CoChat
Sign Up
Cited by 348Cancer Genomics and DiagnosticsGenetics, Aging, and Longevity in Model OrganismsEvolution and Genetic Dynamics

The DOI matches its Crossref record; 113 of the 115 references it cites were found in a registry or an index.

Cancer as a disease of old age: changing mutational and microenvironmental landscapes

Open access
Laconi, Ezio, Marongiu, Fabio, DeGregori, James·2020·British Journal of Cancer
DOI 10.1038/s41416-019-0721-1PMC PMC7109142
Open this paper in CoChat — free accountOpen PDF

Abstract

Why do we get cancer mostly when we are old? According to current paradigms, the answer is simple: mutations accumulate in our tissues throughout life, and some of these mutations contribute to cancers. Although mutations are necessary for cancer development, a number of studies shed light on roles for ageing and exposure-dependent changes in tissue landscapes that determine the impact of oncogenic mutations on cellular fitness, placing carcinogenesis into an evolutionary framework. Natural selection has invested in somatic maintenance to maximise reproductive success. Tissue maintenance not only ensures functional robustness but also prevents the occurrence of cancer through periods of likely reproduction by limiting selection for oncogenic events in our cells. Indeed, studies in organisms ranging from flies to humans are revealing conserved mechanisms to eliminate damaged or oncogenically initiated cells from tissues. Reports of the existence of striking numbers of oncogenically initiated clones in normal tissues and of how this clonal architecture changes with age or external exposure to noxious substances provide critical insight into the early stages of cancer development. A major challenge for cancer biology will be the integration of these studies with epidemiology data into an evolutionary theory of carcinogenesis, which could have a large impact on addressing cancer risk and treatment.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41416-019-0721-1

Cites

113 of 113 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Age-dependent incidence for the most common cancers and leukaemias. The incidence of the five most common cancers (excluding skin cancers) and the two most common leukaemias in the United States from 2012 to 2016. Data are from the National Cancer Institute (www.seer.cancer.gov). a Absolute incidence per 100,000 people per year by age. b Normalised incidence was derived by first subtracting the minimum value from each value in a data set (to set the minimum to zero on the Y-scale) and then dividing the resulting values by the maximal (of those resulting) in the data set (removes vertical scale).
Fig. 1 Age-dependent incidence for the most common cancers and leukaemias. The incidence of the five most common cancers (excluding skin cancers) and the two most common leukaemias in the United States from 2012 to 2016. Data are from the National Cancer Institute (www.seer.cancer.gov). a Absolute incidence per 100,000 people per year by age. b Normalised incidence was derived by first subtracting the minimum value from each value in a data set (to set the minimum to zero on the Y-scale) and then dividing the resulting values by the maximal (of those resulting) in the data set (removes vertical scale).CC BY · PubMed Central
Fig. 2 How differences in age-related tissue landscapes influence cancer. Quality-control mechanisms ensure maintenance of cell fitness throughout reproductive ages (YOUNG; left panel). This maintenance results both from efficient purging of altered cells and from a low probability of their selection in a young and healthy tissue landscape. These two mechanisms reinforce each other. By contrast, quality-control mechanisms wane in post-reproductive ages (OLD, right panel). This decline implies that more altered cells accumulate and there is an increased likelihood for their selection when clones possess mutations that are adaptive in the aged tissue landscape. Again, inefficient elimination of altered cells and reductions in cellular fitness reinforce each other.
Fig. 2 How differences in age-related tissue landscapes influence cancer. Quality-control mechanisms ensure maintenance of cell fitness throughout reproductive ages (YOUNG; left panel). This maintenance results both from efficient purging of altered cells and from a low probability of their selection in a young and healthy tissue landscape. These two mechanisms reinforce each other. By contrast, quality-control mechanisms wane in post-reproductive ages (OLD, right panel). This decline implies that more altered cells accumulate and there is an increased likelihood for their selection when clones possess mutations that are adaptive in the aged tissue landscape. Again, inefficient elimination of altered cells and reductions in cellular fitness reinforce each other.CC BY · PubMed Central
Fig. 3 Decoy fitness peaks and tumour suppression. a Hypothetical evolutionary fitness landscapes depict the relationships between genotype/epigenotype and fitness (shown here for somatic cell fitness). The x–y plane represents potential genetically and epigenetically encoded somatic cell phenotypes. Genetic changes can either decrease (downhill) or increase (uphill) fitness. We have previously proposed that on a young fitness landscape ‘wild-type’ (WT) cells occupy a high peak, as evolution over millions of years has optimised stem and progenitor cell adaptation to their tissue niche.48 Thus phenotypic change resulting from genetic or epigenetic mutations will mostly result in cells with reduced fitness, thus disfavouring evolution towards malignancy. We further propose that some somatic mutations (such as in Notch1) can create ‘decoy peaks’, which confer low risk of further progression to cancer. Progression up the decoy peak may be limited by the required passage through lower fitness intermediates, but the small size of epithelial progenitor pools could facilitate such transitions through neutral drift. Alternatively, a single mutation, such as in Notch1, could mediate the ‘jump’ to the other peak. b By middle age, fitness landscapes engender greater selection for the phenotypes that occupy decoy peaks (often with Notch1 mutations); while partially transformed, cells on these decoy peaks are more benign with reduced malignant potential. At older ages, further tissue degradation and damage accumulation should result in a landscape that increases the odds of mutational adaptation towards both benign (decoy) and more malignant phenotypes. Arrow thickness reflects hypothetical probabilistic phenotypic and fitness effects of mutation. Note that for simplicity this model does not incorporate roles for ageing-dependent mutation accumulation, which should clearly contribute to cancer risk with age. We also note that, while experimental and observational data support changes in somatic selection in aging tissues, the shapes of these landscapes are hypothetical. Figure and legend were modified from Higa and DeGregori104 under a CC-BY licence.
Fig. 3 Decoy fitness peaks and tumour suppression. a Hypothetical evolutionary fitness landscapes depict the relationships between genotype/epigenotype and fitness (shown here for somatic cell fitness). The x–y plane represents potential genetically and epigenetically encoded somatic cell phenotypes. Genetic changes can either decrease (downhill) or increase (uphill) fitness. We have previously proposed that on a young fitness landscape ‘wild-type’ (WT) cells occupy a high peak, as evolution over millions of years has optimised stem and progenitor cell adaptation to their tissue niche.48 Thus phenotypic change resulting from genetic or epigenetic mutations will mostly result in cells with reduced fitness, thus disfavouring evolution towards malignancy. We further propose that some somatic mutations (such as in Notch1) can create ‘decoy peaks’, which confer low risk of further progression to cancer. Progression up the decoy peak may be limited by the required passage through lower fitness intermediates, but the small size of epithelial progenitor pools could facilitate such transitions through neutral drift. Alternatively, a single mutation, such as in Notch1, could mediate the ‘jump’ to the other peak. b By middle age, fitness landscapes engender greater selection for the phenotypes that occupy decoy peaks (often with Notch1 mutations); while partially transformed, cells on these decoy peaks are more benign with reduced malignant potential. At older ages, further tissue degradation and damage accumulation should result in a landscape that increases the odds of mutational adaptation towards both benign (decoy) and more malignant phenotypes. Arrow thickness reflects hypothetical probabilistic phenotypic and fitness effects of mutation. Note that for simplicity this model does not incorporate roles for ageing-dependent mutation accumulation, which should clearly contribute to cancer risk with age. We also note that, while experimental and observational data support changes in somatic selection in aging tissues, the shapes of these landscapes are hypothetical. Figure and legend were modified from Higa and DeGregori104 under a CC-BY licence.CC BY · PubMed Central
Fig. 4 Strategies to target neoplastic-prone tissue landscapes. Various evolution-informed strategies can be envisioned to limit cancer incidence and to improve treatment outcomes. For cancer prevention, the ideas and evidence presented here indicate that the selection for malignant clones can be countered by the preservation of a younger tissue landscape (e.g. through dietary interventions, reducing age-associated inflammation). In addition, while more speculative, interventions that mimic decoy fitness peaks, such as by providing adaptive but non-malignant changes to cells in aged tissues, could potentially delay cancer evolution. For therapeutic interventions for the treatment of a metastatic cancer, recent studies indicate the ability of less aggressive and/or more therapy-sensitive cancer cell populations to limit the dominance of more malignant and therapy-resistant clones, which are associated with a worse prognosis.
Fig. 4 Strategies to target neoplastic-prone tissue landscapes. Various evolution-informed strategies can be envisioned to limit cancer incidence and to improve treatment outcomes. For cancer prevention, the ideas and evidence presented here indicate that the selection for malignant clones can be countered by the preservation of a younger tissue landscape (e.g. through dietary interventions, reducing age-associated inflammation). In addition, while more speculative, interventions that mimic decoy fitness peaks, such as by providing adaptive but non-malignant changes to cells in aged tissues, could potentially delay cancer evolution. For therapeutic interventions for the treatment of a metastatic cancer, recent studies indicate the ability of less aggressive and/or more therapy-sensitive cancer cell populations to limit the dominance of more malignant and therapy-resistant clones, which are associated with a worse prognosis.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

1 retracted

Show all 115 references

Research works printed but not linked: 1

  1. Martincorena, I., Fowler, J. C., Wabik, A., Lawson, A. R. J., Abascal, F., Hall, M. W. J. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018). · DOI · Paper page

Cited by

Sorted by citations

Items 201–300 of 347

Related

See all

Metadata

Authors
Laconi, E., Marongiu, F., & DeGregori, J.
Affiliations
  • University of Cagliari (IT)
  • University of Colorado Anschutz (US)
Corresponding author
  • Laconi, Ezio
  • DeGregori, James
ORCID
Venue
British Journal of Cancer
Publisher
Springer Science and Business Media LLC
Published
2020-02-11
Volume
122
Issue
7
Pages
943-952
ISSN
  • 0007-0920 (ISSN-L)
  • 1532-1827
DOI
10.1038/s41416-019-0721-1
PMC
PMC7109142
OpenAlex
W3005392003
Cited by
348
References
115
Citations by year
2026: 41 · 2025: 68 · 2024: 80 · 2023: 59 · 2022: 57 · 2021: 29 · 2020: 13
FWCI
4.03
Type
journal article
Language
en
Open access
Licence
Locations
  • British Journal of Cancer (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
Funders
  • Associazione Italiana per la Ricerca sul Cancro (10604)
  • U.S. Department of Health & Human Services | National Institutes of Health (U01AG066099)
  • American Association for Cancer Research (18-90-52-DEGR)
  • J.D. is also supported by the Courtenay C. and Lucy Patten Davis Endowed Chair in Lung Cancer Research.
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
somatic mutations, tumor microenvironment, clonal architecture, tissue maintenance, natural selection, oncogenic mutations, cell fitness, carcinogenesis, cancer risk
MeSH terms
Aged, Aged, 80 and over, Aging, Biological Evolution, Female, Humans, Male, Middle Aged, Mutation, Neoplasms, Tumor Microenvironment

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 ›

Are you an author, or do you see a problem with this page? Request a review