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Supplementary methods, figures, and tables Figure S1: Variant filters applied to Lymphopanel NGS data. Figure S2: Validation of variants according to SIFT and CADD scores. Figure S3: PMBL samples harbor more mutations than other DLBCL subtypes. Figure S4: Role of AID-induced SHM in DLBCL. Figure S5: Mutation frequency by subtype. Figure S6: Clustering of gene mutations according to subtype. Figure S7: Protein representations of observed mutations. Figure S8: Correlations between age and specific gene mutations. Figure S9: Prognosis according to IPI and subtype. Figure S10: Prognostic impact of gene mutations among the Lymphopanel. Table S1: Clinical characteristics of patients in our cohort. Table S2: Overview of the Lymphopanel used for NGS analysis. Table S3: Overview of pathway selection Table S4: Validation of variant filtering by an independent study of seven non-tumoral samples from DLBCL patients Table S5: Validated variants. Table S6: Lymphopanel NGS detection of genes with high AID target frequencies. Table S7: Prognostic impact of mutations in Lymphopanel genes

Patient clinical characteristics for the whole population and for the cohort with molecular analysis.

Supplementary Figures 1-10 Figure S1. MYD88 mutation VAF according to L265P or non-L265P variants. Figure S2. Age according to MYD88 mutation status and variant among ABC patients. Figure S3. Genomic profiles of DLBCL according to the presence of MYD88 L265P or non-L265P variants. Figure S4. Relative L265P allele expression level according to L265P VAF. Figure S5. Gene expression according to MYD88 status. Figure S6. NFkB pathway gene expression according to MYD88 mutational status. Figure S7. LPS score according to subtype and downstream NFkB alterations. Figure S8. Clustering according to expression levels of 27 genes involved in LPS response and differentially expressed according to MYD88 L265P. Figure S9. TNFAIP3 and CARD11 expression according to their copy number status. Figure S10. Additional survival analyses.

Clinical characteristics of the 17 patients with CNS relapse are provided, included time to CNS relapse, first-line treatment and response after first-line treatment. CR, Complete Response; PR, Partial Response; PD, Progressive Disease.

Supplementary Figure 1, related to main Figure 1

Supplementary Figure 3, related to main Figure 5.

Purpose: Next-generation sequencing (NGS) has detailed the genomic characterization of diffuse large B-cell lymphoma (DLBCL) by identifying recurrent somatic mutations. We set out to design a clinically feasible NGS panel focusing on genes whose mutations hold potential therapeutic impact. Furthermore, for the first time, we evaluated the prognostic value of these mutations in prospective clinical trials.Experimental Design: A Lymphopanel was designed to identify mutations in 34 genes, selected according to literature and a whole exome sequencing study of relapsed/refractory DLBCL patients. The tumor DNA of 215 patients with CD20+de novo DLBCL in the prospective, multicenter, and randomized LNH-03B LYSA clinical trials was sequenced to deep, uniform coverage with the Lymphopanel. Cell-of-origin molecular classification was obtained through gene expression profiling with HGU133+2.0 Affymetrix GeneChip arrays.Results: The Lymphopanel was informative for 96% of patients. A clear depiction of DLBCL subtype molecular heterogeneity was uncovered with the Lymphopanel, confirming that activated B-cell–like (ABC), germinal center B-cell like (GCB), and primary mediastinal B-cell lymphoma (PMBL) are frequently affected by mutations in NF-κB, epigenetic, and JAK–STAT pathways, respectively. Novel truncating immunity pathway, ITPKB, MFHAS1, and XPO1 mutations were identified as highly enriched in PMBL. Notably, TNFAIP3 and GNA13 mutations in ABC patients treated with R-CHOP were associated with significantly less favorable prognoses.Conclusions: This study demonstrates the contribution of NGS with a consensus gene panel to personalized therapy in DLBCL, highlighting the molecular heterogeneity of subtypes and identifying somatic mutations with therapeutic and prognostic impact. Clin Cancer Res; 22(12); 2919–28. ©2016 AACR.See related commentary by Lim and Elenitoba-Johnson, p. 2829

All relevant clinical data is included. Immunohistochemical results for BCL2, CD10, BCL6, MUM1, FOXP1, IgM and MYC are transcribed: 0 indicates negativity and 1 indicates positivity. For BCL2 and MYC, positivity is according to a threshold of 50% or 40% respectively.

Purpose: MYD88 mutations, notably the recurrent gain-of-function L265P variant, are a distinguishing feature of activated B-cell like (ABC) diffuse large B-cell lymphoma (DLBCL), leading to constitutive NFκB pathway activation. The aim of this study was to examine the distinct genomic profiles of MYD88-mutant DLBCL, notably according to the presence of the L265P or other non-L265P MYD88 variants.Experimental Design: A cohort of 361 DLBCL cases (94 MYD88 mutant and 267 MYD88 wild-type) was submitted to next-generation sequencing (NGS) focusing on 34 genes to analyze associated mutations and copy number variations, as well as gene expression profiling, and clinical and prognostic analyses.Results: Importantly, we highlighted different genomic profiles for MYD88 L265P and MYD88 non-L265P–mutant DLBCL, shedding light on their divergent backgrounds. Clustering analysis also segregated subgroups according to associated genetic alterations among patients with the same MYD88 mutation. We showed that associated CD79B and MYD88 L265P mutations act synergistically to increase NFκB pathway activation, although the majority of MYD88 L265P–mutant cases harbors downstream NFκB alterations, which can predict BTK inhibitor resistance. Finally, although the MYD88 L265P variant was not an independent prognostic factor in ABC DLBCL, associated CD79B mutations significantly improved the survival of MYD88 L265P–mutant ABC DLBCL in our cohort.Conclusions: This study highlights the relative heterogeneity of MYD88-mutant DLBCL, adding to the field's knowledge of the theranostic importance of MYD88 mutations, but also of associated alterations, emphasizing the usefulness of genomic profiling to best stratify patients for targeted therapy. Clin Cancer Res; 23(9); 2232–44. ©2016 AACR.

Supplementary methods, supplementary figure & table legends, and supplementary tables 3, 4, 8 & 9. Table S3: Overview of the Lymphopanel used for NGS analysis. Table S4: Overview of pathway selection Table S8. Genes involved in the LPS score. Table S9. Expression levels of genes involved in response to LPS stimulation according to MYD88 L265P.

Supplementary Figure 4, related to main Figure 5. Data pertaining to independent validation in the BEAT-AML cohort.

Statistically significant differences (p

Purpose: The prognostic impact of total metabolic tumor volume (TMTV) measured on pretreatment 18F-FDG PET/CT and its added value to molecular characteristics was investigated in patients with diffuse large B-cell lymphoma (DLBCL).Experimental Design: For 81 newly diagnosed patients with DLBCL treated with rituximab and CHOP/CHOP-like regimen, TMTV was computed using the 41% SUVmax thresholding method. According to the gene expression profile, determined using DASL (cDNA-mediated Annealing, Selection, Ligation and extension) technology, a subset of 57 patients was classified in germinal center B (GCB) or activated B-cell (ABC) subtypes and MYC or BCL2 overexpressed.Results: Median follow-up was 64 months. Five-year progression-free survival (PFS) and overall survival (OS) were 60% and 63% in the whole population. Median pretherapy TMTV was 320 cm3 (25th–75th percentiles 106–668 cm3). With a 300 cm3 cutoff, patients with high TMTV (n = 43) had a 5-year PFS and OS of 43% and 46% compared with 76% and 78% for patients with a low TMTV (P = 0.0023, P = 0.0047). ABC status, MYC, or BCL2 overexpression and both overexpression (“dual expressor,” DE) were significantly associated with a worse PFS and OS. TMTV combined with molecular data allowed a significant better risk substratification of ABC/GCB patients, on PFS and OS. High TMTV individualized in molecular-low-risk patients a group with a poor outcome (MYC, PFS=51%, OS=55% BCL2, PFS=49%, OS=49% or DE PFS=50%, OS=50%) and a group with a good outcome (MYC, PFS=93%, OS=93% BCL2, PFS=86%, OS=86%, or DE PFS=81%, OS=81%).Conclusions: The combination of molecular and imaging characteristics at diagnosis could lead to a more accurate selection of patients, to increase tailor therapy. Clin Cancer Res; 22(15); 3801–9. ©2016 AACR.

Purpose:RNA splicing is a fundamental biological process that generates protein diversity from a finite set of genes. Recurrent somatic mutations of splicing factor genes are common in some hematologic cancers but are relatively uncommon in acute myeloid leukemia (AML, < 20% of patients). We examined whether RNA splicing differences exist in AML, even in the absence of splicing factor mutations.Experimental Design:We developed a bioinformatics pipeline to study alternative RNA splicing in RNA-sequencing data from large cohorts of patients with AML.Results:We have identified recurrent differential alternative splicing between patients with poor and good prognosis. These splicing events occurred even in patients without any discernible splicing factor mutations. Alternative splicing recurrently occurred in genes with specific molecular functions, primarily related to protein translation. Developing tools to predict the functional impact of alternative splicing on the translated protein, we discovered that approximately 45% of the splicing events directly affected highly conserved protein domains. Several splicing factors were themselves misspliced and the splicing of their target transcripts were altered. Studying differential gene expression in the same patients, we identified that alternative splicing of protein translation genes in ELNAdv patients resulted in the induction of an integrated stress response and upregulation of inflammation-related genes. Finally, using machine learning techniques, we identified a splicing signature of four genes which refine the accuracy of existing risk prognosis schemes and validated it in a completely independent cohort.Conclusions:Our discoveries therefore identify aberrant alternative splicing as a molecular feature of adverse AML with clinical relevance.See related commentary by Bowman, p. 3503

This table presents all variants identified among our cohort by Lymphopanel NGS. Variant type and variant allele frequency (VAF) are indicated.

Supplementary Figure 2, related to main Figure 3

This table presents all CNVs identified among our cohort using the ONCOCNV software. Duplication was considered when copy number was superior to 2 and deletion was considered when copy number was inferior to 2. False discovery rate (FDR) cutoff was set at 0.01.

COO information according to the three techniques used in this study is given according to the various MYD88 variants present in our cohort.

Supplementary Methods - revised, plus Supplementary Tables 1-6

Background: Gene expression profiling (GEP), next-generation sequencing (NGS) and copy number variation (CNV) analysis have led to an increasingly detailed characterization of the genomic profiles of DLBCL. The aim of this study was to perform a fully integrated analysis of mutational, genomic, and expression profiles to refine DLBCL subtypes. A comparison of our model with two recently published integrative DLBCL classifiers was carried out, in order to best reflect the current state of genomic subtypes. Methods: 223 patients with de novo DLBCL from the prospective, multicenter and randomized LNH-03B LYSA clinical trials were included. GEP data was obtained using Affymetrix GeneChip arrays, mutational profiles were established by Lymphopanel NGS targeting 34 key genes, CNV analysis was obtained by array CGH, and FISH and IHC were performed. Unsupervised independent component analysis (ICA) was applied to GEP data and integrated analysis of multi-level molecular data associated with each component (gene signature) was performed. Findings: ICA identified 38 components reflecting transcriptomic variability across our DLBCL cohort. Many of the components were closely related to well-known DLBCL features such as cell-of-origin, stromal and MYC signatures. A component linked to gain of 19q13 locus, among other genomic alterations, was significantly correlated with poor OS and PFS. Through this integrated analysis, a high degree of heterogeneity was highlighted among previously described DLBCL subtypes. Interpretation: The results of this integrated analysis enable a global and multi-level view of DLBCL, as well as improve our understanding of DLBCL subgroups. Keywords: Diffuse large B-cell lymphoma, Independent component analysis, Transcriptomic variability, Gene signatures, prognosis

International audience; Peripheral T cell lymphomas (PTCLs) represent a significant unmet medical need with dismal clinical outcomes. The T cell receptor (TCR) is emerging as a key driver of T lymphocyte transformation. However, the role of chronic TCR activation in lymphomagenesis and in lymphoma cell survival is still poorly understood. Using a mouse model, we report that chronic TCR stimulation drove T cell lymphomagenesis, whereas TCR signaling did not contribute to PTCL survival. The combination of kinome, transcriptome, and epigenome analyses of mouse PTCLs revealed a NK cell-like reprogramming of PTCL cells with expression of NK receptors (NKRs) and downstream signaling molecules such as Tyrobp and SYK. Activating NKRs were functional in PTCLs and dependent on SYK activity. In vivo blockade of NKR signaling prolonged mouse survival, demonstrating the addiction of PTCLs to NKRs and downstream SYK/mTOR activity for their survival. We studied a large collection of human primary samples and identified several PTCLs recapitulating the phenotype described in this model by their expression of SYK and the NKR, suggesting a similar mechanism of lymphomagenesis and establishing a rationale for clinical studies targeting such molecules.

RNA sequencing holds great promise to improve the diagnostic of haematological malignancies, because this technique enables to detect fusion transcripts, to look for somatic mutations in oncogenes, and to capture transcriptomic signatures nosological entities. However, the analytical performances of targeted RNA sequencing have not been extensively described in diagnostic samples. Using a targeted panel of 1385 cancer-related genes in a series of 100 diagnosis samples and 8 controls, we detected all the already known fusion transcripts, and also discovered unknown and/or unsuspected fusion transcripts in 12 samples. Regarding the analysis of transcriptomic profiles, we show that targeted RNA sequencing is performant to discriminate acute lymphoblastic leukemia entities driven by different oncogenic translocations. Additionally, we show that 86% of the mutations identified at the DNA level are also detectable at the mRNA level, except for nonsense mutations which are subjected to mRNA decay. We conclude that targeted RNA sequencing might improve the diagnosis of haematological malignancies. Standardization of the preanalytical steps and further refinements of the panel design and of the bioinformatical pipelines will be an important step towards its use in standard diagnostic procedures.

Although copy number alterations (CNAs) and translocations constitute the backbone of the diagnosis and prognostication of acute myeloid leukemia (AML), techniques used for their assessment in routine diagnostics have not been reconsidered for decades. We used a combination of 2 next-generation sequencing–based techniques to challenge the currently recommended conventional cytogenetic analysis (CCA), comparing the approaches in a series of 281 intensively treated patients with AML. Shallow whole-genome sequencing (sWGS) outperformed CCA in detecting European Leukemia Net (ELN)–defining CNAs and showed that CCA overestimated monosomies and suboptimally reported karyotype complexity. Still, the concordance between CCA and sWGS for all ELN CNA–related criteria was 94%. Moreover, using in silico dilution, we showed that 1 million reads per patient would be enough to accurately assess ELN-defining CNAs. Total genomic loss, defined as a total loss ≥200 Mb by sWGS, was found to be a better marker for genetic complexity and poor prognosis compared with the CCA-based definition of complex karyotype. For fusion detection, the concordance between CCA and whole-transcriptome sequencing (WTS) was 99%. WTS had better sensitivity in identifying inv(16) and KMT2A rearrangements while showing limitations in detecting lowly expressed PML-RARA fusions. Ligation-dependent reverse transcription polymerase chain reaction was used for validation and was shown to be a fast and reliable method for fusion detection. We conclude that a next-generation sequencing–based approach can replace conventional CCA for karyotyping, provided that efforts are made to cover lowly expressed fusion transcripts.

Relevant molecular tools for treatment stratification of patients ≥65 years with acute myeloid leukemia (AML) are lacking. We combined clinical data with targeted DNA- and full RNA-sequencing of 182 intensively and palliatively treated patients to predict complete remission (CR) and survival in AML patients ≥65 years. Intensively treated patients with NPM1 and IDH2R172 mutations had longer overall survival (OS), whereas mutated TP53 conferred lower CR rates and shorter OS. FLT3-ITD and TP53 mutations predicted worse OS in palliatively treated patients. Gene expression levels most predictive of CR were combined with somatic mutations for an integrated risk stratification that we externally validated using the beatAML cohort. We defined a high-risk group with a CR rate of 20% in patients with mutated TP53, compared to 97% CR in low-risk patients defined by high expression of ZBTB7A and EEPD1 without TP53 mutations. Patients without these criteria had a CR rate of 54% (intermediate risk). The difference in CR rates translated into significant OS differences that outperformed ELN stratification for OS prediction. The results suggest that an integrated molecular risk stratification can improve prediction of CR and OS and could be used to guide treatment in elderly AML patients.

RNA splicing is a fundamental biological process that generates protein diversity from a finite set of genes. Recurrent somatic mutations of splicing factor genes are relatively uncommon in Acute Myeloid Leukemia (AML, < 20%). We examined whether RNA splicing differences exist in AML even in the absence of splicing factor mutations. Analyzing RNA-seq data from two independent cohorts of AML patients, we identified recurrent differential alternative splicing between patients with poor and good prognosis. These alternative splicing events occurred even in patients without any discernible splicing factor mutations. The alternative splicing events recurrently occurred in genes involved in specific molecular functions, primarily related to protein translation. Developing informatics tools to predict the functional impact of alternative splicing on the translated protein, we discovered that ~45% of the splicing events directly affected highly conserved protein domains. Several splicing factors were themselves misspliced in patients, and the splicing of their target transcripts were also altered. By studying differential gene expression in the same patients, we identified that alternative splicing of protein translation genes in ELNAdv patients resulted in the induction of an integrated stress response and up- regulation of inflammation-related genes. Lastly, using machine learning techniques, we identified a set of four genes whose alternative splicing can refine the accuracy of existing risk prognosis schemes and validated it in a completely independent cohort. Our discoveries therefore identify aberrant alternative splicing as a molecular feature of adverse AML with clinical relevance.

Purpose: RNA splicing is a fundamental biological process that generates protein diversity from a finite set of genes. Recurrent somatic mutations of splicing factor genes are common in some hematologic cancers but are relatively uncommon in acute myeloid leukemia (AML, < 20% of patients). We examined whether RNA splicing differences exist in AML, even in the absence of splicing factor mutations. Experimental Design: We developed a bioinformatics pipeline to study alternative RNA splicing in RNA-sequencing data from large cohorts of patients with AML. Results: We have identified recurrent differential alternative splicing between patients with poor and good prognosis. These splicing events occurred even in patients without any discernible splicing factor mutations. Alternative splicing recurrently occurred in genes with specific molecular functions, primarily related to protein translation. Developing tools to predict the functional impact of alternative splicing on the translated protein, we discovered that approximately 45% of the splicing events directly affected highly conserved protein domains. Several splicing factors were themselves misspliced and the splicing of their target transcripts were altered. Studying differential gene expression in the same patients, we identified that alternative splicing of protein translation genes in ELNAdv patients resulted in the induction of an integrated stress response and upregulation of inflammation-related genes. Finally, using machine learning techniques, we identified a splicing signature of four genes which refine the accuracy of existing risk prognosis schemes and validated it in a completely independent cohort. Conclusions: Our discoveries therefore identify aberrant alternative splicing as a molecular feature of adverse AML with clinical relevance. See related commentary by Bowman, p. 3503

Background: Mature T-cell lymphomas and leukemias (MTCL) are heterogeneous diseases with dismal prognosis. Differentiating between the numerous entities requires specialized pathology expertise and studies show up to 20% change in diagnosis after expert review of cases (Laurent, JCO, 2017). Assay for transposase accessible chromatin sequencing (ATAC-seq) is a simple technique to profile open chromatin regions (OCR) proven to be highly discriminant for cell-of-origin identification regardless of cell activation status (Shih, Cell, 2016). We applied ATAC-seq to MTCL in order to explore the epigenetic landscape of these diverse entities, compared them to normal T-cell subtypes and built a predictive model to help diagnosis. Method: Ten-thousand FACS-sorted single cells from primary MTCL samples and 50µm section of frozen tumoral tissue from the TENOMIC French T-cell Lymphoma Consortium were processed according to the previously published FAST-ATAC and OMNI-ATAC protocols respectively (Corces, Nat Genetics, 2016 & Nat. Methods, 2017). Concurrently we applied FAST ATAC to different normal T- and NK-cell subsets sorted from healthy donor PBMC or lymph node suspensions. Sequencing data were processed by an adapted version of ENCODE ATAC-seq pipeline. Matrix of insertion events in peaks by sample was obtained, normalized and most variant peaks were selected for UMAP projection. Results: In total, 678 normal and tumoral samples were sequenced to provide a comprehensive landscape of chromatin accessibility in MTCL. Epigenetic profiling by ATAC-seq of FACS-sorted tumoral samples resulted in a complete segregation of the known MTCL entities (AITL, TFH-PTCL, ALK+ and ALK- ALCL, HSTL, CTCL, ATLL, LGL and T-PLL). Most PTCL-NOS (13/17) clustered with a pre-defined MTCL subtype (mainly AITL/TFH-phenotype PTCL, CTCL and lymphomas exhibiting cytotoxic features). All but one discordant diagnosis between pathology and ATAC-seq (1/11) led to revised diagnosis after pathology review. Unsupervised clustering of normal NK- and T-cell subtypes (N=49) and sorted tumoral lymphoma cells (N=104) confirmed that AITL derive from TFH cells. HSTL and LGL closely segregated with NK- and gamma-delta T cells, in line with their known innate-like phenotype. Surprisingly, the cell-of-origin of T-PLL seems to be naïve T cells despite the known expression of central memory markers on leukemic cells. Beyond epigenetic classification, background reads from ATAC-seq profiles were used to detect copy number variation (CNV), such as isochromosome 7q in HSTL. In addition, HTLV1 and EBV viral sequence detection in ATAC-seq reads strengthened identification of ATLL and NKTCL cases. Finally, using unsupervised deconvolution approaches, we were able to discriminate different MTCL subtypes from 223 processed bulk frozen samples. All known MTCL subtypes were differentiated (AITL/PTCL-TFH, HSTL, NKTCL, ATLL, ALK- and ALK+ ALCL, MEITL, EATL). A subgroup of PTCL-NOS harboring GATA3 OCRs and a distinctly high CNV number was isolated that might correspond to previously described PTCL-GATA3 subtypes (Iqbal, Blood, 2019). A random forest model was trained to predict diagnosis based on chromatin-accessibility clusters defined in the discovery cohort of patients. The model showed accurate prediction performance by cross-validation. External validation on 172 samples collected from 5 tertiary care centers will be presented at the meeting. Conclusion: ATAC-seq is a fast and cost-effective technique to help and refine MTCL pathological classification and allows for putative cell-of-origin identification in lymphoma. Training of a machine learning model to predict MTCL entity diagnosis based on ATAC-seq analysis of fresh or frozen samples shows promising results. Figure 1 Figure 1. Disclosures Sibon: Janssen: Consultancy; Abbvie: Consultancy; iQone: Consultancy; Takeda: Consultancy; Roche: Consultancy. Drieux: Genexpath: Patents & Royalties: The author is a potential inventor on a patent application for the LymphoSign, which has been licensed for by Genexpath Patents & Royalties.. Ruminy: Genexpath: Patents & Royalties: The author is a potential inventor on a patent application for the LymphoSign, which has been licensed for by Genexpath Patents & Royalties. . Salles: Takeda: Consultancy; Velosbio: Consultancy; Ipsen: Consultancy; Allogene: Consultancy; Miltneiy: Consultancy; Genentech/Roche: Consultancy; Genmab: Consultancy; Janssen: Consultancy; Loxo: Consultancy; Kite/Gilead: Consultancy; Regeneron: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Novartis: Consultancy; Incyte: Consultancy; Rapt: Consultancy; Epizyme: Consultancy, Honoraria; Debiopharm: Consultancy; BMS/Celgene: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Honoraria; Bayer: Honoraria. Gaulard: Alderaan: Research Funding; Sanofi: Research Funding; Innate Pharma: Research Funding; Gilead: Consultancy; Takeda: Consultancy, Honoraria. Bachy: Kite, a Gilead Company: Honoraria; Novartis: Honoraria; Daiishi: Research Funding; Roche: Consultancy; Takeda: Consultancy; Incyte: Consultancy.

Motivation Although sequencing-based technologies are becoming the new reference in genome analysis, comparative genomic hybridization arrays (aCGH) still constitute a simple and reliable approach for copy number analysis. The most powerful algorithms to analyze such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task. Results The cghRA framework combines a user-friendly graphical interface and a powerful object-oriented command-line interface to handle a full aCGH analysis, as is illustrated in an original series of 107 Diffuse Large B-Cell Lymphomas. New algorithms for copy-number calling, polymorphism detection and minimal common region prioritization were also developed and validated. While their performances will only be demonstrated with aCGH, these algorithms could actually prove useful to any copy-number analysis, whatever the technique used. Availability and implementation R package and source for Linux, MS Windows and MacOS are freely available at http://bioinformatics.ovsa.fr/cghRA. Supplementary information Supplementary data are available at Bioinformatics online.

Little is known on the phylogenetic relationship between diagnostic and relapse clones of diffuse large B-cell lymphoma (DLBCL). We applied high throughput sequencing (HTS) of the VDJ locus of Immunoglobulin heavy chain (IGHV) on 14 DLBCL patients with serial samples, including tumor biopsies and/or peripheral blood mononuclear cells (PBMC). Phylogenetic data were consolidated with targeted sequencing and cytogenetics. Phylogeny clearly showed that DLBCL relapse could occur according either an early or a late divergent mode. These two modes of divergence were independent from the elapsed time between diagnosis and relapse. We found no significant features for antigen selection pressure in complementary determining region both at diagnosis and relapse for 9/12 pairs and a conserved negative selection pressure for the three remaining cases. Targeted HTS and conventional cytogenetics revealed a branched vs. linear evolution for 5/5 IGHV early divergent cases, but unexpected such "oncogenetic" branched evolution could be found in at least 2/7 IGHV late divergent cases. Thus, if BCR signaling is mandatory for DLBCL emergence, oncogenetic events under chemotherapy selection pressure may be the main driving forces at relapse. Finally, circulating subclones with divergent IGHV somatic hypermutations patterns from initial biopsy could be detected in PBMC at diagnosis for 4/6 patients and, for two of them, at least one was similar to the ones found at relapse. This study highlights that oncogenetic intraclonal diversity of DLBCL should be evaluated beyond the scope a single biopsy and represents a rationale for future investigations using peripheral blood for lymphoid malignancies genotyping. Am. J. Hematol. 92:68-76, 2017. © 2016 Wiley Periodicals, Inc.

Purpose: The prognostic impact of total metabolic tumor volume (TMTV) measured on pretreatment 18F-FDG PET/CT and its added value to molecular characteristics was investigated in patients with diffuse large B-cell lymphoma (DLBCL). Experimental Design: For 81 newly diagnosed patients with DLBCL treated with rituximab and CHOP/CHOP-like regimen, TMTV was computed using the 41% SUVmax thresholding method. According to the gene expression profile, determined using DASL (cDNA-mediated Annealing, Selection, Ligation and extension) technology, a subset of 57 patients was classified in germinal center B (GCB) or activated B-cell (ABC) subtypes and MYC or BCL2 overexpressed. Results: Median follow-up was 64 months. Five-year progression-free survival (PFS) and overall survival (OS) were 60% and 63% in the whole population. Median pretherapy TMTV was 320 cm3 (25th–75th percentiles 106–668 cm3). With a 300 cm3 cutoff, patients with high TMTV (n = 43) had a 5-year PFS and OS of 43% and 46% compared with 76% and 78% for patients with a low TMTV (P = 0.0023, P = 0.0047). ABC status, MYC, or BCL2 overexpression and both overexpression (“dual expressor,” DE) were significantly associated with a worse PFS and OS. TMTV combined with molecular data allowed a significant better risk substratification of ABC/GCB patients, on PFS and OS. High TMTV individualized in molecular-low-risk patients a group with a poor outcome (MYC, PFS=51%, OS=55% BCL2, PFS=49%, OS=49% or DE PFS=50%, OS=50%) and a group with a good outcome (MYC, PFS=93%, OS=93% BCL2, PFS=86%, OS=86%, or DE PFS=81%, OS=81%). Conclusions: The combination of molecular and imaging characteristics at diagnosis could lead to a more accurate selection of patients, to increase tailor therapy. Clin Cancer Res; 22(15); 3801–9. ©2016 AACR.

Primary mediastinal B-cell lymphoma (PMBL) is an entity of B-cell lymphoma distinct from the other molecular subtypes of diffuse large B-cell lymphoma (DLBCL). We investigated the prevalence, specificity, and clinical relevance of mutations of XPO1, which encodes a member of the karyopherin-β nuclear transporters, in a large cohort of PMBL. PMBL cases defined histologically or by gene expression profiling (GEP) were sequenced and the XPO1 mutational status was correlated to genetic and clinical characteristics. The XPO1 mutational status was also assessed in DLBCL, Hodgkin lymphoma (HL) and mediastinal gray-zone lymphoma (MGZL).The biological impact of the mutation on Selective Inhibitor of Nuclear Export (SINE) compounds (KPT-185/330) sensitivity was investigated in vitro. XPO1 mutations were present in 28/117 (24%) PMBL cases and in 5/19 (26%) HL cases but absent/rare in MGZL (0/20) or DLBCL (3/197). A higher prevalence (50%) of the recurrent codon 571 variant (p.E571K) was observed in GEP-defined PMBL and was associated with shorter PFS. Age, International Prognostic Index and bulky mass were similar in XPO1 mutant and wild-type cases. KPT-185 induced a dose-dependent decrease in cell proliferation and increased cell-death in PMBL cell lines harboring wild type or XPO1 E571K mutant alleles. Experiments in transfected U2OS cells further confirmed that the XPO1 E571K mutation does not have a drastic impact on KPT-330 binding. To conclude the XPO1 E571K mutation represents a genetic hallmark of the PMBL subtype and serves as a new relevant PMBL biomarker. SINE compounds appear active for both mutated and wild-type protein.

HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1, HACE1, located on chromosome 6q, encodes an E3 ubiquitin ligase and is downregulated in many human tumors. Here, we report HACE1 as a candidate tumor suppressor gene down-regulated by a combination of deletion and epigenetic mechanisms. HACE1 deletions were observed in 40% of B-cell lymphoma tumors. Hypermethylation of the HACE1 promoter CpG177 island was found in 60% (68/111) of cases and in all tested B-cell lymphoma lines. Using HDAC inhibitors, we observed predominantly inactive chromatin conformation (methylated H3 histones H3K9me2) in HACE1 gene promoter region. We demonstrated in Ramos and Raji cells that down-regulation of HACE1 expression was associated with a significant decrease in apoptosis and an accumulation of cells in the S and G2/M phases. Our experiments indicate that HACE1 can act as a haploinsufficient tumor suppressor gene in most B-cell lymphomas and can be downregulated by deacetylation of its promoter region chromatin, which makes HACE1 a potential target for HDAC inhibitors.

Despite the many efforts already spent to enumerate somatic mutations in diffuse large B-cell lymphoma (DLBCL), previous whole-genome and whole-exome studies conducted on patients of mixed outcomes failed at characterizing the 30% of patients who will relapse or resist current immunochemotherapies. To address this issue, we performed whole-exome sequencing of normal/tumoral DNA pairs in 14 relapsed/refractory (R/R) patients subclassified by full-transcriptome arrays (six activated B-cell like, three germinal center B-cell like, and five primary mediastinal B-cell lymphomas), from the LNH-03 LYSA clinical trial program. Aside from well-known DLBCL features, gene and pathway level recurrence analyses proposed several interesting leads including TBL1XR1 and activating mutations in IRF4 or in the insulin regulation pathway. Sequencing-based copy number analysis defined 23 short recurrently altered regions involving genes such as REL, CDKN2A, HYAL2, and TP53. Moreover, it highlighted mutations in genes such as GNA13, CARD11, MFHAS1, and PCLO as associated with secondary variant allele amplification events. The five primary mediastinal B-cell lymphomas (PMBL), while unexpected in a R/R cohort, showed a significantly higher mutation rate (P = 0.003) and provided many insights on this classical Hodgkin lymphoma related subtype. Novel genes such as XPO1, MFHAS1, and ITPKB were found particularly mutated, along with various cytokine-based signaling pathways. Among these analyses, somatic events in the NF-κB pathway were found preponderant in the three DLBCL subtypes, confirming its major implication in DLBCL aggressiveness and pinpointing several new candidate genes.

RNA splicing is a fundamental biological process that generates protein diversity from a finite set of genes. Recurrent somatic mutations of genes involved in RNA splicing are present at high frequency in Myelodysplasia (up to 70%) but less so in Acute Myeloid Leukemia (AML; less than 20%). To investigate whether there were aberrant and recurrent RNA splicing events in the AML transcriptome that were associated with poor prognosis in the absence of splicing factor mutations, we developed a bioinformatics pipeline to systematically annotate and quantify alternative splicing events from RNA-sequencing data (Fig A). We first analysed publicly available RNA-seq data from The Cancer Genome Atlas (TCGA, n=170). We focussed on non-M3 AML patients with no splicing factor mutations (based on reported genomic sequencing and verified by re-analysis of RNA-seq data from all patients) who had received intensive chemotherapy. We segregated these patients based on their European Leukaemia Net (ELN) risk classification and identified 1290 alternatively spliced events impacting 910 genes that were significantly different (FDR In order to prioritise those alternatively spliced events most likely to have a deleterious function, we developed an analytical framework to predict their impact on protein structure (Fig E). 87 alternatively spliced events, 25.81% of the commonly shared splicing events, relating to 78 genes (35.13% of all genes) were predicted to directly alter highly conserved protein domains within the affected genes, leading to either a complete (~25%, Fig E) or a partial loss of a domain (20%, Fig E). These in silico predictions are likely to be an underestimate of the true impact, as splicing alterations mapping to poorly annotated domains or affecting the tertiary structure of proteins would be missed. A number of splicing factors themselves were differentially spliced, with the alternative splicing predicted to have functional consequences. This was exemplified by hnRNPA1, a factor with well-established roles in splicing, is itself alternatively spliced in patients and predicted to be deleterious. Consistent with this, motif scanning analyses indicated that a number of mis-spliced transcripts had hnRNPA1 binding motifs (Fig F). To assess the impact of these alternatively spliced events (that were predicted to also disrupt highly conserved protein domains) on the transcriptome, we simultaneously quantified differential gene expression. IPA analysis of the 602 genes that were differentially expressed between ELNAdv and ELNFav patients and shared between both TCGA and ClinSeq cohorts indicated that they were associated with pathways (Fig G) that were distinct from those associated with aberrantly spliced genes (Fig D). A number of pathways related to inflammation were enriched amongst the genes observed to be upregulated in ELNAdv patients (Fig G). Network analyses integrating the alternatively spliced genes with differentially expressed genes revealed strong interactions (Fig H), indicating functional associations between these biological events. Given these strong network interactions, we investigated the potential prognostic significance of these alternatively spliced events. To this end, we utilised machine-learning methods to derive a "splicing signature" of four mis-spliced genes with a predictive capacity equivalent to the ELN (Fig I). The splicing signature further refined existing risk prediction algorithms to improve the classification of patients (Fig J). Taken together, we report the presence of extensive deregulation of RNA splicing in AML patients even in the absence of splicing factor mutations. Many of these events were shared in patients with adverse outcomes and their impact on the AML transcriptome points towards vulnerabilities that could be targeted. Figure Disclosures Unnikrishnan: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lehmann:TEVA: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Abbive: Membership on an entity's Board of Directors or advisory committees. Pimanda:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Alterations of RUNX1 in acute myeloid leukemia (AML) are associated with either a more favorable outcome in the case of the RUNX1/RUNX1T1 fusion or unfavorable prognosis in the case of point mutations. In this project we aimed to identify genes responsible for the observed differences in outcome that are common to both RUNX1 alterations. Analyzing four AML gene expression data sets (n = 1514), a total of 80 patients with RUNX1/RUNX1T1 and 156 patients with point mutations in RUNX1 were compared. Using the statistical tool of mediation analysis we identified the genes CD109, HOPX, and KIAA0125 as candidates for mediator genes. In an analysis of an independent validation cohort, KIAA0125 again showed a significant influence with respect to the impact of the RUNX1/RUNX1T1 fusion. While there were no significant results for the other two genes in this smaller validation cohort, the observed relations linked with mediation effects (i.e., those between alterations, gene expression and survival) were almost without exception as strong as in the main analysis. Our analysis demonstrates that mediation analysis is a powerful tool in the identification of regulative networks in AML subgroups and could be further used to characterize the influence of genetic alterations.

International audience

// Maxime Fontanilles 1, 2 , Florent Marguet 3, 4 , Elodie Bohers 2 , Pierre-Julien Viailly 2 , Sydney Dubois 2 , Philippe Bertrand 2 , Vincent Camus 1, 2 , Sylvain Mareschal 2 , Philippe Ruminy 2 , Catherine Maingonnat 2 , Stephane Lepretre 1 , Elena-Liana Veresezan 5 , Stephane Derrey 6 , Herve Tilly 1, 2 , Jean-Michel Picquenot 5 , Annie Laquerriere 3, 4 and Fabrice Jardin 1, 2 1 Department of Hematology, Cancer Center Henri Becquerel, 76000 Rouen, France 2 INSERM U1245, Cancer Center Henri Becquerel, Institute of Research and Innovation in Biomedicine (IRIB), University of Normandy, UNIVROUEN, 76000 Rouen, France 3 INSERM U1245 and Hopital Charles Nicolle, NeoVasc Team, University of Normandy, UNIVROUEN, CHU-Hopitaux de Rouen, 76031 Rouen, France 4 Department of Neuropathology, Hopital Charles Nicolle, Normandy Center for Genomic and Personalized Medicine, CHU-Hopitaux de Rouen, 76031 Rouen, France 5 Department of Pathology, Cancer Center Henri Becquerel, 76000 Rouen, France 6 Department of Neurosurgery, Hopital Charles Nicolle, CHU-Hopitaux de Rouen, 76031 Rouen, France Correspondence to: Fabrice Jardin, email: fabrice.jardin@chb.unicancer.fr Keywords: primary central nervous system lymphoma, circulating cell-free tumor DNA, somatic mutation, liquid biopsy, next-generation sequencing Received: March 08, 2017 Accepted: May 03, 2017 Published: June 01, 2017 ABSTRACT Purpose: Primary central nervous system lymphomas (PCNSL) have recurrent genomic alterations. The main objective of our study was to demonstrate that targeted sequencing of circulating cell-free DNA (cfDNA) released by PCNSL at the time of diagnosis could identify somatic mutations by next-generation sequencing (NGS). Patients and Methods: PlasmacfDNA and matched tumor DNA (tDNA) from 25 PCNSL patients were sequenced using an Ion Torrent Personal Genome Machine (Life Technologies ® ). First, patient-specific targeted sequencing of identified somatic mutations in tDNA was performed. Then, a second sequencing targeting MYD88 c.T778C was performed and compared to plasma samples from 25 age-matched control patients suffering from other types of cancer. Results: According to the patient-specific targeted sequencing, eight patients (32% [95% CI 15-54%]) had detectable somatic mutations in cfDNA. Considering MYD88 sequencing, six patients had the specific c.T778C alteration detected in plasma. Using a control group, the sensitivity was 24% [9-45%] and the specificity was 100%. Tumor volume or deep brain structure involvement did not influence the detection of somatic mutations in plasma. Conclusion: This pilot study provided evidence that somatic mutations can be detected by NGS in the cfDNA of a subset of patients suffering from PCNSL.

International audience; Introduction: Primary cutaneous diffuse large B‐cell lymphoma leg‐type (PCLBCL‐LT) is recognized by the World Health Organization (WHO) classification as a rare and aggressive disease accounting for 5 to 10% of primary cutaneous lymphoma. It displays original clinical features occurring mostly in the elderly and preferentially involving the legs but date the genetic specificities of this entities, as compared to other B‐cell aggressive lymphoma is unknown.Methods: To determine whether the mutational profile of primary cutaneous diffuse large B‐cell lymphoma leg‐type (PCLBCL‐LT) is unique by comparison with other diffuse large B‐cell lymphoma (DLBCL) subtypes, we analyzed a total cohort of 28 PCLBCL‐LT cases by next generation sequencing with a Lymphopanel designed for DLBCL. We also analyzed 12 pairs of tumor and control DNA samples by whole exome sequencing which led us to perform resequencing of three selected genes not included in the Lymphopanel: TBL1XR1, KLHL6 and IKZF3. To pinpoint specificities, comparison with a cohort of DLBCL and Primary central nervous System lymphoma (PCNSL) analyzed by the same targeted panel was performed.Results: Our study clearly identifies an original mutational landscape of PCLBCL‐LT with a very restricted set of highly recurrent mutations (>40%) involving MYD88 (p.L265P variant), PIM1 and CD79B. Other genes involved in B‐cell signaling, NFKB activation or DNA modeling were found altered notably TBL1XR1 (33%), MYC (26%) CREBBP (26%) and IRF4 (21%) or HIST1H1E(41%). MYD88L265P variant was associated with copy number variations or copy neutral loss of heterozygosity in 60% of cases. The most frequent genetic losses involved CDKN2A/2B, TNFAIP3/A20, PRDM1, TCF3 and CIITA. The high recurrence of specific gene mutations such as MYD88 and the absence of mutations of KMT2D or FOXO1 are distinct features from nodal DLBCLs of either GC or ABC subtypes. Interestingly PCLBCL‐LT exhibits a mutational pattern that is closer to PCNSL than to ABC‐type nodal DLBCL but also has distinctive features, such as a very high mutational rate for PIM1, a higher rate of CD79B mutations and other original mutations such as CREBBP, MYC and IRF4 mutations (figure 1).Conclusion: This study describes for the first time the genomic landscape of a series of untreated PCLBCL‐LT. Our results obtained by WES and targeted sequencing underscore several similarities with ABC‐DBCL and more specifically with PCNSL subtypes. On the other hand, we pinpoint specificities that may sustain distinctive clinical features and guide therapeutic strategies according to an individual genetic analysis.

Introduction: MYD88 mutations, notably the recurrent gain-of-function L265P variant, are a distinguishing feature of Activated B-Cell like (ABC) Diffuse Large B Cell Lymphoma (DLBCL), leading to constitutive NFkB pathway activation. The frequency of MYD88 mutations in DLBCL and other hematologic malignancies is well described; however, there has not yet been a large-scale study of a MYD88 mutated patient cohort with additional Next Generation Sequencing (NGS), copy number variation (CNV), and gene expression data, in order to thoroughly characterize the associated genomic profiles of these patients. The aims of our study were to compare the L265P and non-L265P mutations in terms of pathological and genetic features, to better detail the genomic background associated with MYD88 mutations in order to delineate patients potentially sensitive to targeted therapies, and to define the prognostic value of MYD88 mutations according to different genomic contexts. Methods: A cohort of 361 DLBCL patients (94 MYD88 mutant and 267 MYD88 wild-type) was selected among the prospective, multicenter and randomized LNH-03B and LNH09-7B (NCT01195714) LYSA trials, as well as among patients sequenced at our institution as part of routine procedure. Cell of origin (COO) classification was obtained with HGU133+2.0 Affymetrix GeneChip arrays for 214 patients, with RT-MLPA for 77 patients1 and with Hans immunohistochemistry (IHC) method for 49 patients. All cases were submitted to next generation sequencing (NGS) focusing on 34 genes (Lymphopanel2) in order to analyze associated mutations and copy number variations (CNVs), as well as IHC, FISH, and clinical and prognostic analyses. Results: Importantly, we highlighted different genomic profiles for MYD88 L265P and MYD88 non-L265P mutant DLBCL, shedding light on their divergent backgrounds. Clustering analysis segregated subgroups according to associated genetic alterations among patients with either MYD88 L265P or non-L265P mutations. As such, clustering separated MYD88 L265P mutated DLBCL with associated PIM1 (52%), CD79B (52%), KMT2D (42%), and PRDM1 (32%) mutations, as well as MYD88 L265P mutated DLBCL with CDKN2A/B (67%/50%), PRDM1 (57%) and TNFAIP3 (52%) CNVs. We showed that associated CD79B and MYD88 L265P mutations act synergistically to increase NFkB pathway activation, although the majority of ABC MYD88 L265P mutant cases harbor downstream NFkB alterations, which can potentially predict BTK inhibitor resistance. Of note, although the MYD88 L265P variant was not an independent prognostic factor in ABC DLBCL, associated CD79B mutations significantly improved the survival of MYD88 L265P mutant ABC DLBCL in our cohort both in OS (p=0.02) and PFS (p=0.01), whereas the association of CARD11 or TNFAIP3 alterations did not impact survival. Interestingly, MYD88 mutant DLBCL cases were significantly more likely to experience central nervous system (CNS) relapse than MYD88 WT cases (p=0.02), as were MYD88 L265P mutant cases specifically (p=0.03). This result still tended toward statistical significance when considering only ABC patients (7 of 11 ABC CNS-relapsing cases were MYD88 mutant, p=0.1) but would have to be confirmed in a larger cohort. Conclusions: This study highlights the relative heterogeneity of MYD88 mutant DLBCBL, adding to the field's knowledge of the distinct genetic backgrounds of these subgroups. Our data highlights the theranostic and prognostic relevance of examining MYD88 and associated genomic alterations, emphasizing the usefulness of genomic profiling to best stratify patients for targeted therapy. 1. Mareschal S, Ruminy P, Bagacean C, et al. Accurate Classification of Germinal Center B-Cell-Like/Activated B-Cell-Like Diffuse Large B-Cell Lymphoma Using a Simple and Rapid Reverse Transcriptase-Multiplex Ligation-Dependent Probe Amplification Assay: A CALYM Study. The Journal of molecular diagnostics : JMD. 2015;17(3):273-283. 2. Dubois S, Viailly P-J, Mareschal S, et al. Next Generation Sequencing in Diffuse Large B Cell Lymphoma Highlights Molecular Divergence and Therapeutic Opportunities: a LYSA Study. Clinical cancer research : an official journal of the American Association for Cancer Research. 2016;22(12):2919-2928. Disclosures Salles: Novartis: Consultancy, Honoraria; Mundipharma: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Gilead: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria, Research Funding.

International audience; To determine whether the mutational profile of primary cutaneous diffuse large B-cell lymphoma, leg type (PCLBCL-LT) is unique by comparison with other diffuse large B-cell lymphoma subtypes, we analyzed a total cohort of 20 PCLBCL-LT patients by using next-generation sequencing with a lymphoma panel designed for diffuse large B-cell lymphoma. We also analyzed 12 pairs of tumor and control DNA samples by whole-exome sequencing, which led us to perform resequencing of three selected genes not included in the lymphoma panel: TBL1XR1, KLHL6, and IKZF3. Our study clearly identifies an original mutational landscape of PCLBCL-LT with a very restricted set of highly recurrent mutations (>40%) involving MYD88 (p.L265P variant), PIM1, and CD79B. Other genes involved in B-cell signaling, NF-κB activation, or DNA modeling were found altered, notably TBL1XR1 (33%), MYC (26%) CREBBP (26%), and IRF4 (21%) or HIST1H1E (41%). MYD88L265P variant was associated with copy number variations or copy neutral loss of heterozygosity in 60% of patients. The most frequent genetic losses involved CDKN2A/2B, TNFAIP3/A20, PRDM1, TCF3, and CIITA. Together, these results show that PCLBCL-LT exhibits a unique mutational landscape, combining highly recurrent hotspot mutations in genes involved in NF-kB and B-cell signaling pathways, which provides a rationale for using selective inhibitors of the B-cell receptor.

International audience; Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. It includes three major subtypes termed germinal center B-cell-like, activated B-cell-like, and primary mediastinal B-cell lymphoma. With the emergence of novel targeted therapies, accurate methods capable of interrogating this cell-of-origin classification should soon become essential in the clinics. To address this issue, we developed a novel gene expression profiling DLBCL classifier based on reverse transcriptase multiplex ligation-dependent probe amplification. This assay simultaneously evaluates the expression of 21 markers, to differentiate primary mediastinal B-cell lymphoma, activated B-cell-like, germinal center B-cell-like, and also Epstein-Barr virus-positive DLBCLs. It was trained using 70 paraffin-embedded biopsies and validated using >160 independent samples. Compared with a reference classification established from Affymetrix U133 + 2 data, reverse transcriptase multiplex ligation-dependent probe amplification classified 85.0% samples into the expected subtype, comparing favorably with current diagnostic methods. This assay also proved to be highly efficient in detecting the MYD88 L265P mutation, even in archival paraffin-embedded tissues. This reliable, rapid, and cost-effective method uses common instruments and reagents and could thus easily be implemented into routine diagnosis workflows, to improve the management of these aggressive tumors.

Diffuse large B cell lymphoma (DLBCL) is an aggressive and heterogeneous malignancy that can be divided in two major subgroups, germinal center B-cell-like (GCB) and activated B-cell-like (ABC). Activating mutations of genes involved in the BCR and NF-κB pathways (CD79A, CD79B, MYD88, and CARD11) or in epigenetic regulation (EZH2) have been recently reported, preferentially in one of the two DLBCL subtypes. We analyzed the mutational status of these five recurrently mutated genes in a cohort of 161 untreated de novo DLBCL. Overall, 93 mutations were detected, in 61 (38%) of the patients. The L265P MYD88 mutation was the most frequent MYD88 variant (n = 18), observed exclusively in the ABC subtype. CD79A/CD79B ITAM domains were targeted in ABC DLBCL (12/77; 16%), whereas CARD11 mutations were equally distributed in the two subtypes. The EZH2 Y641 substitution was found almost exclusively in the GCB subgroup (15/62; 24%). Twenty cases (12%) displayed two activating mutations, including the most frequent CD79/MYD88 variants combination (n = 8) which is observed exclusively in the ABC subtype. When considering only ABC DLBCL patients treated by rituximab plus chemotherapy, the presence of an activating NF-κB mutation was associated with an unfavorable outcome (3-years OS 26% for mutated cases versus 67% for the cases without mutations, P = 0.0337). Our study demonstrates that activating and targetable mutations are observed at a very high frequency in DLBCL at the time of diagnosis, indicating that sequencing of a limited number of genes could help tailor an optimal treatment strategy in DLBCL.