Molecular characteristics of poorly differentiated chordoma
Bibliographic record
Abstract
Pediatric poorly differentiated chordoma is a subtype of chordoma with a much more aggressive clinical course and has been characterized by loss of SMARCB1. This study characterizes the molecular features of these tumors in comparison to conventional chordoma. A search of records between 1990 and 2017 at Massachusetts General Hospital identified two patients with sufficient excess tissue for molecular analysis and a third patient diagnosed with a highly cellular conventional chordoma. The three tumors were sent for array comparative genomic hybridization for genome-wide copy number variants; multiplex PCR for single-nucleotide variants; and RNA-sequencing for fusions. Poorly differentiated chordoma showed chromosome 22q loss, including SMARCB1, with no identifiable mutations on multiplex PCR. The cellular conventional chordoma showed a complex pattern of chromosomal gains and losses involving 12 chromosomes, and an RB1 mutation at low allelic frequency. RNA-Seq identified no disease-defining gene fusion events. Poorly differentiated chordoma appears to represent a distinct type of tumor that is genetically unrelated to conventional chordoma. Recognition of this subtype is important because these malignancies should be treated aggressively with multimodality therapy, and possibly targeted therapy.
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 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".