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Tumour heterogeneity and metastasis at single-cell resolution
Open accessAbstract
Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.
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“Indeed, single-cell methods applied to cancer cells have described heterogeneity in terms of transcriptomics, epigenomics, metabolomics, and proteomics,5,6”
From Dynamic IL-6R/STAT3 signaling leads to heterogeneity of metabolic phenotype in pancreatic ductal adenocarcinoma cells (2024) · cites this paper as [6]
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In the citing paper
“evidence points to a substantial level of metabolic heterogeneity even in aggressive cancers”
From Dynamic IL-6R/STAT3 signaling leads to heterogeneity of metabolic phenotype in pancreatic ductal adenocarcinoma cells (2024) · cites this paper as [6]
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“Desynchronized dynamics would ration resources evenly, while maintaining a relatively “normal” bulk metabolic rate, as shown in primary tumors.”
From Dynamic IL-6R/STAT3 signaling leads to heterogeneity of metabolic phenotype in pancreatic ductal adenocarcinoma cells (2024) · cites this paper as [6]
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Metadata
- Authors
- Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N., & Werb, Z.
- Affiliations
- University of California, Irvine (US)
- University of California, San Francisco (US)
- Corresponding author
- Lawson, Devon A.
- Kessenbrock, Kai
- Venue
- Nature Cell Biology
- Published
- 2018-11-26
- Volume
- 20
- Issue
- 12
- Pages
- 1349-1360
- ISSN
- 1465-7392 (ISSN-L)
- 1476-4679
- PMC
- PMC6477686
- OpenAlex
- W2900850487
- Cited by
- 606
- References
- 157
- Citations by year
- 2026: 51 · 2025: 78 · 2024: 89 · 2023: 74 · 2022: 90 · 2021: 98 · 2020: 76 · 2019: 48 · 2018: 1
- FWCI
- 5.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Cell Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Cancer Institute (R00CA181490, U01CA199315, K22CA190511, U54CA217378, T32CA009054)
- National Institute of Biomedical Imaging and Bioengineering (T32EB009418)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Keywords
- tumor heterogeneity, metastasis, single-cell technologies, single-cell profiling, tumor progression, drug resistance, tumor cell subpopulations
- MeSH terms
- Humans, Neoplastic Cells, Circulating, Neoplasm Metastasis, Neoplasms, Biomarkers, Tumor, Gene Expression Regulation, Neoplastic, Genetic Heterogeneity, Single-Cell Analysis, Tumor Microenvironment
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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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