GFI1’s role in DNA repair suggests implications for tumour cell response to treatment
Bibliographic record
Abstract
Despite recent advances in cancer treatment through personalized and precision medicine and new avenues such as immunotherapy and chimeric antibodies, the induction of DNA damage either through irradiation or specific compounds remains the primary approach to kill tumour cells. Improvements in our understanding of how tumour cells respond to DNA damage, and especially how this response differs from that of normal cells, are crucial to the development of better and more efficient therapies. We have recently shown that the activity of the oncogenic transcription factor GFI1, which is required for the development and maintenance of T and B cell leukemia, increases the ability of tumour cells to repair their DNA following damage (Vadnais et al. Nat Commun 9(1):1418). GFI1 accomplishes this by regulating the post-translational modifications (PTM) of key DNA repair proteins, including MRE11 and 53BP1, by the methyltransferase PRMT1. Here, GFI1 acts as an accessory protein required for the interaction between the enzyme and its substrates. This has implications for the treatment response of tumour cells overexpressing GFI1, which includes T cell leukemia, neuroendocrine lung carcinomas and aggressive subtypes of medulloblastoma, and suggests that targeting GFI1's activity and with this its capacity to aid DNA repair may open avenues for new therapeutic approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".