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Utilization of Proteomic Technologies for Precision Oncology Applications.
Pierobon, Mariaelena; Wulfkuhle, Julie; Liotta, Lance A; Petricoin Iii, Emanuel F.
Afiliación
  • Pierobon M; Center for Applied Proteomics and Molecular Medicine, George Mason University, 20110, Manassas, VA, USA.
  • Wulfkuhle J; Center for Applied Proteomics and Molecular Medicine, George Mason University, 20110, Manassas, VA, USA.
  • Liotta LA; Center for Applied Proteomics and Molecular Medicine, George Mason University, 20110, Manassas, VA, USA.
  • Petricoin Iii EF; Center for Applied Proteomics and Molecular Medicine, George Mason University, 20110, Manassas, VA, USA. epetrico@gmu.edu.
Cancer Treat Res ; 178: 171-187, 2019.
Article en En | MEDLINE | ID: mdl-31209845
ABSTRACT
Genomic analysis of tumor specimens has revealed that cancer is fundamentally a proteomic disease at the functional level driven by genomically defined derangements, but selected for in the proteins that are encoded and the aberrant activation of signaling and biochemical networks. This activation is measured by posttranslational modifications such as phosphorylation and other modifications that modulate cellular signaling, and these events cannot be effectively measured by genomic analysis alone. Moreover, these signaling networks by and large represent the targets for many FDA-approved and experimental molecularly targeted therapeutics. Consequently, it is important that we consider new classification schemas for oncology based not on tumor site of origin or histology under the microscope but on the functional protein signaling architecture. There are numerous proteomic technologies that could be discussed from a purely technological standpoint, but this chapter will concentrate on an overview of the main proteomic technologies available for conducting protein pathway activation analysis of clinical specimens such as multiplex immunoassays, phospho-specific flow cytometry, reverse phase protein microarrays, quantitative immunohistochemistry, and mass spectrometry. This chapter will focus on the application of these technologies to cancer-based clinical studies evaluating prognostic/predictive markers or for stratifying patients to personalized treatments.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteómica / Medicina de Precisión / Neoplasias Límite: Humans Idioma: En Revista: Cancer Treat Res Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteómica / Medicina de Precisión / Neoplasias Límite: Humans Idioma: En Revista: Cancer Treat Res Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos