Synergy of WEE1 and mTOR Inhibition in Mutant <i>KRAS</i> -Driven Lung Cancers
Bibliographic record
Abstract
Abstract Purpose: KRAS-activating mutations are the most common oncogenic driver in non–small cell lung cancer (NSCLC), but efforts to directly target mutant KRAS have proved a formidable challenge. Therefore, multitargeted therapy may offer a plausible strategy to effectively treat KRAS-driven NSCLCs. Here, we evaluate the efficacy and mechanistic rationale for combining mTOR and WEE1 inhibition as a potential therapy for lung cancers harboring KRAS mutations. Experimental Design: We investigated the synergistic effect of combining mTOR and WEE1 inhibitors on cell viability, apoptosis, and DNA damage repair response using a panel of human KRAS-mutant and wild type NSCLC cell lines and patient-derived xenograft cell lines. Murine autochthonous and human transplant models were used to test the therapeutic efficacy and pharmacodynamic effects of dual treatment. Results: We demonstrate that combined inhibition of mTOR and WEE1 induced potent synergistic cytotoxic effects selectively in KRAS-mutant NSCLC cell lines, delayed human tumor xenograft growth and caused tumor regression in a murine lung adenocarcinoma model. Mechanistically, we show that inhibition of mTOR potentiates WEE1 inhibition by abrogating compensatory activation of DNA repair, exacerbating DNA damage in KRAS-mutant NSCLC, and that this effect is due in part to reduction in cyclin D1. Conclusions: These findings demonstrate that compromised DNA repair underlies the observed potent synergy of WEE1 and mTOR inhibition and support clinical evaluation of this dual therapy for patients with KRAS-mutant lung cancers. Clin Cancer Res; 23(22); 6993–7005. ©2017 AACR.
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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 | 0.000 | 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".