Today, oncogenic mutations in more than 300 genes are known in the scientific community, so it is often necessary to sort them to determine the target of the main role in treatment. But in most cases, mutations are sorted by frequency of occurrence, which does not necessarily correspond to their importance in the disease. MIPT scientists suggest that it is based on the intensity of the mutation. Using bioinformatic analysis of TCGA, the largest oncogenic mutation database, they developed a new approach to sorting the driver genes that trigger the disease.
Research results published in the scientific journal PeerJ Life & Environment. According to the popular view today, cancer is triggered and stimulated by mutations in the so-called "driven" gene, while mutations in the "passenger" gene have no effect, but simply "traveling" with the driver gene. somatic evolution. There are several ways to identify and sort cancer initiation mutations, the main ones are based on the frequency of occurrence, the effect on the three-dimensional structure of protein , and the effect on protein interactions. These methods have their pros and cons.
One method is to use the frequency of mutations in patients with a specific type of cancer, usually adjusted according to the frequency of mutations in the gene background. This approach does reveal the most common mutations, but that doesn't mean they can cause cancer on their own. At the same time, this mutation may be rare, but is enough to cause cancer. The first case is a common but weak drive example, while the second case is a rare but strong drive example. Therefore, algorithms based on mutation rate cannot reliably determine the intensity of the driver gene.
has a large set of algorithms designed to predict and arrange driver genes based on the effects of mutations on protein structure and activity. These methods can determine whether the structure and function of proteins are damaged and how damaged, but they are less suitable to determine the role of specific proteins in other cellular proteins and their microenvironment . That said, this is crucial to determine whether it can cause cancer. Therefore, the extent of the impact of mutations on protein structure does not determine whether the protein is carcinogenic.
The third method determines the effect of mutations on protein interactions by identifying the key molecules that interact the most with other proteins in the cell. It is understood that mutations in such proteins will have the most serious consequences for cells. This will usually prove to be the case, but more often this leads to cell death rather than oncogenic conversion. Therefore, this approach is also not suitable for sorting mutations by intensity.
"We quantified the driver mutations before and found that even in patients with the same type of cancer, the variability in the number of mutations is very large. So we asked ourselves: Why is a driver mutation sufficient in some patients, and In other patients, cancer does not develop until dozens of mutations accumulate? We hypothesize that the main reason is their strength: a powerful driver can be comparable to several weak drivers in its tumor-induced activity.
Therefore, strong driver genes are more likely to be statistically more common in patients with a smaller number of mutations, as some strong driver gene mutations are sufficient to trigger cancer. In contrast, weak drivers are more likely to be more common in patients with a large number of driver mutations. It is common because many weak drivers are needed to cause cancer. Based on this principle, we developed mathematical formulas that enable us to create bioinformatics algorithms that automatically calculate any driver based on the distribution of driver mutations in different numbers of driver mutation patients. Numerical equivalent of mutation intensity. "Study.
Scientists used the formulas of the Driver Strength Index (DSI) and the Standardized Driver Strength Index (NDSI). They performed a large-scale screening of mutations in the largest TCGA PanCanAtlas database and determined their intensity. When analyzing the data, scientists found that the strongest drivers usually belong to several known gene families, , and constitute some signal cascades that often mutate in tumors.
"Our research shows that the proposed sorting method does reveal biological entities that cannot be detected by some traditional methods in the driver gene, which we believe is the real power of driving mutations.Therefore, our ratings can be used to select the highest priority target for tumor treatment, as well as select genes from a scientific point of view for more in-depth research. Sergey Leonov, head of the MIPT Innovation Drugs and Agricultural Biotechnology Development Laboratory, concluded that the priority genes and signaling pathway may make the greatest contribution to the occurrence and progress of cancer and may become future therapeutic targets. Formulate and implement.
