CAPRI: Efficient inference of cancer progression models from cross-sectional data

Bioinformatics

Volumn 31, Issue 18, 2015, Pages 3016-3026

  Ramazzotti, Daniele ^a   Caravagna, Giulio ^a   Olde Loohuis, Loes ^b   Graudenzi, Alex ^a   Korsunsky, Ilya ^c   Mauri, Giancarlo ^a,d   Antoniotti, Marco ^a   Mishra, Bud ^c  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c NEW YORK UNIVERSITY   (United States)

^d SYSBIO Centre of Systems Biology   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGY; CHRONIC MYELOID LEUKEMIA; CROSS-SECTIONAL STUDY; DISEASE COURSE; GENETIC DATABASE; GENETICS; HUMAN; MUTATION; PATHOLOGY; PROBABILITY; PROCEDURES; SIGNAL TRANSDUCTION; THEORETICAL MODEL;

ALGORITHMS; COMPUTATIONAL BIOLOGY; CROSS-SECTIONAL STUDIES; DATABASES, GENETIC; DISEASE PROGRESSION; HUMANS; LEUKEMIA, MYELOGENOUS, CHRONIC, BCR-ABL POSITIVE; MODELS, THEORETICAL; MUTATION; PROBABILITY; SIGNAL TRANSDUCTION;

EID: 84941792023     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv296     Document Type: Article

References (49)

1. Abdel-Wahab, O. et al. (2012) Asxl1 mutations promote myeloid transformation through loss of prc2-mediated gene repression. Cancer Cell, 22, 180-193.
2. Antoniotti, M. et al. (2014) The TRONCO package for translational oncology. Available at standard R repositories.
3. Attolini, C. S.-O. et al. (2010) A mathematical framework to determine the temporal sequence of somatic genetic events in cancer. Proc. Natl. Acad. Sci., 107, 17604-17609.
4. Beerenwinkel, N. et al. (2005) Learning multiple evolutionary pathways from cross-sectional data. J. Comput. Biol., 12, 584-598.
5. Beerenwinkel, N. et al. (2007) Conjunctive bayesian networks. Bernoulli, 13, 893-909.
6. Beerenwinkel, N. et al. (2014) Cancer evolution: mathematical models and computational inference. Syst. Biol., 64, e1-e25.
7. Carvalho, A. M. (2009) Scoring functions for learning Bayesian networks. Inesc-id Tec. Rep.
8. Cheng, Y.-K. et al. (2012) A mathematical methodology for determining the temporal order of pathway alterations arising during gliomagenesis. PLoS Comput. Biol., 8, e1002337.
9. Desper, R. et al. (1999) Inferring tree models for oncogenesis from comparative genome hybridization data. J. Comput. Biol., 6, 37-51.
10. Desper, R. et al. (2000) Distance-based reconstruction of tree models for oncogenesis. J. Comput. Biol., 7, 789-803.
11. Efron, B. (1982) The Jackknife, the Bootstrap and Other Resampling Plans, Volume 38 of CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM, Philadelphia, Pennsylvania, USA.
12. Efron, B. (2010) Large-scale inference: empirical Bayes methods for estimation, testing, and prediction, Volume 1. Cambridge University Press, Cambridge, UK.
13. Figueroa, M. E. et al. (2010) Leukemic idh1 and idh2 mutations result in a hypermethylation phenotype, disrupt tet2 function, and impair hematopoietic differentiation. Cancer Cell, 18, 553-567.
14. Gelsi-Boyer, V. et al. (2009) Mutations of polycomb-associated gene asxl1 in myelodysplastic syndromes and chronic myelomonocytic leukaemia. Br. J. Haematol., 145, 788-800.
15. Gerstung, M. et al. (2009) Quantifying cancer progression with conjunctive bayesian networks. Bioinformatics, 25, 2809-2815.
16. Gupta, A. and Bar-Joseph, Z. (2008) Extracting dynamics from static cancer expression data. IEEE/ACM Trans. Comput. Biol. Bioinf., 5, 172-182.
17. Hanahan, D. and Weinberg, R. A. (2000) The hallmarks of cancer. Cell, 100, 57-70.
18. Hanahan, D. and Weinberg, R. A. (2011) Hallmarks of cancer: the next generation. Cell, 144, 646-674.
19. Heckerman, D. et al. (1995) Learning bayesian networks: the combination of knowledge and statistical data. Mach. Learn., 20, 197-243.
20. Hitchcock, C. (2012) Probabilistic causation. In: Zalta, E. N. (ed. ) The Stanford Encyclopedia of Philosophy. Stanford University, winter 2012 edition.
21. Hjelm, M. et al. (2006) New probabilistic network models and algorithms for oncogenesis. J. Comput. Biol., 13, 853-865.
22. Hofree, M. et al. (2013) Network-based stratification of tumor mutations. Nat. Methods, 10, 1108-1115.
23. Huang, S. et al. (2009) Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Semin. Cell Dev. Biol., 20, 869-76.
24. Inoue, D. et al. (2014) Setbp1 mutations drive leukemic transformation in asxl1-mutated mds. Leukemia, 29, 847-857.
25. Kanehisa, M. and Goto, S. (2000) Kegg: kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 28, 27-30.
26. Kleinberg, S. (2012) Causality, Probability, and Time. Cambridge University Press, Cambridge, UK.
27. Knutsen, T. et al. (2005) The interactive online sky/m-fish & cgh database and the entrez cancer chromosomes search database: Linkage of chromosomal aberrations with the genome sequence. Genes Chromosomes Cancer, 44, 52-64.
28. Koller, D. and Friedman, N. (2009) Probabilistic Graphical Models: Principles and Techniques-Adaptive Computation and Machine Learning. The MIT Press, Cambridge, Massachusetts, USA.
29. Leiserson, M. et al. (2015) Comet: A statistical approach to identify combinations of mutually exclusive alterations in cancer. In: Proceedings of the 19th Annual Research in Computational Biology Conference (RECOMB).
30. Lin, C.-C. et al. (2014) Sf3b1 mutations in patients with myelodysplastic syndromes: The mutation is stable during disease evolution. Am. J. Hematol., 89, E109-E115.
31. Magwene, P. M. et al. (2003) Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics, 19, 842-850.
32. Meggendorfer, M. et al. (2013) Setbp1 mutations occur in 9% of mds/mpn and in 4%; of mpn cases and are strongly associated with atypical cml, monosomy 7, isochromosome i (17)(q10), asxl1 and cbl mutations. Leukemia, 27, 1852-1860.
33. Merlo, L. M. et al. (2006) Cancer as an evolutionary and ecological process. Nat. Rev. Cancer, 6, 924-935.
34. Misra, N. et al. (2014) Inferring the paths of somatic evolution in cancer. Bioinformatics, 30, 2456-2463.
35. Muto, T. et al. (2013) Concurrent loss of ezh2 and tet2 cooperates in the pathogenesis of myelodysplastic disorders. J. Exp. Med., 210, 2627-2639.
36. NCI and the NHGRI. (2005) The Cancer Genome Atlas.
37. Olde Loohuis, L. et al. (2014) Cancer hybrid automata: model, beliefs & therapy. Inf. Comput., 236, 68-86.
38. Olde Loohuis, L. et al. (2014) Inferring tree causal models of cancer progression with probability raising. PloS one, 9, e115570.
39. Pearl, J. (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco, California, USA.
40. Pearl, J. (2000) Causality: models, reasoning and inference, volume 29. Cambridge Univ Press, Cambridge, UK.
41. Piazza, R. et al. (2013) Recurrent setbp1 mutations in atypical chronic myeloid leukemia. Nat. Genet., 45, 18-24.
42. Schwarz, G. (1978) Estimating the dimension of a model. Ann. Stat., 6, 461-464.
43. Spirtes, P. et al. (2000) Causation, prediction, and search, Volume 81. MIT press, Cambridge, Massachusetts, USA.
44. Suppes, P. (1970) A Probabilistic Theory of Causality. North-Holland Publishing Company, Amsterdam, Holland.
45. Szabo, A. and Boucher, K. (2002) Estimating an oncogenetic tree when false negatives and positives are present. Math. Biosci., 176, 219-236.
46. Tamborero, D. et al. (2013) Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep., 3, 1-9.
47. Tsamardinos, I. et al. (2003) Algorithms for large scale markov blanket discovery. In: FLAIRS Conference, Volume 2003, p. 376-381.
48. Vogelstein, B. et al. (1988) Genetic alterations during colorectal-tumor development. New Engl. J. Med., 319, 525-532.
49. Vogelstein, B. et al. (2013) Cancer genome landscapes. Science, 339, 1546-1558.