Loss of LATS1 and LATS2 promotes ovarian tumor formation by enhancing AKT activity and PD-L1 expression
Bibliographic record
Abstract
High-grade serous ovarian cancer (HGSOC) is the deadliest and most common subtype of ovarian cancer. Unfortunately, most patients develop recurrence and, ultimately, resistance to standard platinum chemotherapy. Large tumor suppressors LATS1 and LATS2, the core Hippo signaling kinases, have been implicated in various cancer types, including ovarian cancer. The mechanism by which LATS1/2 suppresses ovarian cancer progression is currently elusive, but the expression of LATS1/2 is frequently reduced or lost in these cancers. In this study, we demonstrate that the inactivation of LATS1/2 is sufficient to transform normal mouse ovarian epithelium into tumorigenic cells associated with increased cell proliferation, invasion, and stemness and epithelial-mesenchymal transition (EMT) characteristics. The knockout of Lats1/2 in the epithelial cells also leads to higher expression levels of the immune checkpoint molecule PD-L1, suggesting a regulatory role of LATS1/2 in modulating immune responses and immune evasion. In addition to the loss of LATS1/2 activating the downstream transcriptional coactivators YAP and TAZ, PI3K-AKT activity was also increased, likely contributing to enhanced tumor proliferation and survival. The stimulatory effect of Lats1/2 knockout on cell proliferation can be partially reversed by treatment with the AKT inhibitor MK2206. Treatment with verteporfin, a potent inhibitor of YAP/TAZ, decreases ovarian tumor progression and reduces the activated AKT in the tumors. In summary, this study uncovers several biological mechanisms for the initiation of HGSOC and identifies LATS1/2 as potential prognostic indicators and therapeutic targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".