A Rapid and Cost-Effective Gene Expression Assay for the Diagnosis of Well-Differentiated and Dedifferentiated Liposarcomas
Bibliographic record
Abstract
Histologic examination neither reliably distinguishes benign lipomas from atypical lipomatous tumor/well-differentiated liposarcoma, nor dedifferentiated liposarcoma from other pleomorphic sarcomas, entities with different prognoses and management. Molecular confirmation of pathognomonic 12q13-15 amplifications leading to MDM2 overexpression is a diagnostic gold standard. Currently the most commonly used assay for this purpose is fluorescence in situ hybridization (FISH), but this is labor intensive. This study assessed whether newer NanoString-based technology could allow for more rapid and cost-efficient diagnosis of liposarcomas on standard formalin-fixed tissues through gene expression. Leveraging large-scale transcriptome data from The Cancer Genome Atlas, 20 genes were identified, most from the 12q13-15 amplicon, that distinguish dedifferentiated liposarcoma from other sarcomas and can be measured within a single NanoString assay. Using 21 cases of histologically ambiguous low-grade adipocytic tumors with available MDM2 amplification status, a machine learning-based analytical pipeline was built that assigns a given sample as negative or positive for liposarcoma based on quantitative gene expression. The effectiveness of the assay was validated on an independent set of 100 sarcoma samples (including 40 incident prospective cases), where histologic examination was considered insufficient for clinical diagnosis. The NanoString assay had a 93% technical success rate, and an accuracy of 97.8% versus an MDM2 amplification FISH gold standard. NanoString had a considerably faster turnaround time and was cheaper than FISH.
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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 | 0.001 |
| 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 | 0.001 |
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".