovio Pharmaceuticals: Other: Family member; Foundation Medicine: Other: Family member; Forma Therapeutics: Other: Family member; Agios Pharmaceuticals: Other: Family member. Olszewski:Spectrum Pharmaceuticals: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding; Genentech, Inc.: Research Funding. Evens:Epizyme: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Mylteni: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria. Collins:Gilead: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding; Taekda: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; BeiGene: Consultancy; Roche: Consultancy, Honoraria, Other: travel, accommodations, expenses , Speakers Bureau; Celleron: Consultancy, Honoraria, Research Funding; ADC Therapeutics: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Speakers Bureau; Celgene: Research Funding; Amgen: Research Funding; Pfizer: Honoraria. Danilov:Astra Zeneca: Consultancy, Research Funding; Verastem Oncology: Consultancy, Research Funding; Takeda Oncology: Research Funding; Gilead Sciences: Research Funding; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Aptose Biosciences: Research Funding; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy; Karyopharm: Consultancy; Pharmacyclics: Consultancy; BeiGene: Consultancy; Abbvie: Consultancy. Jagadeesh:Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Debiopharm Group: Research Funding; MEI Pharma: Research Funding; Verastem: Membership on an entity's Board of Directors or advisory committees; Regeneron: Research Funding. Reddy:Genentech: Research Funding; Abbvie: Consultancy; BMS: Consultancy, Research Funding; Celgene: Consultancy; KITE Pharma: Consultancy. Farooq:Kite, a Gilead Company: Honoraria. Bond:Seattle Genetics: Honoraria. Khan:Celgene: Research Funding; Janssen: Honoraria; Pharmacyclics: Honoraria; Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Yazdy:Bayer: Honoraria; Genentech: Research Funding; Octapharma: Consultancy; Abbvie: Consultancy. Karmali:Karyopharm: Honoraria; Takeda: Research Funding; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Martin:Janssen: Consultancy; Regeneron: Consultancy; Bayer: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Beigene: Consultancy; Cellectar: Consultancy; Incyte: Consultancy; Kite: Consultancy; Morphosys: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; Karyopharm: Consultancy, Research Funding. Diefenbach:Bristol-Myers Squibb: Consultancy, Research Funding; Denovo: Research Funding; Genentech, Inc.: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Millenium/Takeda: Research Funding; Trillium: Research Funding. Klein:Takeda: Membership on an entity's Board of Directors or advisory committees. Haverkos:Viracta THerapeutics: Consultancy. Epperla:Verastem Oncology: Speakers Bureau; Pharmacyclics: Honoraria. Caimi:Amgen: Other: Advisory Board; Bayer: Other: Advisory Board; Kite Pharma: Other: Advisory Board; ADC Therapeutics: Other: Advisory Board, Research Funding; Celgene: Speakers Bureau; Verastem: Other: Advisory Board. Kamdar:Roche: Research Funding. Feldman:Eisai: Research Funding; Pfizer: Research Funding; Kyowa Kirin: Consultancy, Research Funding; Portola: Research Funding; Janssen: Speakers Bureau; AstraZeneca: Consultancy; Trillium: Research Funding; Cell Medica: Research Funding; Amgen: Research Funding; Pharmacyclics: Honoraria, Other, Speakers Bureau; Abbvie: Honoraria; Bayer: Consultancy, Honoraria; Viracta: Research Funding; Rhizen: Research Funding; Corvus: Research Funding; BMS: Consultancy, Honoraria, Research Funding; Kite: Honoraria, Other: Travel expenses, Speakers Bureau; Celgene: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses; Seattle Genetics, Inc.: Consultancy, Honoraria, Other: Travel expenses, Research Funding, Speakers Bureau. Smith:AstraZeneca: Consultancy; Millenium/Takeda: Consultancy; Karyopharm: Consultancy; Beigene: Consultancy; Seattle Genetics: Research Funding; Ayala: Research Funding; Bayer: Research Funding; AstraZeneca: Research Funding; Acerta Pharma BV: Research Funding; Bristol Meyers Squibb: Research Funding; Portola: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Incyte: Research Funding; Ignyta: Research Funding; Genentech: Research Funding; De Novo Biopharma: Research Funding. Portell:Amgen: Consultancy; Pharmacyclics: Consultancy; AbbVie: Research Funding; Janssen: Consultancy; TG Therapeutics: Research Funding; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Xencor: Research Funding; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding; Infinity: Research Funding; Roche/Genentech: Consultancy, Research Funding. Naik:Celgene: Other: advisory board; Sanofi: Other: advisory board. Lossos:Janssen Biotech: Honoraria; Verastem: Consultancy, Honoraria; Stanford University: Patents & Royalties; NCI: Research Funding; Seattle Genetics: Consultancy, Other; Janssen Scientific: Consultancy, Other. Cwynarski:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau.

Background. CD5 expression is rare in MZL and has been historically characterized in case series of splenic (SMZL) and nodal (NMZL) MZL (Baseggio et al., haematologica, 2010; Jaso et al., Am J Clin Pathol, 2013; Salido et al., Blood 2010). These studies reported similar outcomes with CD5+ and CD5- MZL but did not examine the significance of CD5 in the context of modern immunochemotherapy. BR is a regimen increasingly used for treatment of MZL, but many patients can initially receive R monotherapy (R-mono) with no detriment to survival (Olszewski et al., ASH 2019). There are no biomarkers predictive of differential response to R-mono or BR. We examined outcomes of these treatments in MZL according to CD5 expression. Methods. We collected clinicopathologic data on patients with MZL (SMZL, NMZL, and extranodal MZL of the mucosa-associated lymphoid tissue [MALT]) diagnosed in our center between 2010 and 2020. CD5+ MZL was diagnosed by expert academic hematopathologists after excluding chronic lymphocytic leukemia and mantle cell lymphoma by immunophenotypic evaluation and cytogenetic/fluorescent in-situ hybridization studies. For prognostication, we assigned subtype-specific prognostic scores: MALT-IPI (Thieblemont et al., Blood 2017), IIL score for SMZL (Arcaini et al., Blood 2006), and standard IPI for NMZL. We examined overall response rate (ORR), progression-free (PFS) and overall survival (OS), calculated from the start of therapy, according to CD5 expression and first-line therapy. Survival was compared using log-rank tests stratified by age, MZL subtype, and stage. Results. The study included 213 patients with SMZL (N=51, 24%), NMZL (N=46, 22%), and MALT (N=116, 54%), with median age of 68 years at diagnosis, and with 52% women. Overall, 23 MZLs (10.8%) were CD5+. CD5 expression was most common in SMZL (24%), then NMZL (11%), and rare in MALT lymphoma (5%; P=.003). CD5+ cases showed del(7q) in 9%, trisomy 12 in 17%, and CD23 expression in 9%. All tested CD5+ cases had a positive peripheral blood flow cytometry (100% vs 65% of CD5- cases, P The most common first-line treatments were R-mono (29%), radiation therapy (23%), BR (20%), and surgery (15%). CD5 expression was not prognostic for PFS (stratified log-rank, P=.29, Fig. A) or OS (P=.08; Fig. B) in the entire cohort, or within the subgroup of NMZL/SMZL only (P=.46 for PFS and P=.31 for OS). However, outcomes differed according to CD5+/- status between patients treated with first-line R-mono or BR. ORR to R-mono was significantly lower for CD5+ than CD5- MZL (22% vs. 78%, P=.003), whereas ORR to BR did not differ (100% and 93%, respectively, P=.72). Similarly, PFS after R-mono was significantly worse for CD5+ than CD5- MZL (P=.036; Fig. C), while it did not significantly differ after BR (P=.64; P for interaction between treatments=.030). CD5 expression was also associated with significantly worse OS among patients receiving R-mono (P=.047) but not among those receiving BR (P=.56; Fig. D). Results were similar in the subgroup of SMZL/NMZL cases. Conclusions. To our knowledge, this is the first study examining CD5 expression as a prognostic and predictive biomarker for outcomes of R-based immunochemotherapy in MZL. CD5+ MZL was characterized by disseminated presentation with universal peripheral blood involvement, but after adjustment for other factors, CD5 expression was not independently prognostic. However, we observed worse response rates, PFS, and OS for CD5+ MZL treated with first-line R monotherapy, but not among those treated with BR, suggesting that the BR combination might be preferable in this subgroup. Disclosures Olszewski: Spectrum Pharmaceuticals: Research Funding; Genentech, Inc.: Research Funding; Adaptive Biotechnologies: Research Funding; TG Therapeutics: Research Funding.

Introduction: T cell lymphomas are heterogenous with an overall poor prognosis. Peripheral T cell lymphoma not otherwise specified (PTCL-NOS) and angioimmunoblastic T cell lymphoma (AITL) are the two most common subtypes in the US accounting for 45% of diagnoses. Based on overlapping recurrent molecular and cytogenetic abnormalities, the WHO created an umbrella category of nodal T-cell lymphomas with T-follicular helper phenotype (TFH lymphoma), which includes AITL and PTCL with TFH phenotype (Swerdlow SH, et al. Blood. 2016). 25-30% of patients (pts) are refractory to initial therapy and even amongst responders to initial therapy, relapse is common. Survival after relapse or progression (R/P) is typically measured only in months. Outcomes have not changed significantly even with the introduction of several new agents in the last 10 years (Chihara D, et al. Br J Haematol. 2017). There remains no standard 2nd line therapy or guidance on sequencing of therapies as interpretation of clinical data in T-cell lymphoma is frequently hampered by the heterogeneity of the patient population and small sample size. The aim of our study was to determine outcomes in a large, well-defined group of pts with either primary refractory PTCL-NOS or TFH lymphoma. Methods: We performed a multi-center retrospective study to determine outcomes to 2nd line therapy for adult pts diagnosed between 1.1.09-6.30.18 with PTCL-NOS or TFH lymphoma, who were primary refractory defined by either induction failure, less than CR to initial therapy, or relapse within 6 months (mo) of completion of initial therapy. We performed time to event analysis using Kaplan-Meier method and compared groups using log-rank test. All other statistics were descriptive. Results: Patient and disease characteristics at diagnosis and relapse are summarized in Table 1. We identified 80 eligible pts from 7 US academic centers. Median FU was 17.2 mo from diagnosis (0.5-70). Overall, pts had mostly advanced stage with multiple high-risk features including bone marrow (BM) or extranodal (EN) involvement and B symptoms at diagnosis and R/P. Most pts received CHOP (52%) or CHOEP (24%) as initial therapy. 60% of patients did not attain a CR, while 40% of pts relapsed after initial CR.14% had received a consolidative transplant in 1st CR prior to relapse (almost all autologous hematopoietic cell transplant [HCT]). At R/P, 48% received single agent therapy [mostly romidepsin (15/38)], while 36% received multi-agent salvage therapy [mostly ICE (9/29)]. 13/80 pts were placed on hospice or only received local therapy after R/P. Median OS from diagnosis and following R/P was 19 mo/12.9 mo respectively. Median PFS2 (defined as time from 2nd line therapy to 2nd progression) for pts receiving systemic therapy was 73 days (d) (range 41-175). On univariate and multivariate analysis, there was no significant difference in PFS2 by histologic subtype (74 d for PTCL and 73 d for TFH lymphoma; p=0.29), platelet count Conclusions: Outcomes in this large, well-defined population of primary refractory PTCL-NOS and TFH lymphoma were poor, but better compared to most other retrospective series in R/R T-cell lymphoma. EN disease and advanced stage were common in this cohort of primary refractory pts. Relapse after HCT in CR1 had a particularly poor prognosis. Our findings suggest that single agent therapy following R/P in primary refractory pts and transplant may be beneficial, though our statistical power was limited due to small sample size. In a next step, we plan to expand the cohort and perform genetic/molecular characterization of available biospecimens following central pathology review. Disclosures Rhodes: DAVA Oncology: Honoraria. Olszewski:Spectrum Pharmaceuticals: Research Funding; Genentech: Research Funding; Adaptive Biotechnologies: Research Funding; TG Therapeutics: Research Funding. Brammer:Verastem, Inc: Research Funding; Viracta Therapeutics, Inc.: Research Funding; Bioniz Therapeutics, Inc.: Research Funding. Ghosh:TG Therapeutics: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Forty Seven Inc: Research Funding; AstraZeneca: Honoraria, Speakers Bureau; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding, Speakers Bureau; Genentech: Research Funding; SGN: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau. Dwivedy Nasta:Merck: Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding; 47 (Forty Seven): Research Funding; Rafael: Research Funding; Celgene: Honoraria; ATARA: Research Funding; Aileron: Research Funding; Debiopharm: Research Funding; Millenium/Takeda: Research Funding; Pharmacyclics: Research Funding. Barta:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Mundipharma: Honoraria; Seattle Genetics: Honoraria, Research Funding; Takeda: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Bayer: Consultancy, Research Funding; Mundipharma: Honoraria; Merck: Research Funding.

Introduction: Historically, outcomes for BL have improved in adults using dose intensive chemotherapy regimens and early CNS prophylaxis. More recent data using a less intensive regimen, DA-EPOCH, have been reported. We analyzed detailed patient (pt) & disease characteristics and treatment patterns across 26 US CCs over a recent 9 year (yr) period and also determined survival rates & prognostication. Methods: We conducted a large multicenter retrospective study of newly diagnosed (dx) adult BL pts (6/2009 - 6/2018). Dx was established by institutional expert pathology review; all cases were verified for BL based on 2016 WHO criteria (high grade B cell lymphoma, BL like, etc were excluded). Survival rates were estimated by Kaplan-Meier with differences assessed by log rank test. Univariate (UVA) associations were derived via Cox model with variables P ≤0.05 entered stepwise into a multivariate (MVA) model. Using significant factors from the MVA, a prognostic survival model was constructed. Results: Among N=557 verified BL cases, clinical features included: median age 47 yrs (17-88 yrs; 24% ≥60 yrs); male 76%; HIV+ 22%; ECOG PS 0/1 76%; B symptoms 51%; elevated LDH 78% (3, 5, & 10x elevation: 44%, 29% & 15%, respectively); hemoglobin 1 EN 43%; and 76% stage 3-4 disease (10% stage 1). Additionally, 16% and 3% of pts had baseline leptomeningeal (CSF or cranial nerve palsy) or parenchymal CNS involvement, respectively (see Zayac A et al. ASH 2019 for details). For MYC partner, 68% had t(8;14), 4% light chain, 5% negative FISH (otherwise classic BL) and 23% + break apart probe. HIV+ pts had several clinical differences: CSF+ 23% vs 12% P=0.003; CNS 19% vs 8% P1 EN 60% vs 38% P For all pts, 87% had Tx at an academic CC (13% at community CC). Tx regimens were: CODOX-M/IVAC (Magrath) 31%, HyperCVAD/MA 29%, DA-EPOCH 28%, other 10% (mostly CHOP-based & CALGB Tx) & 1% were never treated. 90% of pts received rituximab as part of Tx (69% inpatient (inpt) & 31% outpatient) & 2% had consolidative autologous SCT. Response among all pts were CR 72%, PR 6%, SD 1%, PD 14%, 7% not evaluable. The treatment related mortality (TRM) rate across all pts was 8.9% (HIV+ vs not: 13% vs 8% P=0.09); most common: sepsis 48%, GI bleed/perforation 14% & respiratory failure 12%. TRM by Tx: hyperCVAD/MA 11.5%, EPOCH 6.4%, Magrath 6.3% & other 18.9%. With 39 month median follow-up, 3 year progression-free survival (PFS) and overall survival (OS) were 65% and 72%, respectively (Fig 1A). Among all pts who experienced disease progression, 90% occurred For prognostication, outcomes were inferior for pts ages ≥40 yrs & LDH >3x normal (Fig 1B/C). Notably, survival rates were not different based on HIV status (Fig 1D) or by the 3 most common Tx regimens (Fig 1E). However, there were important Tx differences based on presence of CNS involvement (see Zayac A et al. ASH 2019). Additionally, use of rituximab was associated with improved PFS & OS (Fig 1F), while outcomes were similar whether rituximab was given inpt vs outpatient (inpt PFS HR 1.25, P=0.19). Furthermore, Tx at an academic CC was associated with improved outcomes, which persisted on MVA (PFS HR 0.54, 95%CI 0.33-0.88 P=0.01; OS HR 0.50, 95%CI 0.29-0.87 P=0.01) & achievement of initial CR was strongly prognostic (Fig 1G). Baseline factors significant on UVA for PFS & OS were: age ≥40 yrs; PS 2-4; LDH >3x; anemia, low albumin; BM involvement; Stage 3-4; CSF+; & >1 EN. On MVA, factors associated with inferior survival were: age ≥40 yrs (PFS HR 1.57, P3x (PFS HR 2.28, P Conclusions: Outcomes for adult BL in this contemporary, large, multicenter RW analysis appear inferior to smaller published series. Interestingly, despite increased adverse prognostic factors, survival rates appeared similar in HIV+ pts. In addition, use of rituximab, achievement of initial CR, and Tx at an academic CC were associated with improved survival. Finally, a novel BL-specific survival model identified pts with markedly divergent outcomes. Disclosures Evens: Seattle Genetics: Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria; Tesaro: Research Funding; Verastem: Consultancy, Honoraria. Danilov:Janssen: Consultancy; Seattle Genetics: Consultancy; MEI: Research Funding; Abbvie: Consultancy; Pharmacyclics: Consultancy; Takeda Oncology: Research Funding; Janssen: Consultancy; TG Therapeutics: Consultancy; Curis: Consultancy; Pharmacyclics: Consultancy; Aptose Biosciences: Research Funding; Verastem Oncology: Consultancy, Other: Travel Reimbursement , Research Funding; AstraZeneca: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Bayer Oncology: Consultancy, Research Funding; Celgene: Consultancy; Bristol-Meyers Squibb: Research Funding; MEI: Research Funding; Gilead Sciences: Consultancy, Research Funding; Abbvie: Consultancy; Bristol-Meyers Squibb: Research Funding. Reddy:KITE Pharma: Consultancy; BMS: Consultancy, Research Funding; Celgene: Consultancy; Genentech: Research Funding; Abbvie: Consultancy. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Khan:Janssen: Other: Educational Content/Symposium; Abbvie: Membership on an entity's Board of Directors or advisory committees; Bristol Myers: Other: Research Funds; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Yazdy:Genentech: Research Funding; Bayer: Honoraria; Abbvie: Consultancy; Octapharma: Consultancy. Karmali:Gilead/Kite; Juno/Celgene: Consultancy, Speakers Bureau; Astrazeneca: Speakers Bureau; Takeda, BMS: Other: Research Funding to Institution. Martin:Janssen: Consultancy; Teneobio: Consultancy; Celgene: Consultancy; Karyopharm: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy. Diefenbach:LAM Therapeutics: Research Funding; Incyte: Research Funding; Genentech: Consultancy, Research Funding; Trillium: Research Funding; Millenium/Takeda: Research Funding; Seattle Genetics: Consultancy, Research Funding; Merck: Consultancy, Research Funding; MEI: Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Denovo: Research Funding. Epperla:Pharmacyclics: Honoraria; Verastem Oncology: Speakers Bureau. Feldman:Eisai: Research Funding; Amgen: Research Funding; Cell Medica: Research Funding; Roche: Research Funding; Corvus: Research Funding; Kyowa Hakko Kirin: Research Funding; Pfizer: Research Funding; Trillium: Research Funding; Viracta: Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Other: Travel expenses, Speakers Bureau; AbbVie: Honoraria, Other: Travel expenses, Speakers Bureau; Pharmacyclics: Honoraria, Other: Travel expenses, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Kite Pharma: Honoraria, Other: Travel expenses, Speakers Bureau; Portola Pharma: Research Funding; Roche: Research Funding. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Naik:Celgene: Other: Advisory board. Kamdar:Celgene: Consultancy; AstraZeneca: Consultancy; Seattle Genetics: Speakers Bureau; University of Colorado: Employment; Pharmacyclics: Consultancy. Portell:AbbVie: Research Funding; Pharmacyclics: Consultancy; Janssen: Consultancy; Genentech: Consultancy, Research Funding; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding; TG Therapeutics: Research Funding; Xencor: Research Funding; Roche/Genentech: Research Funding; Infinity: Research Funding; Amgen: Consultancy. Olszewski:Spectrum Pharmaceuticals: Research Funding; TG Therapeutics: Research Funding; Genentech: Research Funding; Adaptive Biotechnologies: Research Funding. Alderuccio:Agios: Other: Immediate family member; Puma Biotechnology: Other: Immediate family member; Foundation Medicine: Other: Immediate family member; Targeted Oncology: Honoraria; Inovio Pharmaceuticals: Other: Immediate family member; OncLive: Consultancy.

Background: Allogeneic stem cell transplant (allo-SCT) is a cornerstone in the treatment of Acute Myeloid Leukemia (AML) exerting much of its therapeutic efficacy through the graft-versus-leukemia effect. The administration of immunoregulatory cells via engrafted donor lymphocytes is essential to the eradication of residual malignancy and long-term survival. Many patients, however, either do not have an allo-SCT donor or cannot withstand the toxicity of allo-SCT. Previous studies have shown durable responses in AML patients following donor lymphocyte infusions in the absence of engraftment in both the frontline and relapsed setting (Dey et al, BJH 2005, Colvin et al, BBMT 2009, Ai et al, Blood 2010). In this clinical trial we propose a role for donor leukocyte infusions (DLI) in the absence of engraftment. Without the need for engraftment patients will not need to receive high dose chemotherapy or radiation and the toxicities that accompany these therapies. Instead, allogeneic donor cells are infused into patient with relapsed/refractory (R/R) AML to serve as a potent immune stimulator. Prior to DLI, patients will receive fractionated dosing of gemtuzumab ozogamicin (GO). GO is an anti-CD-33 antibody drug conjugate approved in combination with induction therapy for de novo AML and in R/R disease. Patients who demonstrate a CR or CRi to therapy will go on to have up to 2 additional GO + DLI treatments. Bone marrow and blood samples will be obtained from patients before, during, and after treatment to determine immune effector cells (both donor and patient), cytokine release profiles, and extracellular vesicle components. Study Design and Methods: Our study, BrUOG 345 [NCT03374332], evaluates the combination of fractionated GO with non-engraftment DLI in the treatment of patients with R/R AML. Adults patients 18 years of age and older with histologically confirmed R/R AML who have had recurrence or progression after at least 1 prior standard treatment are eligible. Enrollees must have no active systemic infections and have adequate lung, liver, cardiac, and renal function with an ECOG PS 0-1. Fractionated GO 6-9mg/m2 will be administered on days 1, 4, and 7 followed by infusion of 1-2x108 CD3 cells/kg from a 0-3/6 HLA mismatched related donor cell. Patients that are in CR or CRi can receive up to 2 additional treatments with GO+DLI (Figure 1). The primary objective of the phase 1 portion is to determine the maximum tolerated dose (MTD) of DLI in combination with GO while the primary objective of Phase 2 portion is to assess the response rate after one cycle of fractionated GO followed by non-engraftment DLI in patients with relapsed/refractory AML. The study will initially utilize a 3+3 design in Phase 1 to determine if 1-2x108 CD3 cells/kg can be safely administered with GO. This study will target a response rate of 57% considered to be interesting enough to warrant further study in a randomized setting. With this hypothesis in mind, the phase 2 portion of the study will use Simon's two-stage design. The null hypothesis that the true response rate (CR and CRi) is only 29% will be tested against a one-sided alternative. In the first stage, 9 subjects will be accrued. Patients treated at MTD in the Phase 1 portion of the study will be included in this cohort. If there are 3 or fewer responses in these 9 patients, the study will be stopped for futility. Otherwise, 6 additional subjects will be accrued for a total of 15. The null hypothesis will be rejected if 7 or more responses are observed in 15 patients. This design yields a type I error rate of 0.1 and power of 80% if the true response rate is 57%. A continuous assessment of toxicity will be utilized for the Phase 2 portion of this study. Sequential boundaries will be used to monitor dose-limiting toxicity rate for patients after the initial MTD is determined. Accrual will be halted if excessive numbers of dose-limiting toxicities are seen. The primary outcome of the Phase 2 portion is the CR/CRi rate following GO and non-engraftment DLI. Secondary outcomes will include survival, both progression-free (PFS) and overall (OS) until two years post treatment, and dose limiting toxicities until 16 weeks post-infusion. Additional lab correlative studies will be performed including CD33 expression before, during, and after GO infusion and T-cell activation markers, antigen presenting cell/macrophage amounts, cytokine release profiles, and extracellular vesicle measurements (Figure 1). Disclosures Olszewski: TG Therapeutics: Research Funding; Spectrum Pharmaceuticals: Research Funding; Adaptive Biotechnologies: Research Funding; Genentech: Research Funding. Reagan:Pfizer: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees.

Background: BL is associated with a high risk of primary or secondary CNS involvement, warranting intrathecal (IT) and/or systemic therapy that penetrates the blood-brain barrier (BBB). The lower-intensity DA-EPOCH-R regimen has recently shown high survival rates in BL (Dunleavy, NEJM 2013), but it omits drugs traditionally used for CNS prophylaxis (like high-dose methotrexate [HDMTX]). The objective of this multi-institutional retrospective study was to examine treatments, risk factors, and CNS-related outcomes among patients (pts) with BL. Methods: We collected data from 26 US centers on adult BL pts diagnosed (dx) in 6/2009-6/2018. Using institutional expert pathology review and 2016 WHO criteria, we excluded other high-grade lymphomas (including BL-like/unclassifiable), or cases with inadequate clinicopathologic data. We studied factors associated with baseline CNS involvement (CNSinv) using logistic regression reporting odds ratios (OR). Progression-free (PFS), overall survival (OS), and cumulative incidence function of CNS recurrence (in a competing risk analysis) were examined in Cox or Fine-Gray models reporting hazard (HR) or subhazard ratios (SHR), respectively. All estimates report 95% confidence intervals (in square brackets). Results: Among 557 BL pts (median age, 47 years [yr], 24% women, 23% HIV+), 107 (19%) had CNSinv at dx, including 89 (16%) with leptomeningeal, and 15 (3%) with parenchymal CNS disease. In a multivariable model, factors significantly associated with CNSinv at dx included stage 3/4 (OR, 11.2 [1.47-85.9]), poor performance status (PS; OR, 2.12 [1.22-3.69]), ≥2 extranodal sites (OR, 3.77 [2.02-7.03]), or marrow involvement (OR, 2.44 [1.35-4.39]), whereas intestinal involvement conferred low risk of CNSinv (OR, 0.27 [0.11-0.65]). CNSinv at dx was not significantly associated with use of specific chemotherapy regimens (Fig. A,P=.75) or receipt of IT chemotherapy (91% vs 84%, P=.065). Pts with CNSinv were less likely to achieve a complete response (62% vs 76%, P=.005), had worse 3 yr PFS (47% vs 69%; P3x upper limit of normal [LDH>3x]; see Evens AM et al, ASH 2019 for further details). With median follow up of 3.6 yrs, 33 pts (6%) experienced a CNS recurrence (82% within 1 yr from dx; 79% purely in CNS, and 21% with concurrent systemic BL). The cumulative risk of CNS recurrence was 6% [4-8%] at 3y (Fig. D). Univariate significant predictors of CNS recurrence included baseline CNSinv, HIV+ status, stage 3/4, poor PS, LDH>3x, involvement of ≥2 extranodal sites, marrow, or testis. However, in a multivariate model only baseline CNSinv (SHR, 3.35 [1.53-7.31]) and poor PS (SHR, 2.24 [1.03-4.90]) retained significance. The 3 yr risk of CNS recurrence varied from 3% for pts with no risk factor, to 10% with one, and 17% with both factors (Fig. E). In addition, the risk of CNS recurrence differed according to chemotherapy regimen, and was significantly higher for pts treated with DA-EPOCH (12% at 3y [8-18%]; Fig. F) compared with CODOX-M/IVAC (4% [2-8%]) or hyperCVAD/MA (3% [1-6%]; SHR for DA-EPOCH vs. others, 3.50 [1.69-7.22]). All pts recurring after DA-EPOCH had received IT chemotherapy. Higher risk of CNS recurrence persisted with DA-EPOCH regardless of baseline CNSinv (Pinteraction=.70), poor PS (Pint=.14), or HIV status (Pint=.89). Baseline CNSinv was the strongest factor associated with CNS recurrence after DA-EPOCH (3 yr risk, 30% vs 8%, P Conclusions: In adult BL, baseline CNSinv and poor PS predicted subsequent CNS recurrence, an outcome that is associated with a dismal prognosis. Furthermore, treatment with DA-EPOCH was associated with a significantly increased risk of CNS recurrence in this real-world analysis. For BL pts with baseline CNSinv treated in routine clinical practice, regimens with highly BBB-penetrant drugs (e.g. CODOX-M/IVAC, hyperCVAD/MA) may be preferred. Studies should delineate ways to mitigate the risk of CNS recurrence with lower-intensity programs. Disclosures Evens: Seattle Genetics: Consultancy, Honoraria; Research to Practice: Honoraria; Verastem: Consultancy, Honoraria; Affimed: Consultancy, Honoraria; Pharmacyclics: Honoraria, Other: DMC; Bayer: Consultancy, Honoraria; Takeda: Research Funding; Merck: Research Funding. Smith:Incyte Corporation: Research Funding; Seattle Genetics: Research Funding; Portola Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Acerta Pharma BV: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Ignyta (spouse): Research Funding; Merck Sharp & Dohme Corp: Consultancy, Research Funding; Denovo Biopharma: Research Funding; Bristol-Myers Squibb (spouse): Research Funding; Ayala (spouse): Research Funding. Naik:Celgene: Other: Advisory board. Reddy:KITE Pharma: Consultancy; Abbvie: Consultancy; Genentech: Research Funding; BMS: Consultancy, Research Funding; Celgene: Consultancy. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Epperla:Verastem Oncology: Speakers Bureau; Pharmacyclics: Honoraria. Khan:Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Janssen: Other: Educational Content/Symposium; Abbvie: Membership on an entity's Board of Directors or advisory committees; Bristol Myers: Other: Research Funds. Alderuccio:Puma Biotechnology: Other: Immediate family member; Agios: Other: Immediate family member; Inovio Pharmaceuticals: Other: Immediate family member; Targeted Oncology: Honoraria; OncLive: Consultancy; Foundation Medicine: Other: Immediate family member. Yazdy:Bayer: Honoraria; Genentech: Research Funding; Octapharma: Consultancy; Abbvie: Consultancy. Diefenbach:Bristol-Myers Squibb: Consultancy, Research Funding; Denovo: Research Funding; Genentech: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Millenium/Takeda: Research Funding; Trillium: Research Funding. Karmali:Astrazeneca: Speakers Bureau; Takeda, BMS: Other: Research Funding to Institution; Gilead/Kite; Juno/Celgene: Consultancy, Speakers Bureau. Martin:Celgene: Consultancy; Teneobio: Consultancy; Karyopharm: Consultancy; Janssen: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy. Magarelli:Tevan Oncology: Speakers Bureau. Kamdar:Seattle Genetics: Speakers Bureau; Pharmacyclics: Consultancy; AstraZeneca: Consultancy; Celgene: Consultancy; University of Colorado: Employment. Portell:Xencor: Research Funding; Roche/Genentech: Research Funding; Infinity: Research Funding; TG Therapeutics: Research Funding; AbbVie: Research Funding; Pharmacyclics: Consultancy; Janssen: Consultancy; Genentech: Consultancy, Research Funding; Amgen: Consultancy; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding. Olszewski:Genentech: Research Funding; Adaptive Biotechnologies: Research Funding; TG Therapeutics: Research Funding; Spectrum Pharmaceuticals: Research Funding.

Background: Among many causes of thrombocytopenia acquired during hospitalization, heparin-induced thrombocytopenia (HIT), an immune complex-mediated thrombogenic state with high morbidity and mortality, is one of the most feared etiologies. The clinical '4T' score is an established method to assess risk for HIT and guide immediate management(Lo, Juhl et al. 2006). However, the 4T score is not consistently used by clinicians, leading to potential over-testing and suboptimal management of suspected HIT. Our objective in this Quality Improvement (QI) project was to implement a mandatory 4T calculator (4TC) into the electronic medical record (EMR) to improve the positive predictive value of HIT antibody (HITA) testing, decrease the incidence of testing of low-risk patients, and to improve clinical management of suspected HIT. Methods: We developed a 4TC that assisted clinicians by integrating EMR-derived data on heparin exposure and the five most recent platelet counts with a link to an online calculator as well as clinical suggestions (Figure). As a part of this QI project, clinicians were required to enter the calculated 4T score before ordering a polyspecific HITA assay, which serves as the screening test for HIT in our institution. We reviewed laboratory records for all HITA tests performed over a six-month period, including 3 months before and after the implementation of a "hard stop" for HITA order in the EMR. We extracted data on the ordering hospital service team, 4T score documented in notes (if any), as well as quality of care indicators including rates of clinically recommended heparin cessation and initiation of alternative anticoagulation in the subgroup of patients with intermediate or high 4T score at the time of HITA order. In this QI project, we refrained from any statistical testing and summarized pre-specified outcome measures: incidence of HITA testing, positive predictive value of the HITA assay, and quality of care indicators. Results: We identified 99 (53 prior to the 4TC and 46 post) cases of HITA orders in the study period, among which there were 4 cases of confirmed HIT (either with positive IgG or positive serotonin-release assay [SRA]). Three HIT cases occurred after the 4TC was implemented. After the implementation of the 4TC, the positive predictive value for the HITA assay increased from 9% to 21%. The rate of clinically appropriate heparin order discontinuation increased from 72% to 88%, as did the initiation of alternative anticoagulation from 14% to 27%. HITA assay was most commonly ordered by the internal medicine service (35% of tests), non-cardiac intensive care service (30%), coronary care and cardiothoracic surgery services (21%), neurology (5%), and general surgery/other services (9%). Implementation of the 4TC did not significantly change this distribution, but the overall incidence of HITA orders decreased from 4.0 to 3.4 per 1000 admissions. We observed a high frequency of confirmatory SRA testing requested (18%), which is the reflex confirmatory test performed at our institution, even in cases of negative polyspecific HITA or HIT IgG. Conclusions: Addition of a 4T score calculator into the EMR which provides immediate feedback on patient's prior heparin exposure and recent platelet counts allows clinicians to consistently use the 4T score when considering HIT. Importantly, it also helps to educate non-hematologists on the proper management of HIT. Improved predictive value of the HITA assay suggests that our 4TC led to ordering HITA in a more appropriate patient population. We also demonstrated improved quality of care for suspected HIT, with increased rates of heparin cessation and initiation of alternative anticoagulation in patients with intermediate or high pre-test probability. Identifying clinical teams that order a disproportionate number of HITA assays will help us to direct education to improve clinical management. This QI project has led to other areas of improvement, particularly with regard to rational ordering of the SRA. At only three months of review since implementation of the 4TC, we have noted an improvement in the management of suspected HIT and a slight reduction in the incidence of testing. With continued review, we hope to demonstrate further improvement, as practitioners continue to use the 4TC and rely on clinical rationale rather than reflexive laboratory testing when evaluating thrombocytopenia in the hospital. Disclosures Olszewski: TG Therapeutics: Research Funding; Spectrum Pharmaceuticals: Consultancy, Research Funding; Genentech: Research Funding. Reagan:Alexion: Honoraria; Takeda Oncology: Research Funding; Pfizer: Research Funding.

Background Febrile neutropenia (FN) is an oncologic emergency associated with high morbidity and mortality, particularly in patients (pts) with hematologic malignancies. Delays in antibiotic administration, which can occur in busy emergency departments (EDs), lead to worse outcomes. We instituted a FN pathway (FNP) in the Cleveland Clinic (CC) ED to reduce antibiotic delays. Methods This prospective study comparing patients from 06/12 - 06/13 to historical pts from 02/10 - 05/12, represented a collaboration among cancer center, ED, infectious disease, pharmacy, and electronic medical record representatives. Fever was defined as temperature >38°C either at home or in the ED, while neutropenia as absolute neutrophil count Results In total, 137 consecutive FN episodes in 115 pts with hematologic malignancies occurred during the 12 month study period, 63 episodes in 44 pts in the historical cohort. All pts were triaged and treated using the ED FNP, but use of the specific FN order set was variable: episodes were thus classified as treated per the order set (n=53) or not (n=84 – pts still received antibiotics, but not necessarily per the order set). Overall 60% of pts (n=89) were male and the median age at the time of first ED encounter was 59 years (range 20-88). Cancers were: non-Hodgkin lymphoma (38%), acute myeloid leukemia (21%), other leukemias (15%), and myelodysplastic syndromes (8%). Compared to historical pts, FNP study pts had a higher median ANC (2.0 vs. 0.2, p Comparing the two FNP groups treated or not treated per the order set, those treated using the order set had shorter times to antibiotics being ordered (median 28.0 vs. 60.5 minutes, p0.28). Conclusion The FNP significantly decreased time from ED registration to all set time-points, including time to antibiotics by almost three-fold, compared to historical controls in pts with hematologic malignancies. Rate of hospitalization was significantly lower, and ICU and length of stay numerically lower. The FNP is an effective clinical tool to provide prompt antibiotic administration to FN pts and likely represents a significant mechanism for improved outcomes and cost-savings to patients with hematologic malignancies presenting with FN. Disclosures: No relevant conflicts of interest to declare.