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<10-3) fashion in patterns consistent with their described functions within the subnetworks. For instance, among genes comprising the NFkB subnetwork, positive regulators of the pathway such as MYD88 and CARD11 showed activating patterns (copy number gains and recurrent hotspot mutations), whereas negative regulators such as TNFAIP3, NFKBIE, and NFKBIA were inactivated through genetic deletions or frequent nonsense or frameshift mutations. 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<0.01 in the validation set (Figure 2). The cell of origin classification was strongly associated with survival in our cases and was independent of MYC and BCL2 co-expression, which was separately associated with survival (Figure 2A). Figure 2B shows hazard ratios for select genes, as well as associated Kaplan-Meier survival curves for a subset of those genes. We further identified combinations of different genetic and expression features that point to context dependence for survival associations (Figure 2C). For instance, mutations in KLHL14 were associated with a particularly poor prognosis in ABC DLBCL, while CREBBP mutations in ABC DLBCL patients were associated with better prognosis than average GCB DLBCLs. Mutations in EZH2 and CD70 were associated with a highly favorable prognosis within the GCB DLBCL subgroup. TP53 mutations were found to be prognostic only in the presence of MLL2 mutations and high BCL2 expression. Importantly, these risk groups are mutually exclusive and inform clinical outcome significantly better than existing metrics. 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.
Introduction Enteropathy-associated T cell lymphoma (EATL) is an intestinal tumor of the intraepithelial T lymphocytes, with a median survival time of less than 1 year. It is a rare disease in general and has two main subtypes described. Type 1 EATL is a complication in patients with celiac disease, a chronic gluten-sensitive enteropathy. Type 2 EATL, characterized by smaller monomorphic lymphocytes, typically occurs sporadically in patients without celiac disease. Very little is known about the genetic mutations and gene expression signatures that define this disease, or the extent to which the two types of EATL are genetically distinct. It has been suggested that the two types of EATLs should be reclassified as separate diseases in future WHO categories. Methods In this study, we performed whole exome sequencing to 100-fold depth of 41 EATL tumors including 23 type 1 cases and 18 type 2 cases. Both alpha-beta (65%) and gamma-delta (35%) T cell receptor rearrangements were seen among these cases. Paired normal DNA was sequenced in most (N=30) cases. We defined somatic mutations, copy number alterations, and HLA genotypes in these cases from sequencing data. Additionally, we generated RNA sequencing data on the same EATL tumors. Corresponding clinical and outcome data was collected on the same cohort. Results We found that both type 1 and type 2 EATLs had overlapping patterns of mutations and similar overall survival. The most commonly mutated genes were chromatin modifier genes (34%) including ATRX and ARID1B. We also identified recurrent somatic mutations in signal transduction genes, including JAK1 and BCL9L. TP53 mutations were also recurrent (12%). Copy number amplifications in 9q, 1q, and 8q occurred most frequently and were present in both subtypes. We further compared the mutational profiles to peripheral T cell lymphoma, angioimmunoblastic T cell lymphoma, cutaneous T cell lymphoma, natural killer/T cell lymphoma, diffuse large B cell lymphoma, and Burkitt lymphoma. These comparisons identify EATL as a genetically distinct disease with a very different pattern of mutations. RNAseq identified the gene expression patterns that are unique to EATL and also identified gene expression signatures that distinguish the two types of EATL. The DQ2 or DQ8 HLA genotype is present in the majority of type 1 cases (73%) while occurring infrequently in type 2 cases (27%). Conclusions Our study defines the genetic landscape of enteropathy associated T cell lymphoma and highlights the genetic and clinical overlap between the two types. While the two types have differences in mutations and gene expression patterns, they have more in common with each other compared to other lymphoma types. Our data may inform future decisions regarding the potential separation of the two EATL types as distinct entities. Disclosures No relevant conflicts of interest to declare.
<p>Supplementary Table S1. Sanger validated variants. Supplementary Table S2. Mutations in HSTL driver genes. Supplementary Table S3. Other mutations identified by exome sequencing. Supplementary Table S4. Copy number of HSTL patients and cell lines. Supplementary Table S5. Clinical and pathological characteristics of HSTL patients. Supplementary Tables S6, S7. Comparison to HSTL clinical data in the literature. Supplementary Table S8. Mutational frequencies in other lymphomas. Supplementary Table S9. HSTL mutations in other lymphoma driver genes. Supplementary Table S10. Gene set enrichment analysis of DERL2 SETD2 shRNA.</p>
<p>Supplementary Figure S1. Sanger sequencing chromatograms. Supplementary Figure S2. Cancer cell fraction for driver genes. Supplementary Figure S3. Ideogram with chromosome 7 alterations. Supplementary Figure S4. Examples of Exome Copy Number. Supplementary Figure S5. Exploratory Kaplan-Meier plots for clinical covariates. Supplementary Figure S6. Exploratory Kaplan-Meier plots for molecular covariates. Supplementary Figure S7. Sanger and exome sequencing validation of SETD2 biallelic mutation in one HSTL case. Supplementary Figure S8. SETD2 expression in mutant vs. wildtype cases. Supplementary Figure S9. Mutual exclusivity of STAT5B, PIK3CD, and STAT3 mutations.</p>
<p>Supplementary Table S1. Sanger validated variants. Supplementary Table S2. Mutations in HSTL driver genes. Supplementary Table S3. Other mutations identified by exome sequencing. Supplementary Table S4. Copy number of HSTL patients and cell lines. Supplementary Table S5. Clinical and pathological characteristics of HSTL patients. Supplementary Tables S6, S7. Comparison to HSTL clinical data in the literature. Supplementary Table S8. Mutational frequencies in other lymphomas. Supplementary Table S9. HSTL mutations in other lymphoma driver genes. Supplementary Table S10. Gene set enrichment analysis of DERL2 SETD2 shRNA.</p>
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