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Cited by 364T-cell and B-cell ImmunologyGenetics, Aging, and Longevity in Model OrganismsArtificial Immune Systems Applications

The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.

Thymic involution and rising disease incidence with age

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Palmer, Sam, Albergante, Luca, Blackburn, Clare C., Newman, T. J.·2018·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1714478115PMC PMC5828591
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Abstract

Significance: Understanding the risk factors of carcinogenesis is a major goal of biomedical research. Historically, the focus has been on the role of somatic mutations, and the reason for cancer typically occurring late in life is predominantly attributed to a gradual accumulation of such mutations. We challenge that view and propose that the decline of the immune system is the primary reason why cancer is an age-related disease. The immunological model featured here captures risk profiles for many cancer types and infectious diseases, suggesting that therapies reversing T cell exhaustion or restoring T cell production will be promising avenues of treatment.

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Fig. 1. Declining T cell production leads to increasing disease incidence. Our model assumes that immunogenic cells arise with the same probability at any age, and, after a period of being targeted, the population may overwhelm the immune system by crossing an immune escape threshold. This threshold is assumed to be proportional to T cell production, which decreases with age. This provides a prediction for the possible forms of disease incidence curves.
Fig. 1. Declining T cell production leads to increasing disease incidence. Our model assumes that immunogenic cells arise with the same probability at any age, and, after a period of being targeted, the population may overwhelm the immune system by crossing an immune escape threshold. This threshold is assumed to be proportional to T cell production, which decreases with age. This provides a prediction for the possible forms of disease incidence curves.CC BY-NC-ND · PubMed Central
Fig. 2. Infectious disease incidence. Log-linear plots of incidence (per 100,000 person-years) by age group for all ABC bacterial infections, West Nile virus (WNV) disease, and Influenza A, ordered from best fit to worst. Bacterial and viral diseases are shaded yellow and green, respectively. The two-parameter IM-II is in red, while the one-parameter IM-I is in orange. Incidence often decreases initially from birth due to an underdeveloped immune system in infants; therefore, models are fitted only to data points for ages greater than 18 y. Error bars show 99% CIs for all diseases.
Fig. 2. Infectious disease incidence. Log-linear plots of incidence (per 100,000 person-years) by age group for all ABC bacterial infections, West Nile virus (WNV) disease, and Influenza A, ordered from best fit to worst. Bacterial and viral diseases are shaded yellow and green, respectively. The two-parameter IM-II is in red, while the one-parameter IM-I is in orange. Incidence often decreases initially from birth due to an underdeveloped immune system in infants; therefore, models are fitted only to data points for ages greater than 18 y. Error bars show 99% CIs for all diseases.CC BY-NC-ND · PubMed Central
Fig. 3. Cancer incidence. Log-linear plots of incidence (per 100,000 person-years). Data taken from SEER (11). (A and B) Some cancer types rise exponentially fitting IM-I (A), while some cancer types rise like power laws, although can still be fit by IM-II (B). Fitting curves for IM-II and PLM are shown in red and green, respectively. (C) The top 20 best-fitting incidence curves as measured by Akaike Information Criterion (AIC) for IM-II. (D and E) Universal scaling functions for all cancers with defined pivot ages (84 out of 101 cancer types) plotted in gray with the top 20 incidence curves highlighted. Data shown for both genders (D) and gender-separated data (E), with dotted lines showing the model predictions for IM-I and IM-II. The gender-separated curves are fitted with higher independently determined values for α in males than females, reflecting the gender bias in T cell production (Methods). A purely exponential incidence curve would correspond to a pivot age of negative infinity, and therefore, for the purposes of plotting, we set a minimum pivot age of −50 y. Models are fitted only for ages greater than 18 y. Error bars show 95% CIs for all diseases.
Fig. 3. Cancer incidence. Log-linear plots of incidence (per 100,000 person-years). Data taken from SEER (11). (A and B) Some cancer types rise exponentially fitting IM-I (A), while some cancer types rise like power laws, although can still be fit by IM-II (B). Fitting curves for IM-II and PLM are shown in red and green, respectively. (C) The top 20 best-fitting incidence curves as measured by Akaike Information Criterion (AIC) for IM-II. (D and E) Universal scaling functions for all cancers with defined pivot ages (84 out of 101 cancer types) plotted in gray with the top 20 incidence curves highlighted. Data shown for both genders (D) and gender-separated data (E), with dotted lines showing the model predictions for IM-I and IM-II. The gender-separated curves are fitted with higher independently determined values for α in males than females, reflecting the gender bias in T cell production (Methods). A purely exponential incidence curve would correspond to a pivot age of negative infinity, and therefore, for the purposes of plotting, we set a minimum pivot age of −50 y. Models are fitted only for ages greater than 18 y. Error bars show 95% CIs for all diseases.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Palmer, S., Albergante, L., Blackburn, C. C., & Newman, T. J.
Affiliations
  • University of Dundee (GB)
  • Inserm (FR)
  • MRC Centre for Regenerative Medicine (GB)
Corresponding author
  • Palmer, Sam
  • Newman, T. J.
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2018-02-05
Volume
115
Issue
8
Pages
1883-1888
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1714478115
PMC
PMC5828591
OpenAlex
W2788857602
Cited by
364
References
45
Citations by year
2026: 51 · 2025: 53 · 2024: 45 · 2023: 43 · 2022: 37 · 2021: 36 · 2020: 56 · 2019: 26 · 2018: 17
FWCI
15.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Discovery Research Portal (University of Dundee) (submittedVersion, cc-by-nc-nd)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
Funders
  • SULSA (U54 CA143682)
  • EC | Seventh Framework Programme (602587)
Topics
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
Keywords
thymic involution, immunosenescence, cancer incidence, infectious disease incidence, power law model, mutation accumulation, aging
MeSH terms
Aging, Female, Humans, Male, Models, Biological, Mutation, Neoplasms, Thymus Gland, Genetic Predisposition to Disease

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