NGMA-1. Quantification of IDH mutant alleles predicts outcome in diffuse gliomas
Bibliographic record
Abstract
Abstract Background IDH mutation is the main factor used in the prognostication of diffuse gliomas, however within IDH mutated gliomas there still remains a high variability in both tumor progression and overall survival.1 Digital droplet polymerase chain reaction (ddPCR) is one of the latest molecular amplification techniques that offers high precision in addition to the ability of absolute quantification of mutant allele copies.2 Methods A total of 102 IDH mutant diffuse glioma tumor samples ranging from WHO grade 2 to 4 were collected. This cohort includes a total of 45 paired samples collected at two distinct surgical timepoints: initial and recurrent. All samples underwent DNA extraction. A total of 5 ng of tumor DNA from each sample was analyzed using ddPCR for the detection and quantification of IDH1 R132H mutant alleles. Sanger sequencing was performed on all samples as a gold standard. Results ddPCR was highly sensitive (100%) and specific (99%) for the detection of IDH mutations. Initial tumor samples with a high number of IDH mutant copies split by median demonstrated decreased overall survival (p = 0.04) and shorter progression free survival (p = 0.024). The number of IDH mutant copies was independent of WHO grade (p = 0.6) and 1p19q codeletion status (p = 0.86). Tumor pairs that had IDH mutant copies increase at recurrence were trending but not significantly related to a decrease in remaining survival (p = 0.1). Conclusions ddPCR is a highly sensitive and specific method of detecting IDH mutations in diffuse gliomas. The number of IDH mutant copies in tumors at initial surgery can serve as an independent prognostic factor to help guide future treatment and follow-up.
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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".