Multiple cancers escape from multiple MAPK pathway inhibitors and use DNA replication stress signaling to tolerate aberrant cell cycles
Bibliographic record
Abstract
Many cancers harbor pro-proliferative mutations of the mitogen-activated protein kinase (MAPK) pathway. In BRAF-driven melanoma cells treated with BRAF inhibitors, subpopulations of cells escape drug-induced quiescence through a nongenetic manner of adaptation and resume slow proliferation. Here, we found that this phenomenon is common to many cancer types driven by EGFR, KRAS, or BRAF mutations in response to multiple, clinically approved MAPK pathway inhibitors. In 2D cultures and 3D spheroid models of various cancer cell lines, a subset of cells escaped drug-induced quiescence within 4 days to resume proliferation. These "escapee" cells exhibited DNA replication deficits, accumulated DNA lesions, and mounted a stress response that depended on the ataxia telangiectasia and RAD3-related (ATR) kinase. We further identified that components of the Fanconi anemia (FA) DNA repair pathway are recruited to sites of mitotic DNA synthesis (MiDAS) in escapee cells, enabling successful completion of cell division. Analysis of patient tumor samples and clinical data correlated disease progression with an increase in DNA replication stress response factors. Our findings suggest that many MAPK pathway-mutant cancers rapidly escape drug action and that suppressing early stress tolerance pathways may achieve more durable clinical responses to MAPK pathway 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 | 0.000 | 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 | 0.001 |
| 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".