GP.5 Identifying clinically relevant prognostic epigenetic subtypes of chordoma and their non-invasive detection in plasma
Bibliographic record
Abstract
Background: Chordomas are rare malignant skull-base/spine cancers with devastating neurological morbidities and mortality. Unfortunately, no reliable prognostic factors exist to guide treatment decisions. This work identifies DNA methylation-based prognostic chordoma subtypes that are detectable non-invasively in plasma. Methods: Sixty-eight tissue samples underwent DNA methylation profiling and plasma methylomes were obtained for available paired samples. Immunohistochemical staining and publicly available methylation and gene expression data were utilized for validation. Results: Unsupervised clustering identified two prognostic tissue clusters (log-rank p=0.0062) predicting disease-specific survival independent of clinical factors (Multivariable Cox: HR=16.5, 95%CI: 2.8-96, p=0.0018). The poorer-performing cluster showed immune-related pathway promoter hypermethylation and higher immune cell abundance within tumours, which was validated with external RNA-seq data and immunohistochemical staining. The better-performing cluster showed higher tumour cellularity. Similar clusters were seen in external DNA methylation data. Plasma methylome-based models distinguished chordomas from differential diagnoses in independent testing sets (AUROC=0.84, 95%CI: 0.52-1.00). Plasma methylomes were highly correlated with tissue-based signals for both clusters (r=0.69 & 0.67) and leave-one-out models identified the correct cluster in all plasma cases. Conclusions: Prognostic molecular chordoma subgroups are for the first time identified, characterized, and validated. Plasma methylomes can detect and subtype chordomas which may transform chordoma treatment with personalized approaches tailored to prognosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".