Probing for a deeper understanding of rhabdomyosarcoma: Insights from complementary model systems

Venkatesh P. Kashi; Mark E. Hatley; Rene L. Galindo

doi:10.1038/nrc3961

Probing for a deeper understanding of rhabdomyosarcoma: Insights from complementary model systems

Venkatesh P. Kashi, Mark E. Hatley, Rene L. Galindo

Research output: Contribution to journal › Review article › peer-review

94 Scopus citations

Abstract

Rhabdomyosarcoma (RMS) is a mesenchymal malignancy composed of neoplastic primitive precursor cells that exhibit histological features of myogenic differentiation. Despite intensive conventional multimodal therapy, patients with high-risk RMS typically suffer from aggressive disease. The lack of directed therapies against RMS emphasizes the need to further uncover the molecular underpinnings of the disease. In this Review, we discuss the notable advances in the model systems now available to probe for new RMS-targetable pathogenetic mechanisms, and the possibilities for enhanced RMS therapeutics and improved clinical outcomes.

Original language	English (US)
Pages (from-to)	426-439
Number of pages	14
Journal	Nature Reviews Cancer
Volume	15
Issue number	7
DOIs	https://doi.org/10.1038/nrc3961
State	Published - Jul 26 2015

ASJC Scopus subject areas

Oncology
Cancer Research

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abstract = "Rhabdomyosarcoma (RMS) is a mesenchymal malignancy composed of neoplastic primitive precursor cells that exhibit histological features of myogenic differentiation. Despite intensive conventional multimodal therapy, patients with high-risk RMS typically suffer from aggressive disease. The lack of directed therapies against RMS emphasizes the need to further uncover the molecular underpinnings of the disease. In this Review, we discuss the notable advances in the model systems now available to probe for new RMS-targetable pathogenetic mechanisms, and the possibilities for enhanced RMS therapeutics and improved clinical outcomes.",

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Probing for a deeper understanding of rhabdomyosarcoma: Insights from complementary model systems

Abstract

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