Prediction of Lung Cancer Metastasis Risk Based on Single-Cell Metabolic Profiling of Circulating Tumor Cells

Area of Science:

• Oncology
• Metabolomics
• Precision Medicine

Background:

• Lung cancer metastasis is a major cause of cancer mortality.
• Early prediction and personalized management are crucial for improving patient outcomes.
• Current methods for assessing metastasis risk have limitations.

Purpose of the Study:

• To develop a novel strategy for predicting lung cancer metastasis risk.
• To combine single-cell metabolic profiling of circulating tumor cells (CTCs) with advanced CTC isolation techniques.
• To identify distinct metabolic signatures associated with different metastatic potentials.

Main Methods:

• Development of a high-efficiency, high-viability CTC sorting and capture platform.
• Single-cell metabolomic profiling of 301 CTCs using nanoelectrospray ionization-atmospheric pressure chemical ionization mass spectrometry.
• Construction of a classification model based on identified metabolic profiles.

Main Results:

• Identification of 390 unique metabolites with distinct metabolic signatures linked to brain and bone metastasis.
• Classification model achieved AUCs of 0.74 for brain metastasis and 0.92 for bone metastasis.
• Prospective validation confirmed the model's accuracy in predicting one-year metastasis risk.

Conclusions:

• Single-cell metabolomics of CTCs offers a powerful tool for uncovering novel prognostic markers.
• This approach advances liquid biopsy towards qualitative analysis for personalized lung cancer management.
• The developed strategy represents a significant advancement in precision medicine for predicting metastasis and guiding treatment.