Diffuse gliomas classified by 1p/19q co-deletion, TERT promoter and IDH mutation status are associated with specific genetic risk loci
Bibliographic record
Abstract
Recent genome-wide association studies of glioma have led to the discovery of single nucleotide polymorphisms (SNPs) at 25 loci influencing risk. Gliomas are heterogeneous, hence to investigate the relationship between risk SNPs and glioma subtype we analysed 1659 tumours profiled for IDH mutation, TERT promoter mutation and 1p/19q co-deletion. These data allowed definition of five molecular subgroups of glioma: triple-positive (IDH mutated, 1p/19q co-deletion, TERT promoter mutated); TERT-IDH (IDH mutated, TERT promoter mutated, 1p/19q-wild-type); IDH-only (IDH mutated, 1p/19q wild-type, TERT promoter wild-type); triple-negative (IDH wild-type, 1p/19q wild-type, TERT promoter wild-type) and TERT-only (TERT promoter mutated, IDH wild-type, 1p/19q wild-type). Most glioma risk loci showed subtype specificity: (1) the 8q24.21 SNP for triple-positive glioma; (2) 5p15.33, 9p21.3, 17p13.1 and 20q13.33 SNPs for TERT-only glioma; (3) 1q44, 2q33.3, 3p14.1, 11q21, 11q23.3, 14q12, and 15q24.2 SNPs for IDH mutated glioma. To link risk SNPs to target candidate genes we analysed Hi-C and gene expression data, highlighting the potential role of IDH1 at 2q33.3, MYC at 8q24.21 and STMN3 at 20q13.33. Our observations provide further insight into the nature of susceptibility to glioma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".