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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".