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 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 | 0.001 |
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".