LG-19IMMUNOHISTOCHEMISTRY IS HIGHLY SENSITIVE AND SPECIFIC FOR THE DETECTION OF BRAF V600E STATUS IN PEDIATRIC LOW-GRADE GLIOMA
Bibliographic record
Abstract
INTRODUCTION: The BRAF-V600E mutation has been described in a broad spectrum of adult and pediatric cancers. Recently, tissue-specific discordances have been identified between immunohistochemistry (IHC) and sequencing methods for determining BRAFV600E status in colorectal carcinoma and melanoma, suggesting that the sensitivity and specificity of IHC needs to be determined for each cancer type. As BRAFV600E status in PLGG has been shown to have prognostic and therapeutic implications, there is a critical need to ensure accurate identification of patients whose PLGG harbour this mutation. We sought to investigate agreement between sequencing and IHC for BRAFV600E status specifically for PLGG. METHODS: Archival formalin-fixed, paraffin-embedded tissue was collected from 100 PLGG cases. BRAFV600E status was determined by both sequencing (digital-droplet PCR, ddPCR) and IHC (VE1 antibody). Discordant cases were re-evaluated by TaqMan PCR. RESULTS: Compared with ddPCR, the antibody had a sensitivity of 90% (34/38) and a specificity of 98% (52/53) for detecting the presence of the BRAFV600E mutation. For 8 cases there was insufficient tissue for sequencing but a result was obtained by IHC. There were 5 discordant cases. Additional molecular analysis confirmed the IHC result in 2 cases and the ddPCR result in 1 case. For 2 cases the re-evaluation is pending. CONCLUSIONS: The BRAFV600E IHC is a fast, accurate and cost-effective method for mutation detection in PLGG patients. Clinical use of the BRAFV600E antibody is a valuable alternative to conventional sequencing, allowing identification of patients with a potentially more aggressive clinical course and who may be eligible for targeted therapy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".