High-Throughput Screening of Myxoid Liposarcoma Cell Lines: Survivin Is Essential for Tumor Growth
Bibliographic record
Abstract
Myxoid liposarcoma (MLS) is a soft tissue sarcoma characterized by a recurrent t(12;16) translocation. Although tumors are initially radio- and chemosensitive, the management of inoperable or metastatic MLS can be challenging. Therefore, our aim was to identify novel targets for systemic therapy. We performed an in vitro high-throughput drug screen using three MLS cell lines (402091, 1765092, DL-221), which were treated with 273 different drugs at four different concentrations. Cell lines and tissue microarrays were used for validation. As expected, all cell lines revealed a strong growth inhibition to conventional chemotherapeutic agents, such as anthracyclines and taxanes. A good response was observed to compounds interfering with Src and the mTOR pathway, which are known to be affected in these tumors. Moreover, BIRC5 was important for MLS survival because a strong inhibitory effect was seen at low concentration using the survivin inhibitor YM155, and siRNA for BIRC5 decreased cell viability. Immunohistochemistry revealed abundant expression of survivin restricted to the nucleus in all 32 tested primary tumor specimens. Inhibition of survivin in 402-91 and 1765-92 by YM155 increased the percentage S-phase but did not induce apoptosis, which warrants further investigation before application in the treatment of metastatic MLS. Thus, using a 273-compound drug screen, we confirmed previously identified targets (mTOR, Src) in MLS and demonstrate survivin as essential for MLS survival.
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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 | 0.000 |
| 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 | 0.001 |
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".