Senescent Cells and Their Secretory Phenotype as Targets for Cancer Therapy
Open accessAbstract
Cancer is a devastating disease that increases exponentially with age. Cancer arises from cells that proliferate in an unregulated manner, an attribute that is countered by cellular senescence. Cellular senescence is a potent tumor-suppressive process that halts the proliferation, essentially irreversibly, of cells at risk for malignant transformation. A number of anti-cancer drugs have emerged that induce tumor cells to undergo cellular senescence. However, although a senescence response can halt the proliferation of cancer cells, the presence of senescent cells in tissues has been associated with age-related diseases, including, ironically, late-life cancer. Thus, anti-cancer therapies that can induce senescence might also drive aging phenotypes and age-related pathology. The deleterious effects of senescent cells most likely derive from their senescence-associated secretory phenotype or SASP. The SASP entails the secretion of numerous inflammatory cytokines, growth factors and proteases that can render the tissue microenvironment favorable for tumor growth. Here, we discuss the beneficial and detrimental effects of inducing cellular senescence, and propose strategies for targeting senescent cells as a means to fight cancer.
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Cells undergoing damage-induced senescence are often cleared by the immune system, as several SASP factors are cytokines and chemokines that can modulate the local immune environment.
“Cells undergoing damage-induced senescence are often cleared by the immune system, as several SASP factors are cytokines and chemokines that can modulate the local immune environment.2,3,5,70,71”
From Paracrine roles of cellular senescence in promoting tumourigenesis (2018) · cites this paper as [71]
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- Authors
- Velarde, M. C., Demaria, M., & Campisi, J.
- Affiliations
- Buck Institute for Research on Aging (US)
- Venue
- Interdisciplinary Topics in Gerontology and Geriatrics
- Publisher
- S. Karger AG
- Published
- 2013
- Volume
- 38
- Pages
- 17-27
- ISSN
- 2297-3486 (ISSN-L)
- 2297-3508
- PMC
- PMC4167737
- OpenAlex
- W1526274673
- Cited by
- 112
- References
- 78
- Citations by year
- 2025: 3 · 2024: 9 · 2023: 2 · 2022: 6 · 2021: 15 · 2020: 3 · 2019: 12 · 2018: 12 · 2017: 6 · 2016: 13 · 2015: 15 · 2014: 14 · 2013: 2
- FWCI
- 56.40
- Type
- book chapter
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Interdisciplinary topics in gerontology and geriatrics (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- PubMed Central (acceptedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- National Institute on Aging (R37AG009909, P01AG041122, P01AG017242)
- National Cancer Institute (R21CA170610)
- Topics
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Geriatrics and Gerontology › Sirtuins and Resveratrol in Medicine
- Keywords
- cellular senescence, senescence-associated secretory phenotype, cancer therapy, senolytics, tumor suppression, aging, age-related cancer, tumor microenvironment, inflammatory cytokines, senescent cells
- MeSH terms
- Aged, Clinical Trials as Topic, Cytokinins, DNA Damage, Drug Evaluation, Preclinical, Humans, Neoplasms, Peptide Hydrolases, Phenotype, Up-Regulation, Genes, Tumor Suppressor, Anticarcinogenic Agents, Cellular Senescence, Intercellular Signaling Peptides and Proteins, Systems Biology, Molecular Targeted Therapy, Tumor Microenvironment, Carcinogenesis
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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