Mutational analysis of primary and advanced chordoma tissue using next‐generation sequencing
Bibliographic record
Abstract
BACKGROUND: Chordomas are rare ectodermal bone malignancies derived from transformed notochordal remnants. Histologic variants include conventional (80%-90%), chondroid (5%-15%), and dedifferentiated (2%-8%). Because chordomas are relatively resistant to chemotherapy and radiotherapy, novel targeted agents are needed to expand treatment approaches and improve outcomes. This study analyzes the genomic landscape of chordoma and identifies potential pathogenic and druggable targets. METHODS: Eighty-six tumor samples derived from chordoma patients treated at Massachusetts General Hospital, University of California, Los Angeles, and the University of Miami were included. Tumor specimens were sent for comprehensive molecular profiling using next-generation sequencing. The most frequently mutated genes were identified and categorized by subtype, and microsatellite instability and programmed death ligand-1 (PD-L1) staining were assessed. RESULTS: Histologic subtypes included 70 conventional (81.4%), nine chondroid (10.5%), and seven dedifferentiated chordomas (8.1%). The most common mutations were cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) (28 of 86, 33%), low-density lipoprotein receptor-related protein 1B (10 of 86, 12%), polybromo-1 (9 of 86, 11%), and epidermal growth factor receptor (EGFR) (8 of 86, 9%). By subtype, CDKN2A/B mutation was most common in conventional chordoma (24 of 70, 34%), and chondroid chordoma (3 of 9, 33%). CDKN2A/B and EGFR mutations were most common in dedifferentiated chordoma (2/7, 29%). Microsatellite instability was not detected in seven of 69 (10.1%) samples. PD-L1 staining of tumor and immune cells was scarce, with scores <1 in 38 of 41 (92.7%) and 22 of 25 (88%) patients, respectively. CONCLUSIONS: This study provides a robust, high-dimensional sequencing assessment from 86 chordoma tissue samples and a descriptive overview of the genomic landscape of this rare, difficult to treat malignancy. Future studies should include in vitro assessment of gain and loss of function of frequently altered pathways to validate these findings.
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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".