Frequent <i><scp>IDH2</scp></i><scp>R172</scp> mutations in undifferentiated and poorly‐differentiated sinonasal carcinomas
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Bibliographic record
Abstract
Abstract Sinonasal undifferentiated carcinoma ( SNUC ) is a high‐grade malignancy with limited treatment options and poor outcome. A morphological spectrum of 47 sinonasal tumours including 17 (36.2%) SNUCs was analysed at genomic level. Thirty carcinomas (cohort 1) were subjected to a hybridization exon‐capture next‐generation sequencing assay ( MSK‐IMPACT TM ) to interrogate somatic variants in 279 or 410 cancer‐related genes. Seventeen sinonasal tumours (cohort 2) were examined only for the presence of IDH1 /2 exon 4 mutations by Sanger sequencing. IDH2 R172 single nucleotide variants were overall detected in 14 (82.4%) SNUCs , in two (20%) poorly‐differentiated carcinomas with glandular/acinar differentiation, and in one of two high‐grade neuroendocrine carcinomas, large cell type ( HGNECs ). No IDH2 mutation was detected in any of five olfactory neuroblastomas or in any of five SMARCB1 ‐deficient carcinomas. Among 12 IDH2 ‐mutated cases in cohort 1, five (41.7%) harboured co‐existing TP53 mutations, four (33.3%) CDKN2A / 2B loss‐of‐function alterations, four (33.3%) MYC amplification, and three (25%) had concurrent SETD2 mutations. AKT1 E17K and KIT D816V hotspot variants were each detected in one IDH2 ‐mutated SNUC . The vast majority of SNUCs and variable proportions of other poorly‐differentiated sinonasal carcinomas may be amenable to IDH2 ‐targeted therapy. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it