Clinical performance of diagnostic, prognostic and predictive immunohistochemical biomarkers for hormone receptor-negative breast cancer
Bibliographic record
Abstract
Gene expression profiling of breast cancer delineates a particularly aggressive subtype referred to as “basal-like”, which comprises ~15% of all cases, afflicts younger women and is refractory to endocrine and anti-HER2 therapies. Immunohistochemical surrogate definitions for basal-like breast cancer, such as the ER/PR/HER2 triple negative phenotype and models incorporating positive expression of cytokeratin 5 (CK5/6) and/or epidermal growth factor receptor are more amenable to implementation in a clinical setting. Despite this and the fact that basal-like breast carcinomas are being increasingly recognized as a distinct clinical entity, there is no diagnostic method used and reported in routine practice. Without a reproducible test to identify this aggressive subtype in the clinic there will be no ability to establish clearly defined intake criteria for subtype-specific clinical trials, translating to no progress in the management of this form of the disease and little change in breast cancer survival rates for the foreseeable future. A first evaluation of performance of the triple negative definition and various surrogate immunopanels for basal-like breast cancer in clinical laboratories is described in the initial chapters of this dissertation. Considerable staining variability of individual biomarkers included in immunopanels typically led to only moderate concordance with a gene expression gold standard for identification of basal-like breast carcinomas. Lack of standardization was the underlying reason for all of the observed variability, supporting the notion that further standardization efforts through continual participation in external quality assurance programs are needed before routine diagnosis of basal-like breast carcinomas could be made in a clinical setting. In light of this, we sought to identify more easy-to-interpret and robust biomarkers for this disproportionately deadly type of breast cancer. A parallel comparison of 46 proposed immunohistochemical biomarkers of basal-like breast cancer was performed against a gene expression profile gold standard. Results from that survey determined that loss of expression of INPP4B and positive expression of nestin had the strongest associations with this aggressive subtype. Paving the way for further studies, this comprehensive immunohistochemical biomarker survey is a necessary step to determine an optimized surrogate immunopanel that best defines basal-like breast cancer in a practical and clinically-accessible way.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".