DNA Clutch Probes for Circulating Tumor DNA Analysis
Bibliographic record
Abstract
Progress toward the development of minimally invasive liquid biopsies of disease is being bolstered by breakthroughs in the analysis of circulating tumor DNA (ctDNA): DNA released from cancer cells into the bloodstream. However, robust, sensitive, and specific methods of detecting this emerging analyte are lacking. ctDNA analysis has unique challenges, since it is imperative to distinguish circulating DNA from normal cells vs mutation-bearing sequences originating from tumors. Here we report the electrochemical detection of mutated ctDNA in samples collected from cancer patients. By developing a strategy relying on the use of DNA clutch probes (DCPs) that render specific sequences of ctDNA accessible, we were able to readout the presence of mutated ctDNA. DCPs prevent reassociation of denatured DNA strands: they make one of the two strands of a dsDNA accessible for hybridization to a probe, and they also deactivate other closely related sequences in solution. DCPs ensure thereby that only mutated sequences associate with chip-based sensors detecting hybridization events. The assay exhibits excellent sensitivity and specificity in the detection of mutated ctDNA: it detects 1 fg/μL of a target mutation in the presence of 100 pg/μL of wild-type DNA, corresponding to detecting mutations at a level of 0.01% relative to wild type. This approach allows accurate analysis of samples collected from lung cancer and melanoma patients. This work represents the first detection of ctDNA without enzymatic amplification.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".