Base excision repair accessory factors in senescence avoidance and resistance to treatments
Bibliographic record
Abstract
Cancer cells, in which the RAS and PI3K pathways are activated, produce high levels of reactive oxygen species (ROS), which cause oxidative DNA damage and ultimately cellular senescence. This process has been documented in tissue culture, mouse models, and human pre-cancerous lesions. In this context, cellular senescence functions as a tumour suppressor mechanism. Some rare cancer cells, however, manage to adapt to avoid senescence and continue to proliferate. One well-documented mode of adaptation involves increased production of antioxidants often associated with inactivation of the KEAP1 tumour suppressor gene and the resulting upregulation of the NRF2 transcription factor. In this review, we detail an alternative mode of adaptation to oxidative DNA damage induced by ROS: the increased activity of the base excision repair (BER) pathway, achieved through the enhanced expression of BER enzymes and DNA repair accessory factors. These proteins, exemplified here by the CUT domain proteins CUX1, CUX2, and SATB1, stimulate the activity of BER enzymes. The ensued accelerated repair of oxidative DNA damage enables cancer cells to avoid senescence despite high ROS levels. As a by-product of this adaptation, these cancer cells exhibit increased resistance to genotoxic treatments including ionizing radiation, temozolomide, and cisplatin. Moreover, considering the intrinsic error rate associated with DNA repair and translesion synthesis, the elevated number of oxidative DNA lesions caused by high ROS leads to the accumulation of mutations in the cancer cell population, thereby contributing to tumour heterogeneity and eventually to the acquisition of resistance, a major obstacle to clinical treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".