Automated Quantitative Analysis of p53, Cyclin D1, Ki67 and pERK Expression in Breast Carcinoma Does Not Differ from Expert Pathologist Scoring and Correlates with Clinico-Pathological Characteristics
Bibliographic record
Abstract
There is critical need for improved biomarker assessment platforms which integrate traditional pathological parameters (TNM stage, grade and ER/PR/HER2 status) with molecular profiling, to better define prognostic subgroups or systemic treatment response. One roadblock is the lack of semi-quantitative methods which reliably measure biomarker expression. Our study assesses reliability of automated immunohistochemistry (IHC) scoring compared to manual scoring of five selected biomarkers in a tissue microarray (TMA) of 63 human breast cancer cases, and correlates these markers with clinico-pathological data. TMA slides were scanned into an Ariol Imaging System, and histologic (H) scores (% positive tumor area x staining intensity 0-3) were calculated using trained algorithms. H scores for all five biomarkers concurred with pathologists' scores, based on Pearson correlation coefficients (0.80-0.90) for continuous data and Kappa statistics (0.55-0.92) for positive vs. negative stain. Using continuous data, significant association of pERK expression with absence of LVI (p = 0.005) and lymph node negativity (p = 0.002) was observed. p53 over-expression, characteristic of dysfunctional p53 in cancer, and Ki67 were associated with high grade (p = 0.032 and 0.0007, respectively). Cyclin D1 correlated inversely with ER/PR/HER2-ve (triple negative) tumors (p = 0.0002). Thus automated quantitation of immunostaining concurs with pathologists' scoring, and provides meaningful associations with clinico-pathological data.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".