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This study introduces plasma extracellular vesicle RNA (evRNA) analysis for advanced cancer monitoring. This liquid biopsy approach identifies molecular subtypes, tracks treatment response, and finds therapeutic targets without tissue biopsies.

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Area of Science:

  • Oncology
  • Molecular Biology
  • Genomics

Background:

  • Longitudinal monitoring of advanced cancers is vital for assessing disease progression and treatment efficacy.
  • Current liquid biopsy methods primarily use DNA biomarkers, limiting comprehensive tumor analysis.
  • Plasma RNA analysis offers new avenues for interrogating tumor states and molecular subtypes.

Purpose of the Study:

  • To develop and validate a transcriptomic liquid biopsy approach using plasma extracellular vesicle RNA (evRNA).
  • To enable longitudinal monitoring of molecular subtypes and treatment response in metastatic colorectal cancer.
  • To identify expressed gene fusions and neoepitopes for targeted cancer therapies.

Main Methods:

  • Deep learning algorithms were applied to deconvolved transcriptomes from plasma extracellular vesicle RNA (evRNA).
  • Consensus molecular subtypes were predicted in patients with metastatic colorectal cancer.
  • Changes in transcriptomic subtypes under treatment pressure and recurrence-associated pathways were analyzed.

Main Results:

  • Successfully predicted consensus molecular subtypes in metastatic colorectal cancer patients using plasma evRNA.
  • Demonstrated the ability to monitor transcriptomic subtype shifts during treatment.
  • Identified molecular pathways linked to cancer recurrence and detected expressed gene fusions and neoepitopes.

Conclusions:

  • Plasma evRNA analysis provides a feasible transcriptomic liquid biopsy platform for precision oncology.
  • This approach supports longitudinal monitoring of tumor subtype dynamics.
  • It facilitates the identification of expressed fusions and neoantigens as potential therapeutic targets, reducing reliance on tissue biopsies.