LATS tumor suppressor: A new governor of cellular homeostasis
Bibliographic record
Abstract
Accumulating evidence points to the LATS (Large Tumor Suppressor) family of human tumor suppressors (LATS1 and LATS2) as new resident governors of cellular homeostasis. Loss of function of either LATS1 or LATS2 leads to a variety of tumor types including soft tissue sarcomas, leukemia, as well as breast, prostate, lung and esophageal cancers. Due to their high degree of homology and functional overlap, LATS1 and LATS2 comprise a new tumor suppressor family. Classically identified within the Hippo-LATS signaling pathway, LATS also acts independently of this pathway, possessing multiple functions including regulation of cell proliferation, cell death and cell migration, as well as broad governing roles such as transcriptional regulation and maintenance of genetic stability. Activity of LATS is tightly controlled through various mechanisms including post-translational modifications, differential localization and expression. Although little is known about the specific underlying mechanisms of these activities, current data suggest that LATS signaling intersects with well-established tumor suppressive or oncogenic pathways including the p53, Ras or Akt networks. This review aims to identify what we know about the LATS tumor suppressor family, highlighting LATS1 and LATS2 redundancies and differences in terms of their structure, expression, regulation and functions, thereby establishing a novel tumor suppressor network.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".