An epithelial-mesenchymal-amoeboid transition gene signature reveals subtypes of breast cancer progression and metastasis
Bibliographic record
Abstract
Abstract Cancer cells within a tumor are known to display varying degrees of metastatic propensity but the molecular basis underlying such heterogeneity remains unclear. We analyzed genome-wide gene expression data obtained from primary tumors of lymph node-negative breast cancer patients using a novel metastasis biology-based Epithelial-Mesenchymal-Amoeboid Transition (EMAT) gene signature, and identified subtypes associated with distinct prognostic profiles. EMAT subtype status improved prognosis accuracy of clinical parameters and statistically outperformed traditional breast cancer intrinsic subtypes even after adjusting for treatment variables. Additionally, analysis of 3D spheroids from an in vitro isogenic model of breast cancer progression reveals that EMAT subtypes display progression from premalignant to malignant and pre-invasive to invasive cancer. EMAT classification is a biologically informed method to assess metastasis risk in early stage, lymph node-negative breast cancer patients.
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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 | 0.000 | 0.000 |
| 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".