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Biostatistics. 2004 Oct;5(4):545-56.

Maximum likelihood estimation of oncogenetic tree models.

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  • 1Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany.


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.

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