It has developed a technology for finding optimal targets for the treatment of cancer cells . A study conducted by a team from KAIST Science University in South Korea. The team, led by Professor Kwang-Hyun Cho used a systems biology to analyze the dynamics of molecular networks, which reflects the genetic mutations in cancer cells and predicts drug response. This study will help in the future according to the invention of drugs against cancer.
In cancer cells, there are many types of genetic variation. They may vary within one and the same type of cancer, so medicament reaction does not act equally on cancer cells. Scientists have investigated only common genetic variation in oncology patients and in particular, to mutations that can be used as an indicator for specific drugs. Previously, scientists have paid attention to only one genetic mutation or creation of the analysis of the structural characteristics of gene networks, but this method was ineffective, since he could not explain the biological properties of cancer caused by the interaction of genes and proteins in cancer cells, which leads to differences in drug response.
Mutations cells alter gene function and as a consequence, one modification can lead to changes in the dynamic properties of a molecular network. So the answer to cancer therapy varies. The new approach is only effective for those patients who have resistance to anticancer drugs.
KAIST team has integrated large-scale computer simulations to analyze the changes in the dynamics of molecular networks in cancer cells, leading to the creation of optimal search technology purposes, in accordance with the type of cancer cells by predicting drug response.
Perfuratsionny analysis of the reaction product in each molecular network was used to estimate the amount of change in the cancer cells and their positive response to therapy. Computer modeling was used to analyze the synergistic effects in terms of efficiency and level combinations for predicting drug response. Based on the modeling results of various cancer cell lines, including lung , breast, bone, skin, kidney, ovaries, for comparison, in experiments with a reaction to drugs.
This method can be used in any molecular networks to determine optimal drug targets for personalized medicine.
This technology can help in determining the cause of resistance of cancer cells to the drugs.
Professor Cho: "Genetic variation in cancer cells are the cause of various response to drugs, but a full analysis has not yet been made."