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Updated: Jun 24, 2025

Isolation and Functional Assessment of Human Breast Cancer Stem Cells from Cell and Tissue Samples
Published on: October 2, 2020
The transcriptional landscape of cancer stem-like cell functionality in breast cancer
Oana Baldasici1,2, Olga Soritau1, Andrei Roman3
1Department of Genetics, Genomics and Experimental Pathology, The Oncology Institute "Prof. Dr. Ion Chiricuță", Cluj-Napoca, Romania.
Cancer stem-like cells (CSCs) drive breast cancer relapse and resistance. This study links CSC functionality to the tumor microenvironment (TME), finding immune-suppressed TMEs in CSC-rich tumors, and identifies key genes with prognostic value.
Area of Science:
- Oncology
- Cancer Biology
- Immunology
Background:
- Cancer stem-like cells (CSCs) are implicated in breast cancer therapy resistance and relapse.
- Assessing CSCs in solid tumors is challenging due to a lack of specific markers and their phenotypic plasticity.
- The tumor microenvironment (TME) significantly influences CSC phenotype, driving resistance and metastasis.
Purpose of the Study:
- To investigate the relationship between CSC functionality and the tumor microenvironment (TME) in breast cancer.
- To identify molecular markers associated with CSC phenotype and prognostic value.
Main Methods:
- Utilized in vitro mammosphere formation assays.
- Performed bulk tumor microarray profiling.
- Applied deconvolution algorithms to analyze a cohort of 125 breast tumors.
Main Results:
- A significant association was found between the TME score and CSC functionality.
- CSC-rich tumors exhibited an immune-suppressed TME, contrasting with immune-infiltrated tumors lacking CSC potential.
- Gene expression analysis identified IFNG, CXCR5, CD40LG, TBX21, and IL2RG as associated with the CSC phenotype and patient prognosis.
Conclusions:
- Characterizing CSC content and functionality offers a strategy to refine breast cancer treatments.
- This approach can guide clinical decisions to enhance patient therapy response.
- Understanding the CSC-TME interplay is crucial for improving treatment outcomes.
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