Famà, Angelo

The World Health Organization Classification of Tumours of Haematopoietic and Lymphoid Tissues (WHO 2017) included updated criteria for diagnosis and classification of post-transplant lymphoproliferative disorders (PTLDs). This study evaluated the clinicopathologic spectrum using WHO 2017 criteria and adult PTLD patients’ outcomes over 30 years between 1987 and 2017 at Mayo Clinic (Rochester, MN). Patients were retrospectively reviewed for clinical features, outcomes, and diagnostic pathology material and classified based on WHO 2017 criteria. A total of 227 patients were diagnosed with PTLD, with a median time from transplant to PTLD of 45 months. PTLD occurred >1 year after transplant in 149 (66%) patients. Monomorphic PTLD was the most common subtype (173, 76%), with diffuse large B cell lymphoma as the commonest morphology (n = 137). Epstein-Barr virus was positive in 61% of total cases and 90% of PTLD that developed within 1 year from transplant. The median event-free survival (EFS) and overall survival for the entire cohort were 21 months (95% confidence interval [CI]: 9–35) and 82 months (95% CI: 39–115), respectively. The EFS or overall survival was not impacted by Epstein-Barr virus status but differed based on WHO subtypes and year of diagnosis. Management changed over time with increased use of rituximab or chemotherapy + immunosuppression reduction as initial therapy. When compared to the matched general population and de novo diffuse large B cell lymphoma, patients not achieving EFS 24 status (no progression/treatment or death within 24 mo of diagnosis) had a worse standardized mortality ratio 16.75 (95% CI: 13.91–20) versus SMR 1.72 (95% CI: 1.26–2.28) in those who achieved EFS24. Cause of death was mostly attributed to non-lymphoma–related causes in those achieving EFS 24.

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

8052 Background: A significant proportion of patients with FL experience an early relapse and a subsequent poor outcome. While several prognostic indices have been developed, none were designed to predict early failure. Recently, we established that lack of intrafollicular CD4+ T-cell expression predicted risk of early failure, and integrating this microenvironment biomarker with the Follicular Lymphoma International Prognostic Index, termed BioFLIPI, further improved identification of FL patients at risk of early failure ( Blood 2019;134(suppl1):121). However, the microenvironment may be influenced by the genetic composition of tumor. We investigated whether the CD4 biomarker and BioFLIPI were impacted by genetic features of the tumor as assessed by a 23-gene expression prognostic score ( Lancet Oncol 2018;19:549-61). Methods: Of the 186 cases with FL grade 1-3A treated with immunochemotherapy (IC) in our prior study, 152 had digital expression quantification of 23 selected genes (23-GEP score), which used RNA from formalin-fixed, paraffin-embedded samples. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS at 24 months. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals from logistic regression models. We also used Cox regression to assess associations with continuous EFS and overall survival (OS). Results: 28% of patients failed to achieve EFS24. Lack of CD4+ intrafollicular expression (38% of patients, OR = 2.33, p = 0.024) and high risk 23-GEP score (26% of patients, OR = 3.52, p = 0.001) each predicted early failure, and in a multivariable model that included FLIPI, both CD4+ (OR = 2.26, p = 0.046) and 23-GEP score (OR = 2.26, p = 0.0.057) remained predictors. Similarly, BioFLIPI modeled as a continuous score (1-4, OR per one point increase = 2.31, p < 0.001) predicted early failure, and the association remained (OR = 2.14, p < 0.001) when the high risk 23-GEP score (OR = 2.79, p = 0.013) was included in the model. When stratified on 23-GEP score, BioFLIPI was a stronger predictor of early failure in low risk (74%, OR = 2.51, p = 0.002) relative to high risk (26%, OR = 1.55, p = 0.27) patients. Similar patterns were observed for EFS and OS. Conclusions: CD4+ T-cell infiltrate and tumor gene expression appear to be independently predictive of early failure in newly diagnosed FL patients treated with IC. Future studies should integrate and validate these measures.

Introduction. The VES-13 is a simple, self-reported, function based tool originally developed to screen community-dwelling populations to identify persons age 65 years and older at risk of death and functional decline, including death in the next 12 months. The VES-13 items include patient's age, self-rated overall health status, functional limitations in physical activity, and functional disabilities in more complex activities of daily living. As part of the Lymphoma Epidemiology of Outcomes (LEO) cohort study, we collected self-reported VES-13 data at study enrollment on all participants regardless of age, and here we report on the prevalence of vulnerable status defined by the VES-13 and its association with 1-year mortality, overall, stratified at age 65 years, and in the subset of diffuse large B-cell lymphoma (DLBCL) treated with immunochemotherapy. Methods. From 7/2015 to 6/2017, 3253 participants with NHL were enrolled within 6 months of their diagnosis into the LEO cohort. 2004 were evaluable on VES-13, and 1183 (59%) completed it before the initiation of therapy. VES-13 scores range from a minimum of 0 (low risk for decline) to a maximum of 10 (greatest risk for decline), and a score ≥3 was classified as vulnerable. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants were contacted every 6 months for the first three years and then annually thereafter to identify outcomes. Medical records were reviewed by LEO clinicians to classify cause of death according to a standard protocol. Therapy was determined by the treating physician, and this was independent of knowledge of the VES-13 score. The association of VES-13 with 1-year mortality from date of diagnosis was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression models, which also provided model c-statistics. Results. The median age of the 2004 participants in this analysis was 62 years (range 18-94); 57% were male; 54% were ≥65 years; and 28% had a normal body mass index (BMI), 1% were underweight, 35% were overweight, and 36% were obese. Clinically, 59% of participants had an aggressive subtype, 65% were stage III-IV, 24% had B-symptoms, and 11% had a performance status (PS) of ≥2. Overall, 28% of participants were classified as vulnerable (95% CI 26%-30%), with a higher prevalence among those completing the survey after initiation of therapy (38%, 95% 34%-41%) versus before initiation of therapy (22%, 95% CI 20%-24%), and a higher prevalence for those ≥65 years (32%, 95% CI 29%-34%) versus &lt;65 (25%, 95% CI 22%-28%). During the first year of follow-up, there were 147 deaths, with 61% due to lymphoma, 14% therapy-related, 12% due to another malignancy, 3% other causes, and 10% unknown. Significant univariate predictors of 1-year mortality included vulnerable status, older age, PS 2+, stage III-IV, aggressive subtype, lower quality of life score, lower BMI, and 1 or more comorbidities, and were included the backward stepwise modeling. Vulnerable status (OR=3.26, 95% CI 2.15-4.96) was retained in the final model that also included age, PS, stage, and NHL subtype (Table), with a model c-statistic of 0.81. As shown in the Table, these associations were similar whether VES-13 was assessed before (c-statistic 0.77) or after (c-statistic 0.81) initiation of therapy. After adjustment for age, PS, stage and NHL subtype, vulnerable status predicted 1-year mortality in participants ≥65 years (OR=1.96, 95% CI 1.15-3.36; model c-statistic 0.80) as well as &lt;65 years (OR=6.38, 95% CI 3.07-13.3; model c-statistic 0.83). Finally, in the DLBCL patients treated with immunochemotherapy (N=521), vulnerable status (38% of patients) also predicted 1-year mortality (OR=2.11, 95% CI 1.05-4.26) after adjusting for age, PS and stage (model c-statistic 0.78). Conclusion. Based on a self-administered VES-13, 28% of newly diagnosed NHL patients in the LEO cohort were classified as vulnerable, including 25% of those &lt;65 years, and vulnerable status was a strong predictor of 1-year-mortality, even in the subset of DLBCL patients treated with presumed curative intent. These results highlight the potential value of screening for vulnerable status overall and across a wider age spectrum in order to optimize patient management and reduce early mortality. Disclosures Martin: Karyopharm: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Casulo:Gilead: Honoraria, Other: Travel, accommodation, expenses; Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses. Cohen:Takeda Pharmaceuticals North America, Inc.: Research Funding; Bristol-Meyers Squibb Company: Research Funding; Hutchison: Research Funding; Astra Zeneca: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Gilead/Kite: Consultancy; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Kahl:Seattle Genetics: Consultancy; ADC Therapeutics: Consultancy, Research Funding; BeiGene: Consultancy; TG Therapeutics: Consultancy. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Maurer:Nanostring: Research Funding; Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Nastoupil:Spectrum: Honoraria; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; TG Therapeutics: Honoraria, Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding. Rutherford:Heron: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Juno Therapeutics Inc: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees. Flowers:National Cancer Institute: Research Funding; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Bayer: Consultancy; Spectrum: Consultancy; Karyopharm: Consultancy; Acerta: Research Funding; TG Therapeutics: Research Funding; Millenium/Takeda: Research Funding; AstraZeneca: Consultancy; Optimum Rx: Consultancy; V Foundation: Research Funding; Celgene: Consultancy, Research Funding; Burroughs Wellcome Fund: Research Funding; Denovo Biopharma: Consultancy; Gilead: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding.

Background: Follicular lymphoma (FL) is the most frequent indolent lymphoma. While immunochemotherapy (IC) treated FL patients who achieve event-free status at 24 months after diagnosis (EFS24) have the subsequent life expectancy of the general population, those who fail to achieve EFS24 have aggressive disease with poor outcomes. Similarly, in FL patients initially observed or treated with rituximab monotherapy, EFS at 12 months (EFS12) is a strong predictor of subsequent outcome. Thus, an unmet patient need is to predict at diagnosis those at greatest risk of early failure in order to identify better treatments. The lymphoma microenvironment may be a key determinant of early failure. Therefore, we aimed to improve risk stratification of newly diagnosed FL patients by using a discovery and validation study design to identify microenvironment determinants of early failure and then integrate them into the Follicular Lymphoma International Prognostic Index (FLIPI). Patients and Methods: We evaluated 496 patients with FL grade 1-3A who were prospectively enrolled into the Molecular Epidemiology Resource (MER) cohort from 2002-2012. In the discovery set of patients (N=166), we stained tissue microarrays for CD4, CD8, FOXP3, CD32b, CD14, CD68, CD70, SIRPa, TIM3, PD-1 and PDL1. Immunohistochemical staining was determined both within and between malignant follicles. Stains were scored to the nearest decile as the percentages of cells positive. Scores for each stain were dichotomized as 0% vs &gt;0% except for CD32b (&lt;20% vs ≥20%), CD70 (&lt;10% vs ≥10%), and SIRPa (&lt;30% vs ≥30%). EFS was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS24 for patients treated with IC at diagnosis and failing to achieve EFS12 for patients who were observed or received other treatments. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals (CI) from logistic regression models. Markers at P≤0.15 were brought forward for validation in a separate set of patients from the MER cohort (N=330), with P&lt;0.005 being declared statistically significant after a Bonferroni correction (0.05/10 tests). In the combined dataset, we also used Cox regression to assess associations with continuous EFS and overall survival (OS). Mass cytometry (CyTOF) was then performed on a subset of patients (N=51) to comprehensively characterize the phenotype of intratumoral immune cells associated with outcome. Results: In the discovery set, CD4, CD8, FOXP3, PD-1, and SIRPα were associated with risk of early failure at P≤0.15 and were subsequently evaluated in the validation set. Only CD4 expression inside the follicle validated. Specifically, lack of CD4 expression inside the follicle was associated with higher risk of early failure in the discovery (OR=1.77, P=0.15) and validation (OR=2.59, P=0.001) sets, with a pooled OR=2.29 (95% CI 1.47-3.58; P&lt;0.001). CyTOF analysis characterized the favorable CD4+ T-cell population associated with achieving EFS12/24 as activated effector memory T cells lacking exhaustion markers (Figure 1A). Adjustment of CD4 expression for FLIPI in the combined dataset did not weaken this association (OR=2.38, 95% CI 1.51-3.79), and lack of CD4 expression was prognostic within each FLIPI group 0-1 (OR=2.65; p=0.036), 2 (OR=2.07; P=0.064) and 3-5 (OR=2.51; P=0.015). We next established a bio-clinical risk model (termed BioFLIPI) that combined CD4 intrafollicular expression and FLIPI into a 1-4 scale, where any expression of intrafollicular CD4 moved a patient up one FLIPI risk group, adding a fourth (high) risk group for FLIPI 3-5 and any CD4 inside expression. The BioFLIPI score was strongly associated with risk of early failure: compared to a BioFLIPI score of 1 (24% of patients), patients with a score of 2 (35% of patients; OR=2.17, 95% CI 1.08-4.69), 3 (31% of patients; OR=3.53, 95% CI 1.78-7.54), and 4 (10% of patients; OR=8.92, 95% CI 4.00-21.1) had increasing risk of early failure. The c-statistic for BioFLIPI was 0.665, which was higher than that for FLIPI alone (0.636). BioFLIPI was also strongly predictive of continuous EFS [Figure 1B], OS [Figure 1C], and for EFS and EFS24 when restricted to IC-treated patients [Figure 1D]. Conclusion: Integration of CD4 expression inside the follicle at diagnosis with FLIPI improves identification of FL patients at risk for early failure. Disclosures Novak: Celgene Coorperation: Research Funding. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment.

Abstract Background: PTLD is the most common malignancy, other than non-melanoma skin cancer, complicating solid organ transplantation (SOT) and has been one of the most commonly observed fatal consequences in SOT. The clinical presentations, management strategies, histologies, causes of death and outcomes are diverse (Dierickx D, Habermann TM. N Engl J Med 2018;378:549-62). The 2017 WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues has redefined the categories as non-destructive PTLDs (plasmacytic hyperplasia, infectious mononucleosis, and florid follicular hyperplasia), polymorphic PTLD, monomorphic PTLD: B-cell neoplasms (diffuse large B-cell lymphoma, Burkitt lymphoma, high-grade B-cell lymphoma, plasmablastic lymphoma, plasma cell myeloma, plasmacytoma, and other) and T-cell neoplasms (peripheral T-cell lymphoma NOS, hepatosplenic T-cell lymphoma, other), and classic Hodgkin lymphoma (CHL) PTLD. We report the outcomes and long-term follow-up of patients from a single institution based on these categories whose pathology was retrospectively reviewed and reclassified based on the WHO 2017 classification. Methods: Patients with SOT who were diagnosed with PTLD at Mayo Clinic (Rochester, MN) were identified through the Mayo Clinic Lymphoma Data base and the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER). The histology was re-reviewed in 80% of the cases (RLK) and classified according to the WHO Classification of Tumours of Haematopoietic an Lymphoid Tissues 2017. Cases were considered "PTLD, unclassified" if the histology could not be confidently classified based on pathology material available for review. Indolent small B-cell lymphomas were not included among the PTLDs except for EBV-positive marginal zone lymphoma. Cox proportional hazards models were used to assess the association of clinical factors in overall survival (OS). Results: 233 patients diagnosed with PTLD between 1987 and 2017 were identified. The median age at the time of diagnosis of PTLD was 54 years (range 16 to 84) with 85 patients (36%) over the age of 60. 156 (67%) were male. The transplanted organs were kidney (41%), kidney/pancreas (5%), liver (29%), heart (9%), lung (7%), and other (8%). PTLD occurred late (more than one year after transplantation) in 66%. There were 69 stage I, 19 stage II, 8 stage III, and 128 stage IV patients. 84% presented with extranodal disease. 21% had involvement of the engrafted organ. 64% of the patients developed a PTLD that was EBV positive by in situ hybridization. Initial approaches to management included reduction of immunosuppression (N=55), chemotherapy/immunochemotherapy (N=71), reduction in immunosuppression with rituximab (N=58), single agent rituximab (N=14), and radiation therapy (N=5). At a median follow-up of 87 months (range 9-289), 139 (60%) patients had died. All six CHL-PTLD patients are alive, two of whom had an event. The median overall survival (OS) was 85 months (95% CI: 39-144) in 177 monomorphic B-cell lymphoma PTLD, 95.5 months (95% CI: 40-not reached) in 24 polymorphic PTLD, and 60 months in 8 non-destructive PTLD cases. In contrast, the median overall survival was 12 months (95% CI: 4-200) in 8 monomorphic T cell and 3 months (95% CI: 1-unreached) in 9 unclassified PTLD cases. Conclusion: PTLD is a heterogeneous group of immunodeficiency-associated lymphoproliferative disorders. The overall survival in non-destructive, polymorphic, and monomorphic PTLD were similar. Monomorphic T/NK cell types had inferior outcomes. Figure. Figure. Disclosures Maurer: Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Ansell:Celldex: Research Funding; Merck & Co: Research Funding; Takeda: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Pfizer: Research Funding. Cerhan:Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background Diffuse large B cell lymphoma post-transplant lymphoproliferative disorder (DLBCL-PTLD) represents a fatal complication after a solid organ transplant (SOT). Approximately 50% of DLBCL-PTLD can be related to Epstein Barr Virus (EBV) infection. However, the EBV status is not taken into account in the DLBCL-PTLD categorization, although increasing evidence suggests its critical role in biological, clinical and prognostic aspects of the disease. In this single center retrospective study, we compared clinical and histological features, as well as outcomes of DLBCL-PTLDs by EBV status. Methods This study focused on patients with SOT who were diagnosed with DLBCL-PTLD at Mayo Clinic (Rochester, MN). Patients were identified through the Mayo Clinic Lymphoma Data base and the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER), between 1989 and 2017. The histology was re-reviewed (RLK) and classified according to the WHO Classification or Tumours of Haematopoietic and Lymphoid Tissues 2017. Cell of origin (COO) was performed by Hans algorithm. EBV RNA in situ hybridization (EBER) was performed. Cox proportional hazards models were used to assess the association of clinical factors in overall survival (OS). Results 148 DLBCL-PTLD were identified. The median age at SOT and at the time of PTLD diagnosis were 50 years (range: 15-71) and 56 years (range: 18-82), respectively. The median time to PTLD diagnosis from SOT was 38 months (range: 1-499). The types of SOT included renal (44%), liver (36%), cardiac (9%), renal/pancreas (5%), pancreas (8%), lung (7%) and multiorgan (4%). 62% were stage III-IV, 30% had elevated LDH and 67% had an IPI >1. 88% of patients presented with extranodal disease, and 16% had involvement of the grafted organ. Eighty-three (56%) cases were EBV positive. The COO information was available on 35 cases. The non-GCB subtype was identified in 24 cases and GCB subtype in 11 cases. The initial treatment was as follows: reduction in immunosuppression (12%), immunosuppression reduction with rituximab (26%), rituximab alone (5%), chemotherapy with or without rituximab (35%). Patients with EBV positive PTLD were more likely to have grafted organ involvement (20% vs. 8%, p=0.047) and late PTLD (occurring after 12 months from SOT - 60% vs. 8%, p<0.001) than patients with EBV negative PTLD. There was no association between EBV status and age, sex, stage, LDH, and extranodal site involvement. At a median follow-up of 80 months (range 9-269), 83 patients (56%) had died. The 5-year survival rate from PTLD diagnosis was 56% (95% CI: 48-65%). The median OS was 85 months (95% CI: 37-148). There was no difference in OS in patients with EBV positive PTLD (median OS=84.7 months, HR=1.21, 95% CI: 0.77-1.92) versus patients with EBV negative DLBCL-PTLD (median OS=85.4 months, p=0.41). Conclusion Our study assessed the impact of EBV status on the presentation and clinical outcome of DLBCL-PTLD in a 148-patient single-institution cohort. 56% of cases were EBV positive. EBV positivity was associated with grafted organ involvement and late PTLD diagnosis. Patients with EBV-negative and EBV-positive disease had similar survival from PTLD diagnosis. Disclosures Maurer: Celgene: Research Funding; Morphosys: Research Funding; Nanostring: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

AbstractBackgroundHuman pegivirus (HPgV) is a single-strand RNA virus belonging to the Flaviviridae. Although no definitive association between HPgV infection and disease has been identified, previous studies have suggested an association of HPgV viremia with risk of lymphomas.MethodsWe conducted a systematic review and meta-analysis, including 1 cohort study and 14 case-control studies, assessing the association of HPgV viremia with adult lymphomas. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a random-effects model, overall and by geographic region and lymphoma subtype.ResultsThe overall OR for lymphoma was 2.85 (95% CI, 1.98–4.11), with statistically significantly elevated ORs observed in 8 of 15 studies. There was a small amount of heterogeneity among studies (I2 = 28.9%; Q = 18.27, P = .16), and the funnel plot provided no evidence for publication bias. The strongest association with lymphoma risk was observed for studies from Southern Europe (OR, 5.68 [95% CI, 1.98–16.3]), whereas weaker ORs (with 95% CIs) were observed for studies from North America (2.24 [1.76–2.85]), Northern Europe (2.90 [.45–18.7), and the Middle East (2.51 [.87–7.27]), but all of similar magnitude. Participants with HPgV viremia had statistically significantly increased risks (OR [95% CI]) for developing diffuse large B-cell (3.29 [1.63–6.62]), follicular (3.01 [1.95–4.63]), marginal zone (1.90 [1.13–3.18]), and T-cell (2.11 [1.17–3.89]) lymphomas, while the risk for Hodgkin lymphoma (3.53 [.48–25.9]) and chronic lymphocytic leukemia (1.45 [.45–4.66]) were increased but did not achieve statistical significance.ConclusionsThis meta-analysis supports a positive association of HPgV viremia with lymphoma risk, overall and for the major lymphoma subtypes.

SummaryWe evaluated the association of Human Pegivirus (HPgV) viraemia with risk of developing lymphoma, overall and by major subtypes. Because this virus has also been associated with better prognosis in the setting of co‐infection with human immunodeficiency virus, we further assessed the association of HPgV with prognosis. We used risk factor data and banked plasma samples from 2094 lymphoma cases newly diagnosed between 2002 and 2009 and 1572 frequency‐matched controls. Plasma samples were tested for HPgV RNA by reverse transcription polymerase chain reaction (RT‐PCR), and those with RNA concentrations <5000 genome equivalents/ml were confirmed using nested RT‐PCR methods. To assess the role of HPgV in lymphoma prognosis, we used 2948 cases from a cohort study of newly diagnosed lymphoma patients (included all cases from the case‐control study). There was a positive association of HPgV viraemia with risk of lymphoma overall (Odds ratio = 2·14; 95% confidence interval [CI] 1·63–2·80; P < 0·0001), and for all major subtypes except Hodgkin lymphoma and chronic lymphocytic leukaemia/small lymphocytic lymphoma, and this was not confounded by other lymphoma risk factors. In contrast, there was no association of HPgV viraemia with event‐free survival (Hazard ratio [HR] = 1·00; 95% CI 0·85–1·18) or overall survival (HR = 0·97; 95% CI 0·79–1·20) for lymphoma overall, or any of the subtypes. These data support the hypothesis for a role of HPgV in the aetiology of multiple lymphoma subtypes.