Enhancing chemotherapy response through augmented synthetic lethality by co-targeting nucleotide excision repair and cell-cycle checkpoints
Bibliographic record
Abstract
In response to DNA damage, a synthetic lethal relationship exists between the cell cycle checkpoint kinase MK2 and the tumor suppressor p53. Here, we describe the concept of augmented synthetic lethality (ASL): depletion of a third gene product enhances a pre-existing synthetic lethal combination. We show that loss of the DNA repair protein XPA markedly augments the synthetic lethality between MK2 and p53, enhancing anti-tumor responses alone and in combination with cisplatin chemotherapy. Delivery of siRNA-peptide nanoplexes co-targeting MK2 and XPA to pre-existing p53-deficient tumors in a highly aggressive, immunocompetent mouse model of lung adenocarcinoma improves long-term survival and cisplatin response beyond those of the synthetic lethal p53 mutant/MK2 combination alone. These findings establish a mechanism for co-targeting DNA damage-induced cell cycle checkpoints in combination with repair of cisplatin-DNA lesions in vivo using RNAi nanocarriers, and motivate further exploration of ASL as a generalized strategy to improve cancer 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".