Mathematical tree models for cytogenetic development in solid tumors

We present a new approach for modeling the occurrence of genetic changes in human tumors over time. In solid tumors, data on genetic alterations are usually only available at a single point in time, allowing no direct insight into the sequential order of genetic events. In our approach, genetic tumor development and progression is assumed to follow a probabilistic tree model. We use maximum likelihood estimation to reconstruct a tree model for the genetic evolution of a given tumor type. The use of the proposed method is illustrated by an application to cytogenetic data from 173 cases of clear cell renal cell carcinoma, which results in a model for the karyotypic evolution of this tumor.