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Updated: Feb 8, 2026

An Orthotopic Murine Model of Human Prostate Cancer Metastasis
Published on: September 18, 2013
Dynamic CTC phenotypes in metastatic prostate cancer models visualized using magnetic ranking cytometry
Leyla Kermanshah1, Mahla Poudineh, Sharif Ahmed
1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada. shana.kelley@utoronto.ca.
Monitoring circulating tumor cells (CTCs) reveals dynamic phenotypic changes during cancer progression and chemotherapy. This phenotypic profiling can predict treatment response and monitor disease progression.
Area of Science:
- Oncology
- Biotechnology
- Cancer Research
Background:
- Circulating tumor cells (CTCs) are shed by tumors and are key to metastasis.
- Understanding CTC heterogeneity and dynamic phenotypic changes is vital for tracking cancer progression and treatment efficacy.
Purpose of the Study:
- To monitor dynamic phenotypic changes in CTCs during cancer progression and chemotherapy.
- To investigate the metastatic potential of CTCs and their response to treatment.
- To assess the utility of CTC phenotypic profiling for disease monitoring and predicting therapeutic outcomes.
Main Methods:
- Utilized magnetic ranking cytometry (MagRC) to profile phenotypic properties of CTCs.
- Monitored CTCs in mice xenografted with tumors of varying aggressiveness.
- Observed phenotypic changes during cancer progression and following chemotherapy.
Main Results:
- CTCs from metastatic xenografts exhibited dynamic and heterogeneous profiles, unlike static profiles in non-metastatic models.
- Chemotherapy in highly metastatic xenografts led to decreased CTC heterogeneity and reduced metastasis incidence.
- Phenotypic profiles of CTCs correlated with disease progression and treatment response.
Conclusions:
- Dynamic and heterogeneous CTC phenotypes are characteristic of metastatic cancer.
- Chemotherapy can reduce CTC heterogeneity and metastasis.
- CTC phenotypic profiling offers a valuable tool for real-time monitoring of cancer progression and predicting treatment response.
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