A gene expression prognostic signature for overall survival in patients with high-grade serous ovarian cancer.
Bibliographic record
Abstract
5583 Background: Median survival for high-grade serous ovarian cancer (HGSOC) patients is 3-4 years with a wide range of outcomes. Gene expression patterns have been reported to be predictive of outcome, but results have been inconsistent. The aim of this study was to assess the predictive utility of genes previously associated with outcome and to develop a clinically useful predictor for overall survival (OS). Methods: We identified 200 genes associated with prognosis from a meta-analysis of previously published gene expression profiling studies in HGSOC and selected an additional 313 candidate genes. Expression in formalin fixed paraffin embedded (FFPE) HGSOC tumor tissue was measured with Nanostring for 3770 women with known OS. Regression-based and machine learning methods were used to develop a prognostic signature for OS. These approaches included stepwise regression, elastic net penalized regression, random survival forests, and component-wise gradient boosting. Models were trained and tested on approximately 2/3 of the data and the best performing model was evaluated on the remaining 1/3. Results: In single gene Cox models for OS adjusted for age and stage, there were 275 significant associations (false discovery rate (FDR) < 0.05). The two most significant genes were TAP1 (p = 2.2e-17; hazard ratio (HR) = 0.84 (0.81, 0.87)) and ZFHX4 (p = 1.3e-15; HR = 1.19 (1.14, 1.25)). Elastic net yielded the best performing signature in the training data, and in the validation data it was substantially more prognostic than any individual gene (HR = 2.42 (2.09, 2.81), scaled to 1SD). The area-under-the-curve (AUC) for the signature combined with age and stage for 5yr OS was 0.74, substantially larger than the AUC for age and stage alone (0.62). Conclusions: These data confirm previously reported or hypothesized associations between gene expression in HGSOC tumor tissue and overall survival. Our signature could be useful in designing clinical trials for patients who are destined to have poor survival, thereby delivering new agents to the patients who are in the most urgent need. Identification of genes associated with the outcome also provides the opportunity to develop targeted therapeutic approaches.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".