Discovering signals of selection in cancer mutations with Hidden Markov Models

• Applicant: Dr. Andrej Fischer
• Subject Area: Bioinformatics and Theoretical Biology
• Term: from 2012 to 2013
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 219638969

What kind of mutation causes cancer? The answer to this important question lies in the correct interpretation of cancer-cell DNA data, which now exists in abundance. This task is complicated by the fact that the actual “driver mutations“ that causally contribute to the development of cancer are disguised by a large pool of random “passenger mutations“. Sometimes, individual driver mutations can be identified when they systematically appear in many independent tumor samples. But such cases are rare, for it seems that the mechanisms of cancer evolution are intricate and not without alternative. It is the aim of this project to devise statistical and computational methods to robustly identify DNA regions that are important to cancer evolution. This can be done by finding signals of selection: when genes are required by the cancer to be in a state that is different from their configuration in healthy cells, they will exhibit a higher rate of genetic reconfiguration in the form of missense mutations. Moreover, in order to alter the gene considerably, these mutations are more likely to appear at locations that usually do not tolerate too much diversity. Combining these two complementary aspects could be the key to quantify the functional effects of cancer mutations in a biologically meaningful and statistically powerful manner. Practically, the detection of signals of selection requires extensive statistical analysis not only of the observed cancer mutations but also of the potential mutation target - the human genome - itself. Only by comparing what was seen to what could have been seen can one assess the significance of findings. The probabilistic method of Hidden Markov Models is ideally suited to perform this task efficiently on large data sets. The goal is to establish a computational framework to implement an evolutionarily informed analysis of cancer sequencing data with the objective to identify genomic regions that can act as drivers for cancer progression.

• DFG Programme: Research Fellowships
• International Connection: United Kingdom
• Host: Professor Dr. Ville Mustonen