Combined mTOR and MEK inhibition is an effective therapy in a novel mouse model for angiosarcoma
Bibliographic record
Abstract
// Michelle L. Chadwick 1, 2 , Adam Lane 2 , Dana Thomas 2 , Amanda R. Smith 2 , Angela R. White 2 , Dominique Davidson 3 , Yuxin Feng 2 , Elisa Boscolo 2 , Yi Zheng 1, 2 , Denise M. Adams 4 , Anita Gupta 5 , André Veillette 3 and Lionel M.L. Chow 1, 2 1 Department of Cancer and Cell Biology, University of Cincinnati, Cincinnati, OH, USA 2 Cancer and Blood Diseases Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA 3 Institut de Recherches Cliniques de Montréal, Montréal, Canada 4 Vascular Anomalies Center, Boston Children’s Hospital, Boston, MA, USA 5 Department of Pathology and Laboratory Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA Correspondence to: Lionel M.L. Chow, email: lionel.chow@cchmc.org Keywords: angiosarcoma; mouse model; pre-clinical therapeutics; mTOR; MEK Received: March 09, 2018 Accepted: April 21, 2018 Published: May 15, 2018 ABSTRACT Angiosarcoma is an aggressive malignancy of vascular origin that occurs de novo or in the context of previous cancer therapy. Despite multi-modal aggressive treatment including surgical resection, chemotherapy, and radiation, five-year overall survival remains poor at 35%. Due to its rarity, little is known about its molecular pathology and clinical trials have been extremely difficult to conduct. Development of animal models for rare diseases like angiosarcoma is critical to improve our understanding of tumorigenesis and to test novel treatment regimens. A genetically engineered mouse model for angiosarcoma was generated by conditional deletion of Trp53 , Pten , and Ptpn12 in endothelial cells. Tumors arising from these mice recapitulate the histology and molecular pathology of the human disease including hyperactivation of the PI3K/mTOR and MAPK signaling pathways. Treatment of tumor-bearing mice with mTOR or MEK inhibitors effectively inactivated signaling and resulted in reduced proliferation and elevated apoptosis leading to tumor regression. The effect of treatment on tumor growth was transient and proliferation was restored after a period of dormancy. However, combined inhibition of mTOR and MEK resulted in profound tumor regression which was sustained for the duration of treatment. These results suggest that angiosarcoma may be effectively treated by this drug combination.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".