%0 Journal Article %A Ramon Diaz-Uriarte %T Inferring restrictions in the temporal order of mutations during tumor progression: effects of passenger mutations, evolutionary models, and sampling %D 2014 %R 10.1101/005587 %J bioRxiv %P 005587 %X In cancer progression, fixation of some driver mutations (those causally involved in the disease) can depend on the presence of other drivers. The majority of mutations present in cancer cells, however, are not drivers but passenger mutations that are not involved in disease progression.Several methods have been developed to discover the restrictions in the temporal order of accumulation of driver mutations from cross-sectional data, but the few available comparisons of their performance assume that drivers are known, and have not examined the effects of sampling.Here I conduct a comprehensive comparison of the performance of all available methods. In contrast to previous work, I embed order restrictions within evolutionary models of tumor progression that include passengers and drivers. This allows me to asses the effects of having to filter out passengers, of sampling schemes, and of deviations from order restrictions.No combination of method, filtering, and sampling excels in all metrics: rankings depend on how performance is measured. However, poor choices can lead to large errors in all metrics, and when drivers are not known, performance of all methods degrades. One filtering method emerges as the best overall. In most scenarios, Oncogenetic Trees are superior to Conjunctive Bayesian Networks and Progression Networks. Single cell sampling provides no major advantage, but sampling in the final stages of the disease vs. sampling at different stages can have severe effects depending on the evolutionary model. Evolutionary model and deviations from order restrictions can have major, and counterintuitive, interactions with other factors that affect performance.This paper provides practical recommendations for using these methods with experimental data, and shows that it is both possible and necessary to embed assumptions about order restrictions and the nature of driver status within evolutionary models of cancer progression to evaluate the performance of inferential approaches. %U https://www.biorxiv.org/content/biorxiv/early/2014/05/27/005587.full.pdf