Quantitative Immunohistochemistry for Evaluating the Distribution of Ki67 and Other Biomarkers in Tumor Sections and Use of the Method to Study Repopulation in Xenografts after Treatment with Paclitaxel
Bibliographic record
Abstract
BACKGROUND: Surviving cells may repopulate tumors between courses of chemotherapy, thereby reducing the effectiveness of treatment. Using a novel quantitative method, we characterize the influence of the tumor microenvironment on repopulation of surviving cells in human tumor xenografts after paclitaxel treatment and evaluate the potential of gefitinib, an epidermal growth factor receptor (EGFR) inhibitor, to inhibit repopulation. METHODS: High-EGFR-expressing A431 xenografts and low-EGFR-expressing MCF-7 xenografts were treated with paclitaxel or gefitinib. Time-dependent changes in cell proliferation (Ki67) and apoptosis (cleaved caspase 3) in relation to total and functional tumor blood vessels (recognized by CD31 and a flow marker), and regions of hypoxia (recognized by EF5) were quantified using fluorescence microscopy. RESULTS: Decrease in functional tumor vasculature and in cell proliferation and increase in apoptosis were observed in A431 xenografts after treatment with either paclitaxel or gefitinib. There was a rebound in functional vasculature and cell proliferation ≈ 12 days after treatment with paclitaxel, and repopulation was observed from tumor cells close to regions of hypoxia. Cell proliferation increased ≈ 5 days after the last dose of gefitinib. There were minimal effects of paclitaxel or gefitinib on cell proliferation, cell death, or tumor vasculature in MCF-7 xenografts. CONCLUSIONS: Repopulation in A431 xenografts after treatment with paclitaxel was associated with changes in functional tumor vasculature. Gefitinib decreased cell proliferation in EGFR-overexpressing tumor xenografts, suggesting its potential to inhibit repopulation when used in sequence with chemotherapy.
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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".