Novak, Anne J.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p<0.01) and D8 (p<0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p<0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI>2, compared to 33% of non-D4 with IPI>2 (p<0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p<0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p<0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong: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:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

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: 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 >0% except for CD32b (<20% vs ≥20%), CD70 (<10% vs ≥10%), and SIRPa (<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<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<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.

AbstractHistological transformation in Waldenström macroglobulinemia (WM) is an uncommon complication, with limited data, particularly regarding the impact of MYD88 L265P mutation on transformation. We examined risk factors and outcomes associated with transformation in WM, highlighting the role of MYD88 L265P mutation. Patients with WM seen at Mayo Clinic, Rochester, USA and University Hospital of Reims, France, between 01/01/1996 and December 31, 2017 were included; 50 (4.3%) of 1147 patients transformed to a high‐grade lymphoma, with median time‐to‐transformation of 4.5 (range 0‐21) years in the transformed cohort. The MYD88 L265P mutation status was known in 435/1147 (38%) patients (406 with non‐transformed WM and 29 patients in transformed cohort). On multivariate analysis, MYD88 WT status alone was an independent predictor of transformation (odds ratio, 7[95%CI: 2.1‐23]; P = .003). Additionally, the MYD88 WT status was independently associated with shorter time‐to‐transformation (HR 7.9 [95%CI: 2.3‐27; P = .001]), with a 5‐year transformation rate of 16% for MYD88 WT vs 2.8% with MYD88 L265P mutated patients. Patients with transformation demonstrated a significant increase in risk of death compared to patients who did not transform (HR 5.075; 95%CI: 3.8‐6.8; P < .001). In conclusion, the MYD88 WT status is an independent predictor of transformation and associated with a shorter time‐to‐transformation. Additionally, transformation conferred an inferior overall survival in patients with WM.

Abstract Background - The phenotype and biological role of monocytes in B-cell non-Hodgkin lymphoma (NHL) is not fully understood, however, an increased absolute monocyte count in the peripheral blood of lymphoma patients is associated with a poor prognosis. We have previously reported that monocytes from patients with relapsed B-cell NHL displayed an immunosuppressive CD14+HLA-DRlow/- phenotype that correlated with a poor prognosis. However, the underlying mechanism by which CD14+HLA-DRlow/- monocytes develop in patients with B-cell NHL is unknown. The goal of this study was to determine whether cytokines are responsible for the increased number and phenotype of CD14+HLA-DRlow/-monocytes present in lymphoma patients. Methods – Whole blood from patients with newly diagnosed, untreated B-cell NHL (n=20) and healthy donors (n=20) was stained with a panel of antibodies and analyzed by 10-color flow cytometry. Cytokine levels in serum and culture supernatants were measured by Luminex and ELISA, respectively. Polarization of monocytes from healthy donors was performed by incubating CD14+ cells with specific cytokines or supernatants from B-cell NHL cell lines for 24 hours. Activation and proliferation of CD4+T cells cocultured with IL-10-pretreated monocytes were measured by flow cytometry. Results – By flow cytometry, we observed that the absolute number of peripheral monocytes was increased in newly-diagnosed lymphoma patients compared to healthy donors. A significant proportion of these monocytes displayed an immunosuppressive CD14+HLA-DRlow/- phenotype and the numbers of CD14+HLA-DRlow/- cells were significantly higher in lymphoma patients than in healthy donors. To identify the underlying mechanism by which CD14+HLA-DRlow/- monocytes develop thereby leading to increased numbers of CD14+HLA-DRlow/- monocytes in B-cell NHL, we tested a panel of cytokines and found that IL-10 played a key role in the process. Firstly, treatment of monocytes with IL-10 in vitro resulted in the generation of CD14+HLA-DRlow/- cells. Second, monocytes co-cultured with IL-10 producing lymphoma B-cells, or treated with supernatants from lymphoma cell cultures, developed a CD14+HLA-DRlow/- phenotype. Thirdly, IL-10 levels were increased in the serum of DLBCL (p<0.0001) and FL patients (p=0.010) compared to healthy controls, and serum IL-10 levels correlated with increased numbers of peripheral monocytes (p=0.02). Finally, IL-10-pretreated CD14+HLA-DRlow/- monocytes were significantly immunosuppressive and inhibited the proliferation and activation of CD4+T cells in co-culture assays. Conclusions- Taken together, our results suggest that IL-10 signaling contributes to the increased numbers of CD14+HLA-DRlow/- monocytes in B-cell NHL. Strategies to inhibit IL-10 production may therefore have therapeutic potential in B-cell NHL. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Waldenstršms macroglobulinemia (WM) is an indolent B-lymphoplasmacytic lymphoma, which is highly sensitive to 20S proteasome inhibitors such as bortezomib. However, clinical resistance to the drug can be observed early at onset of therapy (primary or inherent resistance) or develop over time, through repeated exposure to the drug (acquired resistance). Although prior investigations into the biological mechanisms associated with bortezomib resistance (BR) have identified aberrations at the genomic (PSMB5 mutation) and transcriptional level (loss of XPB-1), there is limited data on the epigenetic abnormalities that support resistance to 20S proteasome inhibition. With this in mind, we conducted a comprehensive methylation and mRNA expression analysis in WM cells focusing on the molecular patterns that characterize inherent BR (iBR) as well as acquired BR (aBR). Materials and Methods: Bortezomib sensitive (BS) human WM cell lines, BCWM.1 and MWCL-1, the iBR WM cell line RPCI-WM1 (developed from a bortezomib-refractory WM patient) as well as aBR subclones (BCWM.1/BR, MWCL-1/BR) were used in the study. Methylation status of WM cells was profiled by Illumina Infinium 450k methylation array. Methylation IDAT files were background subtracted using GenomeStudio v1.9. Beta mixture quantile dilation (BMIQ) method in R watermelon package was used to adjust type II probes into distribution characteristic of type I probes. For each gene, probes were partitioned into 6 regions using R IMA package and the ones belonging to the same region were averaged, including transcription start sites (TSS; both 200 bp and 1500 bp upstream), 5' untranslated region (UTR), and 3'UTR, the first exon, and genebody. Gene regions showing a significant adjusted p-value and a beta difference of >0.1 or <-0.1 were selected for differential hypermethylation and hypomethylation, respectively. Correlation with gene expression was conducted only on genes showing >1.5 fold change in expression. Overview of methodology is shown in Figure 1. Results: We first assessed the differential methylation pattern (TSS, 200bp upstream of gene body) of bortezomib-sensitive (BS, BCWM.1, IC50 6nM) and iBR cells, (RPCI-WM1). A total of 3,801 hypermethylated and 1,865 hypomethylated DNA regions were observed in iBR cells relative to BS cells (Figure 2A). In iBR cells, a negative correlation between hypermethylated DNA and corresponding gene expression was found in 1,039 genes (Spearman r = -0.12) (Figure 2B). In parallel, we compared the methylation pattern (TSS 200bp) of BS WM cells (BCWM.1 and MWCL-1) with their aBR derivatives, which display ~20 – 50 fold resistance to bortezomib. aBR subclones showed differential hypermethylation of 1,521 DNA regions and hypomethylation of 651 genomic regions relative to BS WM cells. These findings were not unexpected as aBR cell lines, being derivatives of BS WM cells, are clonally related to the former, whereas iBR cells are derived from a different clone. Meta-analysis of iBR and aBR followed by pathway enrichment (NextBio, Illumina Inc), revealed a significant clustering of commonly hypermethylated genes within the Validated Transcriptional Targets of TP63 isoforms Canonical Pathway (p<1.9-E5, Figure 2C) and the CEBPA binding site Geneset 2 (p<8.2-E9, Figure 2D); pathways whose genes are actively involved in suppression of cell growth and differentiation. Further validation and functional relevance of candidate gene targets from this analysis, which are relevant to WM pathobiology, are currently being investigated. Conclusion: This is the first study to examine and correlate the global methylation and gene expression profiles in WM tumor cells that are inherently wired to resist 20S proteasome inhibition as well as tumor cells, which have acquired resistance through continuous exposure to bortezomib. Although considerable differences were observed in the methylation profiles of WM cells displaying inherent vs. acquired resistance, a number of common genes emerged and pointed towards transcriptional repressors, whose inactivation through methylation has been linked to other cancers. Altogether, these findings uncover cellular systems in WM cells, which are affected by DNA methylation, subsequent mRNA suppression and which may be associated as a whole with both inherent and acquired forms of bortezomib resistance. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Deubiquitinating enzyme (DUB) inhibitors are an emerging class of compounds, which are increasingly being regarded for their anti-cancer activity and therapeutic potential. The 19S regulatory particle of the proteasome contains two DUBs, USP14 and UCHL5, which are critical for the proper unfolding and deubiquitination of proteins prior to their entry into the 20S proteasome β-catalytic core. In B/plasma cell malignancies such as Waldenströms macroglobulinemia (WM) and multiple myeloma (MM), protein homeostasis and optimal proteasome functionality are paramount for tumor survival. This is evidenced by the success of 20S proteasome inhibition with bortezomib and carfilzomib, which have demonstrated remarkable clinical benefit in patients with WM or MM. As such, the proteasome degradation pathway represents a highly attractive and clinically validated, yet still largely unexplored therapeutic target. Here, we provide first evidence of the cytotoxic effects and molecular sequelae associated with a novel 19S proteasome DUB inhibitor, VLX1570, which selectively targets USP14 and UCHL5, in preclinical models of B/plasma cell cancers and their drug resistant tumor subclones. Materials: VLX1570 was obtained from Vivolux AB, Sweden. Bortezomib was purchased from Sellekchem, Houston TX. Human myeloma cell lines (OPM2, U266, KMS11), human WM cell lines (BCWM.1, MWCL-1 and RPCI-WM1), their respective bortezomib-resistant (BR) subclones as well as ibrutinib-resistant (IR) derivatives of BCWM.1 and RPCI-WM1 were used in experiments (total n=12). Peripheral blood mononuclear cells (PBMCs) from healthy donors were used to assess for cytotoxicity of VLX1570 in non-tumor cells. Results: A 72hr MTS assay first established sensitivity of plasma cell cancer models towards VLX1570, including drug resistant subclones (EC50 range, 20 – 90nM). Next we assessed whether loss of viability was due to apoptosis and observed dose-dependent annexin-V staining in ~50 – 70% of wild-type MM and WM cells, ~45 – 55% in corresponding BR models and ~50% in IR subclones when exposed to VLX1570 (100nm – 500nM) for 24hrs. VLX1570 was tested at similar concentrations in PBMCs and induced minimal cell death. To understand if VLX1570-induced apoptosis was mitochondrial mediated, we assessed mitochondrial outer membrane permeability (MOMP) in all plasma cell cancer models treated with the DUB inhibitor (100 – 500nM) and found a dose-dependent increase in MOMP (p<0.00025 – 0.000001). Activation of the mitochondrial/intrinsic apoptotic pathway was also evidenced by cleavage of caspase-9 and 3 on immunoblot analysis. As inhibition of protein deubiquitination results in accumulation of polyubiquitinated protein conjugates, we examined global ubiquitinated protein levels and found increasing amounts of high molecular weight poly-ubiquitinated protein in VLX1570 treated tumor cells in a dose-dependent manner. This prompted us to further delineate the potential cellular response mechanisms that relay the antitumor activity of VLX1570 in malignant plasma cells. Immunoblot analysis of drug sensitive and (bortezomib or ibrutinib) resistant MM and WM cell lines revealed several changes in ER stress response proteins, notably induction of HSP70 and splicing of XBP1; the latter event which was less prominent in BR cell lines treated with bortezomib. This suggested the ability of VLX1570 to overcome resistance through a rapid and potent proteotoxic effect, which overwhelmed the tumor cells stress response mechanisms beyond its capacity for survival. Finally, as abnormalities in the NFkB signaling pathway are implicated in malignant plasma cell survival, we found markedly decreased nuclear translocation of NFkB (p-P65) protein in tumor cells treated with VLX1570. Conclusions: The DUBs of the 19S proteasome cap offer an attractive target within the proteasome degradation system and whose inhibition by VLX1570 is able to circumvent mechanisms that support resistance to b-catalytic PI (bortezomib) or BTK inhibitor (ibrutinib) therapy. The studies undertaken thus far, coupled with more rigorous mechanistic and in vivo efficacy analyses currently underway, provides rational for the clinical development of DUB inhibitors for use in malignant B/plasma cell malignancies. Disclosures Martin: Janssen: Honoraria.

Abstract Waldenström macroglobulinemia (WM) is a B-cell malignancy that is characterized by the production of a monoclonal IgM protein, a lymphoplasmacytic infiltrate in the bone marrow, and associated symptoms including anemia, lymphadenopathy and hyperviscosity. The aberrant production of a monoclonal IgM in the serum is a major factor causing significant morbidity in patients with this disease, yet little is known about the mechanisms that regulate monoclonal protein synthesis. While recent gene array studies and serum analysis have shown that IL-6 is elevated in WM patients suggesting an important role for this cytokine in this disease, the precise role played by IL-6 in WM is unknown. Using a multiplex ELISA approach to screen sera from WM patients, we confirmed that IL-6 was significantly elevated (p<0.0019) in patients (n=20) compared to controls (n=20). Serum levels of IL-6 in WM patients correlated with elevated levels of β2-microglobulin (p<0.0019). Additionally, we also found that serum levels of CCL5 (Rantes) were significantly elevated in WM patients (p<0.0001). CCL5 has been shown to regulate IL-6 secretion, and we therefore wanted to determine if CCL5 influenced IL-6 expression in WM and what the subsequent consequence of IL-6 stimulation was on WM cells. To define the source of IL-6 in the tumor microenvironment, we used stromal cells from the bone marrow of healthy donors, malignant cells from patients with WM, and the BCWM.1 WM cell line, and tested their ability to secrete IL-6 by ELISA. All cell types secreted IL-6, with stromal cells secreting the most. We then tested the ability of CCL5 to induce IL-6 secretion by WM and stromal cells. CCL5 significantly increased IL-6 secretion by stromal cells (p<0.03) and also increased IL-6 secretion by fresh CD19+ CD138+ cells from WM patients (p<0.02). Using fresh patient WM cells and the BCWM.1 WM cell line as a model, we then determined the effect of IL-6 on growth of WM cells. We found that IL-6 had a modest effect (mean=20% increase, range=5–41%) on cell proliferation (p<0.0039) but had no effect on cell viability. In contrast, when we addressed the role of IL-6 on IgM secretion, we found that IL-6 increased IgM secretion by BCWM.1 cells in a dose-dependent manner. The IL-6 mediated increase in IgM secretion was abolished in the presence of neutralizing antibodies to IL-6. When we analyzed the downstream signaling events activated by IL-6 in WM cells we found that stimulation of BCWM.1 cells, which express the IL-6R, resulted in phosphorylation of Stat1, Stat3 and Erk1/2, but not Akt. Using a mitogen activated protein kinase (MAPK) inhibitor, we could inhibit the IL-6-mediated phosphorylation of Erk1/2. Similarly, using a JAK1 Inhibitor, we could inhibit IL-6 mediated signaling through Stat1 and Stat3. In summary, we have clearly shown that IL-6 significantly upregulates IgM secretion by WM cells and increases their proliferation. We have also demonstrated the ability of both the malignant cells and the stromal cells to secrete IL-6, and that this secretion is regulated in part by CCL5. We have found that WM cells express IL-6R, and that IL-6 induced signaling is through both the MAPK and Jak/Stat pathways. Therapies targeting IL-6 secretion or the IL-6 signaling pathways may therefore provide clinical benefit to patients with WM; not just by inhibiting the malignant cells but by down regulating the production of the monoclonal protein.

Introduction: Current classifications of low-grade B-cell lymphomas (LGL), including splenic marginal zone lymphomas (SMZL), nodal marginal zone lymphomas (NMZL), and Lymphoplasmacytic lymphomas (LPL) are based on a mixture of clinical features and morphologic, immunophenotypic, and genetic findings from the tumor biopsy specimen. While this approach to classification makes pathologic diagnosis more precise, the corresponding clinical impact for the timing and choice of treatment is limited, and differentiating between cases can be challenging. Although LGL are considered indolent and the 10-year overall survival (OS) is about 80%, 70% of cases will eventually require treatment and approximately 30% of patients display a more aggressive phenotype and have a poor prognosis. Consequently, further investigations into the driving genetic, biological, and immune mechanisms of LGL are essential for early identification of high-risk patients and design of personalized treatments. Materials and Methods: RNA-seq was performed on 63 newly diagnosed LGL patient samples from the Mayo Clinic/University of Iowa Lymphoma SPORE: SMZL (N=48), NMZL (N=6), LPL (N=5), MALT (N=2), and BCL (N=2) as well as 5 normal memory B cell controls (CD19+CD27+). For identification of biologic clusters, filtered RNA-seq data was analyzed using the Non-negative Matrix Factorization (NMF) clustering tool from the Broad Institute against the normal samples. Each cluster was analyzed for differential gene expression. This analysis generated cluster-specific T values for each gene. Genes that were significantly associated with a cluster (FDR<0.05) were analyzed for genetic ontology. Cibersort was used to deconvolute the tumor microenvironment (TME). We performed whole exome sequencing (WES) analyses on 60/63 matched LGL to characterize mutations and copy number alterations. Results: WES revealed the presence of previously reported mutations in genes such as MYD88, SPEN, NOTCH, and KLF2 as well as copy number alterations 7q31.2, BCL6, TNFAIP3, and BCL2. NMF clustering of RNA-seq data resulted in a best fit of 5 clusters, named LGL1 through LGL4 and "Normals". Pathologic subtypes were not exclusive to a specific cluster. Differential RNA expression analysis resulted in gene sets that were differentially expressed in each cluster. A cluster signature of the top 1% of associated genes (N=94) was created for each. LGL1 genes were associated with higher BCR signaling. LGL2 was characterized by regulatory dysfunction, particularly concerning mitochondrial integrity. LGL3 genes were associated with high TME and NOTCH and STAT signaling components. Specifically, LGL3 was associated with high CD8+ T-cell and M2 macrophage TME presence. LGL4 was distinguished by the presence of aggressive genetic programs related to B-cell activation such as NF-kB and IRF4. Consequently, LGL4 patients had significantly poorer rates of event-free survival (EFS) (P=0.029) and OS (P=0.006) when compared against the other groups. The top 1% gene signature (N=94) was next validated against an independent cohort of 84 LGL samples with gene expression data (SMZL [N=34], NZML [N=24], and LPL [N=24]). A similar cluster pattern emerged, with pathological subtypes distributing across LGL classifications based on respective transcriptomic signatures. Association of DNA variants within each cluster was also analyzed, but due to the low frequency of genomic variants detected in LGL, nothing was significantly associated. However, we did see an enrichment of BCL2, BCL6, and TNFAIP3 alterations in LGL4 and an overall lower driver gene mutation frequency in LGL2. Conclusions: Gaining a greater understanding of LGL based on their genetic, biologic, and immune profiles will establish a valuable platform for clinicians to identify high-risk cases and make better therapeutic decisions. Using a large cohort of well-annotated cases we identify novel clusters of LGL that present unique genomic and clinical profiles. Our study lays the groundwork for a precision therapy approach in LGL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care and who would benefit from closer monitoring and targeted agents. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding.

The outcomes of lymphoma patients treated with PD1 inhibitors (PD1i) vary significantly across different histologies with objective responses rates that are around 70% in classical Hodgkin lymphoma, but significantly lower, around 10%, in indolent non-Hodgkin lymphomas (iNHL). This may be due in part to the inability of PD1i to restore the function of exhausted T-cells (Tex) in the tumor microenvironment (TME). PD1 is expressed on various T-cell subsets, including effector cells and Tex. In this study we sought to identify markers of exhaustion beyond PD1 that define T-cell subsets with high reactivation potential in marginal zone lymphoma (MZL). To define different stages of exhaustion in MZL, we developed and validated a mass cytometry (CyTOF) panel which integrates T-cell markers of lineage, differentiation, activation, suppression, cytokine production and transcription factors associated with exhaustion. We analyzed 35 MZL spleens obtained at diagnosis and 6 reactive spleens. We identified a population of CD8 cells in MZL that express TIM3 without PD1, compose 1.5% (range 0.14-4.88%) of total CD8 cells and expand with activation through the TCR (6.2% of CD8, range 0.6-14.2%). Prolonged cytokine exposure has been shown to induce exhaustion in healthy T-cells, and so to determine whether PD1-TIM3+ cells are Tex, we treated healthy donor T-cells with activating beads and IL12 and analyzed them by flow cytometry. We showed that PD1-TIM3+ cells expand with repetitive activation and IL12 treatment suggesting that they are Tex. Since Tex have been shown to have a distinct transcription profile compared to functional effector T-cells, we analyzed the expression of several transcription factors (TFs) that are upregulated in Tex by CyTOF and compared them to other subsets of T-cells. PD1-TIM3+ cells expressed higher levels of Eomes, Tbet, TOX and Helios compared to activated effector cells, confirming that they are Tex, but lower levels levels of the same TFs compared to PD1+TIM3+ cells, consistent with a less severely exhausted phenotype. To characterize the function of PD1-TIM3+ cells, we manually gated on subsets of CD8+ non-naïve cells that were stained with the CyTOF panel and compared the different subsets. We showed that PD1-TIM3+ cells arise from effector cells, maintain their polyfunctionality by producing cytokines and chemokines and have a unique cytokine production profile, including perforin, granzyme B and proinflammatory cytokines, such as IL17A. Importantly, PD1-TIM3+ cells produced higher levels of most cytokines compared to more severely Tex expressing both PD1 and TIM3 (Fig 1). We then performed tSNE analysis where similar objects are modeled by nearby points. We determined that PD1-TIM3+ cells cluster together, suggesting that they are phenotypically homogeneous cells in terms of function and expression of activation markers and are significantly different from PD1+TIM3+ cells (Fig 2). We finally blocked PD1 or TIM3 and analyzed the effect of each antibody blockade on function. We showed that TIM3 blockade was able to restore the production of most cytokines to higher degrees than PD1 blockade. In addition, TIM3 blockade led to upregulation of CD28, which is critical for the rescue of Tex through costimulation, while PD1 blockade did not. We then analyzed the clinical outcomes of all 34 MZL patients. The frequency of PD1-TIM3+ cells was lower among observed patients who failed to achieve an event-free survival (EFS) of 12 months (EFS12) (0.8% vs 1.7%, p=0.02) and among treated patients that failed an EFS of 24 months (EFS24) (0.9 vs 1.7%, p=0.03), when compared to patients that reached these endpoints. Higher levels of PD1-TIM3+ cells were also associated with a trend towards improved overall survival (OS). Five-year OS was 88% vs. 69% in patients with PD1-TIM3+ levels >2% and <2% of total CD8 cells respectively (p=0.1) (Fig 3). Our study provides insights into the mechanisms of failure of PD1i in MZL and identifies a novel population of Tex with high reactivation potential which is clinically relevant and could serve as a target for future immunotherapies in iNHL. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding.