CORRELATION OF CLINICAL FEATURES TO DNA METHYLATION-BASED PROGNOSTIC SUBTYPES IN CHORODOMA PATIENTS
Bibliographic record
Abstract
Abstract Chordomas are rare aggressive primary bone cancers affecting the skull-base and spine. We have previously identified robust DNA methylation-based prognostic groups, immune infiltrated and cellular subtypes. Here we further characterize these prognostic subtypes with an extensively annotated clinical database to identify factors that correlate with subtype, and investigate methylation patterns between existing clinical scoring systems groups. METHODS: 68 patients from a multi-institutional 20-year series were identified. These patients’ tumor samples had undergone whole genome DNA methylation profiling on the Illumina EPIC array. Baseline clinical features, including clinical features, treatment details, outcomes parameters and imaging were analyzed using chi-squared or kruskal-wallis test, and differential methylation patterns were examined between clinically distinct groups based on the Sekhar scoring system. RESULTS: Of all the variables tested, age of onset (p-value 0.012), location (skull base vs spine vs sacral: p-value 0.0365) and histological subtype (classical vs chondroid: p-value 0.0132) were the only significant predictors of subgroup placement; older age, spinal location, and classical histological typing were predictors of immune infiltrated chordoma subtype, which has a poorer clinical performance. As expected, death from chordoma was significantly different between subtypes (p-value 0.0056). Patients survival stratified based on Shakur grading, and clustered together on unsupervised hierarchical analysis on methylation. CONCLUSION: Overall, there are limited variables that correlate with methylation subtype, meaning that the epigenetic chordoma subtypes cannot be reliably identified using clinical or imaging features in the absence of molecular data. However clinical grading systems remain a valuable tool for prognostication, and display distinct methylation patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".