Dysregulated PTEN‐PKB and negative receptor status in human breast cancer
Bibliographic record
Abstract
Recent studies demonstrate that abnormalities in PTEN may be one of the most frequent genetic events observed in human cancers. PTEN dysfunction leads to tumorigenesis through unopposed survival signals mediated via activated protein kinase B (PKB), which may also be associated with hormone-independence. We therefore investigated the relationship between PTEN-PKB and receptor status in human breast cancer. Several molecular variables, including immunohistochemical staining for PTEN, PKB (phosphorylated on ser473), p53 and p21 were evaluated. The p53 gene was sequenced from exons 2-11. Seventy-eight participants in a randomised breast cancer trial served as the cohort for our study. Twenty-eight of 77 (36%) patients' tumours demonstrated absent or reduced PTEN expression; 17 of 78 (22%) tumours over-expressed P-PKB. A significant inverse relationship was observed between reduced PTEN and increased P-PKB expression. Reduced PTEN also correlated with reduced ER or PR expression. None of the molecular variables correlated with survival. ER and PR negative tumours, however, experienced a significantly inferior disease-free survival than other ER/PR status tumours. Immunohistochemical analyses of ER expression in mammary carcinomas arising in PTEN heterozygous knockout mice did not demonstrate a reduction in ER immunoreactivity, in comparison to wild-type mice. Our data demonstrate that the PTEN-PKB pathway is abnormal in approximately 1/3 of lymph node negative breast cancer. Dysregulated PTEN-PKB was also associated with reduced ER/PR expression, but this does not appear to be a simple direct causal relationship. These observations support the contention that dysregulation in PTEN-PKB contributes to disease progression and hormone resistance of human breast cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".