165 new cancer genes identified with the help of machine learning
A new algorithm can predict which genes cause cancer, even if their DNA sequence is not changed. A team of researchers in Berlin combined a wide variety of data, analyzed it with “Artificial Intelligence” and identified numerous cancer genes. This opens up new perspectives for targeted cancer therapy in personalized medicine and for the development of biomarkers.
In cancer, cells get out of control. They proliferate and push their way into tissues, destroying organs and thereby impairing essential vital functions. This unrestricted growth is usually induced by an accumulation of DNA changes in cancer genes — i.e. mutations in these genes that govern the development of the cell. But some cancers have only very few mutated genes, which means that other causes lead to the disease in these cases.
A team of researchers at the Max Planck Institute for Molecular Genetics (MPIMG) in Berlin and at the Institute of Computational Biology of Helmholtz Zentrum München developed a new algorithm using machine learning technology to identify 165 previously unknown cancer genes. The sequences of these genes are not necessarily altered — apparently, already a dysregulation of these genes can lead to cancer. All of the newly identified genes interact closely with well-known cancer genes and have been shown to be essential for the survival of tumor cells in cell culture experiments.
Additional targets for personalized medicine
The algorithm, dubbed “EMOGI” for Explainable Multi-Omics Graph Integration, can also explain the relationships in the cell’s machinery that make a gene a cancer gene. As the team of researchers headed by Annalisa Marsico describe in the journal Nature Machine Intelligence, the software integrates tens of thousands of data sets generated from patient samples. These contain information about DNA methylations, the activity of individual genes and the interactions of proteins within cellular pathways in addition to sequence data with mutations. In these data, a deep-learning algorithm detects the patterns and molecular principles that lead to the development of cancer.
“Ideally, we obtain a complete picture of all cancer genes at some point, which can have a different impact on cancer progression for different patients,” says Marsico, head of a research group at the MPIMG until recently and now at Helmholtz Zentrum München. “This is the foundation for personalized cancer therapy.”
Unlike with conventional cancer treatments such as chemotherapy, personalized therapy approaches tailor medication precisely to the type of tumor. “The goal is to select the best therapy for each patient — that is, the most effective treatment with the fewest side effects. Additionally, we would be able to identify cancers already at early stages, based on their molecular characteristics.” More