A multiplex, prime editing framework for identifying drug resistance variants at scale
Bibliographic record
Abstract
Abstract CRISPR-based genome editing has revolutionized functional genomics, enabling screens in which thousands of perturbations of either gene expression or primary genome sequence can be competitively assayed in single experiments. However, for libraries of specific mutations, a challenge of CRISPR-based screening methods such as saturation genome editing is that only one region ( e.g. one exon) can be studied per experiment. Here we describe prime-SGE (“prime saturation genome editing”), a new framework based on prime editing, in which libraries of specific mutations can be installed into genes throughout the genome and functionally assessed in a single, multiplex experiment. Prime-SGE is based on quantifying the abundance of prime editing guide RNAs (pegRNAs) in the context of a functional selection, rather than quantifying the mutations themselves. We apply prime-SGE to assay thousands of single nucleotide changes in eight oncogenes for their ability to confer drug resistance to three EGFR tyrosine kinase inhibitors. Although currently restricted to positive selection screens by the limited efficiency of prime editing, our strategy opens the door to the possibility of functionally assaying vast numbers of precise mutations at locations throughout the genome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".