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Abstract B06: Targeting PI3K/mTOR in mouse rhabdomyosarcoma models driven by FGFR4 activation

2018· article· en· W2885953918 on OpenAlexaff
Timothy McKinnon, Rosemarie E. Venier, Marielle E. Yohe, Berkley E. Gryder, Brendan C. Dickson, Krista Schleicher, Dariush Davani, Winnie Wei, Cynthia J. Guidos, Abha A. Gupta, Javed Khan, Rebecca A. Gladdy

Bibliographic record

VenueClinical Cancer Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsHospital for Sick ChildrenMount Sinai HospitalLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsPI3K/AKT/mTOR pathwayCancer researchProtein kinase BRhabdomyosarcomaBiologyOncogeneCell cultureCellCell cycleSarcomaSignal transductionMedicineCell biologyPathology

Abstract

fetched live from OpenAlex

Abstract Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma and while survival rates have increased over the past few decades, outcomes for intermediate and high-risk patients remain dismal. Genomic analyses show that 93% of RMS have RTK/RAS/PI3K alterations and that fibroblast growth factor receptor 4 (FGFR4) is frequently mutated or overexpressed. The point mutation V550E constitutively activates FGFR4, stimulating downstream signaling pathways. In this translational study, we demonstrate that FGFR4V550E is a potent oncogene in mouse models of RMS and that secondary tumor models can be generated following transplantation of FGFR4V550E overexpressing tumor cells into immunocompetent host mice. These models uncovered mechanisms of transformation in FGFR4V550E-driven RMS and helped identify novel compounds that inhibit RMS growth. Specifically, AKT and mTOR signaling pathways were activated in myoblasts overexpressing FGFR4V550E and similarly, FGFR4V550E overexpressing tumors and tumor-derived cells exhibited increased AKT and mTOR phosphorylation by immunoblot. Murine tumor cells overexpressing FGFR4V550E were tested in an in vitro dose-response drug screen along with human RMS cell lines. Compounds were grouped by target class, and potency was determined using average percent area under the dose response curve (AUC). Using this technique, FGFR4V550E overexpressing tumor cells were highly sensitive to PI3K/mTOR inhibitors. In particular, GSK2126458 (omipalisib) was a potent inhibitor of FGFR4V550E tumor-derived cell and human RMS cell viability. FGFR4V550E overexpressing myoblasts and tumor cells had low nanomolar GSK2126458 EC50 values. Mass cytometry using mouse and human RMS cell lines validated GSK2126458 specificity at single cell resolution, decreasing the abundance of phosphorylated Akt as well as decreasing phosphorylation of the downstream mTOR effectors 4ebp1, Eif4e, and S6. In a preclinical study, GSK2126458 inhibited tumor growth in vivo. A statistically significant increase in disease-specific survival was observed in mice treated with GSK2126458 compared to mice treated with vehicle alone (p<0.001) or standard of care, vincristine (p<0.05). In summary, RMS driver mutations were validated in vivo and our model system was effectively used as a preclinical tumor model to identify and test therapeutic agents. Importantly, these results suggest a role for PI3K/mTOR inhibition in precision therapy regimens for RMS with FGFR4 mutations. This study provides further evidence for RMS clinical studies involving mTOR inhibitors (e.g., temsirolimus). Citation Format: Timothy McKinnon, Rosemarie Venier, Marielle Yohe, Berkley E. Gryder, Brendan Dickson, Krista Schleicher, Dariush Davani, Winnie Wei, Cynthia Guidos, Abha Gupta, Javed Khan, Rebecca Gladdy. Targeting PI3K/mTOR in mouse rhabdomyosarcoma models driven by FGFR4 activation [abstract]. In: Proceedings of the AACR Conference on Advances in Sarcomas: From Basic Science to Clinical Translation; May 16-19, 2017; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(2_Suppl):Abstract nr B06.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.475
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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