Absent p53 Immunohistochemical Staining in a Pituitary Carcinoma
Bibliographic record
Abstract
BACKGROUND: Carcinomatous transformation of pituitary adenomas is uncommon, and is generally accompanied by nuclear accumulation of p53 protein. Pituitary carcinoma lacking accumulation of p53 protein is very rare, only two such cases being previously reported. METHODS: A patient presented with visual disturbance and cranial nerve palsies and was found to have a suprasellar mass. He underwent both transphenoidal and transfrontal excision of a nonfunctioning pituitary adenoma which recurred several times. The third recurrence was accompanied by multiple dural-based metastases. Despite aggressive surgical management, he continued to develop additional intracranial lesions and died two years after the discovery of metastatic disease. Specimens from 1984, 1995, 1997 and 1998 were available for histological and immunocytochemical analysis. Antibodies recognizing the pituitary hormones (ACTH, PRL, GH, FSH, LH and TSH), as well as cytokeratin, epithelial membrane antigen (EMA), glial fibrillary acidic protein (GFAP) and chromogranin A were applied to investigate the lineage of the neoplasm. Antisera specific for Ki-67 (MIB-1) and p53 protein were also applied to further delineate the biology of the tumour. RESULTS: Although cytokeratin and chromogranin A were detected in neoplastic cells. no expression of pituitary hormones was demonstrable, indicative of a nonfunctioning, null-cell pituitary adenoma. Nuclear pleomorphism and mitotic activity increased with subsequent resections. Abnormal accumulation of p53 protein was not observed, neither in early resections nor in the metastatic deposits. CONCLUSIONS: Failure to demonstrate p53 protein accumulation does not ensure a favourable outcome for pituitary adenoma. Accordingly, pituitary carcinoma may occur in the absence of p53 accumulation. The factors which underlie aggressive behaviour of pituitary neoplasms are uncertain but are under investigation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".