Ras Effector Mutant Expression Suggest a Negative Regulator Inhibits Lung Tumor Formation
Bibliographic record
Abstract
Lung cancer is currently the most deadly malignancy in industrialized countries and accounts for 18% of all cancer-related deaths worldwide. Over 70% of patients with non-small cell lung cancer (NSCLC) are diagnosed at a late stage, with a 5-year survival below 10%. KRAS and the EGFR are frequently mutated in NSCLC and while targeted therapies for patients with EGFR mutations exist, oncogenic KRAS is thus far not druggable. KRAS activates multiple signalling pathways, including the PI3K/Akt pathway, the Raf-Mek-Erk pathway and the RalGDS/Ral pathway. Lung-specific expression of BrafV600E, the most prevalent BRAF mutation found in human tumors, results in Raf-Mek-Erk pathway activation and in the formation of benign adenomas that undergo widespread senescence in a Cre-activated Braf mouse model (Braf(CA)). However, oncogenic KRAS expression in mice induces adenocarcinomas, suggesting additional KRAS-activated pathways cooperate with sustained RAF-MEK-ERK signalling to bypass the oncogene-induced senescence proliferation arrest. To determine which KRAS effectors were responsible for tumor progression, we created four effector domain mutants (S35, G37, E38 and C40) in G12V-activated KRAS and expressed these alone or with BrafV600E in mouse lungs... The S35 and E38 mutants bind to Raf proteins but not PI3K or RalGDS; the G37 mutant binds to RalGDS and not Raf or PI3K and the C40 mutant is specific to PI3K. We designed lentiviral vectors to code for Cre recombinase along with KRAS mutants (V12, V12/S35, V12/G37, V12/E38 or V12/C40) or EGFP as a negative control.. These lentiviruses were used to infect Braf(CA) and wild-type mice. Surprisingly there was a significant decrease in tumor number and penetrance with each KRAS effector domain mutant relative to controls, suggesting that KRAS directly activates effectors with tumor suppressive functions.
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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) | 0.002 | 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".