Abstract

Simple SummaryAs life expectancy is increasing, the older population is rapidly growing. However, older patients with cancer are still underrepresented in clinical trials, making treatment of these patients challenging for oncologists. Robust biomarkers that reflect the body’s biological age can be helpful to provide older patients with cancer with an optimal personalized treatment. However, to be able to identify such biomarkers, more in-depth research is needed in this underexplored population. In this review, we have put together the current knowledge concerning the mechanistic connections between aging and cancer, as well as aging biomarkers that could be useful in the field of geriatric oncology.Age is one of the main risk factors of cancer; several biological changes linked with the aging process can explain this. As our population is progressively aging, the proportion of older patients with cancer is increasing significantly. Due to the heterogeneity of general health and functional status amongst older persons, treatment of cancer is a major challenge in this vulnerable population. Older patients often experience more side effects of anticancer treatments. Over-treatment should be avoided to ensure an optimal quality of life. On the other hand, under-treatment due to fear of toxicity is a frequent problem and can lead to an increased risk of relapse and worse survival. There is a delicate balance between benefits of therapy and risk of toxicity. Robust biomarkers that reflect the body’s biological age may aid in outlining optimal individual treatment regimens for older patients with cancer. In particular, the impact of age on systemic immunity and the tumor immune infiltrate should be considered, given the expanding role of immunotherapy in cancer treatment. In this review, we summarize current knowledge concerning the mechanistic connections between aging and cancer, as well as aging biomarkers that could be helpful in the field of geriatric oncology.

Highlights

  • As life expectancy has increased dramatically over the past decades, older persons represent a rapidly growing section of our population

  • The immune system has never been exposed to these neoantigens and with the shrinkage of the naive T-cell pool associated with immunosenescence, immunotherapies targeting these antigens might be less effective in older patients

  • Several other genes have been linked to the aging process, such as low density lipoprotein receptor-related protein 1B (LRP1B), paraoxonase 1 (PON1), ‘ataxia telangiectasia mutated’ (ATM) gene, p21/CDKN1A gene, p53 protein, insulin/insulin‐like growth factor (IGF)-1 signaling (IIS) components, telomerase RNA component (TERC), IL-1 gene, IL-6 gene, Toll-like receptors genes (TLR) [77]

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Summary

Introduction

As life expectancy has increased dramatically over the past decades, older persons represent a rapidly growing section of our population This results in an increasing number of older patients with cancer as well. There is an accumulation of oxidative stress and DNA damage over the years that is caused by a life-long exposure to endogenous metabolic insults (e.g., free radicals) and exogenous factors (e.g., UV irradiation, foods, etc.) This may eventually lead to cell transformation and tumor initiation. There is an urgent need for better tools to select cancer patients for specific therapies These tools should be prognostic, providing information on the patient’s life expectancy, and predictive for the therapeutic benefit that will be achieved by the treatment. We discuss the interplay between aging and cancer based on the current literature, together with potential promising biomarkers that could be useful in geriatric oncology

Mechanistic Interface between Aging and Cancer
Cellular Damage and DNA Damage Response
Immunosenescence
Innate Immunosenescence
Clinical Implications of Immunosenescence
Treatment of Older Patients with Cancer
Aging Biomarkers
Gene Expression
Single Nucleotide Polymorphism
DNA Methylation Profiles
Telomere Attrition
Oxidative Stress Markers
Proteostasis
Markers of Inflammation
3.10. Shifts in Immune Cell Subpopulations
3.11. Markers of Cellular Senescence
3.12. Circadian Clock
3.14. Microbiome
3.15. Biomarker Panels
Findings
Conclusions

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