FUS-CHOP Promotes Invasion in Myxoid Liposarcoma through a SRC/FAK/RHO/ROCK-Dependent Pathway
Bibliographic record
Abstract
Deregulated SRC/FAK signaling leads to enhanced migration and invasion in many types of tumors. In myxoid and round cell liposarcoma (MRCLS), an adipocytic tumor characterized by the expression of the fusion oncogene FUS-CHOP, SRC have been found as one of the most activated kinases. Here we used a cell-of-origin model of MRCLS and an MRCLS cell line to thoroughly characterize the mechanisms of cell invasion induced by FUS-CHOP using in vitro (3D spheroid invasion assays) and in vivo (chicken chorioallantoic membrane model) approaches. FUS-CHOP expression activated SRC-FAK signaling and increased the invasive ability of MRCLS cells. In addition, FAK expression was found to significantly correlate with tumor aggressiveness in sarcoma patient samples. The involvement of SRC/FAK activation in FUS-CHOP-mediated invasion was further confirmed using the SRC inhibitor dasatinib, the specific FAK inhibitor PF-573228, and FAK siRNA. Notably, dasatinib and PF573228 could also efficiently block the invasion of cancer stem cell subpopulations. Downstream of SRC/FAK signaling, we found that FUS-CHOP expression increases the levels of the RHO/ROCK downstream effector phospho-MLC2 (T18/S19) and that this activation was prevented by dasatinib or PF573228. Moreover, the ROCK inhibitor RKI-1447 was able to completely abolish invasion in FUS-CHOP-expressing cells. These data uncover the involvement of SRC/FAK/RHO/ROCK signaling axis in FUS-CHOP-mediated invasion, thus providing a rationale for testing inhibitors of this pathway as potential novel antimetastatic agents for MRCLS treatment.
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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".