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El sistema de soporte de decisiones Knowledge Connector para oncología de precisión basada en multiómica

Daniel Hübschmann1,2,3,4,5, Simon Kreutzfeldt6,7, Benjamin Roth8

  • 1Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between the German Cancer Research Center (DKFZ) and Heidelberg University Hospital, Heidelberg, Germany. d.huebschmann@dkfz-heidelberg.de.

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|January 19, 2026
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Resumen

El sistema Knowledge Connector (KC) mejora la medicina de precisión del cáncer al integrar datos del paciente con conocimiento mundial para las juntas moleculares tumorales multidisciplinarias (MTB). Esto mejora la interpretación de datos moleculares complejos para recomendaciones de tratamiento de cáncer personalizadas.

Palabras clave:
oncología de precisiónmedicina de precisiónjuntas moleculares tumoralesdatos multiómicossoporte de decisionesKnowledge Connectorinformática médicabioinformática

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Área de la Ciencia:

  • Oncología
  • Bioinformática
  • Informática Médica

Sus antecedentes:

  • La medicina de precisión del cáncer se basa en juntas moleculares tumorales multidisciplinarias (MTB) para el tratamiento personalizado del paciente.
  • La interpretación de datos moleculares complejos para la toma de decisiones clínicas es un desafío y requiere métodos confiables y reproducibles.

Objetivo del estudio:

  • Desarrollar e implementar un sistema de soporte de decisiones, el Knowledge Connector (KC), para ayudar a las MTB en la interpretación de datos moleculares.
  • Integrar datos moleculares y clínicos del paciente con conocimiento externo para generar recomendaciones basadas en evidencia.

Principales métodos:

  • Desarrollo del sistema Knowledge Connector (KC) para la curación de datos, integración de bases de datos y análisis de datos multiómicos.
  • Integración de datos específicos del paciente con una base de conocimiento curada para apoyar las discusiones de la MTB.
  • Implementación de funciones para extraer asociaciones genotipo-fármaco y optimizar la concordancia intercurador.

Principales resultados:

  • El sistema KC integra con éxito diversos datos moleculares y clínicos con conocimiento mundial.
  • El sistema facilita la interpretación de datos, reduce la dependencia de fuentes externas y mejora la coherencia entre los curadores.
  • Se demostró la versatilidad del KC para apoyar la toma de decisiones médicas dentro de las MTB.

Conclusiones:

  • El Knowledge Connector (KC) es una herramienta eficaz de soporte de decisiones para las MTB en oncología de precisión.
  • El sistema KC permite la interpretación escalable y eficiente de datos moleculares complejos.
  • La implementación del KC apoya la mejora de la gestión clínica y los resultados de los pacientes a través de recomendaciones personalizadas.