Rewiring oncogenic signaling to RNA vector replication for the treatment of metastatic cancer
Bibliographic record
Abstract
Despite recent advances, improvements to long-term survival in metastatic carcinomas, such as pancreatic or ovarian cancer, remain limited. Current therapies suppress growth-promoting biochemical signals, ablate cells expressing tumor-associated antigens, or promote adaptive immunity to tumor neoantigens. However, these approaches are limited by toxicity to normal cells using the same signaling pathways or expressing the same antigens, or by the low frequency of neoantigens in most carcinomas. Here, we report a fundamentally different strategy for designing safer and more effective anti-cancer therapies through the sensing of cancer-driving biochemical signals and their rewiring to virotherapeutic activation. Specifically, we rationally engineer a RNA vector to self-replicate and cause cytotoxicity in cancer cells exhibiting hyperactive HER2 (ErbB2), but not in normal cells with normal HER2 signaling. Compared to a widely tested virotherapeutic from the same vector family, our hyperactive ErbB2-restricted RNA vector (HERV) exhibits lower toxicity and greater activity against metastatic HER2-positive ovarian cancer in mice, extending survival independently of tumor antigenicity. Most importantly, HERV synergizes with standard-of-care chemotherapy against ovarian cancer metastases in vivo, with 43% of combination-treated subjects surviving for months beyond subjects treated with either therapy alone. Taken together, these results introduce rewiring of cancer-driving signaling pathways to virotherapeutic activation as a strategy for more specific and effective cancer treatment.
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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) | 0.001 | 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".