Systems-level modeling of cancer genome evolution
Cancer genomes are characterized by an accumulation of mutational events such a single nucleotide changes, small insertions /deletions , and complex rearrangements. As the tumor progresses in time the mutational load increases and can thereby provide information about tumor evolution. In addition, heterogeneity can arise during tumor evolution, where different sub-populations emerge from the main population of cancer cells. These sub-populations, also called sub-clonal populations, therefore divert slightly in their mutational architecture from the main line of tumor cells. In this sub-project we are using whole-genome sequencing data to detect all aformentionioned genomic alterations and by using mathematical models to reconstruct the sub-clonal diversity of the sequenced tumors. To draw a picture of tumor evolution, one would ideally compare tumor cells of a single indivdual at different points in time. However, the availability of time-resolved cancer specimens is extremely limited, such that we are using the intra-tumor complexity of tumors drived from single individuals and relate this throughout an entire cohort of specimens. From this we aim to derive insights into the evolutionary forces acting in different cancer types. We envision that this analysis might help to find new oncogenic modulators and may thereby even give rise to novel therapeutic options.
Keywords: Cancer Genome, Tumorevolution