EMT, CSCs, and drug resistance: the mechanistic link and clinical implications
Open accessAbstract
The success of anticancer therapy is usually limited by the development of drug resistance. Such acquired resistance is driven, in part, by intratumoural heterogeneity - that is, the phenotypic diversity of cancer cells co-inhabiting a single tumour mass. The introduction of the cancer stem cell (CSC) concept, which posits the presence of minor subpopulations of CSCs that are uniquely capable of seeding new tumours, has provided a framework for understanding one dimension of intratumoural heterogeneity. This concept, taken together with the identification of the epithelial-to-mesenchymal transition (EMT) programme as a critical regulator of the CSC phenotype, offers an opportunity to investigate the nature of intratumoural heterogeneity and a possible mechanistic basis for anticancer drug resistance. In fact, accumulating evidence indicates that conventional therapies often fail to eradicate carcinoma cells that have entered the CSC state via activation of the EMT programme, thereby permitting CSC-mediated clinical relapse. In this Review, we summarize our current understanding of the link between the EMT programme and the CSC state, and also discuss how this knowledge can contribute to improvements in clinical practice.
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The link between EMT and drug resistance is well established.
“While the link between EMT and drug resistance is well established (Shibue and Weinberg, 2017), our CRISPR screen provided functional evidence for several integrin-related genes, which may represent additional druggable vulnerabilities.”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Shibue and Weinberg, 2017
Mitotic catastrophe is a major mechanism of action of anticancer chemotherapeutics, reflecting increased resistance of neoplastic cells to intrinsic apoptosis.
“However, mitotic catastrophe appears to constitute a major mechanism of action of anticancer chemotherapeutics, mostly reflecting the increased resistance of neoplastic cells to the induction of intrinsic apoptosis [1004–1009].”
From Molecular mechanisms of cell death: recommendations of the nomenclature committee on cell death 2018. (2018) · cites this paper as [1006]
Most human carcinomas have a majority of tumor cells that keep epithelial features, while some cells, mainly at invasive fronts, lose these features via EMT.
“In most human carcinomas, the majority of tumor cells retain epithelial characteristics, including epithelial cell junctions and gene expression signatures, whereas some cells, mostly at invasive fronts, attenuate these characteristics through a transdifferentiation process, named epithelial-mesenchymal transition (EMT)34–37.”
From TGFβ biology in cancer progression and immunotherapy (2021) · cites this paper as [34]
TGF-β promotes epithelial plasticity and EMT, which represses epithelial characteristics and promotes a mesenchymal phenotype, increasing motility and invasion.
“An increasingly appreciated response to TGF-β signaling in normal and transformed epithelial cells is the epithelial plasticity response, which represses epithelial characteristics and promotes transdifferentiation toward a mesenchymal phenotype (Figure 3). This EMT process results in deconstruction of epithelial cell-cell junctions and reorientation of apical-basal polarity toward a front-rear”
From TGFβ biology in cancer progression and immunotherapy (2021) · cites this paper as [34]
In multiple carcinoma types, EMT raises the count of cells with cancer stem cell features.
“In a variety of carcinomas, EMT increases the number of carcinoma cells with stem cell properties, i.e. cancer stem cells (CSCs)34,37,127,137.”
From TGFβ biology in cancer progression and immunotherapy (2021) · cites this paper as [34]
In mice, breast carcinoma cells with mesenchymal characteristics are resistant to checkpoint blockade, while largely epithelial cells are more susceptible.
“Accordingly, in mice, breast carcinoma cells with mesenchymal characteristics show resistance to checkpoint blockades, and those that remain largely epithelial are more susceptible34,141,143.”
From TGFβ biology in cancer progression and immunotherapy (2021) · cites this paper as [34]
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- Authors
- Shibue, T., & Weinberg, R. A.
- Affiliations
- Whitehead Institute for Biomedical Research (US)
- Corresponding author
- Weinberg, Robert A.
- Venue
- Nature Reviews Clinical Oncology
- Published
- 2017-04-11
- Volume
- 14
- Issue
- 10
- Pages
- 611-629
- ISSN
- 1759-4774 (ISSN-L)
- 1759-4782
- PMC
- PMC5720366
- OpenAlex
- W2606001457
- Cited by
- 2,539
- References
- 210
- Citations by year
- 2026: 139 · 2025: 223 · 2024: 289 · 2023: 344 · 2022: 356 · 2021: 374 · 2020: 324 · 2019: 273 · 2018: 159 · 2017: 55 · 2013: 1
- FWCI
- 23.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- Breast Cancer Research Foundation
- Samuel Waxman Cancer Research Foundation
- National Institutes of Health (CA080111)
- Japan Society for the Promotion of Science
- National Cancer Institute (P01CA080111)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › TGF-β signaling in diseases
- Health Sciences › Medicine › Genetics › Mesenchymal stem cell research
- Keywords
- epithelial-mesenchymal transition, cancer stem cells, drug resistance, intratumoral heterogeneity, metastasis, tumor recurrence, therapeutic targeting
- MeSH terms
- Animals, Antineoplastic Agents, Humans, Neoplasms, Neoplastic Stem Cells, Signal Transduction, Drug Resistance, Neoplasm, Epithelial-Mesenchymal Transition
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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