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Definición de un mapa de dependencia del cáncer

Aviad Tsherniak1, Francisca Vazquez2, Phil G Montgomery1

  • 1Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA.

Cell
|July 29, 2017
PubMed
Resumen
Este resumen es generado por máquina.

Los investigadores identificaron 769 genes esenciales en diversas líneas celulares de cáncer utilizando pantallas a escala de genoma. Los modelos predictivos revelaron biomarcadores basados en la expresión para la mayoría de las dependencias del cáncer, ayudando a priorizar el objetivo terapéutico.

Palabras clave:
Las pantallas de RNAiDependencias del cáncerobjetivos de cáncerVulnerabilidades genéticasBiomarcadores genómicosMedicina de precisiónModelado predictivoefectos de las semillasshRNA (en inglés)

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Área de la Ciencia:

  • Investigación sobre el cáncer
  • La genómica
  • Biología molecular

Sus antecedentes:

  • Los tumores epiteliales humanos poseen numerosas alteraciones genéticas, lo que complica la identificación de genes esenciales para la supervivencia del tumor.
  • La identificación sistemática de las dependencias del cáncer es crucial para el desarrollo de terapias eficaces contra el cáncer.

Objetivo del estudio:

  • Identificar sistemáticamente los genes esenciales para la supervivencia de diversas líneas celulares de cáncer humano.
  • Desarrollar modelos predictivos para las dependencias del cáncer utilizando características moleculares.
  • Establecer una base para un mapa de dependencia del cáncer para priorizar los objetivos terapéuticos.

Principales métodos:

  • Análisis de 501 pantallas de pérdida de función a escala genómica en líneas celulares de cáncer humano.
  • Desarrollo del marco analítico DEMETER para distinguir los efectos del ARNi en el objetivo de los efectos fuera del objetivo.
  • Aplicación del modelado de regresión no lineal utilizando 66,646 características moleculares para construir modelos predictivos para las dependencias genéticas.

Principales resultados:

  • Identificación de 769 genes requeridos diferencialmente en subconjuntos de líneas celulares de cáncer.
  • Desarrollo de modelos predictivos para 426 dependencias identificadas (55%).
  • Descubrimiento de que los biomarcadores basados en la expresión son los principales predictores en el 82% de los modelos.
  • Demostración de un modelo predictivo que vincula la hipermetilación del gen UBB con la dependencia de UBC.

Conclusiones:

  • El estudio proporciona un análisis exhaustivo de las dependencias del cáncer en un gran conjunto de datos de líneas celulares.
  • Los biomarcadores basados en la expresión son predictores clave de las dependencias del cáncer, que ofrecen información sobre las estrategias terapéuticas.
  • Los hallazgos sientan las bases para un mapa de dependencia del cáncer para guiar el desarrollo de terapias dirigidas al cáncer.