CRISPR prime editing for unconstrained correction of oncogenic KRAS variants
Bibliographic record
Abstract
KRAS is the most commonly mutated RAS family gene and is a primary cause of the occurrence of several types of cancer. However, KRAS mutations have several unique and diverse molecular identities, making it difficult to find specific treatments. Here, we developed universal pegRNAs which can correct all types of G12 and G13 oncogenic KRAS mutations with CRISPR-mediated prime editors (PEs). The universal pegRNA successfully corrected 12 types of KRAS mutations, accounting for 94% of all known KRAS mutations, by up to 54.8% correction frequency in HEK293T/17 cells. We also applied the universal pegRNA to correct endogenous KRAS mutations in human cancer cells and found that G13D KRAS mutation was successfully corrected to wild-type KRAS sequences with up to 40.6% correction frequency without indel mutations. We propose prime editing with the universal pegRNA as a 'one-to-many' potential therapeutic strategy for KRAS oncogene variants.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".