Data from <i>TET2</i> and <i>DNMT3A</i> Mutations Exert Divergent Effects on DNA Repair and Sensitivity of Leukemia Cells to PARP Inhibitors
Bibliographic record
Abstract
Abstract Somatic variants in TET2 and DNMT3A are founding mutations in hematological malignancies that affect the epigenetic regulation of DNA methylation. Mutations in both genes often co-occur with activating mutations in genes encoding oncogenic tyrosine kinases such as FLT3ITD, BCR-ABL1, JAK2V617F, and MPLW515L, or with mutations affecting related signaling pathways such as NRASG12D and CALRdel52. Here, we show that TET2 and DNMT3A mutations exert divergent roles in regulating DNA repair activities in leukemia cells expressing these oncogenes. Malignant TET2-deficient cells displayed downregulation of BRCA1 and LIG4, resulting in reduced activity of BRCA1/2-mediated homologous recombination (HR) and DNA-PK–mediated non-homologous end-joining (D-NHEJ), respectively. TET2-deficient cells relied on PARP1-mediated alternative NHEJ (Alt-NHEJ) for protection from the toxic effects of spontaneous and drug-induced DNA double-strand breaks. Conversely, DNMT3A-deficient cells favored HR/D-NHEJ owing to downregulation of PARP1 and reduction of Alt-NHEJ. Consequently, malignant TET2-deficient cells were sensitive to PARP inhibitor (PARPi) treatment in vitro and in vivo, whereas DNMT3A-deficient cells were resistant. Disruption of TET2 dioxygenase activity or TET2—Wilms' tumor 1 (WT1)–binding ability was responsible for DNA repair defects and sensitivity to PARPi associated with TET2 deficiency. Moreover, mutation or deletion of WT1 mimicked the effect of TET2 mutation on DSB repair activity and sensitivity to PARPi. Collectively, these findings reveal that TET2 and WT1 mutations may serve as biomarkers of synthetic lethality triggered by PARPi, which should be explored therapeutically. Significance: TET2 and DNMT3A mutations affect distinct DNA repair mechanisms and govern the differential sensitivities of oncogenic tyrosine kinase–positive malignant hematopoietic cells to PARP inhibitors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.070 | 0.022 |
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".