Finding driver pathways in cancer: models and algorithms

Algorithms for Molecular Biology

Fabio Vandin¹  Eli Upfal¹  Benjamin J Raphael¹ 

[1] Department of Computer Science, and Center for Computational Molecular Biology Brown University, 115 Waterman St., 4th Flr, Providence, RI 02912, USA

关键词: Generative models;    Background mutation rate;    Pathways;    Driver mutations;    Somatic Mutations;    Cancer;   

Others  :  794937
DOI  :  10.1186/1748-7188-7-23

 received in 2012-01-03, accepted in 2012-07-26,  发布年份 2012

【 摘 要 】

Background

Cancer sequencing projects are now measuring somatic mutations in large numbers of cancer genomes. A key challenge in interpreting these data is to distinguish driver mutations, mutations important for cancer development, from passenger mutations that have accumulated in somatic cells but without functional consequences. A common approach to identify genes harboring driver mutations is a single gene test that identifies individual genes that are recurrently mutated in a significant number of cancer genomes. However, the power of this test is reduced by: (1) the necessity of estimating the background mutation rate (BMR) for each gene; (2) the mutational heterogeneity in most cancers meaning that groups of genes (e.g. pathways), rather than single genes, are the primary target of mutations.

Results

We investigate the problem of discovering driver pathways, groups of genes containing driver mutations, directly from cancer mutation data and without prior knowledge of pathways or other interactions between genes. We introduce two generative models of somatic mutations in cancer and study the algorithmic complexity of discovering driver pathways in both models. We show that a single gene test for driver genes is highly sensitive to the estimate of the BMR. In contrast, we show that an algorithmic approach that maximizes a straightforward measure of the mutational properties of a driver pathway successfully discovers these groups of genes without an estimate of the BMR. Moreover, this approach is also successful in the case when the observed frequencies of passenger and driver mutations are indistinguishable, a situation where single gene tests fail.

Conclusions

Accurate estimation of the BMR is a challenging task. Thus, methods that do not require an estimate of the BMR, such as the ones we provide here, can give increased power for the discovery of driver genes.

【 授权许可】

   
2012 Vandin et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 1.

Figure 1.

【 参考文献 】

• [1]Sjoblom T, et al.: The consensus coding sequences of human breast and colorectal cancers. Science 2006, 314:268-274.
• [2]Ding L, et al.: Somatic mutations affect key pathways in lung adenocarcinoma. Nature 2008, 455:1069-1075.
• [3]The Cancer Genome Atlas Research Network: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008, 455(7216):1061-1068.
• [4]Getz G, Hofling H, Mesirov JP, Golub TR, Meyerson M, Tibshirani R, Lander ES: Comment on “The consensus coding sequences of human breast and colorectal cancers”. Science 2007, 317:1500.
• [5]Hahn WC, Weinberg RA: Modelling the molecular circuitry of cancer. Nat Rev Cancer 2002, 2:331-341.
• [6]Vogelstein B, Kinzler KW: Cancer genes and the pathways they control. Nat Med 2004, 10:789-799.
• [7]Efroni S, Ben-Hamo R, Edmonson M, Greenblum S, Schaefer CF, Buetow KH: Detecting cancer gene networks characterized by recurrent genomic alterations in a population. PLoS ONE 2011, 6:e14437.
• [8]Boca SM, Kinzler KW, Velculescu VE, Vogelstein B, Parmigiani G: Patient-oriented gene set analysis for cancer mutation data. Genome Biol 2010, 11:R112. BioMed Central Full Text
• [9]Cerami E, Demir E, Schultz N, Taylor BS, Sander C: Automated network analysis identifies core pathways in glioblastoma. PLoS ONE 2010, 5:e8918.
• [10]Vandin F, Upfal E, Raphael BJ: Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol 2011, 18:507-522.
• [11]Vandin F, Upfal E, Raphael BJ: De novo discovery of mutated driver pathways in cancer. Genome Res 2012, 22(2):375-385.
• [12]McCormick F: Signalling networks that cause cancer. Trends Cell Biol 1999, 9:M53-M56.
• [13]Yeang C, McCormick F, Levine A: Combinatorial patterns of somatic gene mutations in cancer. FASEB J 2008, 22(8):2605-2622.
• [14]Varela I, et al.: Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011, 469:539-542.
• [15]Deguchi K, Gilliland DG: Cooperativity between mutations in tyrosine kinases and in hematopoietic transcription factors in AML. Leukemia 2002, 16:740-744.
• [16]Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, Mandelker D, Leary RJ, Ptak J, Silliman N, Szabo S, Buckhaults P, Farrell C, Meeh P, Markowitz SD, Willis J, Dawson D, Willson JK, Gazdar AF, Hartigan J, Wu L, Liu C, Parmigiani G, Park BH, Bachman KE, Papadopoulos N, Vogelstein B, Kinzler KW, Velculescu VE: The consensus coding sequences of human breast and colorectal cancers. Science 2006, 314:268-274.
• [17]Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A, Hong SM, Fu B, Lin MT, Calhoun ES, Kamiyama M, Walter K, Nikolskaya T, Nikolsky Y, Hartigan J, Smith DR, Hidalgo M, Leach SD, Klein AP, Jaffee EM, Goggins M, Maitra A, Iacobuzio-Donahue C, Eshleman JR, Kern SE, Hruban R H, et al.: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 2008, 321:1801-1806.
• [18]Benjamini Y, Hochberg Y: Controlling the false discovery rate. J R Stat Soc 1995, 57:289-300.
• [19]Mitzenmacher M, Upfal E: Probability and Computing: Randomized Algorithms and Probabilistic Analysis. New York: Cambridge University Press; 2005.
• [20]Parmigiani G, et al.: Response to Comments on “The Consensus Coding Sequences of Human Breast and Colorectal Cancers”. Science 2007, 317(5844):1500.
• [21]Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, Greulich H, Muzny DM, Morgan MB, Fulton L, Fulton RS, Zhang Q, Wendl MC, Lawrence MS, Larson DE, Chen K, Dooling DJ, Sabo A, Hawes AC, Shen H, Jhangiani SN, Lewis LR, Hall O, Zhu Y, Mathew T, Ren Y, Yao J, Scherer SE, Clerc K, et al.: Somatic mutations affect key pathways in lung adenocarcinoma. Nature 2008, 455:1069-1075.
• [22]Thomas RK, Baker AC, Debiasi RM, Winckler W, Laframboise T, Lin WM, Wang M, Feng W, Zander T, MacConaill L, Macconnaill LE, Lee JC, Nicoletti R, Hatton C, Goyette M, Girard L, Majmudar K, Ziaugra L, Wong KK, Gabriel S, Beroukhim R, Peyton M, Barretina J, Dutt A, Emery C, Greulich H, Shah K, Sasaki H, Gazdar A, Minna J, et al.: High-throughput oncogene mutation profiling in human cancer. Nat Genet 2007, 39:347-351.