Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Single-cell Transcriptomes
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Abstract
Many behaviors of cancer, such as progression, metastasis and drug resistance etc., cannot be fully understood by genetic mutations or intracellular signaling alone. Instead, they are emergent properties of the cell community which forms a tumor. Studies of tumor heterogeneity reveal that many cancer behaviors critically depend on intercellular communication between cancer cells themselves and between cancer-stromal cells by secreted signaling molecules (ligands) and their cognate receptors. We analyzed public cancer transcriptome database for changes in cell-cell interactions as the characteristic of malignancy. We curated a list (>2,500 ligand-receptor pairs) and identified their joint enrichment in tumors from TCGA pan-cancer data. From single-cell RNA-Seq data for a case of melanoma and the specificity of the ligand-receptor interactions and their gene expression measured in individual cells, we constructed a map of a cell-cell communication network that indicates what signal is exchanged between which cell types in the tumor. Such networks establish a new formal phenotype of cancer which captures the cell-cell communication structure - it may open new opportunities for identifying molecular signatures of coordinated behaviors of cancer cells as a population - in turn may become a determinant of cancer progression potential and prognosis.
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Ligand-receptor pair labels can be obtained from the recent CellPhoneDB and used to interpret the highly expressed genes across clusters using statistical models.
“Ligand–receptor pair labels can be obtained from the recent CellPhoneDB (Vento‐Tormo et al, 2018) and used to interpret the highly expressed genes across clusters using statistical models (Zepp et al, 2017; Zhou et al, 2017; Cohen et al, 2018; Vento‐Tormo et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Zhou et al, 2017
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Metabolic Cross Talk in the Tumor Microenvironment
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- Authors
- Zhou, J. X., Taramelli, R., Pedrini, E., Knijnenburg, T., & Huang, S.
- Affiliations
- Institute for Systems Biology (US)
- University of Insubria (IT)
- Corresponding author
- Huang, Sui
- Venue
- Scientific Reports
- Published
- 2017-08-18
- Volume
- 7
- Issue
- 1
- Pages
- 8815-8815
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5562796
- OpenAlex
- W2949225171
- Cited by
- 85
- References
- 70
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 3 · 2023: 13 · 2022: 16 · 2021: 15 · 2020: 14 · 2019: 10 · 2018: 4
- FWCI
- 3.13
- Type
- journal article
- Language
- en
- Update notices
- correction (2018-12-12)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- IrInSubria (University of Insubria) (submittedVersion)
- Funders
- University of California, San Francisco
- National Institute of General Medical Sciences (R01GM109964, P50GM076547)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- 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
- Keywords
- intercellular communication, ligand-receptor interactions, cell-cell communication networks, tumor heterogeneity, tumor stroma, melanoma, single-cell transcriptomics, tumor progression, cancer prognosis
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