Identification of collaborative driver pathways in XXXX .

@entity5 @entity10688 @entity0
BACKGROUND: An important challenge in @entity5 biology is to computationally screen mutations in @entity5 cells, separating those that might drive @entity5 initiation and progression, from the much larger number of bystanders. Since mutations are large in number and diverse in type, the frequency of any particular mutation pattern across a set of samples is low. This makes statistical distinctions and reproducibility across different populations difficult to establish. RESULTS: In this paper we develop a novel method that promises to partially ameliorate these problems. The basic idea is although mutations are highly heterogeneous and vary from one sample to another, the processes that are disrupted when cells undergo transformation tend to be invariant across a population for a particular @entity5 or @entity5 subtype. Specifically, we focus on finding mutated pathway-groups that are invariant across samples of @entity0 subtypes. The identification of informative pathway-groups consists of two steps. The first is identification of pathways significantly enriched in genes containing non-synonymous mutations; the second uses pathways so identified to find groups that are functionally related in the largest number of samples. An application to 4 subtypes of @entity0 identified pathway-groups that can highly explicate a particular subtype and rich in processes associated with transformation. CONCLUSIONS: In contrast to previous methods that identify pathways across a set of samples without any further validation, we show that mutated pathway-groups can be found in each @entity0 subtype and that such groups are invariant across the majority of samples. The algorithm is available at http://www.visantnet.org/misi/ @entity10688 .
