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PURPOSE Peripheral T-cell lymphoma (PTCL) includes heterogeneous clinicopathologic entities with numerous diagnostic and treatment challenges. We previously defined robust transcriptomic signatures that distinguish common PTCL entities and identified two novel biologic and prognostic PTCL-not otherwise specified subtypes (PTCL-TBX21 and PTCL-GATA3). We aimed to consolidate a gene expression–based subclassification using formalin-fixed, paraffin-embedded (FFPE) tissues to improve the accuracy and precision in PTCL diagnosis. MATERIALS AND METHODS We assembled a well-characterized PTCL training cohort (n = 105) with gene expression profiling data to derive a diagnostic signature using fresh-frozen tissue on the HG-U133plus2.0 platform (Affymetrix, Inc, Santa Clara, CA) subsequently validated using matched FFPE tissues in a digital gene expression profiling platform (nCounter, NanoString Technologies, Inc, Seattle, WA). Statistical filtering approaches were applied to refine the transcriptomic signatures and then validated in another PTCL cohort (n = 140) with rigorous pathology review and ancillary assays. RESULTS In the training cohort, the refined transcriptomic classifier in FFPE tissues showed high sensitivity (> 80%), specificity (> 95%), and accuracy (> 94%) for PTCL subclassification compared with the fresh-frozen–derived diagnostic model and showed high reproducibility between three independent laboratories. In the validation cohort, the transcriptional classifier matched the pathology diagnosis rendered by three expert hematopathologists in 85% (n = 119) of the cases, showed borderline association with the molecular signatures in 6% (n = 8), and disagreed in 8% (n = 11). The classifier improved the pathology diagnosis in two cases, validated by clinical findings. Of the 11 cases with disagreements, four had a molecular classification that may provide an improvement over pathology diagnosis on the basis of overall transcriptomic and morphological features. The molecular subclassification provided a comprehensive molecular characterization of PTCL subtypes, including viral etiologic factors and translocation partners. CONCLUSION We developed a novel transcriptomic approach for PTCL subclassification that facilitates translation into clinical practice with higher precision and uniformity than conventional pathology diagnosis.

Abstract Introduction: Plasmablastic lymphoma (PBL) is an aggressive B-cell lymphoma that predominantly occurs in patients with HIV or other causes of immunodeficiency. Frequent infection by the Epstein-Barr virus (EBV) and MYC translocations have been described as major features contributing to the pathogenesis of PBL. Prior studies examining the genetic landscape of PBL have largely relied on targeted capture-based sequencing approaches. As such, the molecular features of PBL remain to be fully explored. Here, we provide a comprehensive description of the genomic landscape of PBL using whole-genome and whole-exome sequencing to identify commonly perturbed pathways. Method: Archival diagnostic fresh frozen and formalin fixed paraffin embedded tissue biopsies from 58 PBL tumours were accrued from Lymphoma/Leukemia Molecular Profiling Project (LLMPP) sites, including 15 tumours from Ramis-Zaldivar et al., Haematolgica 2021. MYC rearrangements were identified by break-apart fluorescent in situ hybridization (FISH) and rearrangement partner was determined in a subset of tumours from capture or genome sequencing data using structural variant callers Manta, GRIDSS, and Delly. High confidence somatic mutations (SNVs/Indels) were identified in data from whole-genome (n=5) or whole-exome (n=53) sequencing through an ensemble voting approach utilizing four variant callers (Strelka2, Lofreq, Mutect2, SAGE). Mutation frequencies in known lymphoma-related genes were compared to activated B-cell (ABC)-DLBCL (Schmitz et al., NEJM 2018), as the closest tumour entity in terms of putative cell-of-origin differentiation stage, to identify differences in genetic aberrations. Candidate somatic copy number alterations (CNAs) were identified from exome and genome sequencing data, using CopywriteR and ControlFREEC, respectively, and high-confidence CNAs were determined using GISTIC2.0. Results: Within the study cohort, 81% of patients were male with a median age of 59 years (range 11-88). HIV and EBV statuses were available for 47% of patients and 95% of tumours, respectively, with 49% (13/27) of the patients being HIV+ and 69% (38/55) of tumours being EBV+. MYC rearrangement was observed in 60% (35/58) of PBLs, with IGH as the partner gene in 88% (21/24) of tumours. The most frequently mutated genes were STAT3 (38%), TP53 (22%), NRAS (21%), and TET2 (16%), consistent with previous studies, however novel mutations were seen in DUSP2 (21%), KLHL6 (16%), and BHLHE41 (16%). Recurrent CNAs included amplifications in 1q, whole gains of 7, 8q24, 11p12 and deletions affecting 4p16, 5p15, 10q11.22. While the mutational landscapes were similar between samples with and without a MYC translocation, the MYC-translocated PBLs showed more frequent amplification of 1q32.1. When stratifying by EBV status, STAT3 and SOCS1 mutations were more frequent in EBV-positive tumours, whereas TP53, TET2, KRAS, and MMRN2 mutations were associated with EBV-negativity. In comparison to ABC-DLBCL, PBLs were significantly enriched in STAT3 and NRAS mutations, and lacked common mutations affecting the NF-κB pathway (eg. MYD88, CD79B, and NFKBIZ 3' UTR mutations). Mutations in genes that are frequently mutated in ABC-DLBCLs, such as those associated with plasma cell differentiation (eg. PRDM1) or a memory B-cell fate (eg. TBL1XR1), were also not mutated in PBLs. Finally, genetic alterations associated with immune evasion, such as deletion of MHC I and II and mutations in B2M, CIITA, and CD58, were rarely observed. Conclusion: These data present a comprehensive overview of the genomic landscape of PBLs in a large cohort. We show frequent mutations involving the JAK-STAT and MAPK pathways, wherein the genetic landscape can be differentially characterized by EBV status and MYC rearrangement status. We show that PBLs are genetically distinct from ABC-DLBCLs, with absence of mutations in genes affecting the NF-κB pathway, immune evasion, and driving a memory B-cell fate. Disclosures Slack: Seagen: Consultancy, Honoraria. Raess: Scopio Labs: Research Funding. Holte: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Nordic: Membership on an entity's Board of Directors or advisory committees; Nanovector: Membership on an entity's Board of Directors or advisory committees, Other: lectures honorarias; Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Savage: Servier: Consultancy, Honoraria; Roche: Research Funding; BMS: Consultancy, Honoraria, Other: Institutional clinical trial funding; Astra-Zeneca: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Other: Institutional clinical trial funding; AbbVie: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Takeda: Other: Institutional clinical trial funding; Beigene: Other: Institutional clinical trial funding; Genentech: Research Funding. Steidl: Seattle Genetics: Consultancy; Curis Inc.: Consultancy; Bayer: Consultancy; Epizyme: Research Funding; Trillium Therapeutics: Research Funding; AbbVie: Consultancy; Bristol-Myers Squibb: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Morin: Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Epizyme: Patents & Royalties. Scott: Abbvie: Consultancy; AstraZeneca: Consultancy; Rich/Genentech: Research Funding; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. ; Incyte: Consultancy; Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling..

Abstract Background: To address the current and long-term unmet health needs of the growing population of patients diagnosed with non-Hodgkin lymphoma (NHL) and NHL survivors, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/) with recruitment at eight centers (Cornell University, Emory University, Mayo Clinic, MD Anderson, University of Iowa, University of Miami, University of Rochester, and Washington University). Here we report on the first two years of enrollment. Methods: All patients ≥18 years with NHL (exclusive of chronic lymphocytic leukemia) newly diagnosed within 183 days of enrollment are eligible. LEO reviews all pathology diagnoses; annotates and harmonizes all cases with clinical, epidemiologic, pathology and treatment data; builds a NHL tumor bank that includes an H&E slide, a formalin-fixed, paraffin-embedded tissue sample and extracted tumor DNA and RNA; collects peripheral blood and banks DNA, serum, plasma and buffy coat in a central biorepository; and prospectively follows patients regularly to ascertain disease progression/relapse, retreatment, transformation, second cancers, survival, updated exposures, and patient-reported outcomes. We examined the demographics, clinical characteristics, and NHL subtypes for patients enrolled in the first two years (2015-2017) in LEO Cohort and compared these characteristics to population-based registry data from the United States Surveillance, Epidemiology, and End Results (SEER) Program for 2011-2015. We also examined baseline body mass index (BMI), comorbidities and quality of life (QOL) measured with the Functional Assessment of Cancer Therapy - General (FACT-G) scores (0-100 scale) overall and by NHL subtype that were collected in LEO but not available in SEER or any other large, prospective US NHL cohort. Results: From 2015-2017, LEO enrolled 3244 NHL patients across 47 states (Figure) with a median age at diagnosis of 62 years (range, 18-94 years) and 55.9% male. Based on self-identified race/ethnicity, 85% of the participants were White, 7.4% were Black/African American, 2.6% were Asian, and 3.5% other or more than one race; 9.9% were Hispanic (regardless of race). The most common subtypes were diffuse large B-cell (DLBCL, 33.9%), follicular (FL, 22.2%), mantle cell (MCL, 9.3%), marginal zone (MZL, 8.3%), T-cell (TCL, 10.9%), and all other NHL subtypes (other NHL, 15.3%). These distributions were all within 5% of SEER data, except that 6.8% of participant in LEO were in the age 80+ years group compared to 18.5% in SEER (Table). The overall prevalence of self-reported comorbidities at enrollment was 13.5% for heart disease (range, 12.1% for FL to 16.6% for MCL), 8.8% for diabetes (range, 7.1% for FL to 9.8% for DLBCL), 4.6% for osteoporosis (range, 3.0% for MCL to 6.3% for MZL), and 5.9% for autoimmune disease (range, 2.0% for MCL to 12.6% for MZL). The median BMI was 27.9 kg/m2 (range, 26.7 kg/m2 for TCL to 28.3 kg/m2 for MZL). A total of 2151 participants completed the FACT-G with a median score of 78.7 (range, 75.9 for TCL to 83.3 for MZL). Complete IPI data were available on 2296 participants: 40.4% IPI 0-1, 32.7% IPI 2, 18.3% IPI 3, 8.7% IPI 4-5. Overall, 73.7% of participants were initially treated with systemic therapy (range, 40.0% for MZL and 91.8% for DLBCL); 14.5% were initially managed with observation (range, 1.4% DLBCL and 31.8% for FL); and 2.0% were initially manage with radiotherapy (range, 0.2% for MCL and 3.8% for DLBCL). Outcomes are being prospectively collected. Conclusions: LEO is a large and diverse cohort of newly diagnosed NHL patients and the first two years of enrollment reflects the broader United States NHL population. Enrollment is ongoing. This unique resource will enable examination of the interactions among a broad array of clinical and molecular factors and their impact on multiple outcomes, to improve prognostication, identify new approaches to improve outcomes and survivorship, and facilitate clinical trials using this infrastructure. Disclosures Flowers: Acerta: Research Funding; Pharmacyclics/ Janssen: Consultancy; OptumRx: Consultancy; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; Burroughs Wellcome Fund: Research Funding; Spectrum: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Millennium/Takeda: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; Gilead: Consultancy; BeiGene: Research Funding; Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Genentech/Roche: Consultancy; Gilead: Research Funding; Genentech/Roche: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Research Funding. Nastoupil:TG Therappeutics: Research Funding; Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Research Funding; Spectrum: Honoraria; Gilead: Honoraria; Genentech: Honoraria, Research Funding; Karus: Research Funding; Novartis: Honoraria; Juno: Honoraria. Kahl:Acerta: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Juno: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Abbvie: Consultancy. Casulo:Celgene: Research Funding; Gilead: Honoraria. Friedberg:Bayer: Honoraria. Lossos:Affimed: Research Funding. Martin:Seattle Genetics: Consultancy; Kite: Consultancy; Janssen: Consultancy; Gilead: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy. Leonard:Bayer: Consultancy; Celgene: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; BMS: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Sutro: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Pfizer: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Genentech/Roche: Consultancy; Novartis: Consultancy. Bernal-Mizrachi:Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership; Takeda Pharmaceutical Company: Research Funding. Maurer:Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract Introduction: Lenalidomide (Len) in association with standard Rituximab-CHOP (R2CHOP21) has been shown to be safe and effective in newly diagnosed Diffuse Large B-cell Lymphomas (DLBCL) [Nowakowski et al. JCO 2014, Vitolo et al. Lancet Oncol 2014]. The addition of lenalidomide appears to benefit primarily patients with non-Germinal Center B-cell (non-GCB) phenotype as determined by immunohistochemistry (IHC). These early results led to currently ongoing randomized trial in ABC subtype of DLBCL, however efficacy of R2CHOP in ABC DLBCL as defined by Gene Expression Prophyling (GEP) has not been reported. In the present combined analysis of two independent phase 2 studies, we report the long-term follow-up (FU) outcome in DLBCL patients treated with R2CHOP, comparing GCB and ABC according to Nanostring Platform. Methods: We included all newly diagnosed histologically-confirmed de-novo DLBCL patients enrolled in two R2CHOP21 phase 2 trials, conducted by Mayo Clinic (MC) and Italian Lymphoma Foundation (FIL). Inclusion criteria in the two trials were similar, main differences included: all age and all International Prognostic Index (IPI) vs age between 60 and 80 years old and IPI >1, in MC vs FIL study, respectively. All pts received R-CHOP21 plus Len at 25 mg/day for 10 days/cycle and 15 mg/day for 14 days/cycle in MC and FIL trial, respectively. Cell of origin (COO) was determined by IHC, according to Hans algorithm, and retrospectively, in patients with available pathological material, by Nanostring, performed according to Scott algorithm and Masque-Soler signature in MC and FIL study, respectively. We analyzed the long-term FU outcome in terms of progression-free survival (PFS), time to progression (TTP), overall survival (OS), comparing between GCB and ABC phenotypes according to GEP. Results: A total of 112 DLBCL pts (63 MC, 49 FIL) were included. Main characteristics were: median age 69 years (y) (range 22-87), male 65 (58%) pts, advanced stage III-IV in 94 (84%), B symptoms in 38 (34%), International Prognostic Index (IPI) intermediate-high/high in 71 (63%). At a median follow-up of 5.1 years (y), 5y-PFS was 63.5%, 5y-TTP 70.1% and 5y-OS 75.4%. A total of 32 relapses were observed, with only 2 cases of Central Nervous System (CNS) relapse. Late relapse occurring beyond 3 years was observed in 4 cases (3 cases with GCB phenotype and one case with missing COO data). In a subgroup analysis by IPI 0-2 vs 3-5 were: 5y-PFS, 5y-TTP and 5y-OS were: 69.0% vs 59.0% (p=0.100), 73.2% vs 67.4% (p=0.285) and 82.3% vs 70.2% (p=0.059), respectively. Regarding for COO defined by IHC, GCB phenotype vs non-GCB were 45 (40%) vs 41 (37%) patients respectively; 26 (23%) patients were not evaluable. 5y-PFS, 5y-TTP and 5y-OS were: 52.8% vs 64.5% (p=0.198), 61.6% vs 69.6% (p=0.444) and 68.6% vs 74.1% (p=0.238) in GCB vs non-GCB, defined by IHC, respectively. Regarding Nanostring analysis, GCB vs ABC vs Unclassified (Uncl) were 31 (46%) vs 22 (32%) vs 15 (22%) respectively; 44 pts were not evaluable. 5y-PFS, 5y-TTP and 5y-OS were: 62.3% vs 70.8% vs 64.2% (p=0.645), 68.1% vs 79.8% vs 64.2% (p=0.662) and 76.0% vs 74.8% vs 79.0% (p=0.658) in GCB vs ABC vs Uncl, defined by Nanostring, respectively (Table1, Fig1). Conclusions: The association of Len with R-CHOP21 appears to overcome the negative prognostic impact of ABC phenotype determined by GEP, with high PFS, TTP and OS rate, maintained at a long-term FU analysis. Figure 1. Figure 1. Disclosures Chiappella: Teva: Other: lecture fees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: lecture fees; Nanostring: Other: lecture fees; Roche: Other: lecture fees; Amgen: Other: lecture fees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: lecture fees. Gaidano:Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Morphosys: Honoraria. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Vitolo:Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sandoz: Speakers Bureau; Gilead: Speakers Bureau.

Abstract Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.