Triple-Negative Breast Cancer: Distinguishing between Basal and Nonbasal Subtypes
Bibliographic record
Abstract
PURPOSE: Triple-negative (TN; estrogen receptor, progesterone receptor, and HER-2 negative) cancer and basal-like breast cancer (BLBC) are associated with poor outcome and lack the benefit of targeted therapy. It is widely perceived that BLBC and TN tumors are synonymous and BLBC can be defined using a TN definition without the need for the expression of basal markers. EXPERIMENTAL DESIGN: We have used two well-defined cohorts of breast cancers with a large panel of biomarkers, BRCA1 mutation status, and follow-up data to compare the clinicopathologic and immunohistochemical features of TN tumors expressing one or more of the specific basal markers (CK5/6, CK17, CK14, and epidermal growth factor receptor; BLBC) with those TN tumors that express none of these markers (TN3BKE-). RESULTS: Here, we show that although the morphologic features of BLBC are not significantly different from that of TN3BKE- tumors, BLBC showed distinct clinical and immunophenotypic differences. BLBC showed a statistically significant association with the expression of the hypoxia-associated factor (CA9), neuroendocrine markers, and other markers of poor prognosis such as p53. A difference in the expression of cell cycle-associated proteins and biomarkers involved in the immunologic portrait of tumors was seen. Compared with TN3BKE- tumors, BLBC was positively associated with BRCA1 mutation status and showed a unique pattern of distant metastasis, better response to chemotherapy, and shorter survival. CONCLUSION: TN breast cancers encompass a remarkably heterogeneous group of tumors. Expression of basal markers identifies a biologically and clinically distinct subgroup of TN tumors, justifying the use of basal markers (in TN tumors) to define BLBC.
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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 | 0.000 | 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) | 0.001 | 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".