Sequential Binary Gene Ratio Tests Define a Novel Molecular Diagnostic Strategy for Malignant Pleural Mesothelioma
Bibliographic record
Abstract
PURPOSE: To develop a standardized approach for molecular diagnostics, we used the gene expression ratio bioinformatic technique to design a molecular signature to diagnose malignant pleural mesothelioma (MPM) from among other potentially confounding diagnoses and differentiate the epithelioid from the sarcomatoid histologic subtype of MPM. In addition, we searched for pathways relevant in MPM in comparison with other related cancers to identify unique molecular features in MPM. EXPERIMENTAL DESIGN: We conducted microarray analysis on 113 specimens including MPMs and a spectrum of tumors and benign tissues comprising the differential diagnosis of MPM. We generated a sequential combination of binary gene expression ratio tests able to discriminate MPM from other thoracic malignancies. We compared this method with other bioinformatic tools and validated this signature in an independent set of 170 samples. Functional enrichment analysis was conducted to identify differentially expressed probes. RESULTS: A sequential combination of gene expression ratio tests was the best molecular approach to distinguish MPM from all the other samples. Bioinformatic and molecular validations showed that the sequential gene ratio tests were able to identify the MPM samples with high sensitivity and specificity. In addition, the gene ratio technique was able to differentiate the epithelioid from the sarcomatoid type of MPM. Novel genes and pathways specifically activated in MPM were identified. CONCLUSIONS: New clinically relevant molecular tests have been generated using a small number of genes to accurately distinguish MPMs from other thoracic samples, supporting our hypothesis that the gene expression ratio approach could be a useful tool in the differential diagnosis of cancers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".