Machine Learning Meets Genomics: Convergence for Precision Oncology at the Point of Care
As the number of molecular data points per oncology patient grows, this Nature Reviews Cancer paper examines how machine learning can enhance NGS-based diagnostic workflows by improving cancer variant interpretation, streamlining molecular tumour board review, and generating therapeutic hypotheses for biomarker-negative patients. The integration of large clinicogenomic datasets with ML methods offers concrete opportunities for clinical impact. The authors emphasise that responsible implementation and rigorous model evaluation remain essential for adoption.
The original study
Convergence of machine learning and genomics for precision oncology.
- Authors
- Reardon B, Culhane AC, Van Allen EM
- Journal
- Nature reviews. Cancer
- Type
- Journal Article, Review
- PMID
- 41478861
Original 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.