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Record W4379981917 · doi:10.1002/hon.3163_68

BTG2 SUPER‐ENHANCER MUTATIONS DISRUPT TFAP4 BINDING AND DYSREGULATE BTG2 EXPRESSION IN DIFFUSE LARGE B‐CELL LYMPHOMA

2023· article· en· W4379981917 on OpenAlexaff
Élodie Bal, Paola De Simone, Andrew B. Holmes, Lisette Hilton, Katia Basso, Rajesh K. Soni, Ryan D. Morin, Riccardo Dalla‐Favera, Laura Pasqualucci

Bibliographic record

VenueHematological Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsCanada's Michael Smith Genome Sciences CentreGenome British Columbia
Fundersnot available
KeywordsBiologyDiffuse large B-cell lymphomaSomatic hypermutationEnhancerCancer researchGeneGeneticsLymphomaTranscription factorB cellImmunology

Abstract

fetched live from OpenAlex

Introduction: Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy, remains incurable in ∼40% of patients. Coding-genome sequencing efforts identified several genes/pathways altered in this disease, as well as genetic subgroups of potential clinical relevance. However, the large non-coding portion of the genome remained largely unexplored. We recently identified a pervasive hypermutation mechanism targeting active super-enhancers (SEs) in >90% of DLBCL and leading to dysregulation of multiple genes, including well-known lymphoma oncogenes (Bal et al., Nature 2022). As evidence of oncogenic relevance, we demonstrated that mutational hotspots in the BCL6, BCL2 and CXCR4 SEs impair the binding of specific transcriptional repressors, preventing the gene negative regulation and creating oncogenic dependencies in DLBCL cells. Here we aimed to define the pathogenic role of mutations targeting the intragenic SE (iSE) of the BTG2 gene, the second most commonly mutated in DLBCL. BTG2 encodes a member of the B-cell translocation gene (BTG)/TOB family involved in transcriptional co-activation and modulation of mRNA abundance. BTG2 is also a recurrent target of somatic missense mutations (6%–11% of DLBCL), suggesting a major role in the pathogenesis of this disease. Methods: We screened 243 DLBCL cases for the presence of mutational hotspots within the BTG2-iSE, and combined in silico prediction, DNA-binding assays (Reverse-ChIP, electromobility-shift assay, ChIP-qPCR), RNA-seq and CRISPR/Cas9 editing approaches in isogenic BTG2 mutant versus WT DLBCL cell lines to identify transcription factors bound to the SE and disrupted by the mutation. Results: We identified a recurrent mutational cluster affecting the BTG2-iSE in 52/243 (21%) DLBCLs, with preferential enrichment in ST2 subgroup. CRISPR-Cas9 mediated correction of the mutation in 3 DLBCL cell lines led to counter selection and reduced BTG2 expression, consistent with oncogenic addiction. In silico motif prediction and in vitro DNA-binding assays, followed by validation in multiple isogenic DLBCL cell lines, identified TFAP4 as a major transcription factor that binds to the WT, but not to the mutated site. TFAP4 is an important regulator of B-cell proliferation and cell fate decisions, which can function as a transcriptional activator or repressor in germinal center B-cells, and acts downstream of/in parallel with c-MYC. Of note, introduction of SE hotspot mutations in WT DLBCL cells was associated with increased BTG2 expression, confirming a direct link between SE mutations and deregulated gene expression through escape from TFAP4-mediated suppression. Conclusions: These findings suggest a major role for BTG2 deregulation by SE aberrant somatic hypermutation in the pathogenesis and heterogeneity of DLBCL, with implications for precision classification and potential therapeutic targeting of DLBCL. Keywords: aggressive B-cell non-Hodgkin lymphoma, tumor biology and heterogeneity No conflicts of interests pertinent to the abstract.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.296
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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