Efficient molecular subtype classification of high‐grade serous ovarian cancer
Bibliographic record
Abstract
High-grade serous carcinomas (HGSCs) account for approximately 70% of all epithelial ovarian cancers diagnosed. Using microarray gene expression profiling, we previously identified four molecular subtypes of HGSC: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative), which correlate with patient survival and have distinct biological features. Here, we describe molecular classification of HGSC based on a limited number of genes to allow cost-effective and high-throughput subtype analysis. We determined a minimal signature for accurate classification, including 39 differentially expressed and nine control genes from microarray experiments. Taqman-based (low-density arrays and Fluidigm), fluorescent oligonucleotides (Nanostring), and targeted RNA sequencing (Illumina) assays were then compared for their ability to correctly classify fresh and formalin-fixed, paraffin-embedded samples. All platforms achieved > 90% classification accuracy with RNA from fresh frozen samples. The Illumina and Nanostring assays were superior with fixed material. We found that the C1, C2, and C4 molecular subtypes were largely consistent across multiple surgical deposits from individual chemo-naive patients. In contrast, we observed substantial subtype heterogeneity in patients whose primary ovarian sample was classified as C5. The development of an efficient molecular classifier of HGSC should enable further biological characterization of molecular subtypes and the development of targeted clinical trials.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".