How Cancer Tumors Are Different In Older and Younger People

Cancer is an age-related disease. In fact aging is the most important risk factor for cancer overall and for many individual types of cancer. The median age at diagnosis for cervical cancer is 50, breast cancer is 62, ovarian cancer is 63, melanoma is 65, prostate cancer is 66, colon cancer is 67, pancreatic cancer 70, lung cancer is 71, and bladder cancer is 73.

Cancer incidence by age at diagnosis

• People under 20 years - 25 cancer cases per 100,000
• People 45 to 49 years - 350 cancer cases per 100,000
• People over 60 years - 1,000 cancer cases per 100,000

Incidence rates by age at diagnosis, all cancer types, all races, both sexes. Image source National Cancer Institute

Researcher Summarize Major Findings In New Paper

Cancer tumors in older patients are different from tumors in younger patients. A new paper published by scientists João Pedro de Magalhães, Kasit Chatsirisupachai, and Cyril Lagger summarizes the main findings in recent studies and highlight some of the differences between cancer in younger and older patients. The open access paper entitled Age-associated differences in the cancer molecular landscape was published in Trends in Cancer on July 7, 2022.

The authors summarize major findings in four independent studies on age-associated genomic, transcriptomic, and epigenomic patterns and discuss potential aging processes that might contribute to these differences in cancer molecular landscape. They describe how some cancer driver genes are mutated more frequently in younger people while others are mutated more frequently in older people, and discuss how aging shapes differences in the development of cancer. They also provide insights that may be relevant in treating various types of cancer using personalized medicine. These three figures from the paper illustrate some of the recent findings.

Figure 1

This figure shows examples of cancer driver genes that display age-associated patterns in somatic mutations.

• Driver genes in blue represent genes that are mutated more frequently in younger patients.
• Driver genes in red represent genes that are mutated more frequently in older patients.

Figure 2

This figure shows the contribution of the aging tissue microenvironment to cancer. 

• Aging is associated with changes in the tissue microenvironment, many of which have been shown to promote cancer progression.
• These processes include immune system aging, accumulation of senescent cells which secrete inflammatory cytokines, reorganization of the extracellular matrix and changes in circulatory factors such as hormones.

Note: NK cells = natural killer cells, SASP= senescence-associated secretory phenotype

Figure 3

This figure shows how tissue microenvironment changes with age may contribute to the selection of cancer clones with distinct phenotypes.

• One potential explanation may be that tissue microenvironment changes during aging might alter local selection pressures to favor tumor clones driven by different oncogenic driver events.

Directions For Future Research

1. What drives the differences in cancer molecular landscape according to patient’s age?
2. How do age-related changes in the microenvironment, such as cellular senescence, shape the age-related molecular landscape of cancer? And how does it relate to clinical outcomes?
3. How do cell–cell communications in the tumor microenvironment, such as through ligand–receptor interactions and through small vesicles like exosomes, differ according to age?
4. What are the differences in tumor immune landscape according to age, and how do these differences affect response to immunotherapy?
5. How does age influence metastatic patterns of cancer?

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Copyright © 2022 Margaretta Colangelo. All Rights Reserved.

This article was written by Margaretta Colangelo. Margaretta is a leading AI analyst who tracks significant milestones in AI in healthcare. She's consulting at AI healthcare companies and she writes about some of the companies she's consulting with. Margaretta serves on the advisory board of the AI Precision Health Institute at the University of Hawaiʻi Cancer Center @realmargaretta