Identifying restrictions in the order of accumulation of mutations during tumor progression: Effects of passengers, evolutionary models, and sampling
Author
Díaz Uriarte, RamónEntity
UAM. Departamento de BioquímicaPublisher
BioMed CentralDate
2015-12-12Citation
10.1186/s12859-015-0466-7
BMC Bioinformatics 16 (2015): 41
ISSN
1471-2105DOI
10.1186/s12859-015-0466-7Funded by
Work partially supported by Project BIO2009-12458 from the Spanish MINECO; computer hardware partially supported by Project BIO2009-12458 from the Spanish MINECOProject
Gobierno de España. BIO2009-12458Editor's Version
http://dx.doi.org/10.1186/s12859-015-0466-7Subjects
Conjunctive bayesian network; Driver mutation; Oncogenetic tree; Passenger mutation; Tumor evolution; Tumor progression model; MedicinaRights
© 2015 Diaz-Uriarte; licensee BioMed Central; The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this articleAbstract
Background: Cancer progression is caused by the sequential accumulation of mutations, but not all orders of
accumulation are equally likely. When the fixation of some mutations depends on the presence of previous ones,
identifying restrictions in the order of accumulation of mutations can lead to the discovery of therapeutic targets and
diagnostic markers. The purpose of this study is to conduct a comprehensive comparison of the performance of all
available methods to identify these restrictions from cross-sectional data. I used simulated data sets (where the true
restrictions are known) but, in contrast to previous work, I embedded restrictions within evolutionary models of tumor
progression that included passengers (mutations not responsible for the development of cancer, known to be very
common). This allowed me to assess, for the first time, the effects of having to filter out passengers, of sampling
schemes (when, how, and how many samples), and of deviations from order restrictions.
Results: Poor choices of method, filtering, and sampling lead to large errors in all performance measures. Having to
filter passengers lead to decreased performance, especially because true restrictions were missed. Overall, the best
method for identifying order restrictions were Oncogenetic Trees, a fast and easy to use method that, although
unable to recover dependencies of mutations on more than one mutation, showed good performance in most
scenarios, superior to Conjunctive Bayesian Networks and Progression Networks. Single cell sampling provided no
advantage, but sampling in the final stages of the disease vs. sampling at different stages had severe effects.
Evolutionary model and deviations from order restrictions had major, and sometimes counterintuitive, interactions
with other factors that affected performance.
Conclusions: This paper provides practical recommendations for using these methods with experimental data. It
also identifies key areas of future methodological work and, in particular, it 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
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