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Next-Generation Liquid Biopsies: Embracing Data Science in Oncology.

Y R Im1, D W Y Tsui2, L A Diaz2

  • 1School of Clinical Medicine, University of Cambridge, Cambridge, UK.

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|December 15, 2020
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Summary
This summary is machine-generated.

Liquid biopsies are evolving beyond digital mutation signals to analog signals, requiring machine learning for complex cancer detection. This shift broadens the scope of detectable cancer markers in oncology.

Keywords:
cancercell-free DNAcirculating tumor DNAliquid biopsyoncology

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Area of Science:

  • Oncology
  • Genomics
  • Data Science

Background:

  • Circulating tumor DNA (ctDNA) sequencing has revealed numerous cancer markers in blood.
  • Liquid biopsies are transitioning towards data science-driven approaches in cancer diagnostics.
  • The analysis of liquid biopsies is moving from digital mutation signals to analog signals.

Purpose of the Study:

  • To discuss the opportunities and challenges in analyzing complex cancer liquid biopsy data.
  • To highlight the increasing role of machine learning in next-generation liquid biopsies.
  • To address the evolving understanding of cancer signal origins in liquid biopsies.

Main Methods:

  • Review of current trends in ctDNA sequencing and analysis.
  • Discussion of machine learning applications for analog signal detection.
  • Exploration of the paradigm shift in liquid biopsy data interpretation.

Main Results:

  • Next-generation liquid biopsies increasingly rely on analog signal analysis.
  • Machine learning is becoming essential for interpreting complex liquid biopsy data.
  • The origin of detectable cancer signals in circulation is being re-evaluated.

Conclusions:

  • The complexity of liquid biopsy data necessitates advanced analytical methods like machine learning.
  • The field is expanding to include non-tumor-derived cancer signals.
  • Further research is needed to fully leverage the potential of complex liquid biopsy data in oncology.