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.

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.

No abstract

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.

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.