BRAF Mutations in Melanocytic Lesions and Papillary Thyroid Carcinoma Samples Identified Using Melting Curve Analysis of Polymerase Chain Reaction Products
Bibliographic record
Abstract
Abstract Context. —Mutations of the proto-oncogene B- raf ( BRAF ) have been detected in melanocytic lesions and papillary carcinomas of the thyroid, and identification of these mutations could be useful in resolving some diagnostic problems. Objective. —To develop a method to evaluate mutations of BRAF that could provide results much more rapidly than conventional polymerase chain reaction and DNA sequencing assays. Design. —An assay using a LightCycler was developed to evaluate DNA sequences encoding amino acids within the activation loop of BRAF . Results. —Using this real-time polymerase chain reaction method, we analyzed 55 paraffin-embedded melanoma or nevus samples. The V600E mutation was found in 0 (0%) of 13 samples diagnosed histologically as Spitz nevi, 9 (24.3%) of 37 invasive melanomas, and 5 (100%) of 5 other melanocytic nevi. Two additional mutations, V600K and VK600-1E, also were identified in cases of invasive melanoma. We analyzed 14 paraffin-embedded papillary thyroid cancer (PTC) samples, 6 of which showed the V600E mutation. We found that our test worked efficiently with fine-needle aspirate specimens, and it identified 6 V600E mutations in 10 fine-needle aspirate specimens diagnosed as PTC. We also identified 4 V600E mutations in 6 specimens of PTC metastatic to lymph node. Unlike the melanocytic lesions, the PTC specimens yielded only V600E mutations. Comparison of our real-time polymerase chain reaction results with conventional polymerase chain reaction and DNA sequencing demonstrated 100% concordance. Surprisingly, we did not identify the previously reported VK600-1E or K601E mutations in our PTC specimens. Conclusions. —Our results show that the real-time polymerase chain reaction method is a rapid and accurate method for identifying BRAF mutations, such as V600E, in both paraffin-embedded tissue and fine-needle aspirate specimens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".