Maximum likelihood estimation of oncogenetic tree models.
We present a new approach for modelling the dependences between genetic changes in human tumours. In solid tumours, 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 tumour development and progression is assumed to follow a probabilistic tree model. We show how maximum likelihood estimation can be used to reconstruct a tree model for the dependences between genetic alterations in a given tumour type. We illustrate the use of the proposed method by applying it to cytogenetic data from 173 cases of clear cell renal cell carcinoma, arriving at a model for the karyotypic evolution of this tumour.
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany. anja.von.heydebreck@merck.de
Anja
A
von Heydebreck
Bastian
B
Gunawan
László
L
Füzesi
D002292:Carcinoma, Renal Cell / Q000235:genetics; D006801:Humans; D007621:Karyotyping; D016013:Likelihood Functions; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics*; D009857:Oncogenes / Q000235:genetics*