Estrogen independent gene expression defines clinically relevant subgroups of estrogen receptor positive breast cancer
Bibliographic record
Abstract
BACKGROUND: Human breast cancer represents a significantly heterogeneous disease. Global gene expression profiling measurements have been used to classify tumors into multiple molecular subtypes. The capacity to define subtypes of breast tumors provides a framework to enable improved understanding of the mechanisms of breast oncogenesis, as well as to provide opportunities for improved therapeutic intervention in patients. METHODS: We used publicly available gene expression profiling data to identify 'estrogen independent' genes in estrogen receptor alpha (ER+) breast tumors, and subsequently identified 6 subgroups of ER+breast tumors. RESULTS: Each of the 6 identified subgroups exhibited distinct clinical behaviors and biology. Patients whose tumors comprised subgroups 2,5&6 experienced excellent long-term survival, whereas those patients whose tumors belonged to subgroups 1&4 experienced much poorer survival. Breast tumor cell lines representative of the different subgroups responded to therapeutic compounds in accordance with their subgroup classification. CONCLUSIONS: These data support the existence of 6 distinct subgroups of ER+breast cancer and suggest that knowledge of the ER+subgroup status of patient samples have the potential to guide therapy choice.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".