pathTiMEx: Joint Inference of Mutually Exclusive Cancer Pathways and Their Dependencies in Tumor Progression

  • Simona Cristea
  • Jack Kuipers
  • Niko Beerenwinkel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9649)


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 Supplementary Material for this article is available online.


Markov Chain Monte Carlo Optimal Assignment Naive Approach Copy Number Aberration Exclusive Group 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Hesam Montazeri for helpful discussions.

Funding. Simona Cristea was financially supported by the Swiss National Science Foundation (Sinergia project 136247).

Supplementary material

420109_1_En_5_MOESM1_ESM.pdf (614 kb)
Supplementary material 1 (pdf 613 KB)


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Simona Cristea
    • 1
    • 2
  • Jack Kuipers
    • 1
    • 2
  • Niko Beerenwinkel
    • 1
    • 2
  1. 1.Department of Biosystems Science and EngineeringETH ZürichBaselSwitzerland
  2. 2.Swiss Institute of BioinformaticsBaselSwitzerland

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