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. 2017 Oct 20;9(10):2117-2136.
doi: 10.18632/aging.101305.

Identification of Polymorphisms in Cancer Patients That Differentially Affect Survival With Age

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Free PMC article

Identification of Polymorphisms in Cancer Patients That Differentially Affect Survival With Age

Aoife Doherty et al. Aging (Albany NY). .
Free PMC article

Abstract

The World Health Organization predicts that the proportion of the world's population over 60 will almost double from 12% to 22% between 2015 and 2050. Ageing is the biggest risk factor for cancer, which is a leading cause of deaths worldwide. Unfortunately, research describing how genetic variants affect cancer progression commonly neglects to account for the ageing process. Herein is the first systematic analysis that combines a large longitudinal data set with a targeted candidate gene approach to examine the effect of genetic variation on survival as a function of age in cancer patients. Survival was significantly decreased in individuals with heterozygote or rare homozygote (i.e. variant) genotypes compared to those with a common homozygote genotype (i.e. wild type) for two single nucleotide polymorphisms (rs11574358 and rs4147918), one gene (SIRT3) and one pathway (FoxO signalling) in an age-dependent manner. All identified genes and pathways have previously been associated with ageing and cancer. These observations demonstrate that there are ageing-related genetic elements that differentially affect mortality in cancer patients in an age-dependent manner. Understanding the genetic determinants affecting prognosis differently with age will be invaluable to develop age-specific prognostic biomarkers and personalized therapies that may improve clinical outcomes for older individuals.

Keywords: SNP; WRN; ageing; genetics; geriatric oncology; longevity.

Conflict of interest statement

CONFLICTS OF INTEREST

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Kaplan Meier survival estimates of overall cancer survival for rs11574358 among the Framingham Heart Study according to a dominant genotype model for different age categories, in which the wild type is the dominant homozygote, and the variant is the heterozygote and the minor homozygote. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.
Figure 2
Figure 2
Kaplan Meier survival estimates of overall survival for GPX4 among the Framingham Heart Study according to a dominant genotype model, in which the wild type is the dominant homozygote, and the variant is the heterozygote and the minor homozygote. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.
Figure 3
Figure 3
Kaplan Meier survival estimates of overall survival for SIRT3 among the Framingham Heart Study according to a dominant genotype model, comparing patients with a high number of wild types to those with a low number of wild types. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.
Figure 4
Figure 4
Kaplan Meier survival estimates of overall survival for FoxO pathway among the Framingham Heart Study according to a dominant genotype model, comparing patients with a high number of wild types to those with a low number of wild types. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.

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References

    1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108. https://doi.org/10.3322/caac.21262 - DOI - PubMed
    1. Edwards BK, Noone AM, Mariotto AB, Simard EP, Boscoe FP, Henley SJ, Jemal A, Cho H, Anderson RN, Kohler BA, Eheman CR, Ward EM. Annual Report to the Nation on the status of cancer, 1975-2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer. Cancer. 2014;120:1290–314. https://doi.org/10.1002/cncr.28509 - DOI - PMC - PubMed
    1. de Magalhães JP. How ageing processes influence cancer. Nat Rev Cancer. 2013;13:357–65. https://doi.org/10.1038/nrc3497 - DOI - PubMed
    1. Meropol NJ, Schulman KA. Cost of cancer care: issues and implications. J Clin Oncol. 2007;25:180–86. https://doi.org/10.1200/JCO.2006.09.6081 - DOI - PubMed
    1. Luengo-Fernandez R, Leal J, Gray A, Sullivan R. Economic burden of cancer across the European Union: a population-based cost analysis. Lancet Oncol. 2013;14:1165–74. https://doi.org/10.1016/S1470-2045(13)70442-X - DOI - PubMed

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