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Background: Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary refractory or eventually relapse, and their prognosis is very poor. Recent progress by large scale genomic and transcriptomic profiling of DLBCL patients has resulted in a deep understanding of disease drivers in newly diagnosed DLBCL (ndDLBCL). However, previous genomic studies of relapsed and/or refractory DLBCL (rrDLBCL) are limited by small sample sizes and much less is known about the genomic landscape or the changes in clonal populations that occur within a patient undergoing R-CHOP therapy. Analysis of a large cohort of rrDLBCL is needed to uncover the importance of known drivers, define clonal shifts that occur in relapsing patients, and discover novel mutations that impact resistance to therapy. Methods: DNA sequencing (whole exome or genome sequencing) was performed on tumor and matching germline samples from two rrDLBCL patient cohorts (N=143). The first rrDLBCL cohort consisted of 68 formalin fixed paraffin embedded (FFPE) tumor samples with matched germline obtained from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER); including 18 diagnostic/relapse paired samples. The second cohort consisted of 75 FFPE samples from 2 rrDLBCL clinical trials CC-122-ST-001 and CC-122-DLBCL-001 (NCT01421524 and NCT02031419). In addition, a ndDLBCL cohort (n=364) consisting of MER front line samples were also analyzed. CNV and SV calling was performed with Sclust and Manta, respectively. Mutational drivers were identified with cDriver. Cosmic mutational signatures were calculated for all samples. Results: For comparison of overall mutation rates between the ndDLBCL and rrDLBCL we analyzed the frequency of mutations in known driver genes [n=211, Chapuy et al, 2018; Schmitz et al, 2018; Reddy et al 2017]. Of these, the top 5 most mutated genes found in the relapse cohort were KMT2D (34.3%), IGLL5 (25.9%), CREBBP (22.4%), TP53 (21.0%), and HIST1H1E (19.6%) all of which were previously reported as mutated genes in rrDLBCL [Morin et al. 2016; Park et al. 2016;Greenawalt et al. 2017]. None of these frequencies changed significantly from the MER ndDLBCL cohort. However, frequencies of 2 genes NCOR1 and ACTB were increased significantly from 3.3% to 13.3% (p= 5.85 x 10-05) and 0.5% to 6.3% (p=2.56 x 10-04), respectively, in the rrDLBCL cohorts. Analysis of the previously reported driver mutations in 18 paired samples revealed a significant increase in the mean variant allele frequency (VAF) from diagnosis to relapse of mutations in BCL2 and BIRC6 from 18.5% to 28.8%(p=0.016) and 4.3% to 13.7%(p=0.011), respectively. In our analysis of novel drivers in rrDLBCL cohorts, 59 significant driver mutations (FDR 0.1) were identified, 23 were novel driver mutations while the remaining 36 have been previously reported as drivers of newly diagnosed DLBCL. These include novel mutations in genes such as IL4R (8.8%), CDK5R1 (6.7%), LAPTM5 (3.7%) and others whose function in lymphoma are unknown. In a secondary analysis, we performed mutational signature analysis for rrDLBCL and identified groups of patients with COSMIC mutational signatures that are associated with aging, defects in homologous end joining resulting in large insertions/deletions, and with defective mismatch repair often found in microsatellite unstable tumors. Together with the high prevalence of TP53 mutations, these results indicate genetic instability as a hallmark of relapse/refractory DLBCL. Analysis of CNV and SV will also be reported. Conclusion: Here we report to our knowledge the largest genomic dataset in relapsed and /or refractory DLBCL using tumor biopsies. Previously identified mutated genes in rrDLBCL were confirmed and novel drivers identified. Paired samples analysis reveals clonal shifts which occur during treatment and at time of relapse. Whether the novel driver mutations appeared as a function of selection of a rare clone over the course of therapy or a therapy induced mutation will be the focus of ongoing work. Novel variants including SNV, CNV and SV can potentially lead to new insights and deepen our understanding of the mechanisms of resistance in DLBCL. * Wenzl, Ortiz and Stong contributed equally **Novak and Gandhi contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Nikbakht:Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Bayer: Consultancy, Research Funding; Curis: Research Funding; Celgene: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties.

BACKGROUND Determination of blood donor hemoglobin (Hb) levels is a pre‐requisite to ensure donor safety and blood product quality. We aimed to identify Hb measurement practices across blood donation services and to what extent differences associate with low‐Hb deferral rates. METHODS An online survey was performed among Biomedical Excellence for Safer Transfusion (BEST) Collaborative members, extended with published data. Multivariable negative‐binomial regression models were built to estimate adjusted associations of minimum donation intervals, Hb cut‐offs (high, ≥13.5 g/dL in men or ≥ 12.5 g/dL in women, vs. lower values), iron monitoring (yes/no), providing or prescribing iron supplementation (yes/no), post‐versus pre‐donation Hb measurement and geographical location (Asian vs. rest), with low‐Hb deferral rates. RESULTS Data were included from 38 blood services. Low‐Hb deferral rates varied from 0.11% to 8.81% among men and 0.84% to 31.85% among women. Services with longer minimum donation intervals had significantly lower deferral rates among both women (rate ratio, RR 0.53, 95%CI 0.33‐0.84) and men (RR 0.53, 95%CI 0.31‐0.90). In women, iron supplementation was associated with lower Hb deferral rates (RR 0.47, 95%CI 0.23‐0.94). Finally, being located in Asia was associated with higher low‐Hb deferral rates; RR 9.10 (95%CI 3.89‐21.27) for women and 6.76 (95%CI 2.45‐18.68) for men. CONCLUSION Differences in Hb measurement and eligibility criteria, particularly longer donation intervals and iron supplementation in women, are associated with variations in low‐Hb deferral rates. These insights could help improve both blood donation service efficiency and donor care.

Diffuse large B-cell lymphoma is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding two biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies and replicated the subgroups with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4CRBN E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and need for a patient selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients demonstrated an enrichment in response rate and progression-free survival of 44% and 6.2 months versus 19% and 1.6 months for classifier-negative patients (HR=0.49 [0.280, 0.86]; P=.0096). The classifier was not prognostic for R-CHOP or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and non-tumor composition and has potential utility to enrich for clinical response to immunomodulatory agents including avadomide.

BACKGROUNDSingle antigen bead (SAB) assays are used to identify human leukocyte antigen (HLA) antibodies in patients with platelet refractoriness due to HLA Class I alloimmunization. Some laboratories use serum pretreatment regimens to eliminate interference from immunoglobulin M antibodies and complement. These modifications may contribute to interlaboratory variability, which is a recognized problem with the SAB assay.STUDY DESIGN AND METHODSFive patientsʼ sera were overnight shipped to 12 laboratories in the United States and internationally. Recipients used their labʼs SAB procedure to identify HLA Class I antibodies. The resultant mean fluorescence intensity (MFI) data were compared by instrumentation, bead lot, and pretreatment regimens. Laboratory‐specific cutoffs for positive antibodies were applied to the results.RESULTSInterlaboratory variability for MFI values appears to be associated with different pretreatment regimens. The coefficient of variation (CV) of MFI from samples pretreated with ethylenediaminetetraacetic acid, dithiothreitol, or heat inactivation (EDHI) were similar, ranging from 14% to 56% (mean, 22%). For samples with no pretreatment, the CVs were significantly higher than EDHI‐treated samples, ranging from 25% to 74% (mean, 39%; 95% confidence interval, 12.10‐21.90; p < 0.0001). An intralaboratory comparison of pretreatment regimens confirmed these findings. Some positive antibody specificities present in EDHI‐treated samples were negative in corresponding samples with no pretreatment when laboratory‐specific cutoffs for positive antibodies were applied.CONCLUSIONOur results show that greater interlaboratory precision can be achieved when samples are pretreated with EDHI as opposed to no pretreatment, likely because these pretreatments eliminate interference from inhibitors. Inhibitors may mask antibodies, leading to missed (or uncalled) specificities when no pretreatment is used.

Background The epidemiological transition of non-communicable diseases replacing infectious diseases as the main contributors to disease burden has been well documented in global health literature. Less focus, however, has been given to the relationship between sociodemographic changes and injury. The aim of this study was to examine the association between disability-adjusted life years (DALYs) from injury for 195 countries and territories at different levels along the development spectrum between 1990 and 2017 based on the Global Burden of Disease (GBD) 2017 estimates. Methods Injury mortality was estimated using the GBD mortality database, corrections for garbage coding and CODEm—the cause of death ensemble modelling tool. Morbidity estimation was based on surveys and inpatient and outpatient data sets for 30 cause-of-injury with 47 nature-of-injury categories each. The Socio-demographic Index (SDI) is a composite indicator that includes lagged income per capita, average educational attainment over age 15 years and total fertility rate. Results For many causes of injury, age-standardised DALY rates declined with increasing SDI, although road injury, interpersonal violence and self-harm did not follow this pattern. Particularly for self-harm opposing patterns were observed in regions with similar SDI levels. For road injuries, this effect was less pronounced. Conclusions The overall global pattern is that of declining injury burden with increasing SDI. However, not all injuries follow this pattern, which suggests multiple underlying mechanisms influencing injury DALYs. There is a need for a detailed understanding of these patterns to help to inform national and global efforts to address injury-related health outcomes across the development spectrum.

Intraductal carcinoma of the prostate (IDC-P) has been recently recognized by the World Health Organization classification of prostatic tumors as a distinct entity, most often occurring concurrently with invasive prostatic adenocarcinoma (PCa). Whether documented admixed with PCa or in its rare pure form, numerous studies associate this entity with clinical aggressiveness. Despite increasing clinical experience and requirement of IDC-P documentation in protocols for synoptic reporting, the specifics of its potential contribution to assessment of grade group (GG) and cancer quantitation of PCa in both needle biopsies (NBx) and radical prostatectomy (RP) specimens remain unclear. Moreover, there are no standard guidelines for incorporating basal cell marker immunohistochemistry (IHC) in the diagnosis of IDC-P, either alone or as part of a cocktail with AMACR/racemase. An online survey containing 26 questions regarding diagnosis, reporting practices, and IHC resource utilization, focusing on IDC-P, was undertaken by 42 genitourinary subspecialists from 9 countries. The degree of agreement or disagreement regarding approaches to individual questions was classified as significant majority (>75%), majority (51% to 75%), minority (26% to 50%) and significant minority (≤25%). IDC-P with or without invasive cancer is considered a contraindication for active surveillance by the significant majority (95%) of respondents, although a majority (66%) also agreed that the clinical significance/behavior of IDC-P on NBx or RP with PCa required further study. The majority do not upgrade PCa based on comedonecrosis seen only in the intraductal component in NBx (62%) or RP (69%) specimens. Similarly, recognizable IDC-P with GG1 PCa was not a factor in upgrading in NBx (78%) or RP (71%) specimens. The majority (60%) of respondents include readily recognizable IDC-P in assessment of linear extent of PCa at NBx. A significant majority (78%) would use IHC to confirm or exclude intraductal carcinoma if other biopsies showed no PCa, while 60% would use it to confirm IDC-P with invasive PCa in NBx if it would change the overall GG assignment. Nearly half (48%, a minority) would use IHC to confirm IDC-P for accurate Gleason pattern 4 quantitation. A majority (57%) report the percentage of IDC-P when present, in RP specimens. When obvious Gleason pattern 4 or 5 PCa is present in RP or NBx, IHC is rarely to almost never used to confirm the presence of IDC-P by the significant majority (88% and 90%, respectively). Most genitourinary pathologists consider IDC-P to be an adverse prognostic feature independent of the PCa grade, although recommendations for standardization are needed to guide reporting of IDC-P vis a vis tumor quantitation and final GG assessment. The use of IHC varies widely and is performed for a multitude of indications, although it is used most frequently in scenarios where confirmation of IDC-P would impact the GG assigned. Further study and best practices recommendations are needed to provide guidance with regards to the most appropriate indications for IHC use in scenarios regarding IDC-P.

Abstract Background. Recent clinical trials based on immunotherapies targeting the PD-1/PD-L1 pathway have shown striking durable responses in a subset of patients with solid cancers. The Programmed death 1 (PD-1) protein is a key immune checkpoint inhibitor expressed by activated T cells. Its ligand, PD-L1, was reported highly expressed by tumor cells in some diffuse large B-cell lymphomas (DLBCL) of non-Germinal Center phenotype. We recently reported on the French multicenter GOELAMS075 trial that pre-treatment soluble PD-L1 (sPD-L1) in plasma was elevated in DLBCL patients compared to controls, and that elevated sPD-L1 was associated with inferior overall survival (OS), independent of the International Prognostic Index (IPI) and other clinical factors (Rossille et al., Leukemia 2014). Here, we replicate and extend these findings in two independent studies from Australia and the US. Methods. The protein expression of sPD-L1 was evaluated using a commercial ELISA kit. The French discovery cohort consisted of 288 adults with newly diagnosed aggressive DLBCL, age 18 to 60 years, and treated with R-CHOP or high dose chemotherapy plus rituximab followed by autologous stem cell support (clinicaltrials.gov: NCT00561379); there were also 60 controls. The Australian study consisted of 51 DLBCL patients age 18 to 71 years, all stages, treated with R-CHOP14, along with 57 controls. The US study was an observational cohort from the Iowa/Mayo Lymphoma SPORE and consisted of 225 DLBCLs, age 19 to 92 years, all stages, treated with immunochemotherapy, along with 98 controls. Plasma samples were collected pre-treatment using EDTA tubes for the Australian and the US cohorts, BDª P100 tubes for the French cohort. sPD-L1 expression was measured in Rennes, France (French & US samples) and in Brisbane, Australia (Australian samples).The Kaplan-Meier method and Cox regression were used to model the association of sPD-L1 with OS. The 95th percentile of the sPD-L1 levels in each matched control group was used as the cutoff point to define elevated sPD-L1 levels. Results. Replicating the French findings, sPD-L1 levels were significantly higher for DLBCL patients compared to controls in both the US (P<0.0001) and Australian (P<0.0001) studies (Figure 1). The odds ratios for elevated sPD-L1 among patients compared to controls were 10.7 (95% CI 3.3-22.0), 8.9 (95% CI 3.1-25.3) and 12.6 (95% CI 2.7-28.0) for the French, US and Australian studies, respectively. In the French cohort, elevated sPD-L1 levels were associated with inferior OS (Hazard Ratio (HR)=2.05; 95% CI 1.18-3.57), and this was replicated in the US cohort (HR=1.71; 95% CI 0.97-3.01) (Figure 2). Due to the similarity of the results, we next pooled the French and US cohorts to increase power for multivariate and subset analyses. The median age at diagnosis of the 513 patients was 54 years; 29% were more than 60 years old; 69% of the patients had stage III-IV disease; 56% were IPI intermediate-risk and 9% were IPI high-risk patients. The median follow-up was 48 months (range, 0.5-126) overall, and 66 months for the patients still alive; there were 102 deaths. Using a cutpoint of 1729 pg/mL from the combined studies, patients with elevated sPD-L1 had inferior OS compared to patients without elevated sPD-L1, with 3-year OS rates estimated at 78% (95% CI 71.8-84.9) vs. 87.8% (95%CI 84.5-91.4) (P<0.01). In multivariate analysis, elevated sPD-L1 (HR=1.75; 95%CI 1.16-2.64), age (HR=1.03; 95%CI 1.02-1.05), high-risk IPI score (HR=3.71; 95%CI 1.91-7.21) and ALC ² 1 G/L (HR=2.19; 95%CI 1.44-3.33) were all significant at P<0.01. In analyses stratified by IPI, the 3-year OS rates among high-risk patients (IPI 4-5) was 40.9% (95% CI 23.6-71.0) for elevated sPD-L1 vs. 82.1% (95% CI 69.1-97.6) for not elevated sPD-L1 (P<0.01). There was no association of sPD-L1 with 3-year OS among low risk (IPI 0-1) or intermediate-risk (IPI 2-3) patients. Conclusion. We confirmed that sPD-L1 levels are higher in pre-treatment plasma samples of newly diagnosed DLBCL patients compared to controls, and that elevated levels of sPD-L1 are associated with inferior OS in DLBCL patients treated with immunochemotherapy, and this is independent of IPI and other clinical factors. Among high risk patients, we identified a subgroup with less than 50% survival. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures No relevant conflicts of interest to declare.

BACKGROUND: Axi-cel is an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy that is approved for treatment of relapsed/refractory (R/R) large B-cell lymphoma and is associated with high response rates and durable remissions. Recent data show that axi-cel is effective across various adverse prognostic features, namely cell of origin, disease bulkiness, and extranodal disease, among others. Hypoalbuminemia is a known adverse prognostic factor in lymphomas. It is unknown if axi-cel overcomes the adverse prognostic feature of hypoalbuminemia in R/R large B-cell or transformed follicular lymphoma. METHODS: We conducted a retrospective analysis of patients treated with axi-cel across three Mayo Clinic campuses (Rochester, Jacksonville, and Phoenix) from 06/01/2018 until 04/01/2019. The primary objective of this analysis was to assess the impact of hypoalbuminemia (defined at day 0, prior to infusion) on outcome after axi-cel therapy. RESULTS: A total of 50 (male=37, 74%) patients (pts), median age of 53 (26-67) years received axi-cel. The median number of prior lines of therapy was 3 (2-8) (Table 1). Two pts had no available serum albumin levels at time of axi-cel infusion. Seven (15%) of 48 pts had serum albumin levels lower than 3.5 g/dL (median= 3.3 g/dL (range 2.6-3.4)) and the median follow up of survivors was 7.6 (1.9-14.3) months. The best overall response rate (ORR) and complete remission (CR) rates in these pts were 57% and 57%, respectively. One (14%) patient had stable disease and 2 (29%) had disease progression. The median overall survival (OS) for pts with hypoalbuminemia was not reached. On the other hand, 41 (85%) pts had a normal serum albumin level (median=4.0 (range 3.5-5.1) g/dL) and the median follow up for survivors was 6.3 months. The best objective response rate (ORR) and complete remission (CR) rates in these pts were 82% and 44%, respectively. The median OS for pts with normal serum albumin was 14 (95%CI=6.3-29.6) months. There was no significant difference at 6-months and 1-year OS between pts with hypoalbuminemia vs. those with normal baseline serum albumin levels [6-month=100% vs. 79%(95%CI=64-93%); 1-year (100% vs. 54% (95%CI=26-82%), p=0.17] (Figure 1). All grades cytokine release syndrome (CRS) was diagnosed in all 7 pts with hypoalbuminemia (100%) and in 38 of 41 (92%) pts without hypoalbuminemia. There was no difference in the median duration of CRS between pts with or without hypoalbuminemia [6 (range 1-11) days vs 5 (range 1-19) days, p=0.89]. Neurotoxicity (all grades) was observed in 5 (71%) pts with hypoalbuminemia compared 26 (63%) with normal albumin levels. There was no statistically significant difference in median duration of neurotoxicity between pts with hypoalbuminemia and those with normal baseline albumin levels [9 (range 1-10) days vs. 3 (range 0-25) days, p= 0.72]. CONCLUSIONS: Hypoalbuminemia does not have a significant impact on the outcomes of axi-cel therapy, including the incidence of CRS or neurotoxicity. These results need to be validated in a large collaborative multicenter study. Further investigation is needed to assess the prognostic impact of severe hypoalbuminemia (<3g/dL) on axi-cel therapy. Disclosures Ansell: Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding. Bennani:Seattle Genetics: Other: Advisory board; Kite Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board. Paludo:Verily Life Sciences: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding. Tun:DTRM Biopharma: Research Funding; Mundi-pharma: Research Funding; BMS: Research Funding; Celgene: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding. Foran:Agios: Honoraria, Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

Background: Central nervous system (CNS) involvement by peripheral T cell lymphoma (PTCL) is a rare condition. Among primary CNS lymphomas, only 2% are secondary to PTCL, while the risk of CNS relapse in all cases of PTCL is estimated at 2% to 6%. Little is known about the presentation and outcomes of PTCL patients with CNS involvement given the rarity of this entity. In this study, we describe patient characteristics, histology, and clinical course of patients with CNS involvement by PTCL. Methods: The Mayo Clinic Lymphoma Database was used to identify PTCL patients with primary or secondary CNS involvement seen at our institution between 2000 and 2018. A total of 12 patients were identified and their medical records were reviewed for patient and disease characteristics, CNS-directed treatment modality, and outcomes. The Kaplan-Meier method was used for time-to-event analysis. Results: The median age at CNS diagnosis was 63 years (range 41 to 76) and 11 (93%) patients were male. The histological diagnoses were PTCL, NOS in 9 (75%) patients, enteropathy-associated T-cell lymphoma in 2 (17%) patients, and angioimmunoblastic T-cell lymphoma in 1 (8%) patient. Five patients presented with primary T-cell CNS lymphoma (all with a PTCL, NOS histology), while the remaining 7 (58%) patients also had systemic involvement. All patients presented with neurologic symptoms at the time of CNS involvement diagnosis including: focal motor deficits in 6 patients (unilateral upper extremity weakness, gait impairments, and hemiparesis), cognitive decline in 5 patients (memory impairments, reduced attention, and confusion), headache in 4 patients, and seizure in 3 patients. The CNS disease location included the brain parenchyma in 9 (75%) patients, leptomeninges in 1 (8%) patient, and lumbar plexus in 1 (8%) patient. One patient (8%) had positive CSF finding only without radiologic evidence of involvement. CSF analysis was performed in 11 patients. Elevated protein levels were noted in 3 (27%) patients, malignant cells in 2 (18%), and no clear abnormalities in the remaining 6 (55%) patients. Concomitant bone marrow involvement was seen in only 1 patient. Elevated LDH was seen in 2 patients. The a median LDH was 195 U/L (range 139 to 4,360) The most common CNS-directed therapies were: high-dose methotrexate (MTX)-based regimens in 8 (67%) patients, including high-dose MTX in combination with temozolomide (n=2), or cytarabine and thiotepa (n=2). Intrathecal MTX, temozolomide and dexamethasone, lenalidomide, high-dose steroids, and surgical resection were the treatment modality used for one patient each. At a median follow up of 18 months, eight (75%) out of 12 patients were not alive at the time of last follow up. The median overall survival (OS) from diagnosis was 16 months (95% CI: 2.8-173). The median progression free survival (PFS) from initiation of CNS-directed therapy was 9 months (95% CI: 1.6-33) (figure). Four patients had a PFS longer than 12 months. These 4 patients were treated with: temozolomide/dexamethasone, high-dose MTX, lenalidomide, and high-dose MTX followed by cytarabine/thiothepa. Conclusion: CNS involvement by T-cell lymphoma is a rare complication that carries a poor prognosis. Early onset of neurologic symptoms should trigger prompt investigation of CNS involvement. Despite the short OS and PFS, some patients may achieve a relatively longer disease free interval. Disclosures Bennani: Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Kite Pharma: Other: Advisory board. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Celgene: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Curis: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Ansell:Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding. Paludo:Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding.

Introduction: Proteasome inhibitors (PI), immunomodulatory agents (IMiDs) and the CD38 monoclonal antibody daratumumab (dara) have transformed the management of MM, yet eventual refractoriness to these agents seems inevitable. Relapsed and refractory MM (RRMM) which becomes triple class refractory (TCR, i.e. refractory to a PI, an IMiD and Dara) uncommonly responds to further lines of therapy and survival is dismal. Selinexor is a selective inhibitor of nuclear export compound targeting exportin 1 (XPO1) which is overexpressed in MM cells and essential for their survival. In the STORM study, selinexor in combination with low-dose dexamethasone (Sd) demonstrated promising efficacy in TCR, penta-exposed (TCR-PE, i.e. exposed to lenalidomide, pomalidomide, bortezomib, carfilzomib and dara) MM. Establishing the natural history for outcomes in the TCR-PE population can help provide context to understand the outcomes observed with Sd in STORM. In the retrospective MAMMOTH study, we reported the outcomes of patients with RRMM after they became refractory to dara, including a subset of patients who were TCR. We further analyzed the MAMMOTH dataset to generate a cohort of patients similar to patients in STORM in order to compare conventional care vs. Sd. Methods: We included all patients in STORM who received Sd as the first line therapy after they achieved TCR-PE status (n=64). We extracted from the MAMMOTH dataset all patients who were not exposed to Sd in a subsequent line of therapy, became TCR-PE and who received subsequent MM-directed therapy (n=128). Overall response rate (ORR) was evaluated according to IMWG criteria. Overall survival (OS) was calculated from the time of initiation of next line of therapy after TCR-PE status until death or last follow-up. We compared OS in STORM vs. MAMMOTH utilizing cox-regression analysis with adjustment for covariables potentially influencing the outcome. Results: Baseline patient characteristics and prior therapies are per table. The two cohorts were similar in terms of age, number of prior lines of therapy and presence of high-risk cytogenetic abnormalities. STORM patients had longer time between MM diagnosis and post TCR-PE therapy with a higher proportion of refractoriness to carfilzomib. Patients in STORM had ORR to Sd of 32.8% vs 25.0% for patients receiving conventional care in MAMMOTH (P=0.078). In direct comparison, patients in STORM had better OS than patients in MAMMOTH (median 10.4 vs. 6.9 months) (P=0.043, figure). In multivariate analysis, STORM patients had lower risk of death in comparison with MAMMOTH patients (aHR=0.55, 95%C.I. 0.35-0.86, P=0.009). Refractoriness to carfilzomib (aHR=2.20, 95%C.I. 1.16-4.15, P=0.015) and high-risk cytogenetics (aHR-1.66, 95% C.I. 1.13-2.42, P=0.009) were also associated with inferior OS. Conclusion: Despite inherent limitations in comparison of trial enrollees vs. real world patients, this analysis suggests improved OS with Sd vs conventional care in patients with TCR-PE RRMM. Prognosis for these patients remains poor and underscores the need for therapeutic advancements. Disclosures Costa: Janssen: Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Speakers Bureau; GSK: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy; Karyopharm: Consultancy; Fujimoto Pharmaceutical Corporation Japan: Other: Advisor; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Hari:Kite: Consultancy, Honoraria; Amgen: Research Funding; Spectrum: Consultancy, Research Funding; Sanofi: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Cell Vault: Equity Ownership; AbbVie: Consultancy, Honoraria. Kumar:Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. Tang:Karyopharm: Employment. Shah:Karyopharm Therapeutics Inc: Employment, Equity Ownership. Jagannath:BMS: Consultancy; Merck: Consultancy; Medicom: Speakers Bureau; Celgene: Consultancy; Novartis: Consultancy; Multiple Myeloma Research Foundation: Speakers Bureau. Chari:Oncoceutics: Research Funding; Novartis Pharmaceuticals: Research Funding; GlaxoSmithKline: Research Funding; Array Biopharma: Research Funding; Karyopharm: 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, Research Funding; Millennium/Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding. Shacham:Karyopharm Therapeutics Inc: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Ma:Karyopharm: Employment, Equity Ownership. Siegel:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol-Myers Squibb Company: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Biran:Amgen: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Bristol Meyers Squibb: Research Funding. Lonial:BMS: Consultancy; Genentech: Consultancy; GSK: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Amgen: Consultancy; Celgene Corporation: Consultancy, Research Funding; Karyopharm: Consultancy. Richardson:Amgen: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding. Kauffman:Karyopharm Therapeutics Inc: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Malek:Adaptive: Consultancy; Amgen: Speakers Bureau; Janssen: Speakers Bureau; Celgene: Consultancy; Takeda: Consultancy; Sanofi: Consultancy; Medpacto: Research Funding. Fiala:Incyte: Research Funding. Usmani:Amgen, Celgene, Janssen, Sanofi, Takeda: Speakers Bureau; Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, SkylineDX, Takeda: Other: Consultant/Advisor; Amgen Array Biopharma, Bristol-Myers Squibb, Celgene, Janssen, Merck, Pharmacyclics, Sanofi, Takeda: Other: Research Grant. Kang:Takeda Oncology: Consultancy; InCyte Corportation: Research Funding. Cornell:Takeda: Consultancy; KaryoPharm: Consultancy.