Normal development is an integral part of tumorigenesis in T cell-specific PTEN-deficient mice
Bibliographic record
Abstract
PTEN is a tumor suppressor gene but whether cancer can develop in all PTEN-deficient cells is not known. In T cell-specific PTEN-deficient (tPTEN-/-) mice, which suffer from mature T cell lymphomas, we found that premalignancy, as defined by elevated AKT and senescence pathways, starts in immature T cell precursors and surprisingly not in mature T cells. Premalignancy only starts in 6-week-old mice and becomes much stronger in 9-week-old mice although PTEN is lost since birth. tPTEN-/- immature T cells do not become tumors, and senescence has no role in this model because these cells exist in a novel cell cycle state, expressing proliferating proteins but not proliferating to any significant degree. Instead, the levels of p27(kip1), which is lower in tPTEN-/- immature T cells and almost nonexistent in tPTEN-/- mature T cells, correlate with the proliferation capability of these cells. Interestingly, transient reduction of these cancer precursor cells in adult tPTEN-/- mice within a crucial time window significantly delayed lymphomas and mouse lethality. Thus, loss of PTEN alone is not sufficient for cells to become cancerous, therefore other developmental events are necessary for tumor formation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".