Defining Breast Cancer Intrinsic Subtypes by Quantitative Receptor Expression
Bibliographic record
Abstract
PURPOSE: To determine intrinsic breast cancer subtypes represented within categories defined by quantitative hormone receptor (HR) and HER2 expression. METHODS: We merged 1,557 cases from three randomized phase III trials into a single data set. These breast tumors were centrally reviewed in each trial for quantitative ER, PR, and HER2 expression by immunohistochemistry (IHC) stain and by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), with intrinsic subtyping by research-based PAM50 RT-qPCR assay. RESULTS: Among 283 HER2-negative tumors with <1% HR expression by IHC, 207 (73%) were basal-like; other subtypes, particularly HER2-enriched (48, 17%), were present. Among the 1,298 HER2-negative tumors, borderline HR (1%-9% staining) was uncommon (n = 39), and these tumors were heterogeneous: 17 (44%) luminal A/B, 12 (31%) HER2-enriched, and only 7 (18%) basal-like. Including them in the definition of triple-negative breast cancer significantly diminished enrichment for basal-like cancer (p < .05). Among 106 HER2-positive tumors with <1% HR expression by IHC, the HER2-enriched subtype was the most frequent (87, 82%), whereas among 127 HER2-positive tumors with strong HR (>10%) expression, only 69 (54%) were HER2-enriched and 55 (43%) were luminal (39 luminal B, 16 luminal A). Quantitative HR expression by RT-qPCR gave similar results. Regardless of methodology, basal-like cases seldom expressed ER/ESR1 or PR/PGR and were associated with the lowest expression level of HER2/ERBB2 relative to other subtypes. CONCLUSION: Significant discordance remains between clinical assay-defined subsets and intrinsic subtype. For identifying basal-like breast cancer, the optimal HR IHC cut point was <1%, matching the American Society of Clinical Oncology and College of American Pathologists guidelines. Tumors with borderline HR staining are molecularly diverse and may require additional assays to clarify underlying biology.
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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 | 0.007 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".