Ph.D. Student, Karolinska Institutet
Mass spectrometry-based proteomics for cancer drug target discovery and deconvolution
There is an unmet need for discovery of chemical compounds which can treat and eradicate different types of cancer. Although many compounds show anticancer properties, due to the lack of information on their mechanism of action in human cells, they can not be optimized for clinical use. Therefore, development of new tools for identification of the mechanism of such molecules is necessary and can help scientists optimize them for testing on animals and eventually humans. In my research, I am employing cutting edge mass spectrometers which can detect and quantify 5--10000 proteins in parallel from human cancer cells. In the next step, I am working with advanced bioinformatic tools and models, which can predict the mechanism of the desired compound. So far, I have the data for at least 80 compounds, and we have built more than 100 statistical models to classify them and to characterize their mechanisms. These models can facilitate the discovery of new anticancer compounds in future.
Abstract: To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.
Pub.: 30 Sep '06, Pinned: 22 Sep '17