Identification of Genes Frequently Mutated In FL and DLBCL with Transcriptome, Genome and Exome Sequencing
Bibliographic record
Abstract
Abstract Abstract 804 Introduction: Follicular lymphoma (FL) and diffuse large B cell lymphoma (DLBCL) are the two most common types of non Hodgkin lymphoma (NHL). It is widely accepted that DLBCL can be divided into two major subtypes using gene expression profiling: germinal center B-cell (GCB) and activated B-cell (ABC). Both FL and the GCB subtype of DLBCL derive from germinal center B cells and have been found to share some common mutational events such as translocations leading to the deregulation of the BCL2 oncogene and mutations affecting a single tyrosine (Y641) in the histone methyltransferase EZH2. In contrast, ABC DLBCL tumors are characterized by mutations leading to the constitutive activity of NFkB. The clear differences in treatment response between subtypes allow this distinction to be used as a prognostic indicator and may ultimately lead to therapies that target individual features of each subtype. However, besides the gene expression and mutational signatures that differentiate the DLBCL subtypes, there is a paucity of molecular prognostic markers in these NHLs. Further, there is limited knowledge about the genetic events that drive the GCB subtype of DLBCL, which, if better understood, may enable the design of targeted therapeutics. Methods: To identify mutations driving lymphomagenesis and in particular, aggressive cases of NHL, we applied Illumina second-generation sequencing technology to the analysis of tumor genomes and constitutional DNAs from a FL and a DLBCL tumor and the exomes from two additional DLBCLs. In these “omes”, we identified somatic protein-altering point mutations in more than 250 genes including genes known to be involved in cancer, for example TP53, FAS and TNFAIP3 (A20). Many of these mutations may represent passenger rather than driver mutations, the latter of which are involved in disease progression. To identify the likely driver mutations, we sought to identify the genes that are recurrent targets of somatic mutation in these cancers. To this end, we further analyzed the transcriptome sequences we generated using RNA-seq from 95 primary DLBCLs,13 FL cases and 10 DLBCL-derived cell lines. Results: 105 of the genes found mutated in the FL and DLBCL genomes were observed to be recurrent targets of somatic mutation in these diseases. Some of these were known targets of aberrant somatic hypermutation (SHM) including BCL2, PIM1, and IRF4 and others have been previously identified as targets of recurrent mutation in lymphoma, such as EZH2, CD79B and CARD11. One of the most frequently mutated genes was MLL2, a histone methyltransferase never before implicated in lymphomagenesis. MLL2 showed a pattern of mutation characteristic of a dosage-sensitive tumor suppressor gene. Another frequently mutated gene was MEF2B, a calcium-regulated transcriptional co-activator/repressor that cooperates with histone modifying enzymes to epigenetically regulate the expression of genes. We found that mutations affecting MEF2B occur in 11.7% of FL and 9% of DLBCL, with the majority (73%) of these mutations affecting three amino acids (K4, Y69, and D83). Analysis of these 105 recurrently mutated genes for prognostic signatures is ongoing. Conclusions: High-throughput sequencing platforms have enabled the identification of recurrent targets of somatic mutations never suspected to be involved in lymphoma. Some of these mutated genes may have prognostic value while others may represent targets for the rational design of novel therapeutics. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".