pathTiMEx: Joint Inference of Mutually Exclusive Cancer Pathways and Their Dependencies in Tumor Progression
- Cite this paper as:
- Cristea S., Kuipers J., Beerenwinkel N. (2016) pathTiMEx: Joint Inference of Mutually Exclusive Cancer Pathways and Their Dependencies in Tumor Progression. In: Singh M. (eds) Research in Computational Molecular Biology. RECOMB 2016. Lecture Notes in Computer Science, vol 9649. Springer, Cham
In recent years, high-throughput sequencing technologies have facilitated the generation of an unprecedented amount of genomic cancer data, opening the way to a more profound understanding of tumorigenesis. In this regard, two fundamental questions have emerged: (1) which alterations drive tumor progression? and (2) what are the evolutionary constraints on the order in which these alterations occur? Answering these questions is crucial for therapeutic decisions involving targeted agents, which are often based on the identification of early genetic events. Mainly because of interpatient heterogeneity, progression at the level of pathways has been shown to be more robust than progression at the level of single genes. Here, we introduce pathTiMEx, a probabilistic generative model of tumor progression at the level of mutually exclusive driver pathways. pathTiMEx employs a stochastic optimization procedure to jointly optimize the assignment of genes to pathways and the evolutionary order constraints among pathways. On cancer data, pathTiMEx recapitulates previous knowledge on tumorigenesis, such as the temporal order among pathways which include APC, KRAS and TP53 in colorectal cancer, while also proposing new biological hypotheses, such as the existence of a single early causal event consisting of the amplification of CDK4 and the deletion of CDKN2A in glioblastoma. The pathTiMEx R package is available at https://github.com/cbg-ethz/pathTiMEx. Supplementary Material for this article is available online.