7067 Background: Relapsed/refractory (R/R) peripheral T-cell lymphomas (PTCL) are a rare group of non-Hodgkin lymphomas with median survival at relapse of 6-10 months and few treatment options. Duvelisib is a g/∆ phosphatidylinositol 3-kinase inhibitor approved in CLL and NCCN compendium listed in PTCL. In indolent lymphomas, concerns for cumulative duvelisib toxicity limit its use. In contrast, studies in R/R PTCL show duvelisib to be well-tolerated. Therefore, we sought to assess the efficacy and tolerability of duvelisib in a real world setting in R/R PTCL. Methods: We conducted a retrospective analysis of patients with R/R PTCL treated with duvelisib from 2012-2025 at 12 centers. Results: We identified 207 patients with PTCL: nodal PTCL with TFH phenotype (n=106, 51%), PTCL-NOS (n=78, 38%), ALCL (n=16, 8%), enteropathy associated T-cell lymphoma (n=4, 2%), NK/T-cell lymphoma (n=2, 1%), adult T-cell leukemia/lymphoma (n=1, 0.5%). 90 patients (43%) were female. Median age was 64; median International Prognostic Index score 3. Median prior lines of therapy was 2 (range 1-9). 26% received frontline transplant (23% auto, 3% allo). 118 patients received single agent duvelisib; 89 patients received duvelisib in combination with other agents (66 romidepsin, 12 bortezomib, 4 azacitadine, 3 ruxolitinib, 4 other). Of patients with response data, ORR was 51% (94/186; 31% CR, 17% PR) with median OS 12 months. ORR in patients with TFH-phenotype vs. non-TFH phenotype was 60% and 41% respectively. ORR in patients treated with single agent duvelisib and duvelisib combinations was 46% (46/99; 29% CR, 17% PR) and 55% (48/87; 39% CR, 16% PR), respectively with no difference in OS. Median duration of therapy was 84 days; the most common reason for discontinuation was progression (41%, 76/186). 13% (25/196) underwent allo-SCT after duvelisib therapy. Toxicity data is summarized in our table. In patients receiving single agent duvelisib therapy, rates of cytopenia were lower (7% vs. 34%, p<0.001) and rates of colitis were higher (17% vs. 7%, p= 0.03). Conclusions: In this real world, multicenter analysis of patients with R/R PTCL treated with duvelisib-based regimens, the ORR of 51% was consistent with published trials. Duvelisib therapy was well tolerated and a minority required hospitalization for toxicity. This suggests that duvelisib is safe in patients with R/R PTCL who have poor prognosis and limited treatment options. Adverse Event Full Cohortn (%)n=207 Single agent n (%)n=118 Combination therapyn (%)n=89 Single vs. Combination p value SteroidGivenn (% full cohort) Hospitalizationn (% full cohort) Rash 41 (20%) 24 (20%) 17 (19%) 0.83 23 (11%) 6 (3%) Colitis 26 (13%) 20 (17%) 6 (7%) 0.03 20 (10%) 16 (8%) Pneumonitis 7 (3%) 4 (3%) 3 (3%) 0.99 5 (2%) 3 (1%) Cytopenia 38(18%) 8 (7%) 30 (34%) <0.001 * * Transaminitis 33 (16%) 21 (18%) 12 (13%) 0.40 * * Infection 66 (32%) 36 (31%) 30 (34%) 0.63 N/A 42 (20%) *Not available.

Little is known about risk factors for central nervous system (CNS) relapse in mature T- and NK-cell neoplasms (MTNKN). We aimed to describe the clinical epidemiology of CNS relapse in patients with MTNKN and developed the CNS relapse In T-cell lymphoma Index (CITI) to predict patients at highest risk of CNS relapse. We reviewed data from 135 patients with MTNKN and CNS relapse from 19 North American institutions. After exclusion of leukemic and most cutaneous forms of MTNKN, patients were pooled with non-CNS relapse control patients from a single institution to create a CNS relapse-enriched training set. Using a complete case analysis (N=182), of whom 91 had CNS relapse, we applied a LASSO Cox regression model to select weighted clinicopathologic variables for the CITI score, which we validated in an external cohort from the Swedish Lymphoma Registry (N=566). CNS relapse was most frequently observed in patients with PTCL, NOS (25%). Median time to CNS relapse and median overall survival after CNS relapse was 8.0 months and 4.7 months, respectively. We calculated unique CITI risk scores for individual training set patients and stratified them into risk terciles. Validation set patients with low-risk (N=158) and high-risk (N=188) CITI scores had a 10-year cumulative risk of CNS relapse of 2.2% and 13.4%, respectively (HR 5.24, 95%CI 1.50-18.26, P=0.018). We developed an open-access web-based CITI calculator (https://redcap.link/citicalc) to provide an easy tool for clinical practice. The CITI score is a validated model to predict patients with MTNKN at highest risk of developing CNS relapse.

Background Renal function is an important parameter to inform drug-dosing in oncology. In acute myeloid leukemia (AML), commonly used therapies or their metabolites are renally cleared. Several equations are commonly used to estimate renal function, such as Cockcroft-Gault (CG) to estimate creatinine clearance (CrCl) and the Modification of Diet in Renal Disease (MDRD) or Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equations to calculate estimated glomerular filtration rates (eGFR). These equations frequently use endogenous serum markers, such as creatinine (Cr) and cystatin-C due to their ready availability but can have limitations. For example, Cr can be affected by muscle mass, diet, and age. Current ADDIKD international consensus guidelines recommend using the CKD-EPI creatinine formula to calculate eGFR to guide anti-cancer drug dosing unless direct GFR measurement is indicated. Renal function criteria are commonly used in clinical trial eligibility. In this study, we analyzed the types of reported renal eligibility criteria in phase II and III trials in AML and the methodology specified for renal function estimation, which can significantly impact treatment options for patients and potentially confound toxicity evaluation. Methods We examined the clinicaltrials.gov database to review publicly reported renal eligibility criteria for interventional phase II and III clinical trials recruiting adults with AML between 2021 to 2023. Trials including pediatric patients and exclusive phase I studies were excluded. Chi-square tests were used to evaluate for potential associations between eGFR cutoff criteria and disease variables. Results A total of 134 trials met criteria for review, including 84 (63%) trials specifically for AML and 50 (37%) allowing additional diagnoses. Of the total study cohort, 107 trials (80%) were evaluating chemotherapy, small molecule inhibitors, immunotherapy and/or combinations of these whereas 27 (20%) were evaluating primarily cell therapy and/or transplant-based regimens. Among the 134 trials, 21 (16%) did not mention renal or general organ function in eligibility criteria; 16 (12%) mentioned renal or organ function in inclusion/exclusion criteria but did not specify an eGFR cutoff value; and 15 (11%) specified a serum Cr cutoff value without a specific eGFR cutoff value. Among 82 (61%) studies specifying eGFR cutoffs: 18 required eGFR ≥60 ml/min and 64 specified cutoffs between 30 ml/min to 50 ml/min or greater (table 1). We found an association between studies requiring an eGFR of 60 ml/min or greater and studies investigating cell therapy or transplant-based regimens (p<0.01). We did not find associations between eGFR cutoff requirements and trials based on disease status (relapsed/refractory AML versus other AML states, p=0.24). We next evaluated methodologies reported in eligibility criteria to estimate renal function. Among the 113 trials commenting on renal function criteria: 15 (13%) did not report either an eGFR cutoff or methodology of determination; 67 (59%) specified eGFR cutoffs without specifying the methodology of determination and/or provided serum Cr-based cutoffs; and 31 (27%) specified either a method of direct measurement or equations to estimate eGFR (including CG-based in 25, CKD-EPI in 1, MDRD-based in 3 and radioisotope methodology in 1) (table 1). No trials mentioned cystatin-C as part of renal function determination. Conclusion In contemporary phase II/III clinical trials for adult patients with AML, we identified that while most trials include renal function in inclusion/exclusion criteria, there was tremendous heterogeneity in methodologies specified for determination of renal function which may make efficacy and toxicity comparisons challenging and impact patient eligibility across institutions. Several trials specified serum Cr cutoffs which can be impacted by factors besides renal function such as age or muscle mass. This study was limited by what trial criteria were publicly available and additional information specifying methodologies to calculate renal function may be available in full trial protocols. Further research is needed to determine the most suitable method for assessing eGFR in patients with AML to optimize treatment exposures for patients and to help reduce confounding of trial results or trial eligibility by institutional practice variation.

Multiple CD19-directed therapies are available for the treatment of relapsed and refractory (R/R) DLBCL, including CAR T cells, tafasitamab/lenalidomide (TL), and loncastuximab tesirine (lonca-T) (Sermer D., Blood Rev 2023). However, there are few data available on the efficacy of sequential CD19-directed therapies. We performed a retrospective study evaluating all patients with R/R LBCL treated consecutively with TL at 11 institutions from 8/2020 to 8/2022, identifying 178 patients. In this study, we specifically evaluate outcomes in patients who received anti-CD19 therapy prior to or after TL. The primary outcome of interest was progression-free survival (PFS) in TL recipients who previously received CD19-directed CAR T therapy, with overall response rate (ORR), complete response rate (CRR), and overall survival (OS) also evaluated. Outcomes of lonca-T and CAR T after TL therapy were evaluated. Data on CD19 expression was assessed on biopsies after TL; this was determined locally using flow cytometry or immunohistochemistry per institutional protocols. In subgroup analyses, ORR and CRR were compared using Fisher's exact test. PFS and OS were estimated using the Kaplan-Meier method, with differences tested using the log-rank test. Of 178 patients who received TL, 52 had received prior anti-CD19 CAR T therapy, at a median of 7.4 months before TL (Table 1). Median PFS was lower in prior CAR T recipients (1.6 mo, 95% CI 1.1-2.5) vs no prior CAR T (2.1 mo, 95% CI 1.8-3.2), with hazard ratio 1.46 (p = 0.04; Figure 1). Best ORR was lower in patients with prior CAR T exposure (15% vs 33%, p = 0.02) though CRR was not significantly different (15% vs 18%, p = 1.0). We specifically evaluated TL outcomes based on prior response to CAR T, given the previously identified association of refractory disease with poor TL outcomes (Qualls D et al., presented at ASH 2022). Of 19 patients who had progressive disease more than 6 months after CAR T, ORR and CRR to TL therapy were 31.6% (all responses were CRs); in 33 patients with refractory disease (less than CR or relapse within 6 months) after CAR T, ORR and CRR to TL were significantly lower, at 6.1% (p = 0.04). Median PFS after TL was shorter in CAR-refractory than CAR-relapsed disease, at 1.4 vs 3.5 months (HR 2.2, p = 0.02). Of 88 patients who received therapy after TL, 20 (23%) received lonca-T and 14 (16%) received CAR T. Data on survival and initiation of new treatment after CAR T or lonca-T was available, but response and progression data after CAR T or lonca-T therapy is pending. These data allowed an estimate of OS and event-free survival (EFS), defined as survival without initiation of a new treatment after CAR T or lonca-T. With a median 3.9 months' follow-up, median EFS after CAR T administration was 3.7 months (95% CI, 0.9 - 6.5), and median OS was 8.1 months (95% CI, 0.6 - 15.7). In patients who received lonca-T, with median 2.7 months' follow-up from initiation, median EFS was 2.8 months (95% CI, 0.9 - 4.7), and median OS was 3.5 months (95% CI, 1.2 - 5.7). When comparing CAR T and lonca-T to polatuzumab vedotin- or chemotherapy-based therapies after TL, there were no significant differences in OS or EFS. In patients who discontinued TL, 24 had a biopsy a median of 18 days after the last dose of tafasitamab. Of these, 13 (54%) were CD19-positive and 11 (46%) were CD19-negative. All samples obtained more than 21 days after tafasitamab (6/6) were CD19-positive, while 46% obtained within 21 days were CD19-positive. These time-dependent findings may reflect “epitope masking” by tafasitamab rather than CD19 loss, reflecting other recent reports (Fitzgerald K., Leuk Lym 2022; Duell J., Leuk Lym 2022). In patients receiving TL after CAR T, responses and PFS were lower than in patients without prior CAR T therapy; however, with disease that was not refractory to prior CAR T, results were similar to patients who had not had prior CAR T. This may reflect that refractory disease, regardless of prior therapy, is associated with shorter PFS, though the possibility of CD19-mediated mechanisms of resistance remains. Modest EFS and OS were seen in patients receiving CAR T therapy or lonca-T after already receiving TL, though this may also reflect the high-risk disease characteristics of this cohort. Overall, in a high-risk, real-world cohort of TL-treated patients, these data suggest that some patients may benefit from subsequent CD19-directed therapies, but better therapeutic options are needed.

ABSTRACT:In this real-world evaluation of tafasitamab-lenalidomide (TL) in relapsed or refractory LBCL, patients receiving TL had higher rates of comorbidities and high-risk disease characteristics, and substantially lower progression-free survival and overall survival, compared with the L-MIND registration clinical trial for TL.

Background: Central nervous system (CNS) relapse (CNSr) in patients with aggressive non-Hodgkin lymphoma is uncommon but carries a high morbidity and mortality. Data in T- and NK-cell lymphomas (TCL) are sparse, though the few small case series describing CNSr in TCL report an incidence up to 9% with median overall survival (mOS) less than 3 months (mo). We previously reported a large (n=75) case series of CNSr in TCL (Abstract #1382, ASH 2021). Herein, we developed a prediction score for CNSr in newly diagnosed patients with TCL. Methods: We retrospectively collected clinicopathologic and treatment data from 19 US academic centers from patients diagnosed with TCL between 1/1/09-1/1/19 who experienced CNSr (CNSr cohort). We combined these data with that from patients diagnosed with TCL without CNSr from a single institution (Penn cohort) to create a training set (T-set) enriched for patients with CNSr. Patients with leukemic or cutaneous TCL subtypes were excluded. We applied a LASSO Cox proportional hazards model to the T-set to create a predictive score for factors associated with CNSr and validated this score in an independent population-based validation set (V-set) from the Swedish Lymphoma Registry, which was not enriched for CNSr. Results: We evaluated data from 91 and 135 patients in the CNSr and Penn cohorts, respectively, which were combined to form the T-set and compared them to the V-set (n=745). Median age in the T-set was lower than in the V-set (60 vs 67; p<0.001). The most common TCL subtypes in the T-set and V-set were peripheral TCL, not otherwise specified (PTCL, NOS; 23% vs 34%, respectively; p=0.002), TCL with T-follicular helper phenotype (23% vs 14%, respectively; p=0.001), and ALK-negative anaplastic large cell lymphoma (18% vs 15%, respectively; p=0.352). CNS prophylaxis, defined as intrathecal therapy or high-dose methotrexate, was more common in the V-set than in the T-set (15% vs 9%, respectively; p=0.012). Rates of stem cell transplantation (SCT) as consolidation in first remission (CR1) did not differ significantly between the T-set and V-set (p=0.140). The median follow-up time was 37.6 mo in the T-set and 95.9 mo in the V-set. The median progression free survival (mPFS) and mOS after diagnosis were 9.8 mo and 33.2 mo in the T-set, respectively, and 8.5 mo and 17.9 mo in the V set, respectively. Within the T-set, mPFS and mOS were shorter for patients in the CNSr cohort vs the Penn cohort (6.0 mo vs 37.1 mo [p<0.001] and 17.3 mo vs 78.1 mo [p<0.001], respectively). In univariate analysis of the CNSr-enriched T-set, CNS prophylaxis, SCT in CR1, or frontline etoposide did not significantly decrease the risk of CNSr. Using several clinicopathologic characteristics, we fitted a LASSO Cox proportional hazards model to the T-set, which selected histology, LDH, stage, B-symptoms, and ≥2 sites of extranodal involvement (ENI) for a weighted risk score of CNSr (Fig. 1A). Variables most strongly associated with CNSr were PTCL, NOS (HR 2.64), enteropathy-associated TCL (HR 2.92), and ≥2 sites of ENI (HR 2.66). We stratified the T-set into tertiles based on weighted scores (x), which was low-risk if x≤0.14, intermediate-risk if 0.141.11. Cumulative incidence of CNSr in each respective group was 19.6%, 55.1%, and 83.7%. When the same cutoffs were applied to weighted scores in the V-set, we confirmed separation into distinct low-, intermediate-, and high-risk groups with cumulative incidences of CNSr of 1.9%, 4.9%, and 7.3%, respectively (Fig. 1B). As the V-set population was not enriched for CNSr, the relative incidence is expectedly lower compared to the T-set. Compared to the low-risk group in the V-set, risk of CNSr was significantly increased in the high-risk group (HR 5.72, p=0.002) and increased with a trend toward significance in the intermediate-risk group (HR 3.35, p=0.057). Conclusions: In a cohort of patients with TCL enriched for CNSr, we were able to characterize several risk factors associated with CNSr and developed a predictive CNSr in T-cell lymphoma Index (CITI) that could stratify patients into 3 distinct risk groups. PTCL, NOS, EATL, and ≥2 sites of ENI had the highest impact on risk of CNSr. The CITI score was validated in an independent population-based cohort. Although CNSr was uncommon even in high-risk patients (7.3%), our data will inform clinical decision making and may allow for identification of patients with TCL at high-risk of CNSr in future studies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

In many cancers, including lymphoma, males have higher incidence and mortality than females. Emerging evidence demonstrates that one mechanism underlying this phenomenon is sex differences in metabolism, both with respect to tumor nutrient consumption and systemic alterations in metabolism, i.e., obesity. We wanted to determine if visceral fat and tumor glucose uptake with fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) could predict sex-dependent outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We conducted a retrospective analysis of 160 patients (84 males; 76 females) with DLBCL who had imaging at initial staging and after completion of therapy. CT-based relative visceral fat area (rVFA), PET-based SUVmax normalized to lean body mass (SULmax), and end-of-treatment FDG-PET 5PS score were calculated. Increased rVFA at initial staging was an independent predictor of poor OS only in females. At the end of therapy, increase in visceral fat was a significant predictor of poor survival only in females. Combining the change in rVFA and 5PS scores identified a subgroup of females with visceral fat gain and high 5PS with exceptionally poor outcomes. These data suggest that visceral fat and tumor FDG uptake can predict outcomes in DLBCL patients in a sex-specific fashion.

Abstract Introduction Central nervous system (CNS) relapse (CNSr) in patients with aggressive non-Hodgkin lymphoma (NHL) occurs uncommonly (estimated incidence 5%) but carries a high morbidity and mortality. Studies have identified risk factors for CNSr such as high tumor burden and extranodal (EN) disease. However, most focus on B-cell NHL with minimal data in T-cell lymphomas (TCL), which are significantly less common and more heterogenous. The few small series of CNSr in TCL report a median overall survival (OS) less than 3 months (mo) with incidence ranging from 2.6-9%. To better define CNSr in TCL, we performed a multi-institutional retrospective analysis of TCL patients with CNSr and herein describe clinicopathologic characteristics and treatment of CNSr. Methods We performed a retrospective observational study using data from 9 US academic centers with IRB approval at individual sites. We included adult patients diagnosed with a mature T-cell neoplasm as per the 2016 WHO classification between 1/1/2009-1/1/2019, who were found to have CNSr at any time after initial diagnosis, and collected patient, disease, and treatment characteristics at time of initial diagnosis as well as at CNSr. Patients with a diagnosis of a precursor T-cell malignancy or with CNS disease identified at initial TCL diagnosis (TCLd) and/or prior to first-line systemic treatment were excluded. Results In this analysis, we report the outcomes of 75 patients (male n=45, female n=30). At TCLd, the median age was 59 years (range 20-81), and 61% of patients (n=46) had an IPI score of at least 3, 92% (n=69) had EN involvement with 37% (n=28) involving at least 2 EN sites, and 59% (n=44) had BM involvement. The most common pathologic diagnoses were peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS; 24%, n=18), angioimmunoblastic T-cell lymphoma (AITL; 17%, n=13), adult T-cell leukemia/lymphoma (ATLL; 17%, n=13), and mycosis fungoides (MF; 12%, n=9) (Figure 1A). First-line systemic therapy for TCL included anthracyclines for 72% (n=54). Autologous and allogenic transplants were performed prior to CNSr in 12% (n=9) and 8% (n=6) of patients, respectively. Prior to CNSr, 48% (n=36) had non-CNS relapse. Some form of CNS prophylaxis was used during initial systemic lymphoma therapy in 24% of patients (n=18), predominantly intrathecal methotrexate (IT MTX; n=16). Median time from TCLd to CNSr was 8.5mo, though this was significantly longer in MF (46.8mo [range 17.5-187.7]) versus PTCL, NOS (7.6mo [range 1.1-58.4], P=0.0002), AITL (21.2mo [range 2.0-61.6, P=0.008), and ATLL (7.3mo [range 0.7-46.4], P=0.0005) (Figure 1B). CNSr developed within 6mo of TCLd in 31% of patients (n=23) and within 12mo in 57% (n=43). Symptoms related to CNSr occurred in 71% of patients (n=53). CNSr patterns were 61% leptomeningeal (n=46), 21% parenchymal (n=16), and 17% both (n=13) with no significant survival difference between leptomeningeal or parenchymal disease alone (HR 1.45, 95% CI 0.78-2.70, P=0.28). Concomitant systemic relapse was observed in 59% of patients (n=44). The most common CNS-directed therapy for CNSr was IT MTX (56%; n=42), though multiple different IT and/or systemic regimens were used. Patients received a median of 1 line of CNS-directed treatment (range 0-5). The overall response rate to initial CNS directed treatment was 32% (16% CR, 16% PR). Median follow up after CNSr was 40.7mo. At last follow up, 83% had died (n=62). Median OS after CNSr was 4.6mo (range 0.1-68.7) (Figure 1C). Those with ATLL had the shortest median OS after CNSr (2.7mo) versus 6.3mo in MF (HR 3.69, 95% CI 1.44-9.42, P=0.005), 6.5mo in AITL (HR 2.16, 95% CI 0.92-5.06, P=0.054), and 4.8mo in PTCL, NOS (HR 1.49, 95% CI 0.70-3.19, P=0.27) (Figure 1D). The most common cause of death was progressive lymphoma (77%; n=48). Conclusions This is to our knowledge the largest series of CNSr in TCL to date. Most CNSr occurred within 12mo, though CNSr occurred later in patients with MF. Although the prognosis after CNSr was generally poor, we found that median OS in CNSr was longer than previously reported, perhaps reflecting more effective treatments for CNS and systemic relapse, inclusion of MF, or lead time bias. Further analysis of the impact of different treatment strategies and outcomes in CNSr was limited by the small sample size and heterogeneity within our cohort, and future analyses in a larger cohort should focus on factors associated with outcomes. Figure 1 Figure 1. Disclosures Horwitz: ADC Therapeutics, Affimed, Aileron, Celgene, Daiichi Sankyo, Forty Seven, Inc., Kyowa Hakko Kirin, Millennium /Takeda, Seattle Genetics, Trillium Therapeutics, and Verastem/SecuraBio.: Consultancy, Research Funding; Affimed: Research Funding; Aileron: Research Funding; Acrotech Biopharma, Affimed, ADC Therapeutics, Astex, Merck, Portola Pharma, C4 Therapeutics, Celgene, Janssen, Kura Oncology, Kyowa Hakko Kirin, Myeloid Therapeutics, ONO Pharmaceuticals, Seattle Genetics, Shoreline Biosciences, Inc, Takeda, Trillium Th: Consultancy; Celgene: Research Funding; C4 Therapeutics: Consultancy; Crispr Therapeutics: Research Funding; Daiichi Sankyo: Research Funding; Forty Seven, Inc.: Research Funding; Kura Oncology: Consultancy; Kyowa Hakko Kirin: Consultancy, Research Funding; Millennium/Takeda: Research Funding; Myeloid Therapeutics: Consultancy; ONO Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy, Research Funding; Secura Bio: Consultancy; Shoreline Biosciences, Inc.: Consultancy; Takeda: Consultancy; Trillium Therapeutics: Consultancy, Research Funding; Tubulis: Consultancy; Verastem/Securabio: Research Funding. Bennani: Purdue Pharma: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Vividion: Other: Advisory Board; Kymera: Other: Advisory Board; Verastem: Other: Advisory Board. Chavez: AstraZeneca: Research Funding; ADC Therapeutics: Consultancy, Research Funding; Merk: Research Funding; MorphoSys, AstraZeneca, BeiGene, Genentech, Kite, a Gilead Company, and Epizyme: Speakers Bureau; MorphoSys, Bayer, Karyopharm, Kite, a Gilead Company, Novartis, Janssen, AbbVie, TeneoBio, and Pfizer: Consultancy; BMS: Speakers Bureau. Sokol: Dren Bio: Membership on an entity's Board of Directors or advisory committees; Kyowa-Kirin: Membership on an entity's Board of Directors or advisory committees. Saeed: Nektar Therapeutics: Consultancy, Other: research investigator; MEI Pharma Inc: Consultancy, Other: investigator; Celgene Corporation: Consultancy, Other: investigator; MorphoSys AG: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb Company: Consultancy; sano-aventis U.S.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen Pharmaceutica Products, LP: Consultancy, Other: investigator; Kite Pharma: Consultancy, Other: investigator; Other-TG therapeutics: Consultancy, Other: investigator; Other-Epizyme, Inc.: Consultancy; Other-Secura Bio, Inc.: Consultancy; Seattle Genetics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mehta-Shah: Kiowa Hakko Kirin: Consultancy; C4 Therapeutics: Consultancy; Verastem: Research Funding; Karyopharm: Consultancy; Ono Pharmaceuticals: Consultancy; Secura Bio: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; AstraZeneca: Research Funding; Bristol Myers Squibb: Research Funding; Celgene: Research Funding; Innate Pharmaceuticals: Research Funding; Roche/Genentech: Research Funding; Corvus Pharmaceuticals: Research Funding. Olszewski: TG Therapeutics: Research Funding; PrecisionBio: Research Funding; Celldex Therapeutics: Research Funding; Acrotech Pharma: Research Funding; Genentech, Inc.: Research Funding; Genmab: Research Funding. Allen: Epizyme: Consultancy; MorphoSys: Consultancy; ADC Therapeutics: Consultancy; Secure Bio: Consultancy; Kyowa Kirin: Consultancy. Gerson: Abbvie: Consultancy; Kite: Consultancy; TG Therapeutics: Consultancy; Pharmacyclics: Consultancy. Landsburg: Triphase: Research Funding; Takeda: Research Funding; Curis: Research Funding; ADCT: 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; Incyte: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schuster: Adaptive Biotechnologies: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Genentech/Roche: Consultancy, Research Funding; Tessa Theraputics: Consultancy; Juno Theraputics: Consultancy, Research Funding; Loxo Oncology: Consultancy; BeiGene: Consultancy; Alimera Sciences: Consultancy; Acerta Pharma/AstraZeneca: Consultancy; Abbvie: Consultancy, Research Funding; Nordic Nanovector: Consultancy; Novartis: Consultancy, Honoraria, Patents & Royalties, Research Funding; Incyte: Research Funding; TG Theraputics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Svoboda: Atara: Consultancy; Adaptive: Consultancy, Research Funding; Astra Zeneca: Consultancy, Research Funding; Imbrium: Consultancy; Pharmacyclics: Consultancy, Research Funding; Genmab: Consultancy; Merck: Research Funding; Incyte: Research Funding; BMS: Consultancy, Research Funding; TG: Research Funding; Seattle Genetics: Consultancy, Research Funding. Barta: Kyowa Kirin: Honoraria; Acrotech: Honoraria; Daiichi Sankyo: Honoraria; Seagen: Honoraria.

Background: High-dose methotrexate (HD-MTX) with leucovorin rescue is frequently used in the treatment of various lymphomas, breast cancer, and sarcomas and remains an important therapy for lymphoma with CNS involvement. Despite its efficacy, HD-MTX can carry considerable toxicity which can lead to prolonged hospital stays, invasive therapies, and delays/discontinuation of a potentially curative treatments. Previous studies have shown that advanced age, male sex, use of proton pump inhibitors and impaired creatinine clearance are associated with higher rates of MTX toxicity (May et al 2014). However, the impact of body mass index (BMI) on the risk of toxicity with HD-MTX has not previously been reported. We performed a retrospective analysis of all patients at Washington University in St. Louis who were treated with HD-MTX to evaluate the relationship between BMI and risk of toxicity. Methods: Consecutively treated adult patients at Washington University in St. Louis who were treated with HD-MTX (>1,000mg/m2) for any malignancy (excluding leukemia) from 2005-2011 were identified via our pharmacy database. Baseline patient data was collected via retrospective review of the medical record including age, sex, diagnosis, methotrexate dose received, baseline renal and liver function tests, evidence for HD-MTX toxicity, concomitant medications. HD-MTX toxicity was defined as by delayed methotrexate clearance, acute kidney injury, liver function abnormalities, mucositis, or acute kidney injury, disease status and survival. Delayed methotrexate clearance was defined as serum methotrexate level of greater than 15umol/L at 24 hours, greater than 1.5umol/L at 48 hours, or greater than 0.15umol/L at 72 hours based on prior studies (May et al 2014). Results: 147 patients were included who received a total of 496 cycles of methotrexate (58 CNS lymphoma, 26 DLBCL, 11 T-cell lymphoma, 12 Burkitt's lymphoma, 2 mantle cell lymphoma, 27 sarcoma, 10 breast cancer, 1 other). 2 patients with B-ALL were excluded. The median age was 50 years (range 19-80) with 14 patients who were ≥70 years. Patients each received a median of 2.5 cycles of HD-MTX (range 1-12) at doses of ≤3.5g/m2 (n=248) and >3.5g/m2 (n=248). The total incidence of HD-MTX toxicity in this cohort was 52.4% (260/496 administrations) and did not differ between those who received doses ≤3.5g/m2 (n=248) or >3.5g/m2 (n=248) (OR: 0.875, 95% CI: 0.61-1.25). Median OS was not impacted by presence or absence of MTX toxicity (66 mo vs 84 mo p=0.78). Patients who experienced toxicity had longer clearance than those who did not (5.8 days vs 3.1 days, p=<0.001). Use of proton pump inhibitors was associated with a higher risk of MTX toxicity (OR 1.8, 95% CI: 1.25-2.6). Use of concomitant antibiotics (OR 1.024, 95% CI: 067-1.6) and age >70 (OR 0.64, 95% CI: 0.185-2.2) were not independent risk factors for HD-MTX toxicity. The median BMI of patients was 25.3 (range 14.7-44.9). 6.1%, 40.8%, 27.2%, and 25.9% had BMIs of underweight (<18.5), normal (18.5-25), overweight (25-30), obese (>30) respectively. We used the restricted cubic spline smoothing method to model the functional form of the association between BMI and MTX toxicity. We found that there is no significant association between BMI and MTX toxicity (p=0.898). The 95% confidence bands contain a flat line such that there is no associated change in the probability of having a MTX toxicity event for any given change in BMI. When comparing patients who had normal BMI or underweight compared to those who had BMIs above normal, there was no significant difference in the rate of methotrexate toxicity. Conclusions: In this cohort of patients treated with HD-MTX, we found that BMI is not a risk factor in the development of toxicity. Our analysis also suggests that a single incidence of HD-MTX toxicity does not significantly impact patient survival, which is similar with previous findings (May, Leukemia & Lymphoma 2013). Determining potential risk factors for HD-MTX toxicity will allow clinicians to be better prepared to manage complications and tailor treatment regimens based on individual patient characteristics. Disclosures Mehta-Shah: Genetech: Research Funding; Karyopharm Therapeutics: Consultancy; Bristol Myers-Squibb: Research Funding; C4 Therapeutics: Consultancy; Celgene: Research Funding; Innate Pharmaceuticals: Research Funding; Kyowa Kirin: Consultancy; Verastem: Research Funding.

Background: Hodgkin lymphoma (HL) is a rare lymphoma that often affects young people, with a median age of diagnosis of 39. While the 5-year PFS rate for patients after front-line chemotherapy is 94.6%, those who relapse have a 50% to 60% rate of cure (Alinari and Blum, Blood 2016). We recently discovered that increased abdominal visceral fat normalized to subcutaneous fat (rVFA) is associated with poorer outcomes in females, but not males, with diffuse large B-cell lymphoma (DLBCL). (Teja e tal ASH 2018) Interestingly, increased body mass index overall has been found to be associated with favorable prognosis in DLBCL (Carson et al., Journal of Clinical Oncology 2012). The correlation between these findings and elevated fasting blood glucose further supports a relationship between metabolism and prognosis in lymphoma. Since HL patients are generally younger and healthier than DLBCL patients at diagnosis, we sought to determine if rVFA and other metabolic markers had similar prognostic value in relapsed/refractory HL patients. Methods: We conducted a retrospective study of 95 consecutively treated relapsed/refractory HL patients treated at Washington University in St. Louis, who presented with first relapse between 2004 and 2018. We recorded baseline clinical risk factors such as sex, age, BMI, IPI, diabetes status, extranodal sites at relapse, and duration of response to initial therapy. Treatment and response to therapy by PET/CT were also recorded. In 80 patients, pre-treatment CT images were available to determine areas of subcutaneous and visceral fat at the level of the umbilicus. These values were normalized to total fat to calculate rVFA (Nguyen Radiology, 2018). Thresholds for risk stratification and sex differences were obtained using Cutoff Finder (Budczies PLOS One 2012). Data was analyzed with SPSS. Results: Ninety-five patients were eligible for analysis (54M, 41F) and 50 (53%) had refractory disease to initial therapy. (Table 1) The overall response rate to salvage therapy was 77%. There was no significant difference in overall survival (OS) (p = 0.55) or progression-free survival (PFS) (p = 0.49) between males and females in our study. Patients with higher BMI at diagnosis had a trend towards inferior OS and PFS after relapse. Those with BMI<25 had a 5-year PFS and OS of 71% and 90% compared to the BMI ≥25 group with PFS and OS of 58% and 84% respectively (p=0.09). The five patients with type II diabetes had a significantly lower 5-year OS rate of 40% compared to 90% for non-diabetic patients (p<0.001). (Figure 1) Eighty patients had CT images available for rVFA analysis (45M, 35F). Both males and females had a median rVFA of 28%. Patients with rVFA greater than 34% had inferior OS after relapse (HR= 8.85, 95% CI: 2.38-32.93, p<0.001). When controlled for sex, rVFA threshold values above 36% and 27% predicted worse outcomes for males and females respectively (HR=2x109, 95% CI: 0- Inf, p=0.0048 for 0.36 threshold; HR= 8.86, 95% CI: 1.7- 46.16, p=0.0018 for 0.27 threshold). (Figure 1) Conclusion: In this cohort, elevated rVFA was associated with inferior OS in all patients with relapsed/refractory HL, which is consistent with our findings in DLBCL as well as other solid tumors. Our results also suggest an association between diabetes and HL prognosis. Given the association of increased BMI, rVFA, diabetes and poorer outcomes in our cohort, there may be an association between a patient's metabolism and prognosis in Hodgkin lymphoma. Further studies should be conducted to examine the relationship between metabolism and cancer prognosis in a larger cohort. Disclosures Bartlett: Seattle Genetics: Consultancy, Research Funding; Immune Design: Research Funding; Forty Seven: Research Funding; Millennium: Research Funding; Merck: Research Funding; BTG: Consultancy; Roche/Genentech: Consultancy, Research Funding; BMS/Celgene: Research Funding; Pharmacyclics: Research Funding; Janssen: Research Funding; Acerta: Consultancy; Kite, a Gilead Company: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Autolus: Research Funding; ADC Therapeutics: Consultancy; Affimed Therapeutics: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Ippolito:Vital Images, Inc: Research Funding. Mehta-Shah:Karyopharm Therapeutics: Consultancy; C4 Therapeutics: Consultancy; Verastem: Research Funding; Celgene: Research Funding; Genetech/Roche: Research Funding; Corvus: Research Funding; Bristol Myers-Squibb: Research Funding; Kyowa Hakko Kirin: Consultancy; Innate Pharmaceuticals: Research Funding.