Selective Targeting of Ras Mutant Cancers via a New Small Molecule
Bibliographic record
Abstract
ABSTRACT Mutations in the Ras family of oncogenes are implicated in 33% of human cancers, making Ras an intensely pursued target in drug discovery. As an alternative to direct pharmacological inhibition of Ras, we looked for sensitivities in RAS mutant cells. Using a small molecule screen in cell lines with mutations in Ras and its effector Raf, we discovered 249C as a Ras-mutant selective cytotoxic agent against a spectrum of RAS-mutant cancers. By combining CRISPR chemical-genetic screening, comparative profiling and chemoproteomics, we identified that 249C binds to a unique subunit on vacuolar (V)-ATPase with nanomolar affinity, inhibiting its biochemical activity and, unexpectedly, altering V-ATPase translocation in Ras-induced macropinocytosis. Via binding to V-ATPase, 249C prevents lysosomal acidification and inhibits autophagy and macropinocytosis pathways that several Ras-driven cancers rely on for survival. In characterizing 249C’s mechanism, we show that potency varies with the identity of the RAS driver mutation highlighting a mutant-specific dependence on autophagy and macropinocytosis. Indeed, 249C potently inhibits tumor growth without adverse side effects in a mouse xenograft model of KRAS -driven non-small cell lung cancer. These data establish proof-of-concept for targeting V-ATPase as a way to indirectly target specific Ras mutants, and provide a fundamental link between V-ATPase localization and specific Ras mutant tumor-related activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".