Aberrant AKT activation drives well-differentiated liposarcoma
Bibliographic record
Abstract
Well-differentiated liposarcoma (WDLPS), one of the most common human sarcomas, is poorly responsive to radiation and chemotherapy, and the lack of animal models suitable for experimental analysis has seriously impeded functional investigation of its pathobiology and development of effective targeted therapies. Here, we show that zebrafish expressing constitutively active Akt2 in mesenchymal progenitors develop WDLPS that closely resembles the human disease. Tumor incidence rates were 8% in p53 wild-type zebrafish, 6% in p53 heterozygotes, and 29% in p53-homozygous mutant zebrafish (P = 0.013), indicating that aberrant Akt activation collaborates with p53 mutation in WDLPS pathogenesis. Analysis of primary clinical specimens of WDLPS, and of the closely related dedifferentiated liposarcoma (DDLPS) subtype, revealed immunohistochemical evidence of AKT activation in 27% of cases. Western blot analysis of a panel of cell lines derived from patients with WDLPS or DDLPS revealed robust AKT phosphorylation in all cell lines examined, even when these cells were cultured in serum-free media. Moreover, BEZ235, a small molecule inhibitor of PI3K and mammalian target of rapamycin that effectively inhibits AKT activation in these cells, impaired viability at nanomolar concentrations. Our findings are unique in providing an animal model to decipher the molecular pathogenesis of WDLPS, and implicate AKT as a previously unexplored therapeutic target in this chemoresistant sarcoma.
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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) | 0.000 | 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".