Next-Generation Liquid Biopsies: Embracing Data Science in Oncology

Deeper and broader sequencing of circulating tumor DNA (ctDNA) has identified a wealth of cancer markers in the circulation, resulting in a paradigm shift towards data science-driven liquid biopsies in oncology. Although panel sequencing for actionable mutations in plasma is moving towards the clinic, the next generation of liquid biopsies is increasingly shifting from analyzing digital mutation signals towards analog signals, requiring a greater role for machine learning. Concomitantly, there is an increasing acceptance that these cancer signals do not have to arise from the tumor itself. In this Opinion, we discuss the opportunities and challenges arising from increasingly complex cancer liquid biopsy data.

Automated summary

This study discusses the opportunities and challenges arising from increasingly complex cancer liquid biopsy data, how to analyze analog signals using machine learning, and the acceptance that these cancer signals do not necessarily come from the tumor itself.