Inhibition of tumor growth by a novel engineered chimeric toxin that cleaves activated mutant and wild-type RAS
Bibliographic record
Abstract
Summary Despite nearly four decades of effort, broad inhibition of oncogenic RAS using small molecule approaches has proven to be a major challenge. Here we describe the development of a novel pan-RAS biologic inhibitor comprised of the RAS-RAP1-specific endopeptidase fused to the protein delivery machinery of diphtheria toxin. We show that this engineered chimeric toxin irreversibly cleaves and inactivates intracellular RAS at low picomolar concentrations terminating downstream signaling in receptor-bearing cells. Further, we demonstrate in vivo target engagement and reduction of tumor burden in three mouse xenograft models driven by either wild-type or mutant RAS . Intracellular delivery of a potent anti-RAS biologic through a receptor-mediated mechanism represents a promising new approach to developing RAS therapeutics against a broad array of cancers. Significance RAS oncoproteins have long been considered among the most elusive drug targets in cancer research. At issue is the lack of accessible drug binding sites and the extreme affinity for its GTP substrate. Covalent inhibitors against the KRAS G12C mutant have shown early clinical promise, however, targeting the other oncogenic RAS mutants across three RAS isoforms has proven challenging. Inhibition of activated wild-type RAS in the absence of canonical RAS mutations is also highly desirable in certain tumors. Here, we demonstrate delivery of an extremely potent pan-RAS and RAP1 cleaving enzyme in therapeutic quantities to specific receptor-bearing cells in vitro and in vivo . We aim to advance this approach to engineer the first targeted pan-RAS inhibitor for cancer therapy. One Sentence Summary Engineered chimeric toxin halts tumor growth in vivo via RAS cleavage
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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 | 0.000 | 0.000 |
| 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".