An emerging role for the nuclear localization of maspin in the suppression of tumor progression and metastasis<sup>1</sup>This article is part of Special Issue entitled Asilomar Chromatin and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Maspin, a member of the serpin family of serine protease inhibitors, was originally identified as a tumor suppressor that is expressed in normal mammary epithelial cells but is reduced or absent in breast carcinomas. Early enthusiasm for maspin as a biomarker for disease progression has been tempered by clinical data that associates maspin with favourable outcomes in some studies and poor prognosis in others. Here, we review all of the published clinical studies for maspin in breast and ovarian cancers and propose that the apparent discordance between clinical reports is a consequence of differential cellular distribution of maspin. Indeed, it was thought that an extracellular pool of maspin possessed tumor suppressor activity, acting by inhibiting migration and increasing cell adhesion. Recent evidence from our group and others indicates, however, that the nuclear localization of maspin in cancer cells is necessary for its tumor suppressor activity. We provide additional data here to demonstrate that nuclear-localized maspin binds to chromatin and is required to effectively prevent cells from metastasizing. Our knowledge of other serpins that localize to the nucleus should help to inform future studies of nuclear maspin. Elucidation of the molecular mechanisms regulating the localization and activities of maspin should pave the way for the development of improved diagnostics and therapies for 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".