Abstract A027: Characterization of WEE1 kinase activity in myxoid liposarcoma
Bibliographic record
Abstract
Abstract Introduction: Myxoid liposarcomas (MLS), malignant soft tissue tumors of adipocyte origin, are genetically characterized by a chromosomal t(12;16)(q13;p11) translocation encoding the chimeric FUS-DDIT3 fusion gene. The resulting fusion protein drives MLS pathogenesis via (dys-)regulation of oncogenic signaling pathways. Since FUS-DDIT3 is not selectively antagonizable, counteracting the oncogenic effects of FUS-DDIT3 fusion protein represents the most promising strategy to target MLS cells. In this study, we identified cell cycle checkpoint kinase WEE1 as FUS-DDIT3 depending effector and investigated the functional requirement for WEE1 kinase activity in MLS pathogenesis. Experimental Procedures: Characterization of WEE1 expression and kinase activity was performed in multiple MLS cell lines, cell lines derived from other liposarcoma subtypes and a mesenchymal stem cell system. Modulation of WEE1 signaling was carried out by means of small-molecule inhibitor Adavosertib (MK-1775) and RNA interference (RNAi)-mediated depletion, and effects were analyzed in immunoblots, cell proliferation assays and caspase 3/7 activity-based apoptosis assays in vitro. Results: Functional genomic RNAi screening uncovered dependence of FUS-DDIT3-expressing mesenchymal stem cells and MLS cell lines on WEE1 kinase activity. Additional expression analysis revealed increased WEE1 protein levels in MLS cell lines. Moreover, pharmacologic inhibition of WEE1 demonstrated significant reduction of MLS cell viability. Accordingly, functional loss of WEE1 by inhibition or RNAi-mediated depletion was found to induce DNA damage accompanied by unscheduled mitotic entry and cell death via activation of the apoptotic program in MLS cells. Conclusions: Our results identify WEE1 kinase activity as functional liability of FUS-DDIT3 expressing MLS cells and provide first evidence that overactive WEE1 signaling represents a promising target for therapeutic intervention in MLS. Citation Format: Lorena Heinst, Ruth Berthold, Ilka Isfort, Svenja Wosnig, Thomas Kindler, Pierre Åman, Eva Wardelmann, Claudia Scholl, Stefan Fröhling, Wolfgang Hartmann, Marcel Trautmann. Characterization of WEE1 kinase activity in myxoid liposarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr A027.
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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) | 0.002 | 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".