Evolution of cancer

Cancer arises from alterations of the genome that transform cooperative cells in homeostatic tissues into non-cooperatively expanding mutants. This transformation typically requires multiple functional changes which have been summarized as the hallmarks of cancer. Genetically, these functional alterations are caused by mutations in several genes or by larger chromosomal aberrations. Hence, the number of point mutations in a single tumor is large and the mutational landscape of cancer is complex.

Genetic alterations accumulate in the cell population over time. This phenomenon is termed somatic evolution of cancer. The process is evolutionary in the sense that mutant cells in an otherwise homeostatic tissue may have a growth advantage compared to normal cells. The process is somatic because the competing individuals are somatic cells of a multicellular organism. Due to their fitness advantage, waves of clonally expanding cell lines are generated, each harboring additional mutations.

We study the progression of cancer using evolutionary models and statistical inference. We have developed conjunctive Bayesian networks, a family of exponential waiting time models that allow for estimating the temporal order of the accumulation process and the parameters of the waiting time distributions. The model has been applied to cytogenetic data of several cancers and it is used for predicting clinical outcomes, such as patient survival.

Selected references

• Gerstung M, Baudis M, Moch H, Beerenwinkel N (2009). Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics, 25(21):2809-2815.
• Gerstung M, Beerenwinkel N (2008). Waiting time models of cancer progression. Mathematical Population Studies 17(3), in press.
• Beerenwinkel N, Sullivant S (2008). Markov models for accumulating mutations. Biometrika, 96(3):645-861.
• Beerenwinkel N, Antal T, Dingli D, Traulsen A, Kinzler KW, Velculescu VE, Vogelstein B, Nowak MA (2007). Genetic progression and the waiting time to cancer. PLoS Computational Biology 3(11):e225.