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Background With increased understanding of the unique entities, subtype-specific approaches for peripheral T-cell lymphoma (PTCL) are emerging, and more precise diagnoses are becoming increasingly important. Patients and Methods We analyzed the approach to the histopathologic diagnosis of PTCL using data from the comprehensive oncology measures of peripheral T-cell lymphoma (COMPLETE) study. The COMPLETE trial is a large prospective cohort study of patients with newly diagnosed PTCL in the United States. Results A total of 499 patients were enrolled from 40 academic and 15 community-based centers. Baseline assessment forms were collected for 493 patients, of which 435 (88%) were available for analysis. The most common diagnoses were PTCL, not otherwise specified (PTCL-NOS), anaplastic large cell lymphoma, and angioimmunoblastic T-cell lymphoma (AITL). A mean of 10 markers (range, 0-21) was assessed per patient. CD30 was assessed frequently but not uniformly in cases that were not anaplastic large cell lymphoma. Only 17% of PTCL-NOS cases were assessed for PD1. CXCL13 was a relatively sensitive marker in AITL, expressed in 84% of tested cases; however, only 3% of PTCL-NOS cases were tested. T follicular helper cell marker assessment differed between academic and community practices, with PD1 more often evaluated by academic centers in cases of AITL (62% vs. 12%; P = .01). Conclusion The diagnostic workup for PTCL in the United States varies widely and often lacks important phenotypic information to fully characterize the lymphoma. Gaps in testing of selected markers should be filled, given the impending revision to the World Health Organization classification. The accuracy of diagnosis will become increasingly important as we enter the era of targeted treatment for PTCL.

Microarray gene expression profiling (GEP) has been used to identify molecular subtypes of diffuse large B-cell lymphoma (DLBCL) based on the similarity of GEP to a putative “cell of origin” (COO) and defines two molecular subtypes: activated B-cell-like (ABC) DLBCL and germinal centre B-cell-like (GCB) DLBCL (Alizadeh, et al 2000). This dichotomization has prognostic and biological significance, with the ABC subtype having a worse outcome and distinct pathobiology that includes activation of the B-cell receptor and nuclear factor (NF)-κB pathways (Lenz, et al 2008). Attempts to reduce this subclassification to practice using immunohistochemistry are fraught with technical and interpretive issues such that a need for practical quantitative molecular assays exists (Coutinho, et al 2013, de Jong, et al 2007, de Jong, et al 2009, Salles, et al 2011). Indeed, studies have refined this COO concept using limited gene sets, including a 14-gene model to assign ABC and GCB DLBCL subtype developed by Wright et al (2003) as well as a recently-described DLBCL subtyping assay based on parsimonious digital gene expression (Nanostring) technology (Scott, et al 2014). In order to support clinical trials for therapies targeting populations enriched in ABC DLBCL and to offer prognostic information for DLBCL patients as part of our clinical service, we developed a novel multiplex, single-tube, gene expression assay on the ICEPlex® system (PrimeraDx, Mansfield, MA), which allows differentiation between GCB and ABC DLBCL subtypes in formalin-fixed paraffin-embedded (FFPE) specimens using a Food and Drug Administration-cleared platform. Institutional review board approval was obtained for review of newly diagnosed DLBCL specimens at our institution. Paired frozen tissue was available for 30 patients and was used to isolate RNA for hybridization to U219 (27 samples) or U133plus2.0 (3 samples) Affymetrix microarrays. Microarray subtype classification was assigned based on the Wright algorithm, which calculates a linear predictor score (LPS) by multiplying a gene weight and Ct value for each gene and summing the resulting numbers for 14 genes (Wright, et al 2003). Samples with LPS above -150 were classified as GCB, scores between -150 and -200 were unclassified, and scores less than -200 were classified as ABC. A multiplex quantitative reverse transcription polymerase chain reaction (RT-PCR) DLBCL subtyping assay was developed on the ICEPlex system (PrimeraDx), targeting expression of the 14 genes used in the Wright algorithm, two reference genes and an internal control (enterobacteriophage MS2) (Table I) (Hlousek, et al 2012). Primers were designed to amplify short template mRNA regions of exon-spanning junctions. To allow discrimination of target-specific amplicons on the ICEPlex platform by fluorescent label and by size using capillary electrophoresis, PCR reverse primers were labelled with FAM or TYE fluorescent dyes, and forward and reverse PCR primers were equipped with 5′-nucleotide tags. RNA was extracted from a 10-μm FFPE slice of a DLBCL specimen using the Qiagen Allprep DNA/RNA FFPE kit (Germantown, MD). cDNA was generated using SuperScript III (Life Technologies, Grand Island, NY) and RT primers. PCR was performed in triplicate for each 10-μm FFPE slice on the ICEPlex system with Roche AptaTaq ΔExo polymerase (Indianapolis, IN), proprietary buffer (PrimeraDx), and primers. The resulting Ct values were normalized for each replicate and averaged for each gene. The integrity of the results was evaluated using a quality (Q) score calculated for each sample replicate [(mean Ct value of all targets)/(total number of target genes + number of target genes with Ct values less than 36)]. Reactions with a Q-score greater than 1.0 were rejected. Table I DLBCL Molecular Subtyping Assay Genes and Primers A subtyping (S) score was calculated using the normalized Ct data and gene weights of the Wright algorithm (Wright et al 2013). If there was no Ct value for a target, the value was set to a defined target-specific, upper-limit Ct value. Based on correlations with the microarray data and paired FFPE, samples corresponding to an S-score between -100 and 0 were unclassified, while S-scores less than -100 were classified as ABC and greater than 0 were classified as GCB. Thus, our training set of 30 DLBCL FFPE samples with matched frozen tissue microarray GEP data served as the gold standard against which initial S-scores were calibrated. The test set showed 96.7% and 90% agreement compared to microarray subtype for assignment of ABC cases and overall (ABC, GBC, and unclassified) cases, respectively (Figure 1 a). Figure 1 Comparison of microarray GEP LPS and DLBCL molecular subtyping assay S-score for test (circles) and validation (diamonds) DLBCL cohorts (n=53) To validate the S-score cut-off values, additional DLBCL specimens, described previously (Zhang, et al 2013), with paired frozen and FFPE samples were analysed. Microarray data from the Affymetrix Genechip Human Gene 1.0 ST (Santa Clara, CA) platform was obtained, and LPS were calculated (Wright, et al 2003). The DLBCL molecular subtyping assay was performed on FFPE-extracted RNA, and S-scores were obtained for 23 samples (Figure 1 a). The validation set performed similarly, with 91.3% and 87.0% agreement compared to microarray subtype for assignment of ABC samples and overall samples, respectively. This validated the chosen S-score cut-off values, giving a sensitivity of 95.2% and specificity of 93.8% for assignment of the ABC subtype for the combined test and validation sets. For the combined sets, the sensitivity was 95.2% and specificity was 90.6% for assignment of the GCB subtype. Inter-assay and intra-assay variation was determined using two DLBCL specimens (Figure 1 b). The coefficient of variance (CV) was 6.1% for the GCB DLBCL specimen and was 2.9% for the ABC DBCL specimen. The assay was performed on freshly-isolated RNA from an additional 45 FFPE DLBCL samples from up to 12 years prior. When included with other samples from our institution in the test and validation cohorts, the assay failed in only 7 out of 88 samples (8.0%). Dilution studies showed that samples with as low as 0.4 μg of RNA per 10-μm FFPE slice yielded acceptable Q-scores and appropriate subtype classification. In summary, we have successfully developed a multiplex quantitative expression profiling assay designed for DLBCL tumour classification into GCB or ABC subtypes from a single 10-μm FFPE section. This DLBCL molecular subtyping assay has the potential to provide rapid and accurate subclassification for DLBCL patients for prognostic implication as well as clinical trial patient selection in a Clinical Laboratory Improvement Amendments-certified laboratory environment. Finally, the assay, validated against GEP, could be used as an external reference for those clinical laboratories attempting to validate their immunohistochemical-based algorithms against a GEP-based standard.

Myeloid sarcoma manifesting in the testis is rare and may occur concomitantly with bone marrow disease or as a separate entity. We describe our experience with a 6-month-old boy who presented with painless scrotal swelling and was found to have bilateral testicular masses on ultrasonography. The patient underwent unilateral radical inguinal orchiectomy. Surgical pathology revealed myeloid sarcoma of the testicle. He developed peripheral blood involvement 1 week postoperatively. Bone marrow biopsy showed acute myeloid leukemia. He is in remission after 2 cycles of induction chemotherapy, local radiation therapy, and allogeneic bone marrow transplantation.

Diffuse large B-cell lymphoma (DLBCL) is a clinically and pathologically heterogeneous entity. Multiple molecular methods have been used to further understand and define this heterogeneity. Gene profiling has identified “cell-of-origin” molecular subtypes of DLBCL based on expression pattern

Melanoma accounts for most skin cancer-related deaths and has an increasing incidence. Accurate diagnosis and distinction from atypical nevi can be at times difficult using light microscopy alone. Fluorescence in situ hybridization (FISH) and melanoma gene expression score (myPath, Myriad Genetics) have emerged as ancillary tools to further aid in this differential diagnosis. Our aim in this study was to correlate FISH results, gene expression score, consensus histopathologic impression and clinical outcome on a series of 117 challenging melanocytic lesions collected from three separate institutions. The lesions were separated into two groups: 39 histopathologically unequivocal lesions (15 malignant, 24 benign) and 78 challenging lesions interpreted by expert consensus (27 favor malignant, 30 favor benign, and 21 ambiguous). Melanoma-FISH was performed using probes for 6p25, 11q13, 8q24, and 9p21/CEP9 and scored according to established criteria. Analysis by myPath gene expression score was performed and interpreted by the manufacturer as 'benign', 'indeterminate,' or 'malignant'. In the unequivocal group, melanoma-FISH and myPath score showed 97 and 83% agreement with the histopathologic diagnosis, respectively, with 93 and 62% sensitivity, 100 and 95% specificity, and 80% inter-test agreement. In the challenging group, FISH and the myPath score showed 70 and 64% agreement with the histopathologic interpretation, respectively, with 70% inter-test agreement and similar sensitivities and specificities. The inter-test agreement was 73% overall, excluding indeterminate results. Discordant test results occurred in 27/117 cases from both unequivocal and challenging groups. Melanoma-FISH and gene expression score are valuable ancillary tools, though both have limitations and return discordant results in a subset of cases. Follow-up studies with more extensive clinical outcome data are warranted to establish the accuracy of these tests for the classification of melanocytic lesions.

Recent studies suggested that MYC and BCL2 protein co-expression is an independent indicator of poor prognosis in diffuse large B-cell lymphoma. However, the immunohistochemistry protocols for dual-expression staining and the scoring cut-offs vary by study. Sixty-nine cases of diffuse large B-cell lymphoma were evaluated for MYC and BCL2 protein expression using various cut-offs that have been recommended in prior studies. Independent of the International Prognostic Index risk group, cases with dual protein expression of BCL2 and MYC using ≥50%/40% cut-offs and ≥70%/40% had significantly shorter overall survival than cases without. It was verified in this patient population that the use of BCL2 and MYC immunohistochemistry, performed with available in vitro diagnostic-cleared antibodies, provides rapid prognostic information in patients with de novo diffuse large B-cell lymphoma. This study has practical implications for diagnostic laboratories and serves as a guide for implementation in the setting of future clinical trials.

Item does not contain fulltext BACKGROUND: Genomic copy number variants have been shown to be responsible for multiple genetic diseases. Recently, a duplication in septin 9 (SEPT9) was shown to be causal for hereditary neuralgic amyotrophy (HNA), an episodic peripheral neuropathy with autosomal dominant inheritance. This duplication was identified in 12 pedigrees that all shared a common founder haplotype. METHODS AND RESULTS: Based on array comparative genomic hybridisation, we identified six additional heterogeneous tandem SEPT9 duplications in patients with HNA that did not possess the founder haplotype. Five of these novel duplications are intragenic and result in larger transcript and protein products, as demonstrated through reverse transcription-PCR and western blotting. One duplication spans the entire SEPT9 gene and does not generate aberrant transcripts and proteins. The breakpoints of all the duplications are unique and contain regions of microhomology ranging from 2 to 9 bp in size. The duplicated regions contain a conserved 645 bp exon within SEPT9 in which HNA-linked missense mutations have been previously identified, suggesting that the region encoded by this exon is important to the pathogenesis of HNA. CONCLUSIONS: Together with the previously identified founder duplication, a total of seven heterogeneous SEPT9 duplications have been identified in this study as a causative factor of HNA. These duplications account for one third of the patients in our cohort, suggesting that duplications of various sizes within the SEPT9 gene are a common cause of HNA. 01 september 2010

Diffuse large B cell lymphoma (DLBCL) is the most common form of blood cancer and is characterized by a striking degree of genetic and clinical heterogeneity. This heterogeneity poses a major barrier to understanding the genetic basis of the disease and its response to therapy. Here, we performed an integrative analysis of whole-exome sequencing and transcriptome sequencing in a cohort of 1,001 DLBCL patients to comprehensively define the landscape of 150 genetic drivers of the disease. We characterized the functional impact of these genes using an unbiased CRISPR screen of DLBCL cell lines to define oncogenes that promote cell growth. A prognostic model comprising these genetic alterations outperformed current established methods: cell of origin, the International Prognostic Index comprising clinical variables, and dual MYC and BCL2 expression. These results comprehensively define the genetic drivers and their functional roles in DLBCL to identify new therapeutic opportunities in the disease.

Merkel cell carcinoma is a rare but highly aggressive cutaneous neuroendocrine carcinoma. Cytokeratin-20 (CK20) is expressed in approximately 95% of Merkel cell carcinomas and is useful for distinction from morphologically similar entities including metastatic small cell lung carcinoma. Lack of CK20 expression may make diagnosis of Merkel cell carcinoma more challenging, and has unknown biological significance. Approximately 80% of CK20-positive Merkel cell carcinomas are associated with the oncogenic Merkel cell polyomavirus. Merkel cell carcinomas lacking Merkel cell polyomavirus display distinct genetic changes from Merkel cell polyomavirus-positive Merkel cell carcinoma, including RB1 inactivating mutations. Unlike CK20-positive Merkel cell carcinoma, the majority of CK20-negative Merkel cell carcinomas are Merkel cell polyomavirus-negative, suggesting CK20-negative Merkel cell carcinomas predominantly arise through virus-independent pathway(s) and may harbor additional genetic differences from conventional Merkel cell carcinoma. Hence, we analyzed 15 CK20-negative Merkel cell carcinoma tumors (ten Merkel cell polyomavirus-negative, four Merkel cell polyomavirus-positive, and one undetermined) using the Ion Ampliseq Comprehensive Cancer Panel, which assesses copy number alterations and mutations in 409 cancer-relevant genes. Twelve tumors displayed prioritized high-level chromosomal gains or losses (average 1.9 per tumor). Non-synonymous high confidence somatic mutations were detected in 14 tumors (average 11.9 per tumor). Assessing all somatic coding mutations, an ultraviolet-signature mutational profile was present, and more prevalent in Merkel cell polyomavirus-negative tumors. Recurrent deleterious tumor suppressor mutations affected TP53 (9/15, 60%), RB1 (3/15, 20%), and BAP1 (2/15, 13%). Oncogenic activating mutations included PIK3CA (3/15, 20%), AKT1 (1/15, 7%)) and EZH2 (1/15, 7%). In conclusion, CK20-negative Merkel cell carcinoma display overlapping genetic changes with CK20-positive Merkel cell carcinoma, including RB1 mutations restricted to Merkel cell polyomavirus-negative tumors. However, some CK20-negative Merkel cell carcinomas harbor mutations not previously described in Merkel cell carcinoma. Hence, CK20-negative Merkel cell carcinomas harbor diverse oncogenic drivers which may represent therapeutic targets in individual tumors.

To identify pretest characteristics of cerebrospinal fluid (CSF) specimens that will allow the rational use of flow cytometric analysis (FCA) in the diagnosis of hematologic malignancy.Retrospective data were collected on 501 consecutive CSF samples submitted for FCA.A positive diagnosis of hematologic malignancy was made in 41 specimens (8.2%). Blasts or atypical lymphocytes were noted on Wright-stained slides in 98% of FCA-positive specimens (40/41), and a history of a hematologic malignancy was present in 89% of specimens (34/38). All FCA-positive specimens had atypical morphology or history of hematologic malignancy. Four hundred six specimens (81%) were FCA negative. Of FCA-negative specimens, 7% (30/406) had atypical morphology, and 3% (12/404) had future central nervous system involvement seen within 30 days.These data support a policy in which FCA of CSF is actively discouraged unless atypical lymphocytes or blasts are seen or a history of hematologic malignancy is present.

Background Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease entity with multiple potential prognostic biomarkers. Cell of origin (COO) molecular subtype classification using gene expression profiling with microarrays and immunohistochemical expression of CD30 have been examined as potential prognostic markers, often with conflicting results. A recent study demonstrated that patients with CD30-positive DLBCL had better prognosis compared to patients with CD30-negative DLBCL and had a distinct gene expression profile (Hu S et al. Blood. 121(14): 2715-24, 2013). In addition, due to the development of targeted therapies such as an anti-CD30 monoclonal antibody drug conjugate, the identification and prognostic relevance of this biomarker has potential therapeutic impact. We evaluated CD30 expression, determined by immunohistochemistry, in a cohort of de novo DLBCL cases at our institution and examined cell of origin molecular subtype in the CD30-positive and CD30-negative groups. Design 94 adult patients with de novo DLBCL uniformly treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) as first line therapy were identified at The Cleveland Clinic. Clinical data was collected for these patients. A tissue microarray was created and stained with antibodies to CD10, CD20, CD30, BCL-6, and MUM-1. COO subtype was determined for the Hans algorithm in all cases. CD30 was considered positive when expression was seen in ≥ 20% of tumor cells. Results There were no significant differences in sex, age, IPI score, or stage between patients in the CD30-positive and CD30-negative groups. The median age of the DLBCL cohort was 63 years (range 17-91 years) with a male : female ratio of 1:1.1. 54% of patients had stage III or IV disease. Median follow-up was 58 months. 9 of 94 DLBCL samples (9.6%) were positive for CD30 by immunohistochemistry. By Kaplan-Meier analysis, the CD30-positive cases showed a decreased overall survival compared to the CD30-negative cases (Figure 1, p=0.044). Multivariate analysis using a Cox proportional hazard model confirmed that CD30 expression was independent of IPI and a significant factor for overall survival (hazard ratio = 3.05; 95% confidence interval = 1.12-8.30; p = 0.0291). All 9 of the CD30-positive DLBCL samples were of the non-germinal center B-cell-like (NGC) subtype using the Hans immunohistochemical algorithm, which was significantly more than the CD30-negative samples (42/85) (p = 0.003). Conclusion CD30 expression was associated with poor prognosis in our cohort, in contrast to recent studies. However, CD30 expression was highly associated with the NGC subtype of DLBCL and might contribute to the pathogenesis of these lymphomas through NF-κB activation. Given the poor prognosis of NGC DLBCLs, targeting CD30 in DLBCL should be explored. Disclosures: No relevant conflicts of interest to declare.

Abstract 2705 Introduction: Molecular characterization of diffuse large B-cell lymphoma (DLBCL) provides important prognostic, and potentially therapeutic, information. Although the original classification of DLBCL into germinal center B cell-like (GCB) and activated B cell-like (ABC) subtypes was based on microarray gene expression profiling, this technique is not currently feasible in a clinical setting. Surrogate studies to assign classification, such as immunohistochemistry, have shown variability between laboratories and may not yield reliable results. Therefore, we have developed a novel multiplex real-time assay on the automated ICEPlex® System which allows differentiation between germinal center B cell-like (GCB) and activated B cell-like (ABC) subtypes of DLBCL from formalin-fixed, paraffin-embedded (FFPE) specimens. Materials and Methods: A 17-gene panel was constructed from genes utilized previously in DLBCL subtype classification (Wright classifier and Lossos outcome predictor). Primers were designed for this panel, and two reference genes, using extension tag amplification (ETA). This method allows amplification from FFPE tissue with a template size less than 100 nucleotides. Thirty de novo DLBCL clinical samples, with paired frozen tissue and FFPE tissue, from patients at our institution were selected. Gene expression profiling using microarrays was performed on frozen tissue, and molecular subtype was assigned based on Wright classification. The ICEPlex® assay was performed on isolated RNA from two cells lines, one GCB subtype and one ABC subtype. The ICEPlex® assay was also completed on RNA extracted from three DLBCL clinical samples, and molecular subtype was determined for each sample. Analysis of FFPE clinical samples and cell lines is underway using the multiplex assay on the ICEPlex® instrument for determination of molecular subtype. Results: The real-time 19-plex expression profiling assay for DLBCL molecular subtyping was developed and optimized on the ICEPlex® System. Samples from two cell lines and from three clinical samples with frozen tissue were analyzed. The assay identified the molecular subtype of both cell lines. All three clinical samples were classified into the identical molecular subtype by the multiplex assay and the microarray gene expression profile. Results from the profiling of additional FFPE clinical samples and cell lines using the multiplex assay will be presented. Conclusion: A novel 19-plex quantitative real-time expression profiling assay designed for DLBCL molecular classification into GCB or ABC subtypes was developed. This assay correctly assigned DLBCL cell lines and clinical samples into the correct subtype based on microarray gene expression profiling as the gold standard. Further work involves assessment of additional clinical DLBCL FFPE samples. This potentially robust multiplex assay may provide rapid molecular classification that is feasible for routine laboratory use in a clinical setting. * ICEPlex® System is for Research Use Only. Not for clinical diagnostic use. Disclosures: Nolling: PrimeraDx: Employment. Kong:PrimeraDx: Employment.

Abstract 950 INTRODUCTION: Diffuse large B-cell lymphoma (DLBCL) can be categorized by its cell of origin (CoO) as either being derived from a germinal center B-cell (GCB) or activated B-cell (ABC). Primary mediastinal DLBCL represents a third, distinct entity. This classification was initially defined by gene expression profiling (GEP), which remains the gold standard for such determination. Determination of CoO will likely become the basis for patient selection for clinical trials of targeted therapies. Several algorithms and methods have been developed that use immunohistochemistry (IHC) to differentiate GCB-DLBCL from non-GCB DLBCL. These include the Hans algorithm (utilizes staining for CD10, Bcl-6 and Mum1), the Choi algorithm (utilizes additional staining for GCET1 and FoxP1) as well as the Tally method (does not use Bcl-6 and utilizes LMO2 as a tie-breaker stain for otherwise equivocal cases). Recently, it has been recognized that IHC approaches to assign CoO may not be reproducible even at highly experienced laboratories. We sought to determine the performance of these IHC assays in our laboratory as a necessary step in developing trials based on CoO stratification. METHODS: We reviewed 108 adult (age ≥18) cases of de novo DLBCL, the majority of which were treated with chemoimmunotherapy (R-CHOP or R-CVP) at the Cleveland Clinic from 2000–2010. Diagnostic biopsies were available for all cases. IHC staining was performed on tissue microarrays (TMAs), and published algorithms (Hans, Choi and Tally) were applied to categorize cases as GCB or non-GCB. In addition, gene expression profiling was completed in a subset of these cases, for which frozen tissue was available. A linear predictor score for gene expression profiling (GEP) was used to assign cases in 31 of 33 cases with 2 technical failures at the array stage (overall success rate 84.8%). Clinical details including age, sex, International Prognostic Index (IPI) stage at diagnosis, treatment, progression free survival (PFS) and overall survival (OS) were captured for 69 of the 108 patients. Actuarial survival analysis was performed according to the Kaplan and Meier method, and the curves compared by the log-rank test. RESULTS: For the 69 patients with adequate clinical follow-up, the median age was 64 years old (range 18–88). There were 49% males and 51% females. The distribution of patients with stage I, II, III, and IV disease at the time of diagnosis was 20%, 14%, 20%, and 32% (14% had unknown stage). The 5-year overall survival of patients was 88%. Results of the Hans algorithm, Choi algorithm and Tally method were interpretable in 98 (90.7%), 95 (87.9%) and 88 (81.5%) of 108 cases, respectively. Inability to assign subtypes was due to suboptimal staining of the TMA (tissue loss or poor staining of an individual core). Using GEP to assign CoO, 42% of cases were classified as GCB, 42% as ABC and 14% were unclassifiable. The sensitivities of the Hans, Choi and Tally approaches to identify the CoO predicted by GEP were 0.83, 0.83, and 0.58 for correctly identifying GCB cases, respectively, and were 0.70, 0.70 and 0.80 for identifying non-GCB cases, respectively. The positive predictive values of the Hans, Choi and Tally approaches were 0.83, 0.83, and 1.0 for GCB and 0.78, 0.78, and 0.89 for non-GCB. As shown in the figure, 5-year overall survival was significantly superior for GCB relative to ABC cases using GEP (100% vs. 58.9%, P < 0.001) and for GCB vs. non-GCB cases for the algorithms of Hans (100% vs. 82.3%, P = 0.0197) and Choi (95.6% vs. 78.0%, P = 0.0482). The Tally method was not predictive of outcome, possibly due to insufficient power (5-year OS 94.4% for GCB vs. 80.7% for non-GCB, P = 0.1725). Similar findings were observed for progression-free survival. CONCLUSIONS: The Hans and Choi algorithms are reasonable methods for identifying PFS and OS differences based on CoO for de novo DLBCL treated with chemoimmunotherapy. The positive predictive value is universally high for all algorithms tested, but the sensitivity of IHC for identifying CoO was fair, particularly for the Tally method. IHC represents a valid biomarker to identify non-GCB cases. Clinical trials of DLBCL that stratify patients by IHC are feasible provided the performance characteristics of the algorithms are taken into consideration during study design. Disclosures: No relevant conflicts of interest to declare.

Introduction Diffuse large B cell lymphoma (DLBCL), the most common lymphoma world-wide, is strikingly heterogeneous. This heterogeneity creates a daunting challenge for conducting well-powered studies connecting molecular features to clinical outcome. Not only is the association of genetic mutations with clinical outcome in DLBCL mostly unknown, the relative importance of other well-described features, such as MYC and BCL2 translocation/expression and cell of origin based subsets (ABC and GCB DLBCL), is difficult to interpret due to conflicting reports. We sought to comprehensively define the spectrum of genetic mutations and their association with clinical outcome in DLBCL. Our calculations indicated that 500 tumor-normal pairs would provide 95% power to define mutations occurring in at least 5% of patients, and that 800 cases would be required to define the clinical correlations with cross-validation. Methods We enrolled 1001 de novo DLBCL patients, with complete IPI and survival data, who were treated uniformly with standard rituximab and anthracycline containing regimens. All tumors were subjected to whole exome and transcriptome sequencing (RNAseq), as well as SNP arrays to confirm genetic alterations. ABC (38%) and GCB DLBCL (36%) subtypes were defined using microarrays and RNAseq in these patients to examine subgroup-based differences in mutations and outcome. MYC and BCL2 expression were quantified separately. Results Gene discovery analysis of somatic mutations and copy number alterations in exome sequencing data from 502 tumor-normal pairs of DLBCL identified 197 recurrently mutated genes, including 155 genes previously identified to be mutated in DLBCLs. In addition, our study uncovered 42 novel driver genes in DLBCL (e.g. BTK, SPEN, CD70). Exome sequencing results were validated by Sanger sequencing of 1120 variants with over 90% concordance. We also identified copy number alterations in these genes, with strong agreement (90%) of amplifications and/or deletions to those detected on Illumina high resolution SNP microarrays. These 197 genes were found to comprise 15 functionally related subnetworks, including those related to histone modification, NFkB, B cell receptor, PI3K and cell cycle (Figure 1). Within each subnetwork, the vast majority of the gene alterations occurred in a mutually exclusive (P We examined the associations between the mutations and clinical outcome in all 1001 patients. All survival analyses were conducted using nearly equally split training and validation sets, corrected for multiple comparisons with significance of P Conclusion To our knowledge, this is the largest whole exome sequencing study in any single cancer. Our study answers many long-standing questions in the disease, informing a comprehensive understanding of genetic drivers of DLBCL, their organization into pathways, and their relationship to clinical outcome. Disclosures Leppä: Roche: Consultancy, Honoraria, Other: Travel expenses, Research Funding; Takeda Pharmaceuticals: Consultancy, Honoraria, Other: Travel expenses; Janssen: Consultancy, Research Funding; CTI Bio Pharma: Consultancy; Mundipharma: Research Funding; Bayer: Other: Travel expenses, Research Funding. Flowers:ECOG: Research Funding; Gilead: Consultancy, Research Funding; Millenium/Takeda: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; TG Therapeutics: Research Funding; Mayo Clinic: Research Funding; NIH: Research Funding; Infinity: Research Funding; AbbVie: Research Funding; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Acerta: Research Funding. Hsi:Seattle Genetics: Honoraria, Speakers Bureau; HTG Molecular Diagnostics: Consultancy, Honoraria; Eli Lilly: Research Funding; Cellerant Therapeutics: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding. Evens:Takeda: Other: Advisory board. Reddy:GILEAD: Membership on an entity's Board of Directors or advisory committees; INFINITY: Membership on an entity's Board of Directors or advisory committees; celgene: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees.