Ansell, Stephen M.

Background: With the incorporation of positron emission tomography (PET) imaging as part of the standard staging evaluation of follicular lymphoma (FL), it is generally recommended to obtain the diagnostic biopsy from a lesion with the highest standardized uptake value (SUV) to rule out de novo histologic transformation (HT). In some cases such an approach might be impractical, while in other cases a biopsy from a diseased area with a high SUV may still demonstrate an indolent histology. To date, there is no data to guide treatment choices in patients (pts) with FL with high SUVmax but with no documented evidence of HT. Specifically, it remains unclear whether anthracycline-containing regimens, such as R-CHOP, provide a better outcome than R-Bendamustine (BR). Furthermore, it is unknown whether rituximab (R) maintenance is beneficial in this setting. Therefore, we aimed to compare the efficacy of R-CHOP vs BR in newly diagnosed FL pts with high SUVmax at baseline PET and to clarify the role of maintenance. Patients and Methods: We retrospectively identified 261 consecutive pts with newly diagnosed biopsy-proven FL1-3A and a SUVmax≥13 on baseline PET, who were treated with frontline R-CHOP (n=183) or BR (n=78) at 7 US cancer centers based on the physician's choice. Progression-free survival (PFS) was calculated from starting treatment until progression, relapse or death. Cut-offs of SUVmax ≥13≤18 and SUVmax>18 were used for sub-analysis (Haematologica;2020;105:1907-1913). Results: Median age was 59 years. The baseline characteristics of the two groups differed significantly for a younger age (58 vs 62 years, p=0.009), a higher rate of B-symptoms (26% vs 10%, p=0.005) and baseline SUVmax>18 (49% vs 36%, p=0.04) in the R-CHOP group. A diagnostic biopsy was obtained from the site at the highest SUV in 129 (71%) and 43 (55%) pts, respectively (p=0.01). These suggest a potential selection of R-CHOP in pts with adverse features. At the end of therapy (EoT), R-CHOP treated pts achieved higher PET-CT complete response rates (CR 82% vs 69%, p=0.02). This superiority held in pts with diagnostic biopsy at the highest SUV site (CR 83% vs 67%, p=0.03), but not in the others (CR 80% vs 71%, p=0.37). Progression occurred at EoT in 11 and 14 pts receiving R-CHOP and BR (6% vs 18%, p=0.02), with a higher rate of HT in the BR group (13% vs 4%, p=0.006). After a median follow-up of 76 months (range, 4-208 months), there was no significant difference in PFS between R-CHOP and BR treated pts (hazard ratio [HR] 1.22, p=0.34). PFS was strongly associated with FLIPI 2 (HR 2.32, p=0.01) and 3-5 (HR 2.69, p=0.003) in the univariate as well as in the multivariate analysis (FLIPI 2 HR 2.19, p=0.02; FLIPI 3-5 HR 2.51, p=0.01). There was a trend toward overall survival (OS) benefit in the R-CHOP group (Fig.1A). In the univariate analysis for OS, BR regimen (HR 2.12, p=0.03), age >60 (HR 2.54, p=0.006), FLIPI 3-5 (HR 5.13, p=0.03) and biopsy at the highest SUV site (HR 0.44, p=0.01) were prognostic, however none of those remained significant in the multivariate analysis (BR regimen HR 1.84, p=0.09; age >60 HR 1.92, p=0.08; FLIPI 3-5 HR 3.11, p=0.14; biopsy at the highest SUV site HR 0.60, p=0.13; SUVmax>18 HR 1.87, p=0.055). Among pts with EoT response after R-CHOP or BR (n=170 and n=64, respectively), one third in each group (36% vs 41%, respectively) received R-maintenance for a median of 8 administrations (range, 2-12). A significant PFS advantage (landmark analysis) was observed with R-maintenance (Fig.1B; HR 0.53, p=0.02); however, this did not translate in survival benefit (HR 0.67, p=0.49). In a multivariable model, R-maintenance (HR 0.53, p=0.02), FLIPI 2 (HR 2.47, p=0.03) and 3-5 (HR 2.79, p=0.01) remained independent prognosticators of PFS, while no parameters significantly affected OS. The 2-year HT rate (10% vs 17%) and cumulative incidence did not statistically differ between R-CHOP and BR groups (Fig.1C, p=0.159). Early progression (POD24) occurred in 43 and 22 pts who received R-CHOP and BR, respectively (24% vs 28%) and resulted in a significantly higher risk of death (HR 4.12, p=0.006). Conclusion: In newly diagnosed FL pts with SUVmax≥13 at baseline PET, R-CHOP was more effective in achieving CR and reducing the risk of early HT compared with BR, and it was associated with a trend toward survival advantage. Rituximab maintenance significantly prolonged PFS. A prospective evaluation with a larger population will be needed to confirm our findings. Disclosures Joffe: Epizyme: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees. Ruella:Abclon, BMS, NanoString: Consultancy; Abclon: Consultancy, Research Funding; UPenn/Novartis: Patents & Royalties. Svoboda:Adaptive: Consultancy; Astra-Zeneca: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Imbrium: Consultancy; Incyte: Research Funding; Pharmacyclics: Consultancy, Research Funding; Merck: Research Funding; Seattle Genetics: Consultancy, Research Funding; TG: Research Funding; Genmab: Consultancy; Atara: Consultancy. Witzig:Immune Design: Research Funding; Spectrum: Consultancy; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy. Smith:TG Therapeutics: Consultancy, Research Funding; Janssen: Consultancy; BMS: Consultancy; Celgene: Consultancy, Research Funding; Pharmacyclics: Research Funding; Karyopharm: Consultancy, Research Funding; FortySeven: Research Funding; Acerta: Research Funding; Genentech/Roche: Consultancy, Other: Support of parent study and funding of editorial support, Research Funding. Armand:Affimed: Consultancy, Research Funding; Otsuka: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; IGM: Research Funding; Infinity: Consultancy; Genentech: Research Funding; Sigma Tau: Research Funding; Celgene: Consultancy; Adaptive: Consultancy, Research Funding; Tensha: Research Funding; Roche: Research Funding; Merck & Co., Inc.: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy; ADC Therapeutics: Consultancy. Nastoupil:Janssen: Honoraria, Research Funding; LAM Therapeutics: Research Funding; Gilead/KITE: Honoraria; Gamida Cell: Honoraria; TG Therapeutics: Honoraria, Research Funding; Merck: Research Funding; Novartis: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Karus Therapeutics: Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding. Ansell:Takeda: Research Funding; Regeneron: Research Funding; Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding. Zelenetz:Novartis: Consultancy; Adaptive Biotechnology: Consultancy; Genentech/Roche: Consultancy; Gilead: Consultancy; Gilead: Research Funding; Celgene: Consultancy; Roche: Research Funding; Amgen: Consultancy; BeiGene: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Sandoz: Research Funding; Celgene: Research Funding; MorphoSys: Research Funding; MEI Pharma: Research Funding. Younes:BioPath, Xynomic, Epizyme, and F. Hoffmann-La Roche: Consultancy; Janssen, Curis, Merck, Bristol-Myers Squibb, Syndax Pharmaceuticals, F. Hoffmann-La Roche, Curis (Inst), Johnson & Johnson (Inst), Novartis (Inst): Research Funding; Janssen, AbbVie, Merck, Curis, Epizyme, F. Hoffmann-La Roche, Takeda, Bristol-Myers Squibb, Bayer HealthCare Pharmaceuticals, Celgene, Incyte, Janssen Pharmaceuticals, Merck, Sanofi, Seattle Genetics, Takeda Millennium: Honoraria; AstraZeneca: Current Employment; MSKCC: Ended employment in the past 24 months.

Background: Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma and presents with significant clinical, cellular, molecular, and genetic heterogeneity. Despite the name and defining microanatomic location, the genetic and molecular identity and pathogenesis of the FL tumor cell is largely uncharacterized. Prior clinical and molecular classifications of FL have been primarily driven by pathologic classification (Grade 1-3b), genetic classification (M7-FLIPI), or gene expression profiling (IR-1/2). Using a unique cohort of 93 FL tumors, we have explored the transcriptomic signature of purified FL B cells, along with their matched whole tumor, and identified unique molecular subsets which are defined by distinct pathway activation, immune content, and genomic signatures identified through whole exome sequencing (WES). Methods: Frozen tumor biopsies from 93 untreated FL (Grade 1-3b) patients enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE were used for the study. DNA was isolated from whole tumor cell suspensions and RNA was isolated from both whole tumor and B cell enriched cell suspensions. RNA sequencing (RNAseq) and WES was performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using the Mayo Clinic standard pipeline and novel driver genes were identified using 20/20+ driver analysis. Copy number variants were identified using GISTIC 2.0. NMF clustering and single sample gene set testing, for B cell lineage and tumor microenvironment (TME) signatures, was performed in R using the NMF and SingScore packages. Results: NMF consensus clustering of FL B cell RNAseq data identified two distinct subsets, C1 (n=32) and C2 (n=57). Clinically, C1 was associated with being FL grade 3 (p<0.001) and more aggressive therapy (e.g. immunochemotherapy) at diagnosis while other clinical factors were similar between the groups. To determine the biologic underpinnings that drove the NMF analysis, the top NMF metagenes were incorporated (n=389 for C1 and n=293 for C2) for GO analysis. Among those genes were AICDA, IRF4, and EZH2 in C1, and BCL2 in C2. Additionally, we identified significant gene set enrichment with NMF metagenes and calculated differential gene set expression comparing C1, C2, and normal B cells (n=5). Together, these analyses suggest C1 displays significantly (p<0.05) enhanced DNA replication and metabolic activity, whereas C2 displays significantly enhanced cytoskeleton rearrangement, repressed inflammatory cytokine signaling, and NF-kB activity. Based on recent scRNAseq data suggesting that FL derives from distinct germinal center B cells, the B lineage difference between C1 and C2 was explored. Both subgroups were heterogeneous for dark (DZ), intermediate (INT), light (LZ) zones, and plasmablast (PBL) gene signatures. When compared to benign B cell samples, C1 was enriched for a pre-PBL (p<0.01), and C2 for an INT phenotype related to a LZ signature (p<0.01). To define if a genomic signature was associated with C1 and C2, mutations and copy number variants (>5% frequency) were examined. TNFAIP3, TP53, and BCL6 alterations were enriched in C1 samples, whereas C2 associated with alterations in BCL2, KMT2D, CREBBP, REL, and MYC. Finally, B cell clusters were analyzed for TME signatures. C1 samples displayed significant enrichment of macrophage, cytotoxic cell, gamma-delta-Tcell, and endothelial cell TME elements (p<0.05) consistent with an inflamed/hot tumor. In contrast, C2 samples exhibited significantly lower signatures (p<0.05) in all entities except for the fibroblast signature, compared to C1 and normal, consistent with an immune desert phenotype. Conclusion: Our results suggest that B cells from FL patients display two distinct transcriptomic signatures. C1 identifies an immunologically active tumor, driven by TNFAIP3 alterations, with pre-PBL characteristics, DNA replication and repair, inflammatory cytokine secretion/signaling, and hyper-metabolic characteristics. C2 identifies an immunologically quiet tumor, driven by alterations in BCL2 and chromatin modifiers, with an intermediate GC phenotype, repressed cytokine signaling, and active cell cycle progression and cytoskeleton rearrangement. This study improves our understanding of the mechanisms driving FL tumors and motivates further investigation into the relationship between tumor intrinsic factors that may influence the TME. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene / BMS: Research Funding. Ansell:ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Novak:Celgene/BMS: Research Funding.

Introduction: PD-1 inhibits the cytotoxic T-cell functions via the interaction with its ligands (PDL1 and PDL2). Recently, our group showed a selective response (~40%) with PD-1 blocking antibody pembrolizumab in patients with Richter transformation (RT), particularly after prior exposure to ibrutinib (Wei Ding et al, Blood, 2017). Additionally, PD-1 showed an increased expression in tumor B-cells of patients with RT (Rong He et al, AJSP, 2018). Here we investigated the functional implications of the PD-1 signaling axis in lymphoma B-cell pathobiology. Methods: 26 CLL-involved lymph node (LN) and 20 RT-involved LN samples were tested for PD-1 expression by immunohistochemistry (mouse clone NAT105, Abcam). Then, we checked PD-1 expression in 10 lymphoma cell lines and 1 CLL cell line (MEC-1) by both, flow cytometry and Western blot (WB) analysis. Over-expression of PD-1 using pLEX-lentiviral system (Thermo Scientific) was evaluated for in vitro cell cycle regulation and in vivo tumor growth. Overexpression of PD-1 was further examined on the regulation of cell signaling pathways using human phospho kinase array kit (R&D) and WB analysis. Gene expression signatures in CLL and RT patients were also identified by Illumina-based RNA sequencing using Formalin-Fixed Paraffin-embedded (FFPE)-nodal tissue obtained by clinical biopsy (Tempus Labs; Chicago, IL). Results: The expression of PD-1 was significantly increased in RT-LN compared to CLL-LN (mean ± SEM in RT vs. CLL, 30.6% ± 4.7% vs. 11.5% ± 2.8%, p < 0.001) [Fig 1A]. PD-1 expression was highest in patients with RT where the last prior CLL therapy was ibrutinib. To test the role of PD-1 in lymphoma B-cells, its expression was assessed in lymphoma and CLL cell lines. The expression of PD-1 was found to be variable, but at very low-levels in lymphoma lines OCI-LY7 and OCI-LY19 (data not shown). Constitutive lentiviral (pLEX-PD-1)-mediated overexpression of PD-1 in OCI-LY7 and OCI-LY19 cells led to increased cell growth (1.4 and 1.9 fold compared to original lines on day 4, respectively, Fig 1BI-II), which was further confirmed by cell cycle analysis which showed an increase in S phase by 20.4% and 24.53% in OCI-LY7 and OCI-LY19 cells (Fig 1B III), respectively. When the luciferase + PD-1 expressing OCI-LY7 cells were injected intravenously (1X106 cells) into NSG mice, increased tumor growth was observed at 22 days of follow up by bioluminescence imaging (p<0.01, Fig 1C). Using phospho-kinase array, an overall decrease of phosphorylation was detected on multiple sites of p53 (S392, S15, S46) and CHK2 (T68) (data not shown). This finding was further confirmed by WB analyses (Fig1DI). In addition, decreased of total p53 and increased phosphorylation on both SHP-1 and SHP-2 were found in PD-1 overexpressing OCI-LY19 and OCI-LY17 cell lines (Fig 1DI). Immunoprecipitation experiments with anti-PD1 antibody showed that PD-1 was in the same complex with SHP-1 and SHP-2 in OCI-LY7 cells but only with SHP-2 in OCI-LY19 cells (Fig 1DII). In parallel, mRNA sequencing was performed on 11 nodal tissues from either RT (n=8) or progressive CLL (n=3) after they developed clinical progression. The expression of PD-1 (PDCD1) was positively correlated with the expression of SHP-1 (PTPN6, r=0.56) and Cyclin E1 (CCNE1, r=0.62), implicating a direct role of PD-1 in regulating cellular proliferation and the induction in phosphatase expression in RT and CLL. Conclusion: Our data support the notion that PD-1 overexpression in lymphoma cells modifies cell proliferation in vitro and in vivo and regulates the function of phosphatase SHP and p53- pathways. These findings also indicate that aggressive lymphoma or CLL leukemic cells have the ability to hijack the PD-1 pathway and may result in downregulation of the p53 mediated DNA repair. This novel information provide for new strategies to further evaluate the interaction of checkpoint signals with DNA repair pathway, and possibly provide novel targets for Richter's transformation. Disclosures Ding: alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Astra Zeneca: Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; DTRM: Research Funding; MEI Pharma: Membership on an entity's Board of Directors or advisory committees. Sakemura:Humanigen: Patents & Royalties. Parikh:Janssen: Honoraria, Research Funding; Merck: Research Funding; Pharmacyclics: Honoraria, Research Funding; Ascentage Pharma: Research Funding; GlaxoSmithKline: Honoraria; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Verastem Oncology: Honoraria; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; TG Therapeutics: Research Funding. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Liu:Eisal: Research Funding; Genentech: Research Funding; GRAIL: Research Funding; Menarini Silicon Biosystems: Research Funding; Merck: Research Funding; Seattle Genetics: Research Funding; Tesaro: Research Funding. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ansell:Seattle Genetics: Research Funding; Takeda: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol Myers Squibb: Research Funding. Kenderian:Sunesis: Research Funding; Tolero: Research Funding; Torque: Consultancy; BMS: Research Funding; Gilead: Research Funding; Kite: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Mettaforge: Patents & Royalties; Novartis: Patents & Royalties, Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding; Juno: Research Funding. Kay:Acerta Pharma: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Sunesis: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees.

Magnesium is an essential element that is involved in the regulation of cell growth, division and differentiation. Epidemiologic studies have demonstrated that a diet poor in magnesium is associated with an increased risk of developing cancer. There are few studies investigating whether magnesium deficiency is associated with a poor prognosis in lymphomas. The recent discovery of X-linked immunodeficiency with magnesium defect, Epstein-Barr virus, and neoplasia (XMEN) disease, suggests a role of magnesium in the development of hematologic malignancies and lymphocyte function. Furthermore, a recently published study found that hypomagnesemia prior to chemotherapy is associated with inferior overall survival (OS) in patients with Burkitt Lymphoma. We hypothesized that low magnesium levels prior to autologous hematopoietic stem cell transplant (AHSCT) may lead to inferior outcomes via impaired lymphocyte recovery. We evaluated whether low serum magnesium levels were associated with inferior survival in patients with relapsed or refractory diffuse large B cell lymphoma (DLBCL) who underwent autologous hematopoietic stem cell transplant (AHSCT) between January 1998 and May 2020 using a retrospective study design. We cross-referenced our search with patients who had a magnesium level as part of their pre-transplant workup between 7 and 28 days prior to their first transplant. Two-sided Wilcoxon rank sum test and Chi square/Fischer's exact test were used to compare the continuous and categorical variables, respectively. Kaplan-Meier and log-rank tests were used to perform all time to event analysis. Hazard ratios (HR) with confidence intervals (CI) were calculated using Cox-proportional hazards. Our study included 581 patients of which 82 (14.1%) had hypomagnesemia (magnesium < 1.7 mg/dL) prior to AHSCT. Our study found that hypomagnesemia was associated with inferior OS and event-free survival (EFS) defined as re-transplant, relapse or death, compared to those with a normal magnesium level; median OS: 7.3 years (95% CI: 2.91 - upper limit not estimable) versus 9.7 years (95% CI: 6.92 - 12.3 years), p=0.0041; and median EFS: 3.9 years (95% CI: 1.63 - 8.98 years) versus 6.29 years (95% CI: 4.73 - 8.95 years), p = 0.016 respectively(B). Higher serum magnesium (HR 0.199 per doubling, 95% CI 0.078 - 0.503; p<0.001) and higher albumin (HR 0.090 per doubling, 95% CI 0.021 - 0.394; p=0.001) were associated with lower mortality rates, and higher creatinine (HR 1.669 per doubling, 95% CI 1.03 - 2.52; p=0.04) was associated with greater mortality up to 1 year post-transplantation. In a multivariate Cox regression excluding albumin, hypomagnesemia (p < 0.001) and elevated creatinine (p = 0.038) were independent predictors of poor overall survival(A). There was no significant difference between day 15 absolute lymphocyte count between patients with normal magnesium levels and low serum magnesium levels prior to their conditioning regimen. Although we hypothesized that we would find a significant difference between 15 day absolute lymphocyte count recovery and hypomagnesemia, this was not apparent. However, magnesium is an important nutrient for immune function and immune recovery is important after the bone marrow is restored post ablative therapy. Thus, low serum magnesium levels may lead to impaired OS and EFS via impaired lymphocyte function. In vitro studies will be necessary to understand this mechanism further. Magnesium may simply be a marker of illness and replacement of magnesium may not improve outcomes. However, further investigations are needed to determine whether outcomes would improve with replacement of magnesium in patients with hypomagnesemia, which our study did not address. Overall, our preliminary data suggests that hypomagnesemia is an independent prognostic factor and predictor of inferior OS and EFS in this cohort of patients. Figure Disclosures Ansell: ADC Therapeutics:Research Funding;AI Therapeutics:Research Funding;Trillium:Research Funding;Affimed:Research Funding;Regeneron:Research Funding;Seattle Genetics:Research Funding;Bristol Myers Squibb:Research Funding;Takeda:Research Funding.Witzig:Spectrum:Consultancy;Celgene:Consultancy, Research Funding;Immune Design:Research Funding;Karyopharm Therapeutics:Research Funding;Acerta:Research Funding;Incyte:Consultancy;AbbVie:Consultancy;MorphSys:Consultancy.

Background: Real world multi-institutional data of anti-CD19 CAR-T therapy from across the US shows similar response rates and toxicities as that of the registration trials. For axicabtagene ciloleucel (axi-cel), bridging therapy (BT) during CAR-T manufacturing was not allowed. Real world data shows that patients who received BT had lower complete remission (CR) rates, duraiton of response (DOR) and overall survival (OS). Whether the poorer outcome is due to the effect of the BT or the aggressive biology of the disease is unknown. Patients with lower disease burden had higher response rate in the registration study ZUMA-1 for axi-cel. We examined the impact of response to BT on CAR-T outcome. Specifically, we also describe our experience using polatuzumab vedotin, bendamustine and rituximab (Pola-BR), also a recently approved therapy for relapsed/refractory diffuse large B-cell lymphoma (DLBCL), prior to CAR-T as salvage (ST) and BT. Methods: A retrospective review of 63 patients who received axi-cel for NHL from June 2016 - March 2020 at Mayo Clinic, Rochester was performed. ST was defined as the last therapy prior to leukapheresis and grouped into categories - autologous stem cell transplant (ASCT), and non-ASCT (chemotherapy and immunotherapy) . BT was defined as lymphoma-directed therapy given between leukapheresis and CAR-T infusion. Response to all lymphoma-directed therapy was evaluated using 2014 Lugano criteria. Event free survival (EFS) was defined as time from axi-cel infusion to progression, next treatment, or death. Overall survival (OS) was defined as time from axi-cel infusion to death. Results: Patients received BT if there were concerns for symptomatic progression of disease during CAR-T manufacturing that would reduce the likelihood of eligibility to receive CAR-T. The decision and choice of BT was at the discretion of the treating physician. Sixty-eight percent (n=43) of the patients received BT (19 chemo, 24 immunotherapy). Demographics of the patients based on their response to BT are shown in table 1. More patients in the BT responder group had received chemotherapy as type of bridge (58% vs. 33%). With a median follow up of 12-months for those still alive, significant improvement in EFS was seen in patients who had partial response (PR) or stable disease (SD) to BT than patients who continued to progress (median 12 months vs. 3 months, p = 0.01) [Figure 1A]. OS however, was not significant between the two groups (median OS 18 months - responders vs. 12 months non-responders, p = 0.19) [Figure 1B]. We also describe in the case series below 11 patients who received Pola-BR in salvage setting (ST) and/or BT (Table 2). Due to the lymphopenic effects of bendamustine, it was avoided before T-cell collection if possible. Two patients, however, received bendamustine as part of Pola-BR for salvage therapy as its need was determined per the managing physician. Axi-cel was successfully manufactured for all 10 patients. One patient did not have measurable disease after Pola-BR BT and remains on observation. A second patient died before CAR-T infusion from progressive disease. Four of the nine patients treated with CAR-T remained in response. Updated outcome will be presented. Conclusion: While patients who received BT have worse EFS than those who didn't, having some control of lymphoma after BT was associated with better EFS compared to patients who continued to have progressive disease after BT. Preliminary data from Pola-BR case series shows 60% (6/10) patients achieved PR/SD before CAR-T infusion, but requires longer follow up for its impact on survival. Disclosures Bennani: Purdue Pharma: Other: Advisory Board; Kite/Gilead: Research Funding; Affimed: Research Funding; Verastem: Other: Advisory Board. Ansell:AI Therapeutics: Research Funding; Takeda: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; ADC Therapeutics: Research Funding; Trillium: Research Funding. Lin:Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy; Legend BioTech: Consultancy; Merck: Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy.

Introduction: Transformation of low grade follicular lymphoma (FL) to diffuse large B cell lymphoma (DLBCL) carries a poor prognosis. In retrospective studies, 5-year survival of transformed DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) without autologous stem cell transplant is 40-60%, underscoring need to improve frontline treatment of transformed DLBCL beyond R-CHOP. The overall response rate (ORR) to lenalidomide used as a single agent for relapsed transformed non Hodgkin lymphoma was 45% with 21% complete response (CR) rate and a median duration of response of 12 months (Witzig et al, Ann Onc 2011). These data provided the rationale to include patients with transformed DLBCL (with historical and concurrent FL) in MC078E, a phase II clinical trial testing lenalidomide plus R-CHOP (R2CHOP) for patients with new and untreated de novo and transformed DLBCL (NCT00670358). Here we present analysis of the subset of transformed DLBCL patients. Methods: Adult patients with transformed DLBCL and either historical or concurrent FL, stage >=2, measurable disease by Positron Emission Tomography/computed tomography (PET/CT) and adequate organ function were included. Patients with Central Nervous System (CNS) involvement, significant comorbidities, active non-lymphomatous malignancy, life-threatening thromboembolism (TE) and contraindication to aspirin prophylaxis were excluded. Study participants received up to 6 cycles of rituximab (375 mg/m2), cyclophosphamide (750 mg/m2), doxorubicin (50 mg/m2), and vincristine (1.4 mg /m2) on day 1, prednisone (100 mg) on day 1-5, pegfilgrastim on day 2, and Lenalidomide 25 mg day 1-10 of 21 day cycle. Tumor lysis prophylaxis was per local practice; patients also received TE prophylaxis with aspirin. Primary outcome was event free survival (EFS) at 12 months, where an event was defined as death, progression or subsequent anti-lymphoma therapy. Secondary outcomes included ORR, CR, progression free survival (PFS), and overall survival (OS). Response was evaluated by PET/CT after cycle 2 and cycle 6 with revised response criteria (Cheson et al, 2007). Adverse events were recorded according to CTCAE version 3.0. The Kaplan-Meier method was used to estimate time to event endpoints. Results: Thirty-nine patients were accrued from August 5, 2013 to July 28, 2020 and 33 were eligible by central pathology review. Median age was 64 (range 24-80) years and 18 (54%) were >60 years old. Eighteen (54%) were male, and 32 (97%) had ECOG performance status <2. Twenty-three (70%) had historical FL and 10 (30%) had concurrent FL. Twenty-six (79%) had advanced stage (III-IV). Median number of extra nodal sites were 1 (0-4). Thirteen (39%) had high international prognostic index (IPI) (4-5). Thirty-two (97%) completed at least 2 cycles (30 completed all 6 cycles) and were evaluable for response. ORR was 97% (32/33), 29 (88%) had CR and 3 had PR. EFS at 12 months was 87.9% (95% CI: 71.8, 96.6). Two-year PFS and OS were 84.5% (95% CI: 72.8%-98%) and 96.9% (95% CI: 91-100%) (Figure 1). Twenty nine completed study protocol, 4 discontinued protocol early for disease progression (1), adverse event (AE) (1), refusal (1) and noncompliance (1). Thirty (91%) had hematologic AE of grade 3 or above, 27 (82%) had neutropenia, 16 (48%) had thrombocytopenia, and 7 (21%) had anemia. Sixteen (48%) had grade 3 or above non-hematologic AE. Eight (24%) had febrile neutropenia. There were 3 deaths on this study, 1 due to progressive DLBCL, 1 due to AML and 1 due to malignant melanoma. Conclusion: R2CHOP appears effective in transformed DLBCL with high response rates, event free, progression free and overall survival seen in current study. This study supports the inclusion of anthracycline naive patients with transformed DLBCL in future randomized studies of lenalidomide or other novel immunomodulatory (IMiD) analogues. Disclosures Wang: Novartis: Research Funding; Innocare: Research Funding; Incyte: Research Funding. Ansell:Bristol Myers Squibb: Research Funding; Takeda: Research Funding; AI Therapeutics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; ADC Therapeutics: Research Funding. Witzig:Spectrum: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Incyte: Consultancy; AbbVie: Consultancy; MorphSys: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Celgene/BMS: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Kymera: Consultancy; Curis: Consultancy; Seattle Genetics: Consultancy; NanoString: Research Funding.

Background: Patients with Waldenström Macroglobulinemia (WM) tend to present at an older age. Frailty, resulting from comorbidities and reduced organ function, poses unique challenges to the management of WM in the elderly. Age above 65 years, is an established unfavorable prognostic factor in WM. However, data specifically pertaining to the very elderly patient population are scant. The impact of advanced age (≥75 years at diagnosis) on the clinical features and outcomes of patients with symptomatic/active WM were studied. Methods: Patients with active WM, evaluated at Mayo Clinic, Rochester between January 1996 and December 2018 were included in the study. The characteristics and laboratory parameters at presentation of the cohort of patients with the age ≥75 years were compared to those of the younger (age <75 years) cohort. The median follow-up and overall survival (OS) were assessed from active disease to last follow-up or death using the Kaplan Meier method. Because older age is a confounding factor for deaths from unrelated causes, the cause-specific survival (CSS) was calculated. Deaths were deemed WM-related if patients died from progressive disease, transformation with antecedent WM, concomitant light chain (AL) amyloidosis, infection associated with WM or anti-WM therapy related toxicity, while patients who died from other causes were considered to have a WM-unrelated death. The patients who died from causes unrelated to WM were censored at death for CSS analysis. The OS of the cohort of the very elderly patients was compared to that of a cohort of the general US population, matched for age, sex as well as follow-up from the calendar-year of diagnosis, to assess the degree to which WM impacts survival. Results: Among 949 consecutive patients, with a median follow-up of 8.4 years [95% Confidence Interval (CI): 7.7-9.3 years] from active disease, the median age at diagnosis was 65 years (range: 31-93 years). Of 177 patients (19%) who were ≥75 years of age at the onset of active WM, 173 (98%) received anti-WM therapy. The baseline characteristics of the 2 cohorts (patients ≥75 years of age versus <75 years) are outlined in Table 1. The patients in the age ≥75 years cohort were noted to have lower IgM levels and serum albumin, and higher serum LDH and serum β-2 microglobulin compared to the age <75 years cohort. A higher proportion of patients in the age <75 years cohort had coexisting AL amyloidosis at diagnosis. The frequency of MYD88L265P mutation was similar between the 2 cohorts (p=0.2). During the 23-year period, there was an increase in the proportion of patients ≥75 years who were diagnosed with active WM (12% during the 1996-2003 period, 18% during 2004-2010, 25% during 2011-2018; P<0.0001). In the elderly WM cohort, the most frequently used frontline treatment was rituximab monotherapy (36%), followed by rituximab-alkylating agent-based regimen (31%). Of 177 patients with age ≥75 years, 89 (50%) patients were deceased at the time of last follow-up and 58% (52/89) of all deaths in age ≥75 years were attributable to WM. By contrast, 303 (39%) out of the 772 patients in the age <75 years cohort were deceased at last follow-up, with 73% (224/303) deaths attributable to WM. The CSS for patients with age ≥75 years was 10.9 years (95% CI: 7.3-11.7 years) versus 14.7 years (95% CI: 13.2-15.7 years) for age <75 years [Risk ratio 2.1(95% CI: 1.5-2.8); p<0.0001]. At the 1 and 2 year follow-up, WM-related mortality was 6.8% and 11.9%, respectively, for patients with age ≥75 years versus 2.6% and 4.9%, respectively, for the younger cohort. The median OS of patients with age ≥75 years from the diagnosis of active WM was 5.9 years (95% CI: 4.2-7.3 years) compared to the OS of 8.3 years for an age, sex and calendar-year-matched cohort, derived from the general US population (p<0.001), Figure 1. Conclusion: Patients who are ≥75 years of age at the time of diagnosis of active WM present with certain poor prognostic features, including higher serum LDH and β-2 microglobulin and lower serum albumin compared to their younger counterparts. The impact of advanced age is underscored by a poorer WM-specific survival for the subset of patients with the age ≥75 years compared to the younger cohort. Furthermore, although active/symptomatic WM is typically an indolent malignancy, it adversely affects OS of the very elderly patients as suggested by the comparison with an age, sex and calendar-year matched cohort of the general US population. Disclosures Ansell: Bristol Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; ADC Therapeutics: Research Funding; Seattle Genetics: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding. Gertz:Ionis/Akcea: Other: personal fee; Alnylam: Other: personal fee; Prothena: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Annexon: Other: personal fee; Appellis: Other: personal fee; Amgen: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Physicians Education Resource: Other: personal fee; Abbvie: Other; Celgene: Other; Research to Practice: Other; Sanofi: Other; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Other; Springer Publishing: Patents & Royalties; Aurora Bio: Other; Janssen: Other: personal fee. Nowakowski:NanoString: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Kymera: Consultancy; Kite: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; MorphoSys: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding. Witzig:Spectrum: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Incyte: Consultancy; MorphSys: Consultancy; AbbVie: Consultancy; Celgene: Consultancy, Research Funding. Dispenzieri:Pfizer: Research Funding; Janssen: Research Funding; Intellia: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Dingli:Sanofi-Genzyme: Consultancy; Janssen: Consultancy; Bristol Myers Squibb: Research Funding; Rigel: Consultancy; Apellis: Consultancy; Karyopharm Therapeutics: Research Funding; Alexion: Consultancy; Millenium: Consultancy. Kumar:Sanofi: Research Funding; Kite Pharma: Consultancy, Research Funding; Cellectar: Other; Genecentrix: Consultancy; Novartis: Research Funding; Karyopharm: Consultancy; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; MedImmune: Research Funding; Adaptive Biotechnologies: Consultancy; Tenebio: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria; BMS: Consultancy, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments. Kapoor:Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding; Celgene: Honoraria.

Introduction The molecular basis for the clinical heterogeneity observed in DLBCL was initially elucidated with gene expression profiling, identifying three distinct subgroups with prognostic relevance based on COO namely activated B-cell like (ABC), germinal center B-cell like (GCB) and a type 3 subtype. Shortly afterwards the Han's algorithm was developed allowing for widespread applicability and correlation of gene expression with protein expression. GCB profile at diagnosis is generally associated with superior long term outcomes compared to non-GCB in patients treated with conventional chemo-immunotherapy. Patients with relapsed/refractory disease (R/R) after front-line therapy undergo salvage treatment and, upon demonstration of objective response, proceed with high dose therapy and autologous transplantation (auto-HCT). Whether COO by Han's algorithm retains its prognostic significance post auto-HCT in patients with R/R DLBCL is not well established. Methods A retrospective analysis was carried out using the Mayo database across all three sites. It included all patients between 18-75 years of age who were diagnosed with de-novo DLBCL and underwent first auto-HCT for R/R disease. Patients must have received salvage treatment and shown chemotherapy sensitive disease. Patients with transformed DLBCL or other variants and those who underwent auto-HCT in first complete remission for high risk disease were excluded. Patients who had less than a partial response at time of transplantation and still underwent auto-HCT were also excluded. Primary endpoints included overall survival (OS); relapse and relapse-free survival (RFS). Continuous variables were summarized as mean (standard deviation) and median (range). Categorical variables were reported as frequency (percentage). Continuous baseline variables were compared between GCB and non-GCB using Wilcoxon rank sum test and categorical baseline variables were compared with Chi-squared test. Kaplan-Meier method was used to estimate 5 and 10-year rates of freedom from long-term events and draw corresponding survival curves. Multivariate Cox regression models were used to evaluate the impact of cell of origin on long-term survival after adjusting for baseline factors. All tests were two-sided with alpha level set at 0.05 for statistical significance. Results A total of 357 patients underwent auto-HCT between 2005 and 2018. Cell of origin was determined for 284 patients and these were included in the analysis. Median age was 61 (range: 19-78) years, and 64% patients were male. GCB and non-GCB patients did not differ significantly with regards to baseline factors (Table 1). During a median follow up of 1.7 years, 139 patients died, 151 had relapse and 175 either died or had relapse. Five year OS for GCB patients versus (vs) non-GCB was at 44% vs 64% (p=0.004) (Figure 1); relapse was at 67% vs 49% (p=0.012) and RFS was at 28% vs 50% (p=0.003). The difference between GCB and non-GCB groups remained statistically significant in multivariate analysis, with GCB patients 60% more likely to die and 50% more likely to experience relapse compared to non-GCB patients. The only other factor that retained significance in multivariate analysis for both endpoints was response at the time of transplant (Table 2A-C). Conclusions In this analysis, GCB DLBCL results in worse OS and higher relapse vs. non-GCB DLBCL following an auto-HCT. It is unclear if relapse/progression of GCB subtype after front-line therapy alters its natural history into a more aggressive disease. Disclosures Ansell: Trillium: Research Funding; ADC Therapeutics: Research Funding; Affimed: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding. Foran:Xencor: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Kura Oncology: Research Funding; Aptose: Research Funding; Aprea: Research Funding; Actinium: Research Funding; Boehringer Ingelheim: Research Funding; Abbvie: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Revolution Medicine: Consultancy; H3Biosciences: Research Funding; Agios: Honoraria, Research Funding. Tun:Celgene: Research Funding; Mundipharma: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding; Acrotech: Research Funding; DTRM Biopharma: Research Funding; Bristol-Myers Squibb: Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

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.