Huang, Chuican

ABSTRACTDiffuse large B‐cell lymphoma (DLBCL) patients that fail to achieve a complete metabolic response with frontline immunochemotherapy have a poor prognosis. Genomic profiling has led to a broader understanding of the molecular drivers in DLBCL, but it is unknown how well current classifiers identify patients that will experience primary treatment resistance (PTR). Using whole exome and RNA sequencing data from newly diagnosed DLBCL patients, we evaluated the genomic landscape of PTR and compared it to that of non‐PTR DLBCL. We found a significant increase in the frequency of TP53 (34% vs. 15%, p = 0.005) and ARID1A mutations (21% vs. 7%, p = 0.007) in PTR cases, with pathway analysis further demonstrating a downregulation of TP53 and an increase in chromatin modifying pathways. These results suggest that TP53 and ARID1A may be key mediators of PTR and important pathways contributing to the poor outcomes. We found that the current molecular classifiers were unable to identify PTR cases at diagnosis. However, our newly identified high‐risk signature identified 46% of PTR cases at diagnosis. Overall, these results contribute to our understanding of the genomic landscape of patients with primary treatment resistance.

Background Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma subtype with a 5-year survival of ~64%. While DLBCL is treated using immunochemotherapy (IC) with curative intent, 20%-40% of patients do not reach remission or relapse post IC. Clonal somatic mutations have been associated with aging, hematologic malignancies (predominately myeloid), and reduced OS in the general population. The objective of this study was to evaluate the association of clonal somatic mutations with event-free survival (EFS) and overall survival (OS). Methods We studied newly diagnosed DLBCL cases treated with IC who were enrolled in the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). Clinical and treatment data were abstracted from medical records and all patients were systematically followed for disease outcomes. Pre-treatment DNA was extracted from matched peripheral blood and paraffin-embedded tumor tissue, and whole exome sequencing was conducted at 100x coverage. From the peripheral blood, allele counts from GVCF files produced by HaplotypeCaller in GATK were extracted for 42 genes commonly associated with clonal hematopoiesis (e.g. DNMT3A, TET2, and ASXL1). Mutations were deemed clonal somatic if the population minor allele frequency was <0.5% in ExAC and had a variant allele fraction (VAF) that deviated from 0.5 using a binomial distribution test at p<0.001. Furthermore, mutations had to have a minimum alternate read count of at least 5 reads, a VAF greater than or equal to 10%, and met at least one of the following criteria: CAVA impact of "HIGH"; pathogenic/likely pathogenic per ClinVar; or reported in COSMIC at least 10 times. EFS was defined as time from diagnosis to disease progression, relapse, retreatment, or death due to any cause. The association of clonal somatic mutations with prognosis was estimated using Kaplan-Meier curves, and hazard ratios (HR) and 95% confidence intervals (CI) from Cox regression. Associations were adjusted for age at diagnosis, sex, and the International Prognostic Index (IPI). Results The study consisted of 261 DLBCL patients treated with IC. The median age at diagnosis was 65 years (range 20-90) and 56% were male. With a median follow-up time of 5.1 years (range 0.1-15.0), there were 100 events and 80 deaths. A total of 17 (6.5%) patients had clonal somatic mutations, and16 patients were over the age of 60. A total of 8 (of 42) genes had clonal somatic mutations, with SF3B1, ASXL1, and TET2, being the most frequent (4 individuals per gene). VAFs ranged from 0.10 to 0.28 and none of the patients had multiple mutations. Additionally, the clonal somatic variants found in the peripheral blood were abscent in the tumor sample. Of the 17 patients with clonal somatic mutations, 12 had an event while 88 patients without a mutation had an event. In a univariate analysis, clonal somatic mutations were associated with inferior EFS (HR=2.55, 95% CI 1.39-4.68, p=0.002; Figure 1A). After adjusting for age, sex, and IPI, clonal somatic mutations remained associated with inferior EFS (HR=2.02, 95% CI 1.09-3.74, p=0.026). Clonal somatic mutations were also associated with inferior OS in the univariate analysis (HR=2.06, 95% CI 0.99-4.29, p=0.053), which attenuated after multivariate adjustment (HR=1.59, 95% CI: 0.76-3.34, p=0.22, Figure 1B). Although based on small numbers, mutations in SF3B1 were associated with inferior EFS (HR=3.25, 95% CI 1.16-9.12, p=0.025), but did not reach significance for OS (HR=2.56, 95% CI 0.78-8.38, p=0.120). Conclusions In this novel study of newly diagnosed DLBCL patients, clonal somatic mutations were identified in 6.5% of patients and were associated with inferior outcomes. Additional research is required at deeper sequencing to validate these findings and integration with tumor genomics is required to understand the prognosis of DLBCL patients with smaller clonal populations. Disclosures Shah: Dren Bio: Consultancy. Maurer:Celgene / BMS: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Martin:Celgene: Consultancy; Karyopharm: Consultancy, Research Funding; Morphosys: Consultancy; Regeneron: Consultancy; Incyte: Consultancy; Kite: Consultancy; Cellectar: Consultancy; Bayer: Consultancy; Beigene: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Witzig:AbbVie: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding; MorphSys: Consultancy. 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. Novak:Celgene/BMS: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding.

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.