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Updated: Sep 30, 2025
Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
Published on: November 17, 2023
Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis
Gareth Morrison1, Jonathan Buckley2,3, Dejerianne Ostrow3
1Division of Medical Oncology, Department of Medicine and Department of Biochemistry & Molecular Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA.
Integrating liquid biopsies with imaging offers a comprehensive, non-invasive approach for metastatic castration-resistant prostate cancer (mCRPC) profiling. This multi-parametric analysis combines circulating tumor cells (CTCs), cell-free DNA (cfDNA), and radiomics for advanced disease modeling.
Area of Science:
- Oncology
- Genomics
- Radiology
Background:
- Metastatic castration-resistant prostate cancer (mCRPC) management benefits from comprehensive non-invasive monitoring.
- Liquid biopsies (circulating tumor cells [CTCs] and cell-free DNA [cfDNA]) offer promising avenues for disease profiling.
- Integrating multiple data types may enhance understanding of mCRPC heterogeneity.
Purpose of the Study:
- To evaluate the feasibility of concurrent cellular and molecular analysis of CTCs and cfDNA.
- To combine liquid biopsy data with radiomic analysis of CT scans in mCRPC patients.
- To assess the potential of a multi-parametric approach for non-invasive disease modeling.
Main Methods:
- Enumeration and machine learning-based clustering of CTCs from 22 mCRPC patients.
- Targeted sequencing of DNA from single CTCs, cfDNA, and buffy coats.
- Radiomic analysis of bone metastases from CT scans.
- Correlation analysis between cellular, molecular, and radiomic data.
Main Results:
- CTCs were detected in 77% of patients and clustered reproducibly.
- High sensitivity (98.8%) for cfDNA germline variant detection.
- Somatic variants detected in cfDNA (45%) and CTCs (92%).
- Radiomic signature correlated strongly with CTC count and cfDNA levels.
Conclusions:
- Concurrent cellular, molecular, and radiomic analysis is feasible in mCRPC.
- This multi-parametric approach yields complementary, non-invasive disease profiles.
- Integration enables more comprehensive non-invasive disease modeling and prediction.
