Detecting EGFR mutations (L858R, T790M) using allele specific multiplex sequencing: A comparison with Pyrosequencing and TruSeq
Bibliographic record
Abstract
We are presenting an evaluation of Allele Specific Multiplex Sequencing (ASMS) to detect two EGFR somatic mutations (L858R, T790M). Late stage lung cancer samples were tested for both EGFR mutations and were compared to either pyrosequencing or TruSeq. The analytical lower limit of detection (LLOD) for the ASMS-L858R assay was found to be 36 copies, and 72 copies for the ASMS-T790M assay. The forty-one FFPE samples that were tested for T790M showed 100% concordance with the respective comparative method. The forty-five FFPE samples tested previously by Truseq for L858R showed 100% concordance with ASMS. Out of the twenty L858R samples previously tested by pyrosequencing, there was 95% concordance with ASMS. Additionally, twenty-one normal blood samples were tested by ASMS were found to be negative for L858R and T790M. In conclusion, the detection of L858R and T790M by ASMS are in acceptable concordance with both pyrosequencing and TruSeq in detecting EGFR mutations from late stage lung cancer. Further, ASMS was able to detect EGFR (L858R) with 10 picograms (3 copies gDNA) of FFPE extracted DNA, and hence could be used to detect mutations from samples carrying low copy numbers.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 0.000 |
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".