Targeted profiling of human extrachromosomal DNA by CRISPR-CATCH
Bibliographic record
Abstract
ABSTRACT Extrachromosomal DNA (ecDNA) is a common mode of oncogene amplification but is challenging to analyze. Here, we present a method for targeted purification of megabase-sized ecDNA by combining in-vitro CRISPR-Cas9 treatment and pulsed field gel electrophoresis of agarose-entrapped genomic DNA (CRISPR-CATCH). We demonstrate strong enrichment of ecDNA molecules containing EGFR , FGFR2 and MYC from human cancer cells. Targeted purification of ecDNA versus chromosomal DNA enabled phasing of genetic variants and provided definitive proof of an EGFRvIII mutation on ecDNA and wild-type EGFR on chromosomal DNA in a glioblastoma neurosphere model. CRISPR-CATCH followed by nanopore sequencing enabled single-molecule ecDNA methylation profiling and revealed hypomethylation of the EGFR promoter on ecDNA compared to the native chromosomal locus in the same cells. Finally, separation of ecDNA species by size and sequencing allowed accurate reconstruction of megabase- sized ecDNA structures with base-pair resolution. CRISPR-CATCH is a new addition to the toolkit for studying focal amplifications in cancer and will accelerate studies aiming to explore the genetic and epigenetic landscapes of ecDNA.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".