Abstract IA023: Therapeutic efficacy of trametinib and ganitumab in RAS-mutated rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Background: PAX-fusion negative rhabdomyosarcoma (FN RMS) is driven by alterations in the RAS/MAP kinase pathway and is partially responsive to MEK inhibition. Overexpression of IGF1R and its ligands is also observed in FN RMS. Preclinical and clinical studies have suggested that IGF1R is itself an important target in FN RMS. Our previous studies revealed preclinical efficacy of the MEK1/2 inhibitor, trametinib, and an IGF1R inhibitor, BMS75807, but this combination was not pursued clinically due to excessive toxicity in preclinical murine models. Here, we sought to identify a combination of an MEK1/2 inhibitor and IGF1R inhibitor that would be better tolerated in murine models and effective in both cell line and patient derived xenograft models of RAS-mutant FN RMS. Methods: Using proliferation and apoptosis assays, we studied the factorial effects of trametinib and ganitumab (AMG 479), a monoclonal antibody with specificity for human and murine IGF1R, in a panel of RAS-mutant FN RMS cell lines. The molecular mechanism of the observed synergy was determined using conventional and capillary immunoassays. The efficacy and tolerability of the combination was assessed using a panel of RAS-mutated cell-line and patient-derived RMS xenograft models. Results: Treatment with trametinib and ganitumab resulted in synergistic cellular growth inhibition in all cell lines tested and inhibition of tumor growth in five out of six models of RAS-mutant RMS. Evidence suggests that the combination had little effect on body weight loss, thrombocytopenia, neutropenia, or hyperinsulinemia in tumor-bearing SCID beige mice. Mechanistically, ganitumab treatment prevented the AKT phosphorylation that is induced by MEK inhibition alone. Therapeutic response to the combination was observed in models with an intact PI3K/PTEN axis. Conclusions: We demonstrate that combined trametinib and ganitumab is effective in a genomically diverse panel of RAS-mutated FN RMS preclinical models. The trametinib/ganitumab combination also likely has an improved tolerability profile compared to other IGF1R/MEK inhibitor combinations. These data support testing this combination in a phase I/II clinical trial for pediatric patients with relapsed or refractory RAS-mutated FN RMS. Citation Format: Marielle E. Yohe, Katie E. Hebron, Xiaolin Wan, Jacob S. Roth, David J. Liewehr, Nancy E. Sealover, Stacey Stauffer, Olivia Feehan-Nelson, Wenyue Sun, Kristine A. Isanogle, Christina M. Robinson, Amy James, Parirokh Awasthi, Priya Shankarappa, Xiaoling Liu, Haiyan Lei, Donna Butcher, Roberta Smith, Elijah F. Edmonson, Jin-Qui Chen, Noemi Kedei, Cody S. Peer, Jack F. Shern, W. Douglas Figg, Lu Chen, Matthew D. Hall, Simone Difillipantonio, Frederic G. Barr, Robert L. Kortum, Angelina V. Vaseva, Javed Khan. Therapeutic efficacy of trametinib and ganitumab in RAS-mutated rhabdomyosarcoma [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 IA023.
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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) | 0.001 | 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) | 0.001 | 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".