Important progress has been made in our understanding of cancer thanks to the ever growing amount of data originated by sequencing technologies. One useful approach for better under- standing the process of accumulation of somatic mutations in cancer is given by the integration of mathematical modeling with sequencing data of cancer tissues.
In this course we will review some results in stochastic processes and then formulate and analyze a new mathematical model for the evolution of somatic mutations before and during cancer pro- gression, that is, all relevant phases of a tissue’s history will be considered. The model will provide a way to estimate the in-vivo tissue-specific somatic mutation rates from the sequencing data of tumors. The model will also give us novel predictions on the expected number of somatic mutations found in tumors of self-renewing tissues. These results will be then compared and validated by the empirical findings which will be presented.
Moreover, using these results, we will also analyze the dynamics of drug resistance in chronic myeloid leukemia, shedding light on some of the general principles behind this phenomena.
Overall, the material taught in this class has substantial implications for the interpretation of the large number of genome-wide cancer studies now being undertaken.
