A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapy.

Journal: ESMO open
PMID: 39591805

Abstract

BACKGROUND: The low probability of identifying druggable mutations through comprehensive genomic profiling (CGP) and its financial and time costs hinder its widespread adoption. To enhance the effectiveness and efficiency of cancer precision medicine, it is critical to identify patient characteristics that are most likely to benefit from CGP.

Authors

  • H Ikushima
    Department of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Electronic address: hikushima-tky@umin.ac.jp.
  • K Watanabe
    Department of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Next-Generation Precision Medicine Development Laboratory, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • A Shinozaki-Ushiku
    Division of Integrative Genomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • K Oda
    Division of Integrative Genomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • H Kage
    Department of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Keywords

External Resources

Popular Topics
Recent Journals