Year: 2025
Type: article
Abstract: Predicting cancer risk with computational biology Andrew Teschendorff, Professor at the Chinese Academy of Sciences, is developing computational systems-biological tools to identify cells at risk of turning cancerous. Predicting an individual’s risk of developing cancer is a huge challenge. With the exception of pediatric cancers and some adult cancer types that are caused by inherited genetic mutations (e.g., BRCA1 mutations in breast cancer), the great majority of cancer types are believed to be caused by genetic mutations that are acquired as we age, following prolonged exposure to cancer risk factors like smoking, inflammation or viral infections. These genetic mutations alter the underlying DNA sequence, altering gene and protein function and hence also cell function. We now know, however, that other types of molecular alterations, referred to as ‘epigenetic changes,’ also contribute to cancer development. One of these is DNA methylation, where the chemical bond of particular sites in the DNA is modified by the attachment of a methyl group (a carbon atom with three hydrogen atoms). DNA methylation does not alter the underlying DNA sequence, but it can nevertheless affect the activity of a nearby gene and, hence, alter a cell’s normal function. Thus, predicting an individual’s risk of cancer would require us to measure all genetic and epigenetic changes in every tissue of the human body, which is currently impossible. However, some organs are accessible, and clinical samples representative of the tissue can be obtained through routine screening programs: for example, colonic polyps are routinely removed via colonoscopy since polyps are a common precursor lesion of colon cancer. Collecting such clinical specimens from precancerous lesions and subsequently examining the cells from these lesions may thus make it possible to quantify a person’s cancer risk.
Source: Open Access Government
Author: Andrew Teschendorff
Institution: Chinese Academy of Sciences
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4409340717