Harnessing transcriptional regulation of alternative end-joining to predict cancer treatment
Bibliographic record
Abstract
Abstract Alternative end-joining (alt-EJ) is an error-prone DNA repair pathway that cancer cells deficient in homologous recombination rely on, making them vulnerable to synthetic lethality via inhibition of poly(ADP-ribose) polymerase (PARP). Targeting alt-EJ effector DNA polymerase theta (POLθ), which synergizes with PARP inhibitors and can overcome resistance, is of significant preclinical and clinical interest. However, the transcriptional regulation of alt-EJ and its interactions with processes driving cancer progression remain poorly understood. Here, we show that alt-EJ is suppressed by hypoxia while positively associated with MYC (myelocytomatosis oncogene) transcriptional activity. Hypoxia reduces PARP1 and POLQ expression, decreases MYC binding at their promoters, and lowers PARylation and alt-EJ-mediated DNA repair in cancer cells. Tumors with HIF1A mutations overexpress the alt-EJ gene signature. Inhibition of hypoxia-inducible factor 1α or HIF1A expression depletion, combined with PARP or POLθ inhibition, synergistically reduces the colony-forming capacity of cancer cells. Deep learning reveals the anticorrelation between alt-EJ and hypoxia across regions in tumor images, and the predictions for these and MYC activity achieve area under the curve values between 0.70 and 0.86. These findings further highlight the critical role of hypoxia in modulating DNA repair and present a strategy for predicting and improving outcomes centered on targeting alt-EJ.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".