Convergence of machine learning and genomics for precision oncology.

Journal: Nature reviews. Cancer
Published Date: Jan 2, 2026

Abstract

The number of data points per patient considered at the point-of-care in precision cancer medicine continues to increase, and it is accompanied by a growing challenge of translating these observations into clinical insights. This is a time-intensive and laborious process for oncology professionals and molecular tumour boards. As large clinicogenomic datasets and data-sharing protocols mature alongside machine learning methods, molecular diagnostic workflows have an opportunity to integrate these tools. This integration can help extract more information from next-generation sequencing data, enhance cancer variant interpretation, streamline case review and generate therapeutic hypotheses for biomarker-negative patients at the point-of-care. Although machine learning holds promise for precision oncology, responsible implementation and model evaluation remain essential for clinical adoption.

Authors

  • Brendan Reardon
    Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, Massachusetts.
  • Aedin C Culhane
    School of Medicine and Limerick Digital Cancer Research Centre, Health Research Institute, University of Limerick, Limerick, Ireland. [email protected].
  • Eliezer M Van Allen
    Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, Massachusetts.

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