Methylation Profiling Identifies Stability of Isocitrate Dehydrogenase Mutation Over Time
Bibliographic record
Abstract
OBJECTIVE: Isocitrate dehydrogenase (IDH) mutation status is a key diagnostic and prognostic feature of gliomas. It is thought to occur early in glioma tumorigenesis and remain stable over time. However, there are reports documenting a loss of IDH mutation status in a subset of patients with glioma recurrence. Here, we identified patients with a documented loss of IDH mutation status longitudinally and performed multi-platform analysis in order to determine if IDH mutations are stable throughout glioma evolution. METHODS: We retrospectively identified patients from our institution from 2009 to 2018 with immunohistochemistry (IHC)-recorded IDH mutation status changes longitudinally. Archived formalin-fixed paraffin-embedded and frozen tissue samples from these patients were collected from our institution's tumour bank. Samples were analysed using methylation profiling, copy number variation, Sanger sequencing, droplet digital PCR (ddPCR) and IHC. RESULTS: We reviewed 1491 archived glioma samples including 78 patients with multiple IDH mutant tumour samples collected longitudinally. In all instances of documented loss of IDH mutation status, multi-platform profiling identified a mixture of low tumour cell content and non-neoplastic tissue including perilesional, reactive or inflammatory cells. CONCLUSIONS: All patients with a documented loss of IDH mutation status longitudinally were resolved through multi-platform analysis. These findings support the hypothesis that IDH mutations occur early in gliomagenesis and in the absence of copy number changes at the IDH loci and are stable throughout tumour treatment and evolution. Our study highlights the importance of accurate surgical sampling and the role of DNA methylome profiling in diagnostically uncertain cases for integrated pathological and molecular diagnosis.
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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 | 0.000 |
| 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) | 0.001 | 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 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".