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Although translocation events between chromosome 4 (NSD2 gene) and chromosome 14 (immunoglobulin heavy chain [IgH] locus) (t(4;14)) is considered high risk in newly diagnosed multiple myeloma (NDMM), only ∼30% to 40% of t(4;14) patients are clinically high risk. We generated and compared a large whole genome sequencing (WGS) and transcriptome (RNA sequencing) from 258 t(4;14) (n = 153 discovery, n = 105 replication) and 183 non-t(4;14) NDMM patients with associated clinical data. A landmark survival analysis indicated only ∼25% of t(4;14) patients had an overall survival (OS)

Large-scale analyses of genomic data from patients with newly diagnosed multiple myeloma (ndMM) have been undertaken, however, large-scale analysis of relapsed/refractory MM (rrMM) has not been performed. We hypothesize that somatic variants chronicle the therapeutic exposures and clonal structure of myeloma from ndMM to rrMM stages. We generated whole-genome sequencing (WGS) data from 418 tumors (386 patients) derived from 6 rrMM clinical trials and compared them with WGS from 198 unrelated patients with ndMM in a population-based case-control fashion. We identified significantly enriched events at the rrMM stage, including drivers (DUOX2, EZH2, TP53), biallelic inactivation (TP53), noncoding mutations in bona fide drivers (TP53BP1, BLM), copy number aberrations (CNAs; 1qGain, 17pLOH), and double-hit events (Amp1q-ISS3, 1qGain-17p loss-of-heterozygosity). Mutational signature analysis identified a subclonal defective mismatch repair signature enriched in rrMM and highly active in high mutation burden tumors, a likely feature of therapy-associated expanding subclones. Further analysis focused on the association of genomic aberrations enriched at different stages of resistance to immunomodulatory agent (IMiD)–based therapy. This analysis revealed that TP53, DUOX2, 1qGain, and 17p loss-of-heterozygosity increased in prevalence from ndMM to lenalidomide resistant (LENR) to pomalidomide resistant (POMR) stages, whereas enrichment of MAML3 along with immunoglobulin lambda (IGL) and MYC translocations distinguished POM from the LEN subgroup. Genomic drivers associated with rrMM are those that confer clonal selective advantage under therapeutic pressure. Their role in therapy evasion should be further evaluated in longitudinal patient samples, to confirm these associations with the evolution of clinical resistance and to identify molecular subsets of rrMM for the development of targeted therapies.

The acquisition of a multidrug refractory state is a major cause of mortality in myeloma. Myeloma drugs that target the cereblon (CRBN) protein include widely used immunomodulatory drugs (IMiDs), and newer CRBN E3 ligase modulator drugs (CELMoDs), in clinical trials. CRBN genetic disruption causes resistance and poor outcomes with IMiDs. Here, we investigate alternative genomic associations of IMiD resistance, using large whole-genome sequencing patient datasets (n = 522 cases) at newly diagnosed, lenalidomide (LEN)-refractory and lenalidomide-then-pomalidomide (LEN-then-POM)-refractory timepoints. Selecting gene targets reproducibly identified by published CRISPR/shRNA IMiD resistance screens, we found little evidence of genetic disruption by mutation associated with IMiD resistance. However, we identified a chromosome region, 2q37, containing COP9 signalosome members COPS7B and COPS8, copy loss of which significantly enriches between newly diagnosed (incidence 5.5%), LEN-refractory (10.0%), and LEN-then-POM-refractory states (16.4%), and may adversely affect outcomes when clonal fraction is high. In a separate dataset (50 patients) with sequential samples taken throughout treatment, we identified acquisition of 2q37 loss in 16% cases with IMiD exposure, but none in cases without IMiD exposure. The COP9 signalosome is essential for maintenance of the CUL4-DDB1-CRBN E3 ubiquitin ligase. This region may represent a novel marker of IMiD resistance with clinical utility.

Supplementary Data from Interactome of Aiolos/Ikaros Reveals Combination Rationale of Cereblon Modulators with HDAC Inhibitors in DLBCL

Purpose:Cereblon (CRBN), a substrate receptor of the E3 ubiquitin ligase complex CRL4CRBN, is the target of the small molecules lenalidomide and avadomide. Upon binding of the drugs, Aiolos and Ikaros are recruited to the E3 ligase, ubiquitylated, and subsequently degraded. In diffuse large B-cell lymphoma (DLBCL) cells, Aiolos and Ikaros are direct transcriptional repressors of interferon-stimulated genes (ISG) and degradation of these substrates results in increased ISG protein levels resulting in decreased proliferation and apoptosis. Herein, we aimed to uncover the mechanism(s) Aiolos and Ikaros use to repress ISG transcription and provide a mechanistic rationale for a combination strategy to enhance cell autonomous activities of CRBN modulators (CELMoD).Experimental Design:We conducted paired RNA sequencing with histone modification and Aiolos/Ikaros chromatin immunoprecipitation sequencing to identify genes regulated by these transcription factors and to elucidate correlations to drug sensitivity. We confirmed Aiolos/Ikaros mediated transcriptional complex formation in DLBCL patient samples including those treated with avadomide.Results:In DLBCL, the repression of ISG transcription is accomplished in part through recruitment of large transcriptional complexes such as the nucleosome remodeling and deacetylase, which modify the chromatin landscape of these promoters. A rational combination approach of avadomide with a specific histone deacetylase inhibitor leads to a significant increase in ISG transcription compared with either single agent, and synergistic antiproliferative activity in DLBCL cell lines.Conclusions:Our results provide a novel role for lineage factors Aiolos and Ikaros in DLBCL as well as further insight into the mechanism(s) of Aiolos and Ikaros–mediated transcriptional repression and unique therapeutic combination strategies.

Supplementary Figure from Interactome of Aiolos/Ikaros Reveals Combination Rationale of Cereblon Modulators with HDAC Inhibitors in DLBCL

Supplementary Table from Interactome of Aiolos/Ikaros Reveals Combination Rationale of Cereblon Modulators with HDAC Inhibitors in DLBCL

Although translocation events between chromosome 4 (NSD2 gene) and chromosome 14 (IgH locus) (t(4;14)) is considered high-risk in newly diagnosed multiple myeloma (NDMM), only ~30-40% of t(4;14) patients are clinically high-risk. We generated and compared a large whole genome sequencing (WGS) and transcriptome (RNA-sequencing) from 258 t(4;14) (N=153 discovery, N=105 replication) and 183 non-t(4;14) NDMM patients with associated clinical data. A landmark survival analysis indicated only ~25% of t(4;14) patients had an overall survival (OS)24 months, and a comparative analysis of the patient subgroups identified biomarkers associated with this poor outcome, including translocation breakpoints located in NSD2 gene and expression of IGH-NSD2 fusion transcripts. Three breakpoints were identified and are designated as: "no-disruption" (upstream of NSD2), "early-disruption" (in the 5' UTR) and "late-disruption" (within the NSD2 gene). Our results show a significant difference in OS based on the location of DNA breakpoints (mOS 28.6 "late-disruption" vs 59.2 "early disruption" vs 75.1 mo "no disruption"). These findings have been replicated in an independent replication dataset. Also, univariate and multivariate analysis suggest high-risk markers such as del17p, 1p independently contribute to poor outcome t(4;14) MM patients.

Emergence of drug resistance to all available therapies is the major challenge to improving survival in myeloma. Cereblon (CRBN) is the essential binding protein of the widely used immunomodulatory drugs (IMiDs) and novel CRBN E3 ligase modulator drugs (CELMoDs) in myeloma, as well as certain proteolysis targeting chimeras (PROTACs), in development for a range of diseases. Using whole-genome sequencing (WGS) data from 455 patients and RNA sequencing (RNASeq) data from 655 patients, including newly diagnosed (WGS, n = 198; RNASeq, n = 437), lenalidomide (LEN)-refractory (WGS, n = 203; RNASeq, n = 176), and pomalidomide (POM)-refractory cohorts (WGS, n = 54; RNASeq, n = 42), we found incremental increases in the frequency of 3 CRBN aberrations, namely point mutations, copy losses/structural variations, and a specific variant transcript (exon 10 spliced), with progressive IMiD exposure, until almost one-third of patients had CBRN alterations by the time they were POM refractory. We found all 3 CRBN aberrations were associated with inferior outcomes to POM in those already refractory to LEN, including those with gene copy losses and structural variations, a finding not previously described. This represents the first comprehensive analysis and largest data set of CBRN alterations in myeloma patients as they progress through therapy. It will help inform patient selection for sequential therapies with CRBN-targeting drugs.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients do not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involve assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post challenge analysis identified a novel expression-based risk marker and histone modifier,PHF19, which featured prominently in several independent models. Lastly, we show that a simple four feature predictor composed of age, International Staging System stage (ISS), and expression ofPHF19andMMSETperforms similarly to more complex models with many more gene expression features included.Key pointsMost comprehensive and unbiased assessment of prognostic biomarkers in MM resulting in a robust and parsimonious model.Identification ofPHF19as the expression based biomarker most strongly associated with rapid progression in MM patients.

Background Despite significant therapeutic advances in improving lives of Multiple Myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Results Our integrative approach let us identify ndMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank pval − 6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t.test pval

Purpose: Cereblon (CRBN), a substrate receptor of the E3 ubiquitin ligase complex CRL4CRBN, is the target of the small molecules lenalidomide and avadomide. Upon binding of the drugs, Aiolos and Ikaros are recruited to the E3 ligase, ubiquitylated, and subsequently degraded. In diffuse large B-cell lymphoma (DLBCL) cells, Aiolos and Ikaros are direct transcriptional repressors of interferon-stimulated genes (ISG) and degradation of these substrates results in increased ISG protein levels resulting in decreased proliferation and apoptosis. Herein, we aimed to uncover the mechanism(s) Aiolos and Ikaros use to repress ISG transcription and provide a mechanistic rationale for a combination strategy to enhance cell autonomous activities of CRBN modulators (CELMoD). Experimental Design: We conducted paired RNA sequencing with histone modification and Aiolos/Ikaros chromatin immunoprecipitation sequencing to identify genes regulated by these transcription factors and to elucidate correlations to drug sensitivity. We confirmed Aiolos/Ikaros mediated transcriptional complex formation in DLBCL patient samples including those treated with avadomide. Results: In DLBCL, the repression of ISG transcription is accomplished in part through recruitment of large transcriptional complexes such as the nucleosome remodeling and deacetylase, which modify the chromatin landscape of these promoters. A rational combination approach of avadomide with a specific histone deacetylase inhibitor leads to a significant increase in ISG transcription compared with either single agent, and synergistic antiproliferative activity in DLBCL cell lines. Conclusions: Our results provide a novel role for lineage factors Aiolos and Ikaros in DLBCL as well as further insight into the mechanism(s) of Aiolos and Ikaros–mediated transcriptional repression and unique therapeutic combination strategies.

Background Prostate cancer (PCa) is primarily driven by androgen receptor (AR) signaling and has a highly immunosuppressive microenvironment. Although genomic and histopathological differences between low- and high-grade primary PCa (lgPCa and hgPCa) have been reported, an integrative assessment of multiple molecular features in the context of disease grade and metastatic outcome is lacking. We propose that a subset of hgPCa patients who relapse under SOC may benefit from adjuvant immune-checkpoint blockade (ICB) added to SOC to overcome immune suppression. Methods We analyzed treatment naive prostatectomy tissue from a cohort of 124 primary PCa patients (n= 58, Gleason score ≤6; n= 66, Gleason score ≥ 8). We performed RNAseq expression profiling, whole-exome sequencing (WES) and immunohistochemistry. We employed digital spatial analysis in tumor vs. stromal regions to characterize differences in CD8+ T-cell topology between lgPCa and hgPCa Results 1.Comparisons in lg vs. hgPCA: Digital spatial analysis assessing the proximity of CD8+ T-cells to tumor cells revealed a T-cell exclusion phenotype that is more prominent in hgPCa, whereas evaluation of overall CD8+ T-cell density in tumor and stromal regions did not differentiate disease grades. HgPCa had a higher frequency of at least one functional mutation in either TP53, RHPN2, or KMT2D genes compared to lgPCa. Assessment of MHC-I deficiency by IHC and mRNA revealed that hgPCa has significantly lower MHC-I protein expression compared to lgPCa. Interestingly, MHC-I loss in hgPCa associated with a T-cell exclusion phenotype. Moreover, RNAseq gene expression signatures revealed higher expression of tumor-associated macrophage (TAMs), T-regs, Cancer-Associated Fibroblasts (CAFs), DNA damage repair (DDR) genes and lower Interferon-γ (IFN- γ) expression in hgPCa compared to lgPCa. Overall, hgPCa is characterized by a combined phenotype of ‘MHCIloss/IFN- γ low/CAFhigh/TAMhigh/T-reghigh/DDRhigh’. 2.Comparisons within hgPCA that develop metastasis: Unsupervised analysis of molecular features in hgPCa patients that developed metastases identified a subset of patients that exhibit a less immunosuppressive phenotype with lower tumor AR expression, retained tumor MHC-I expression, moderate CD8+ T-cell infiltration and a high IFN-γ RNA signature (figure 1), suggesting potential benefit from ICB therapy Conclusions Our analysis suggests that hgPCa is characterized by low antigenicity as assessed by loss of MHC-I protein expression and an immunosuppressive microenvironment rich in CAFs, macrophages, T-regs and T-cell exclusion phenotypes. Unlike lgPCa, hgPCA can have a poor prognosis (within 5 years relapse). However, a subset of hgPCa patients that metastasized while on SOC exhibited a biomarker profile that might benefit from combination of SOC with ICB Ethics Approval This study was approved by BMS Cambridge Massachusetts Institutional Biosafety Committee, approval number CAM_2020_12050_6 Consent ‘Written informed consent was obtained from the patient for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the Editor of this journal.’

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.

Introduction: The recombination of chromosomes 4 and 14 (t(4;14)) is a primary, predominantly clonal event in newly diagnosed multiple myeloma (ndMM) that is present in ~15% of patients. The translocation results in enhancer regions from the immunoglobulin heavy chain locus upregulating the expression of NSD2 and FGFR3 genes implicated in the disease biology of this subset of MM patients (Chesi et al. Blood. 1998, Keats et al, Leuk Lymph. 2006). The presence of t(4;14) translocation is a considered a biomarker of aggressive disease and is part of the Revised International Staging System (R-ISS) for clinical risk stratification. However, historically only ~40% of t(4;14) patients are high-risk based on the GEP70 gene expression signature. (Weinhold et al. Leukemia. 2016) Our previous analysis of a large cohort of ndMM patients described the genomic features of t(4;14) vs ndMM overall population demonstrating that only ~25% of t(4;14) patients died within 24 months of diagnosis and described biomarkers in this high-risk subset. This analysis identified both known and novel aberrations in ndMM, including some that were associated with high-risk t(4;14) (Ortiz et al Blood. 2019; 134 (Suppl_1):366). In this updated analysis, we provide a more robust analysis of the t(4;14) dataset and demonstrate the prognostic value of the NSD2 breakpoint location. Methods: We generated a large genomic dataset from t(4;14) ndMM patients with whole genome sequencing (WGS) and RNA-seq from a TOUL dataset (t(4;14) N=114) patients treated in routine practice), the IFM2009 trial (N=19), and the Myeloma Genome Project (MGP) (N=34) for discovery and validation. Gene expression, copy number aberration, single nucleotide variant and translocations were derived from RNAseq and WGS profiling of biopsies from patients aged less than 75 years who received transplant, and integrated with clinical information (including age, OS). Cytogenetic assessments from WGS were made by MANTA and used to identify translocation DNA breakpoint location. Results: In all datasets, three DNA breakpoint locations were identified, and based on their position with respect to the NSD2 gene named "no-disruption" (upstream of NSD2 gene), "early-disruption" (in the 5' UTR of NSD2 gene) and "late-disruption" (in the coding region of NSD2 gene). Using paired RNA-seq data, we identified IGH-NSD2 RNA fusion transcripts relative to the breakpoints that corresponded with previously described NSD2 isoforms. "No-disruption" and "early-disruption" breakpoints predominantly produced a fusion transcript (MB4-1) that retained the full coding sequence of the gene, while the "late-disruption" produced truncated fusion transcripts (MB4-2/3). We conducted survival analysis in our datasets based on both DNA breakpoint location and RNA fusion transcripts. This analysis demonstrated a significant difference in outcome between the patient samples with "no-disruption" and the "late-disruption" breakpoints that associated with good and poor OS, respectively (OS pval < 3e-4) in the discovery TOUL dataset. Patients with "late-disruption" had a median OS of 28.64 mo vs 59.18 mo for "early disruption" and 82.26 mo for those with "no disruption" (Figure). This association was replicated in an independent dataset (MGP N=33, replication pval Conclusion: From a large genomic dataset, we were able to discover and validate a clear association between the translocation breakpoints and survival outcome in t(4:14) ndMM patients. While prospective validation is needed before clinical application of our finding, molecular identification of high-risk t(4;14) patients using DNA breakpoint location may enable proper risk classification for this patient group at diagnosis, and would provide improved opportunities for risk-adjusted therapy and identification of a therapeutic target for this high-risk subpopulation. Ongoing work on mutations, copy number, and differential gene expression analyses between translocation breakpoint sub-groups and will be presented. Figure 1 Figure 1. Disclosures Stong: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Ortiz: Bristol Myers Squibb: Current Employment. Towfic: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Pierceall: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Flynt: Bristol Myers Squibb: Current Employment. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties.

Introduction: The Myeloma Genome Project (MGP) characterized the genomic landscape of patients with newly diagnosed multiple myeloma (NDMM) (Walker BA, et al. Blood 2018; 132[6]:587-597). Using a multi-omics unsupervised clustering approach, 12 molecularly-defined disease segments were identified (Ortiz M, et al. Blood 2018; 132[suppl 1]:3165). Here, we performed experimental validation of CDC28 Protein Kinase Regulatory Subunit 1B (CSK1B) that was identified as a putative target from the disease segment with poorest clinical outcome. CKS1B was selected for in-depth validation due to their role in cell cycle pathways associated with high-risk disease, biological mechanisms of chromosome 1q amplification and druggability. Methods: Association of CKS1B with outcomes was analyzed in NDMM patients, across relapses and with clinical outcome datasets from MGP and Mayo clinic. Inducible shRNAs of CKS1B and bromodomain containing protein 4 (BRD4, a member of the BET [bromodomain and extra terminal domain] family) were generated in MM cell lines. BRD4 and Aiolos ChIP-seq datasets were analyzed for binding on CKS1B gene. BRD4 inhibitors JQ1 and CC-90010 were utilized for inhibition studies in MM cell lines. Results: Higher expression of CKS1B was associated with significantly poorer PFS, OS, disease severity and relapse. Knock-down of CKS1B in MM cells led to a significant decrease in proliferation (P Conclusions: In summary, we have identified CKS1B as a key target associated with poor outcome in MM patients. Translational studies suggest a profound downregulation of CKS1B and key pro-survival effector proteins following combination treatment with BRD4inh and IMiD agents/novel CELMoD agents resulting in synergistic anti-tumor effects. These data provide rationale for testing these agents in the clinic for high-risk and IMiD-relapsed patients. Figure: Changes in cell proliferation and protein levels of key signaling mediators were studied in K12PE cell line treated with increasing doses of Lenalidomide, Pomalidomide and CC-92480 in combination with JQ1. Figure 1 Figure 1. Disclosures Ahsan: BMS: Current Employment, Current equity holder in publicly-traded company. Polonskaia: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Hsu: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Bjorklund: BMS: Current Employment, Current equity holder in publicly-traded company. Ortiz Estevez: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Towfic: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Bahlis: Takeda: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; GlaxoSmithKline: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Genentech: Consultancy; Pfizer: Consultancy, Honoraria; BMS/Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria. Pourdehnad: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties: No royalty. Flynt: BMS: Current Employment, Current equity holder in publicly-traded company. Ahsan: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties.

Introduction Identification of the causes of, and biomarkers for, drug resistance in myeloma is important for understanding treatment failures, and for future instigation of targeted therapeutics for myeloma. Using the largest set of whole genome sequencing (WGS) of advanced and drug resistant multiple myelomas to date, we reported that even heterozygous loss of the 3p region, which harbours immunomodulatory drug (IMiD) and CRBN E3 ligase modulator drug (CELMoD)-binding protein Cereblon (CRBN), undergoes strong therapeutic selection on lenalidomide (LEN) and/or pomalidomide (POM) treatment (Gooding et al 2021, PMC7893409). We hypothesized that copy loss of other genes required for IMiD activity may also have clinical relevance. Several groups have reported pharmacogenetic screens identifying genes essential for IMiD sensitivity in vitro, particularly genes required for the maintenance of the CUL4-DDB1-CRBN E3 Ubiquitin Ligase, such as members of the COP9 signalosome complex, function of which prevents CRBN protein degradation. However, loss of these genes has hitherto not been reported in myeloma. Methods and results We identified candidate genes whose loss may favor IMiD drug resistance from published pharmacogenetic screens (n=5), and shortlisted genes consistently identified as essential for LEN or POM function in ≥2 screens (n=23). In our WGS dataset of 455 patients (cohorts: newly diagnosed (ND) n = 198, LEN-refractory n = 203; and LEN-then-POM-refractory n = 54), the incidence of mutation of shortlisted LEN/POM-essential genes in drug-refractory cohorts was rare (10% copy loss at the LEN-then-POM-refractory state, plus incidence of copy loss that increased from ND to LEN-then-POM-refractory states by ≥1.5-fold. This delivered 3 copy loss regions for further investigation: a) 3p, which we had already reported; b) 17p, loss of which is known to be strongly selected in myeloma as the site of TP53; and c) 2q, previously unidentified as relevant in myeloma, but whose minimal common region contained two members of the COP9 signalosome (COPS7B, COPS8). Proportion of loss of this region increased between ND (5.5%), LEN-refractory (9.8%) and LEN-then-POM-refractory states (16.6%), p=0.009. Those patients who had lost a copy of these genes also demonstrated a significant reduction in COPS7B/COPS8 gene expression (p In a separate cohort of myeloma patients (n=24) with sequential sample WGS analysis before and after LEN and/or POM resistance acquisition, we traced acquisition of CNA-defined subclones. 5/24 (21%) patients had acquired either clonal or subclonal loss of the 2q region containing COPS7B and COPS8 at IMID resistance, which had been either absent or below limit of detection pre-IMiD exposure. No other CNA newly-emerged in such a high proportion during IMiD treatment. Relative decrease in even one COP9 signalosome gene has been shown to cause CRBN protein level to fall, and reduce LEN efficacy (Sievers et al 2018, PMC6148446). We are now analysing CRBN protein levels in sequential biopsies from these cases. Conclusion Copy number aberrations have not previously been shown to drive a therapy-specific clonal advantage in myeloma in the clinic. We have now identified a second novel CNA, 2q loss, which increases in incidence through LEN- and POM-refractory states to emerge as a marker of dominant clones in advanced, IMiD-resistant disease. Whether these CNAs will mark resistance to novel CELMoDs remains to be seen. The CRBN protein is key to the function of these drugs, and many novel proteolysis targeting chimeras (PROTACs) in development, but whether the kinetics of their CRBN binding are as sensitive to relative CRBN protein loss remains a key question. CNAs may be easily and cost-effectively detected in the clinic by targeted sequencing approaches, and may prove valuable in future therapeutic decision making. Disclosures Gooding: Bristol Myers Squibb: Research Funding. Ansari-Pour: Bristol Myers Squibb: Consultancy. Karagoz: h.: Research Funding. Ortiz Estevez: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Towfic: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Flynt: BMS: Current Employment, Current equity holder in publicly-traded company. Pierceall: BMS: Current Employment, Current equity holder in publicly-traded company. Yong: Sanofi: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Takeda: Honoraria; GSK: Honoraria; Amgen: Honoraria; BMS: Research Funding; Autolus: Research Funding. Vyas: Astellas: Consultancy, Honoraria; Takeda: Honoraria; Janssen: Honoraria; Novartis: Honoraria; Pfizer: Honoraria; Daiichi Sankyo: Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Gilead: Honoraria; Jazz: Honoraria; AbbVie: Consultancy, Honoraria. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties.

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 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.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding 2 biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies replicated the subgroups, with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4(CRBN) E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and the need for a patient-selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients exhibited an enrichment in response rate and progression-free survival of 44% and 6.2 months vs 19% and 1.6 months for classifier-negative patients (hazard ratio, 0.49; 95% confidence interval, 0.280-0.86; P = .0096). The classifier was not prognostic for rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and nontumor composition and has potential utility to enrich for clinical response to immunomodulatory agents, including avadomide.

Pancreatic ductal adenocarcinoma (PDAC) remains a difficult disease to treat, with few therapies available that target specific patient subgroups. Translational studies in pancreatic cancer can be technically challenging due to biopsy specimen characteristics including low tumor cellularity and dense fibrotic stroma. Resected primary pancreatic tumors obtained during the phase 3 APACT clinical trial (NCT01964430 adjuvant nab-paclitaxel + gemcitabine versus gemcitabine monotherapy n = 866) are a unique resource for characterizing molecular and immune subtypes among PDAC tumors and their association with treatment outcomes in the adjuvant chemotherapy setting. Tumor-infiltrating immune cells were assessed in serial sections of 453 tumors by dual-chromogen immuno-histochemical (IHC) staining (CD4 CD8 CD20 CD163 CMAF CD56 FoxP3 PD-1 PD-L1). Image alignment, segmentation, and spatial localization of stained cells relative to a pan-cytokeratin staining-based tumor epithelial mask was performed using a commercial analysis platform. Expression profiles were obtained for 515 macrodissected tumor biopsy regions by RNA-seq. Transcriptional subtypes were assigned based on schema previously reported by Moffit and Bailey, and molecular pathway correlates were characterized using gene set enrichment analysis. Based on immunochemical staining, higher intratumoral CD8+ or lower CD163+ cell densities were associated with modestly longer disease-free or overall survival in patients treated with nab-paclitaxel plus gemcitabine. The combination of both high CD8+ and low CD163+ cell density was notably associated with longer overall survival compared to other subjects treated with the combination regimen (mOS>55months versus 36 months HR=0.46 p= Citation Format: Thomas Lila, Andrew Biankin, Andrew Browne, David J. Reiss, Brian Lu, Daniel Pierce, Alexander Ratushny, Kao-tai Tsai, Sneh Lata, Sitharthan Kamalakaran, Tomas Babak, Brian Fox, Sam Danziger, Konstantinos Mavrommatis, Matthew W. B. Trotter, David Chang, Fadi Towfic. Multi-omic Profiling of primary pancreatic adenocarcinomas obtained from the APACT adjuvant trial of nab-paclitaxel + gemcitabine vs gemcitabine [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-008.

Deletions of chromosome 17p (del17p) that span the TP53 gene are associated with poor outcome in multiple myeloma (MM), but the prognostic value of del17p cancer clonal fraction (CCF) remains unclear. We applied uniform cytogenetic assessments in a large cohort of newly diagnosed MM (NDMM) patients carrying varying levels of del17p. Incremental CCF change was associated with shorter survival, and a robust CCF threshold of 0.55 was established in discovery and replication data sets. After stratification on the 0.55-CCF threshold, high-risk patients had statistically significantly poorer outcomes compared with low-risk patients (median progression-free survival [PFS] and overall survival [OS], 14 and 32 vs 23.1 and 76.2 months, respectively). Analyses of a third data set comprising whole-exome sequencing data from NDMM patients identified presence of TP53 deletions/mutations as a necessary requirement for high-risk stratification in addition to exceeding the del17p CCF threshold. Meta-analysis conducted across 3 data sets confirmed the robustness of the CCF threshold for PFS and OS. Our analyses demonstrate the feasibility of fluorescence in situ hybridization– and sequencing-based methods to identify TP53 deletions, estimate CCF, and establish that both CCF threshold of 0.55 and presence of TP53 deletion are necessary to identify del17p-carrying NDMM patients with poor prognosis., This work was supported by the Myeloma Disease Team in Celgene (A.T.), by the Association Recherche Cancer (IUC-Oncopole Laboratory), and by Celgene, which provided data processing and storage.

Comparative analyses of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. The rapidly advancing field of systems biology aims to understand the structure, function, dynamics, and evolution of complex biological systems in terms of the underlying networks of interactions among the large number of molecular participants involved including genes, proteins, and metabolites. In particular, the comparative analysis of network models representing biomolecular interactions in different species or tissues offers a powerful means of identifying conserved modules, predicting functions of specific genes or proteins and studying the evolution of biological processes, among other applications. The primary focus of this dissertation is on the biomolecular network alignment problem: Given two or more networks, the problem is to optimally match the nodes and links in one network with the nodes and links of the other. We describe a suite of modular, extensible, and efficient algorithms for aligning biomolecular network models including: (1) undirected graphs in their weighted and unweighted variations (2) undirected graphs in their labeled and unlabeled variants. The resulting algorithms have been implemented as part of the Biomolecular Network Alignment (BiNA) Toolkit, an open source, user-friendly suite of software for comparative analysis of networks. Our experiments show that BiNA is (i) competitive with the state-of-the-art network alignment tools with respect to the quality of alignments (based on a variety of performance measures) and (ii) able to align large networks ranging in size from a few hundreds of nodes and a few thousand edges to tens of thousands of nodes with millions of edges. We describe several applications of BiNA including (1) construction of phylogenetic trees based on protein-protein interaction networks, and (2) identification of biochemical pathways involved in ligand recognition in B cells by aligning gene co-expression networks constructed from mRNA profiles of B cells exposed to different ligands.

Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including IDH1, IDH2, HUWE1, KLHL6, and PTPN11. Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in FGFR3, DIS3, and PRKD2; t(11;14) with mutations in CCND1 and IRF4; t(14;16) with mutations in MAF, BRAF, DIS3, and ATM; and hyperdiploidy with gain 11q, mutations in FAM46C, and MYC rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

We analyzed gene expression levels of CRBN, cMYC, IRF4, BLIMP1, and XBP1 in 224 patients with multiple myeloma treated with pomalidomide and low-dose dexamethasone in the STRATUS study (ClinicalTrials.gov: NCT01712789; EudraCT number: 2012-001888-78). Clinical responses were observed at all CRBN expression levels. A trend in progression-free survival (PFS; p = .038) and a potential trend in overall survival (OS; p = .059) favoring high CRBN expressers were observed; however, no notable difference in overall response rate (ORR) was observed. ORR (30%), median PFS (17.7 weeks), and median OS (52.3 weeks) in low-CRBN expressers were comparable to those in the STRATUS intent-to-treat population (ORR, 33%; median PFS, 20.0 weeks; median OS, 51.7 weeks). A trend in ORR (p = .050) favoring higher cMYC expressers was observed with no notable difference in PFS or OS. This analysis does not support exploring CRBN as a biomarker for selecting patients for pomalidomide therapy., The authors received editorial assistance from William Ho, PhD, and Peter J Simon, PhD, funded by Celgene Corporation.

In order to ensure that patients receive the safest and most effective medicines possible, it is often necessary to compare medicines and assess the extent to which they are similar in their clinical impact. Full clinical trials with appropriate endpoints remain the only method to compare the clinical impact of two medicines with absolute certainty. Other available methods (including physicochemical analysis, genomics, and transcriptomics) can provide partial information about certain aspects of a medicine's biological impact, with possible clinical implications. Especially for biologics and non-biological complex drugs, which are more difficult to characterize by physicochemical means than small molecules, genomics and transciptomic studies can yield valuable insights for physicians, regulators, and drug developers. In this review, we cite and summarize a variety of studies that exemplify the emerging science of applying genomics and transcriptomics technologies to compare medicines. We discuss key aspects of experimental design, conduct of genetic assays, and advanced data analysis, all of which are critical for the successful execution of such studies. Finally, we propose new areas for which such studies can be applied to maximize patient benefit and reduce safety issues.

Introduction: It is strikingly difficult to develop successful treatments for PDAC; even with curative resection, most patients die from early occult metastases. Prior studies identified the presence of tumor-infiltrating lymphocytes (TILs) in primary PDAC tumors as having prognostic significance in the PDAC adjuvant setting, sharpening the questions of what fraction of patients have immune-infiltrated tumors and what therapeutic strategies should be pursued in these patients vs. the non-infiltrated group. The phase 3 APACT trial evaluated the use of adjuvant nab-paclitaxel plus gemcitabine vs. gemcitabine in 866 patients with PDAC who had undergone primary tumor resection, with the primary endpoint of disease-free survival evaluated by independent review. We extended studies of the tumor microenvironment of PDAC to a large set of resected APACT primary tumors in an effort to further refine features of tumor or immune infiltrate that influence disease progression and to determine if chemotherapy regimen–specific predictive signatures are identifiable. Tissue analyses for a large subset of APACT samples included RNA-seq, DNA-seq, multiplexed immunohistochemistry (IHC), and proteomics. Methods: We imaged and quantified markers for tumor cells, 7 different immune cells, and 2 immune checkpoint markers using bright-field chromogenic multiplexed IHC from pretreatment samples for more than 500 APACT primary tumor samples. We computationally defined the tumor, tumor margin, and distal stromal (> 150 μm from tumor boundary) regions, and quantified densities and distributions of immune cells in these regions. As part of an initial analysis of more than 400 samples, we applied both unsupervised clustering and supervised classification to these IHC measurements to identify patient subgroups with similar spatial arrangements of immune cells relative to tumor regions. Results: The preliminary analysis of normalized cell densities across all 3 tissue regions revealed 3 patient subgroups: one in which immune cells are mixed within the tumor regions; a second where immune cells approach the tumor boundary but are depleted within the tumor; and a third in which immune cells are depleted in both tumor and its margin, remaining at high densities only in the distal stromal regions. Within these latter subgroups, CD20+, CD4+, and CD8+ cells were more prevalently depleted from tumor and/or margin, whereas CD163+ and CD163+CMAF+ cells showed less of this arrangement. Nearly 85% of patients fell in the second or third patient group. Conclusions: We are pursuing analyses of these data in conjunction with upcoming molecular and genetic profiling data to further elucidate the association of the immune cell populations and these subgroups with clinical outcomes. These data will provide an unprecedented opportunity for exploratory analysis and discovery of immune, molecular, and genetic biomarkers for PDAC patient stratification. Citation Format: David J. Reiss, Thomas Lila, Suzana Couto, Sitharthan Kamalakaran, Yan Ren, Doug Bowman, Amber Ortiz, Maria Wang, Clifton Drew, Kao-Tai Tsai, Mathieu Marella, Brian Fox, Garth McGrath, Matthew Trotter, Fadi Towfic, Ian Cushman, Alexander Ratushny, Brian Lu, Daniel Pierce, Jim Cassidy. Spatial organization of pancreatic ductal adenocarcinoma (PDAC)–associated immune cells from the Adjuvant Pancreatic Adenocarcinoma Clinical Trial (APACT) study [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr A43.

Introduction Multiple myeloma (MM) is a heterogeneous disease defined by genetic lesions including translocations, chromosomal copy number aberrations (CNA), and mutations. Large-scale analyses of genomic data from newly-diagnosed patients (ndMM) have defined key oncogenic drivers beyond previously known primary events. In relapsed/refractory MM (rrMM), analyses have been completed on small numbers of patient samples for mutational profiles; however, a large-scale analysis of genomic and transcriptomic data has not been performed. In this initial analysis, we describe the rrMM genomic landscape including prevalence of key translocations, oncogenic/tumor suppressor mutational drivers, and chromosomal CNA. Methods We generated whole genome sequencing (WGS) and whole transcriptome expression (RNA-seq) from 485 rrMM patient samples derived from clinical trials: NCT01712789/CC-4047-MM-010 (N=236), NCT02045017/CC-4047-MM-013 (N=17), NCT02773030/CC-220-MM-001 (N=45) and NCT01421524/CC-122-ST-001MM2 (N=10). Somatic single nucleotide variants (SNVs) and indels were derived from WGS from 308 samples using GATK/MuTect2 and annotated using ANNOVAR. Clonal and subclonal CNA were identified using Sclust. Translocations were determined using Manta. Oncogenic/tumor suppressor drivers were identified using cDriver, which utilizes recurrence and functional consequence and cancer cell fraction (CCF) of each mutation. Results Key translocations included: t(11;14) [76/308 (25%)], t(8;14) [79/308 (25%)], t(4;14) [44/308 (14%)], t(14;16) [24/308 (8%)], t(6;14) CCND3 [18/308 (6%)], t(14;20) [17/308 (5.5%)], and t(6;14) [IRF4 17/308 (5.5%)]. Hyperdiploidy was detected in 140/308 (46%) patients. Key chromosomal CNA included del14q 134/308 (44%), del13q 131/308 (43%), del8p 113/308 (37%), del17p 53/308 (17%), amplification of 1q (≥4 copies) 45/308 (15%), and del1q 31/308 (10%). Compared to samples from ndMM patients, we saw an increase of del17p (17% vs. 8%) and t(11;14) (25% vs. 15%) in rrMM. Further, out of the 53 del17p patients, 45 (85%) were high CCF (>0.55) versus 63/107 (59%) reported in ndMM (Thakurta et al, Blood. 2019) Double Hit patients (biallelic inactivation of TP53 or amplification of 1q on a background of ISS3) was detected in 12% patients which is significantly higher than ndMM (6%, p < 0.05) (Walker et al, Leukemia. 2018) We identified a total of 107 driver genes (FDR Conclusions We have established and analyzed the largest molecular rrMM dataset with associated clinical outcome data. These analyses are revealing the evolution of genetic drivers of resistance to therapy and will assist in identification of subsets of poor prognostic groups (eg, Double Hit) and new molecular subsets of rrMM where novel targeted therapies could be developed. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Ansari-Pour:Celgene Corporation: Consultancy. Ortiz:Celgene Corporation: Employment, Equity Ownership. Gooding:Celgene Corporation: Research Funding. Rozelle:Celgene Corporation: Other: Contractor for Celgene. Zadorozhny:Celgene Corporation: Other: Contractor for Celgene. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Tsai:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership.

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 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 (p2, compared to 33% of non-D4 with IPI>2 (p 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 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.