Can detection of Braf p.V600E mutation be improved? Comparison of allele specific multiplex sequencing to present tests
Bibliographic record
Abstract
Objective: This is an investigative study to evaluate a new companion diagnostic platform, allele specific multiplex sequencing(ASMS). Detection of Braf p.V600E from solid tumors is used as the test model with the following objectives: 1) whether ASMScan detect Braf p.V600E/K mutations from a variety of solid tumors, 2) whether ASMS can detect all Braf p.V600E from samplesthat were positive for Braf V600E by SNaPshot or Ion Torrent, and 3) whether ASMS can detect Braf p.V600E among samplesthat were reported negative by SNaPshot or Ion Torrent.Methods: ASMS is a novel modification (US Patent 6197510) of traditional Sanger sequencing, with Lower Limit of Detection(LLOD) of 20 GE (Genome Equivalent) and 0.001% sensitivity. We compared ASMS to clinical samples previously tested eitherby SNaPshot or Ion Torrent methods.Results: We analyzed 83 DNA extracts from FFPE samples (41 tested by SNaPshot and 42 tested by Ion Torrent). There was atotal of thirty-seven samples positive for Braf p.V600E (16 by Ion Torrent; 21 by SNaPshot), and all of these samples testedpositive by ASMS for Braf p.V600E. Out of the 46 negatives for Braf p.V600E (20 by SNaPshot; 26 by Ion Torrent samples),ASMS detected Braf p.V600E positive results in 10 of the SNaPshot and in 18 of the Ion Torrent negative samples. ASMS coulddetect both Braf p.V600E and the wild-type Braf p.V600 simultaneously with 40pg of FFPE DNA extracts.Conclusions: ASMS assay detected all Braf p.V600E positives from different types of solid tumors that previously tested positiveby SNaPshot or Ion Torrent. Further, ASMS was able to detect Braf p.V600E among samples that were reported negative bySNaPshot or Ion Torrent.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".