Abstract A54: Parallel targeting of RAF/MEK/ERK pathway in RAS-mutant embryonal rhabdomyosarcoma
Bibliographic record
Abstract
Abstract RAS pathway mutations are found in nearly 75% of high-risk embryonal rhabdomyosarcoma (ERMS). While RAS oncoproteins are well-established therapeutic targets for many adult human cancers, still very little is known about the role of RAS mutations in the development and maintenance of ERMS. By sequencing, we identified cell lines and PDX tumors harboring activating mutations in H- or NRAS. Further, we showed that mutant H- or NRAS was critical for the growth of all RAS-mutant ERMS cell lines and that RAF/MEK/ERK signaling pathway, but not PI3K/AKT, was mediator of RAS dependency in these cells. However, in vivo treatment of RAS-mutant ERMS xenografts with the MEK inhibitor trametinib showed modest response as compared to BRAF-mutant astrocytoma xenografts. We reasoned that similarly to other RAS-driven cancers, ERMS cells and tumors are able to acquire resistance to inhibitors of the RAF/MEK/ERK pathway. We performed drug-sensitizing pooled CRISPR library screen and identified that inhibition of ERK2 potentiated trametinib treatment. We show that combining trametinib with ERK1/2 inhibitor leads to potent synergistic ERK inhibition and ERMS tumor growth suppression. Citation Format: Angelina V. Vaseva, Abhik Bandyopadhyay, Vanessa Del Pozo, Craig M. Goodwin, Prson Gautam, Krister Wennerberg, Kris C. Wood, Yidong Chen, Channing J. Der, Peter J. Houghton. Parallel targeting of RAF/MEK/ERK pathway in RAS-mutant embryonal rhabdomyosarcoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A54.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".