By: Sanna Heikkinen & Tomas Tanskanen
Many patients with cancer have coexisting diseases called comorbidities. Some comorbidities precede cancer diagnosis more often than might be expected by chance, which suggests a potential role in cancer development. A well-known example is inflammatory bowel disease complicated by colorectal cancer. Moreover, comorbidity can have a major impact on the prognosis of a patient with cancer. Cancers and comorbidities may or may not interact with each other, and the possible relationships between them can result from a variety of shared or unique factors.
The Age Factor
Age increases the risk of many cancers and chronic diseases. Therefore, as populations age, we expect to see a rising number of patients with cancer and comorbidities. Finland has one the oldest populations in Europe, and the share of persons over 65 years of age is expected to further increase in the future. Currently, the median age at cancer diagnosis is around 70 years, and at the end of 2020, there were already over 300,000 persons living with a previous diagnosis of cancer. Due to earlier diagnosis and improved cancer care, more and more of the cases can be cured or brought into remission. With improvements in survival and aging of cancer patients, the number of patients with complex clinical problems is likely to increase as cancer is more often a chronic, long-term disease, and many patients also have other age-related conditions.
The Finnish Center of Excellence in Tumor Genetics Cohort Study
Many diseases are known to affect the risk of cancer, but systematic scientific research on the topic is lacking. To respond to this need, the Finnish Center of Excellence in Tumor Genetics (CoETG) has designed a population-based cohort study with 2.5 million Finns, including data on their cancers and other diseases over several decades. By linking data from the Finnish Cancer Registry and the Care Register for Health Care, we have already identified 100,000 pairs of cancers and non-cancerous diseases for further investigation.
Working with Complex Observational Data
The study is large enough to detect associations even between rare diseases and subsequent cancer. Long-term follow-up permits the detection of increased cancer risk after a latency of years or decades. Multivariable statistical models will be used to study the occurrence of cancer in persons with or without previous medical conditions. Even after accounting for known confounders and random variability, however, we cannot achieve certainty over the nature of the observed relationships. For example, persons with known medical conditions are more likely to undergo medical examinations and diagnostic tests than those without, which can lead to surveillance bias. In some patients, increased cancer risk may be due to treatments such as cytotoxic agents or immunosuppressants. Although causal relationships may be complex, the identification of previously unknown disease associations can benefit the scientific community by highlighting new research areas, while also bringing direct benefit to patients in the form of counselling and monitoring.
After extensive statistical analysis, we aim to identify the relevant tumor samples through accurate, reliable registries and investigate their genetic and other biological characteristics. This may advance our understanding of the pathogenesis of comorbidity-related cancers. A better understanding of the role of comorbidity in carcinogenesis is important for cancer prevention in patient populations and may also suggest hypotheses for the development of personalized cancer treatments. Large population-based studies spanning nearly all cancers and comorbidities are rarely possible. The CoETG is thankful for the opportunity to conduct this study, and we are eagerly looking forward to publishing the first results on autoimmune diseases and cancer.
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