<i>BRAF</i> Rearrangements and <i>BRAF</i> V600E Mutations Are Seen in a Subset of Pancreatic Carcinomas With Acinar Differentiation
Bibliographic record
Abstract
CONTEXT.—: Comprehensive genomic profiling has demonstrated that approximately 20% of pancreatic carcinomas with acinar differentiation harbor potentially targetable BRAF fusions that activate the MAPK pathway. OBJECTIVES.—: To validate the above finding by BRAF break-apart fluorescence in situ hybridization (FISH) in a large series of pure acinar cell carcinomas (ACCs), evaluate tumors for the presence of BRAF V600E mutations, and compare clinicopathologic features of tumors with BRAF rearrangements with those without. DESIGN.—: Thirty cases of pure ACC and 6 cases of mixed acinar-neuroendocrine carcinoma (ACC-NEC) were retrieved. A break-apart FISH probe was used to detect BRAF rearrangements. Immunohistochemistry for BRAF V600E was performed. RESULTS.—: BRAF rearrangements by FISH were found in 6 of 36 cases (17%), 5 of which were pure ACC and 1 was a mixed ACC-NEC. Follow-up was available in 29 of 36 cases (81%). The median survival was 22 months for BRAF-rearranged cases and 16 months for BRAF-intact cases; the 2-year overall survival was 50% for BRAF-rearranged cases and 35% for BRAF-intact cases. No significant clinicopathologic differences were identified in cases with BRAF rearrangement compared with those without BRAF rearrangement. BRAF V600E mutation was identified in 2 of 34 cases (6%), both of which were pure ACC and were BRAF-intact by FISH. CONCLUSIONS.—: This study supports the finding that BRAF rearrangements are present in approximately 20% of cases and identified BRAF V600E mutations in approximately 5% of cases. These cases may benefit from 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".