Proteolytic pan-RAS Cleavage Leads to Tumor Regression in Patient-derived Pancreatic Cancer Xenografts
Bibliographic record
Abstract
The lack of effective RAS inhibition represents a major unmet medical need in the treatment of pancreatic ductal adenocarcinoma (PDAC). Here, we investigate the anticancer activity of RRSP-DTB, an engineered biologic that cleaves the Switch I of all RAS isoforms, in KRAS-mutant PDAC cell lines and patient-derived xenografts (PDX). We first demonstrate that RRSP-DTB effectively engages RAS and impacts downstream ERK signaling in multiple KRAS-mutant PDAC cell lines inhibiting cell proliferation at picomolar concentrations. We next tested RRSP-DTB in immunodeficient mice bearing KRAS-mutant PDAC PDXs. Treatment with RRSP-DTB led to ≥95% tumor regression after 29 days. Residual tumors exhibited disrupted tissue architecture, increased fibrosis and fewer proliferating cells compared with controls. Intratumoral levels of phospho-ERK were also significantly lower, indicating in vivo target engagement. Importantly, tumors that started to regrow without RRSP-DTB shrank when treatment resumed, demonstrating resistance to RRSP-DTB had not developed. Tracking persistence of the toxin activity following intraperitoneal injection showed that RRSP-DTB is active in sera from immunocompetent mice for at least 1 hour, but absent after 16 hours, justifying use of daily dosing. Overall, we report that RRSP-DTB strongly regresses hard-to-treat KRAS-mutant PDX models of pancreatic cancer, warranting further development of this pan-RAS biologic for the management of RAS-addicted tumors.
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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 | 0.000 |
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".