Cornell researchers develop framework for analyzing cancer mutations

Researchers at Cornell University have developed a new framework to better understand genetic mutations that lead to cancer. This tool, named NetFlow3D, can help distinguish between mutations that actively drive cancer and those that do not contribute to the disease. Cancer occurs when cells develop harmful mutations that encourage rapid growth and replication. Identifying the specific mutations responsible for cancer is essential for creating effective treatments. The study, published in January, examines 33 types of cancer by analyzing the 3D structures of human proteins and how they interact. Co-lead author Yingying Zhang explained that when mutations occur in DNA, they can affect how proteins fold, which influences their functions. These proteins also interact with each other, forming complex networks. Disruptions in one protein can have cascading effects on others, making it crucial to understand these interactions. NetFlow3D is unique because it incorporates the 3D structures of all known human proteins and their interactions. This approach builds on decades of research, including insights from The Cancer Genome Atlas Program, which has mapped genetic data of various cancers. Recent advancements, particularly Google's AlphaFold 2, have allowed scientists to predict the 3D shapes of many human proteins. This wealth of data enables researchers to understand mutation patterns across different proteins and biological pathways. The goal of this research is to map the mechanisms behind cancer mutations more clearly. This could lead to improvements in precision medicine, allowing for more personalized treatment options in the future. The study was supported by the National Institutes of Health and the Simons Foundation.