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Abstract:
BACKGROUND: Mixture models of mutagenetic trees are evolutionary models that
capture several pathways of ordered accumulation of genetic events observed in
different subsets of patients. They were used to model HIV progression by
accumulation of resistance mutations in the viral genome under drug pressure
and cancer progression by accumulation of chromosomal aberrations in tumor
cells. From the mixture models a genetic progression score (GPS) can be derived
that estimates the genetic status of single patients according to the
corresponding progression along the tree models. GPS values were shown to have
predictive power for estimating drug resistance in HIV or the survival time in
cancer. Still, the reliability of the exact values of such complex markers
derived from graphical models can be questioned. RESULTS: In a simulation
study, we analyzed various aspects of the stability of estimated mutagenetic
trees mixture models. It turned out that the induced probabilistic
distributions and the tree topologies are recovered with high precision by an
EM-like learning algorithm. However, only for models with just one major model
component, also GPS values for single patients can be reliably estimated.
CONCLUSIONS: It is encouraging that the estimation process of mutagenetic trees
mixture models can be performed with high confidence regarding induced
probability distributions and the general shape of the tree topologies. For a
model with only one major disease progression process, even genetic progression
scores for single patients can be reliably estimated. However, for models with
more than one relevant component, alternative measures should be introduced for
estimating the stage of disease progression.
