Tumour heterogeneity and metastasis at single-cell resolution

Lawson, Devon A.; Kessenbrock, Kai; Davis, Ryan T.; Pervolarakis, Nicholas; Werb, Zena

doi:10.1038/s41556-018-0236-7

Cited by 618Single-cell and spatial transcriptomicsCancer Genomics and DiagnosticsCancer Cells and Metastasis

Tumour heterogeneity and metastasis at single-cell resolution

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Lawson, Devon A., Kessenbrock, Kai, Davis, Ryan T., Pervolarakis, Nicholas, Werb, Zena·2018·Nature Cell Biology

DOI 10.1038/s41556-018-0236-7PMC PMC6477686

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Abstract

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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Single-cell methods applied to cancer cells have described heterogeneity in transcriptomics, epigenomics, metabolomics, and proteomics.
“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]
The review describes transcriptomic, epigenetic and proteomic differences between cancer cells (opening section, page unavailable), and its diagnostics section reports heterogeneity detected by single-cell RNA-seq and mass cytometry. It does not describe single-cell metabolomic heterogeneity in cancer cells; searches for “metabolomics” and “metabolite” found no supporting passage. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Evidence points to a substantial level of metabolic heterogeneity even in aggressive cancers.
“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]
The Abstract describes pervasive heterogeneity in human cancers, supporting the general point. The section ‘Non-genetic heterogeneity and cellular differentiation’ mentions differences in metabolic state among cells in normal tissues, but does not establish substantial metabolic heterogeneity in aggressive cancers. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Desynchronized dynamics would ration resources evenly, while maintaining a relatively normal bulk metabolic rate, as shown in primary tumors.
“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]
The Non-genetic heterogeneity and cellular differentiation section (page unavailable) mentions differences in cellular metabolic state, but does not discuss desynchronized dynamics, even resource allocation, or a normal bulk metabolic rate in primary tumours. Searches for those terms and related concepts found no passage addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured

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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
ORCID: Lawson, Devon A.
Kessenbrock, Kai
Davis, Ryan T.
Pervolarakis, Nicholas
Werb, Zena
Venue: Nature Cell Biology
Publisher: Springer Science and Business Media LLC
Published: 2018-11-26
Volume: 20
Issue: 12
Pages: 1349-1360
ISSN: 1465-7392 (ISSN-L)
1476-4679
DOI: 10.1038/s41556-018-0236-7
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 · 2015: 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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Tumour heterogeneity and metastasis at single-cell resolution

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