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Human Genetics

, Volume 134, Issue 5, pp 479–495 | Cite as

An argument for mechanism-based statistical inference in cancer

  • Donald GemanEmail author
  • Michael Ochs
  • Nathan D. Price
  • Cristian Tomasetti
  • Laurent Younes
Review Paper
Part of the following topical collections:
  1. Computational Molecular Medicine

Abstract

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.

Keywords

Decision Rule Metabolic Network Statistical Learning Prediction Rule Omics Data 
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.

Notes

Acknowledgments

The work of D. Geman and L. Younes was partially supported by the National Science Foundation under NSF DMS1228248. N. Price’s work was supported by a Camille Dreyfus Teacher-Scholar Award and NIH 2P50GM076547.

Author contributions

D.G. supervised the project. All authors wrote the manuscript.

References

  1. Abraham G, Kowalczyk A, Loi S, Haviv I, Zobel J (2010) Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context. BMC Bioinform 11:277. doi: 10.1186/1471-2105-11-277 CrossRefGoogle Scholar
  2. Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using init. PLoS Comput Biol 8(5):e1002518. doi: 10.1371/journal.pcbi.1002518 CrossRefPubMedCentralPubMedGoogle Scholar
  3. Altman R (2012) Translational bioinformatics: linking the molecular world to the clinical world. Clin Pharmacol Ther 91(6):994–1000CrossRefPubMedCentralPubMedGoogle Scholar
  4. Altman RB, Kroemer Ho K, McCarty CA et al (2011) Pharmacogenomics: will the promise be fulfilled. Nat Rev 12:69–73Google Scholar
  5. Anderson AR, Tomlin CJ, Couch J, Gallahan D (2013) Mathematics of the integrative cancer biology program. Interface Focus 3(4):20130023CrossRefPubMedCentralGoogle Scholar
  6. Armitage P, Doll R (1954) The age distribution of cancer and a multi-stage theory of carcinogenesis. Br J Cancer 8(1):1–12. URL http://www.ncbi.nlm.nih.gov/pubmed/13172380
  7. Armitage P, Doll R (1957) A two-stage theory of carcinogenesis in relation to the age distribution of human cancer. Br J Cancer 11(2):161–169. URL http://www.ncbi.nlm.nih.gov/pubmed/13460138
  8. Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1(1):2CrossRefPubMedCentralPubMedGoogle Scholar
  9. Barrett CL, Price ND, Palsson BO (2006) Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition. BMC Bioinform 7:132. doi: 10.1186/1471-2105-7-132 CrossRefGoogle Scholar
  10. Beerenwinkel N, Antal T, Dingli D, Traulsen A, Kinzler KW, Velculescu VE, Vogelstein B, Nowak MA (2007) Genetic progression and the waiting time to cancer. PLoS Comput Biol 3(11):e225. doi: 10.1371/journal.pcbi.0030225. URL http://www.ncbi.nlm.nih.gov/pubmed/17997597
  11. Bender R, Knauer M, Rutgers E, Glas A, de Snoo FA et al (2009) The 70-gene profile and chemotherapy benefit in 1,600 breast cancer patients. J Clin Oncol 27(18 Suppl):512Google Scholar
  12. Binder H, Schumacher M (2009) Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinform 10:18. doi: 10.1186/1471-2105-10-18 CrossRefGoogle Scholar
  13. Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6:422. doi: 10.1038/msb.2010.68 CrossRefPubMedCentralPubMedGoogle Scholar
  14. Boulesteix AL, Sauerbrei W (2011) Added predictive value of high-throughput molecular data to clinical data and its validation. Brief Bioinform 12(3):215–229CrossRefPubMedGoogle Scholar
  15. Boulesteix AL, Tutz G, Strimmer K (2003) A cart-based approach to discover emerging patterns in microarray data. Bioinformatics 19(18):2465–2472Google Scholar
  16. Boveri T (2008) Concerning the origin of malignant tumours by theodor boveri. translated and annotated by henry harris. J Cell Sci 121(Suppl 1):1–84. doi: 10.1242/jcs.025742. URL http://www.ncbi.nlm.nih.gov/pubmed/18089652
  17. Brenner S (2010) Sequences and consequences. Philos Trans R Soc Lond B Biol Sci 365(1537):207–212CrossRefPubMedCentralPubMedGoogle Scholar
  18. Butte AJ (2008) Translational bioinformatics: coming of age. J Am Med Inform Assoc 15(6):709–714CrossRefPubMedCentralPubMedGoogle Scholar
  19. Butte AJ, Kohane IS (2003) Relevance networks: a first step toward finding genetic regulatory networks within microarray data. In: Parmigiani G, Garrett ES, Irizarry RA, Zeger SL (eds) The analysis of gene expression data, pp 428–446Google Scholar
  20. Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS (2000) Discovering functional relationships between rna expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci 97(22):12182–12186CrossRefPubMedCentralPubMedGoogle Scholar
  21. Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci USA 107(41):17845–17850. doi: 10.1073/pnas.1005139107 CrossRefPubMedCentralPubMedGoogle Scholar
  22. Chandrasekaran S, Price ND (2013) Metabolic constraint-based refinement of transcriptional regulatory networks. PLoS Comput Biol 9(12):e1003370. doi: 10.1371/journal.pcbi.1003370 CrossRefPubMedCentralPubMedGoogle Scholar
  23. Chavali AK, Whittemore JD, Eddy JA, Williams KT, Papin JA (2008) Systems analysis of metabolism in the pathogenic trypanosomatid leishmania major. Mol Syst Biol 4:177. doi: 10.1038/msb.2008.15. URL http://www.ncbi.nlm.nih.gov/pubmed/18364711
  24. Chen X, Wang L, Ishwaran H (2010) An integrative pathway-based clinical-genomic model for cancer survival prediction. Stat Probab Lett 80(17–18):1313–1319. doi: 10.1016/j.spl.2010.04.011 CrossRefPubMedCentralPubMedGoogle Scholar
  25. Cohen JE (2004) Mathematics is biology’s next microscope, only better; biology is mathematics’ next physics, only better. PLoS Biol 2(12):e439CrossRefPubMedCentralPubMedGoogle Scholar
  26. Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429(6987):92–96. doi: 10.1038/nature02456 CrossRefPubMedGoogle Scholar
  27. Croce CM (2009) Causes and consequences of microrna dysregulation in cancer. Nat Rev Genet 10(10):704–714. doi: 10.1038/nrg2634 CrossRefPubMedCentralPubMedGoogle Scholar
  28. Cronin M, Sangli C, Liu ML, Pho M, Dutta D, Nguyen A, Jeong J, Wu J, Langone KC, Watson D (2007) Analytical validation of the oncotype dx genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem 53(6):1084–1091CrossRefPubMedGoogle Scholar
  29. Dettling M, Buhlmann P (2003) Boosting for tumor classification with gene expression data. Bioinformatics 19(9):1061–1069. URL http://www.ncbi.nlm.nih.gov/pubmed/12801866
  30. Diaz LA, Williams RT, Wu J, Kinde I, Hecht JR, Berlin J, Allen B, Bozic I, Reiter JG, Nowak MA, Kinzler KW, Oliner KS, Vogelstein B (2012) The molecular evolution of acquired resistance to targeted egfr blockade in colorectal cancers. Nature 486(7404):537–540. doi: 10.1038/nature11219. URL http://www.ncbi.nlm.nih.gov/pubmed/22722843
  31. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97(457):77–87CrossRefGoogle Scholar
  32. Durrett R, Moseley S (2010) Evolution of resistance and progression to disease during clonal expansion of cancer. Theor Popul Biol 77(1):42–48. doi: 10.1016/j.tpb.2009.10.008. URL http://www.ncbi.nlm.nih.gov/pubmed/19896491
  33. Eddy JA, Hood L, Price ND, Geman D (2010) Identifying tightly regulated and variably expressed networks by differential rank conservation (dirac). PLoS Comput Biol 6(5):e1000792. doi: 10.1371/journal.pcbi.1000792
  34. Edelman LB, Toia G, Geman D, Zhang W, Price ND (2009) Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases. BMC Genomics. doi: 10.1186/1471-2164-10-583
  35. Eisen MB, Spellman PT, Brown PO (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci 95(25):14863–14868CrossRefPubMedCentralPubMedGoogle Scholar
  36. Evans JP, Meslin EM, Marteau TM, Caulfield T (2011) Deflating the genomic bubble. Science 331:861–862CrossRefPubMedGoogle Scholar
  37. Fisher JC, Hollomon JH (1951) A hypothesis for the origin of cancer foci. Cancer 4(5):916–918. URL http://www.ncbi.nlm.nih.gov/pubmed/14879355
  38. Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501. doi: 10.1038/msb.2011.35 CrossRefPubMedCentralPubMedGoogle Scholar
  39. Frezza C, Zheng L, Folger O, Rajagopalan KN, MacKenzie ED, Jerby L, Micaroni M, Chaneton B, Adam J, Hedley A, Kalna G, Tomlinson IPM, Pollard PJ, Watson DG, Deberardinis RJ, Shlomi T, Ruppin E, Gottlieb E (2011) Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477(7363):225–228. doi: 10.1038/nature10363 CrossRefPubMedGoogle Scholar
  40. Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303(5659):799–805CrossRefPubMedGoogle Scholar
  41. Geman D, d’Avignon C, Naiman D et al (2004) Gene expression comparisons for class prediction in cancer studies. In: Proceedings 36th symposium on the interface: computing science and statisticsGoogle Scholar
  42. Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias variance dilemma. Neural Comput 4(1):1–58CrossRefGoogle Scholar
  43. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100(1):57–70CrossRefPubMedGoogle Scholar
  44. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674. doi: 10.1016/j.cell.2011.02.013 CrossRefPubMedGoogle Scholar
  45. Hartemink AJ et al (2005) Reverse engineering gene regulatory networks. Nat Biotechnol 23(5):554–555CrossRefPubMedGoogle Scholar
  46. Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  47. Hobert O (2008) Gene regulation by transcription factors and micrornas. Science 319(5871):1785–1786. doi: 10.1126/science.1151651 CrossRefPubMedGoogle Scholar
  48. Hood L, Heath JR, Phelps ME, Lin B (2004) Systems biology and new technologies enable predictive and preventative medicine. Science 306(5696):640–643CrossRefPubMedGoogle Scholar
  49. Hood L, Price ND (2014) Demystifying disease, democratizing health care. Sci Transl Med 6(225):225ed5CrossRefPubMedGoogle Scholar
  50. Ideker T, Galitski T, Hood L (2001) A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2(1):343–372CrossRefPubMedGoogle Scholar
  51. Jamshidi N, Palsson BO (2006) Systems biology of SNPs. Mol Syst Biol 2:38CrossRefPubMedCentralPubMedGoogle Scholar
  52. Jamshidi N, Palsson BO (2007) Investigating the metabolic capabilities of mycobacterium tuberculosis h37rv using the in silico strain inj661 and proposing alternative drug targets. BMC Syst Biol 1:26. doi: 10.1186/1752-0509-1-26. URL http://www.ncbi.nlm.nih.gov/pubmed/17555602
  53. Jerby L, Ruppin E (2012) Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res 18(20):5572–5584. doi: 10.1158/1078-0432.CCR-12-1856 CrossRefPubMedGoogle Scholar
  54. Johannes M, Brase JC, Fröhlich H, Gade S, Gehrmann M, Fälth M, Sültmann H, Beissbarth T (2010) Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics 26(17):2136–2144. doi: 10.1093/bioinformatics/btq345 CrossRefPubMedGoogle Scholar
  55. Kanehisa M, Goto S, Kawashima S, Nakaya A (2002) The KEGG databases at genomenet. Nucleic Acids Res 30(1):42–46CrossRefPubMedCentralPubMedGoogle Scholar
  56. Kern SE (2012) Why your new cancer biomarker may never work: recurrent patterns and remarkable diversity in biomarker failures. Cancer Res 72(23):6097–6101. doi: 10.1158/0008-5472.CAN-12-3232 CrossRefPubMedCentralPubMedGoogle Scholar
  57. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C et al (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(6):673–679CrossRefPubMedCentralPubMedGoogle Scholar
  58. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8(2):1002. doi: 10.1371/Journal.Pcbi375 Google Scholar
  59. Kim YA, Wuchty S, Przytycka TM (2011) Identifying causal genes and dysregulated pathways in complex diseases. PLOS Comput Biol 7(3):e1001095CrossRefPubMedCentralPubMedGoogle Scholar
  60. Knudson AG (1971) Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA 68(4):820–823 (1971). URL http://www.ncbi.nlm.nih.gov/pubmed/5279523
  61. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, CambridgeGoogle Scholar
  62. Kreeger PK, Lauffenburger DA (2010) Cancer systems biology: a network modeling perspective. Carcinogenesis 31(1):2–8CrossRefPubMedCentralPubMedGoogle Scholar
  63. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA, Kiezun A, Hammerman PS, McKenna A, Drier Y, Zou L, Ramos AH, Pugh TJ, Stransky N, Helman E, Kim J, Sougnez C, Ambrogio L, Nickerson E, Shefler E, Cortés ML, Auclair D, Saksena G, Voet D, Noble M, DiCara D, Lin P, Lichtenstein L, Heiman DI, Fennell T, Imielinski M, Hernandez B, Hodis E, Baca S, Dulak AM, Lohr J, Landau DA, Wu CJ, Melendez-Zajgla J, Hidalgo-Miranda A, Koren A, McCarroll SA, Mora J, Lee RS, Crompton B, Onofrio R, Parkin M, Winckler W, Ardlie K, Gabriel SB, Roberts CWM, Biegel JA, Stegmaier K, Bass AJ, Garraway LA, Meyerson M, Golub TR, Gordenin DA, Sunyaev S, Lander ES, Getz G (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499(7457):214–218 (2013). doi: 10.1038/nature12213. URL http://www.ncbi.nlm.nih.gov/pubmed/23770567
  64. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I et al (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594):799–804CrossRefPubMedGoogle Scholar
  65. Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11(10):733–739. doi: 10.1038/nrg2825 CrossRefPubMedGoogle Scholar
  66. Levy R, Borenstein E (2013) Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc Natl Acad Sci USA 110(31):12,804–12,809Google Scholar
  67. Li C, Li H (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24(9):1175–1182. doi: 10.1093/bioinformatics/btn081 CrossRefPubMedGoogle Scholar
  68. Li Q, Seo JH, Stranger B, McKenna A, Pe’er I, Laframboise T, Brown M, Tyekucheva S, Freedman ML (2013) Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152(3):633–641. doi: 10.1016/j.cell.2012.12.034 CrossRefPubMedCentralPubMedGoogle Scholar
  69. Li XJ, Hayward C, Fong PY, Dominguez M, Hunsucker SW, Lee LW, McLean M, Law S, Butler H, Schirm M, Gingras O, Lamontagne J, Allard R, Chelsky D, Price ND, Lam S, Massion PP, Pass H, Rom WN, Vachani A, Fang KC, Hood L, Kearney P (2013) A blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci Transl Med 5(207):207ra142. doi: 10.1126/scitranslmed.3007013. URL http://www.ncbi.nlm.nih.gov/pubmed/24132637
  70. Liu KQ, Liu ZP, Hao JK, Chen L, Zhao XM (2012) Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinform 13:126CrossRefGoogle Scholar
  71. Liu Y, Koyuturk M, Barnholtz-Sloan JS, Chance MR (2012) Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases. BMC Syst Biol 6:65CrossRefPubMedCentralPubMedGoogle Scholar
  72. Lottaz C, Spang R (2005) Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data. Bioinformatics 21(9):1971–1978. doi: 10.1093/bioinformatics/bti292 CrossRefPubMedGoogle Scholar
  73. Luria SE, Delbrück M (1943) Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28(6):491–511. URL http://www.ncbi.nlm.nih.gov/pubmed/17247100
  74. Maathuis MH, Colombo D, Kalisch M, Bühlmann P (2010) Predicting causal effects in large-scale systems from observational data. Nat Methods 7(4):247–248CrossRefPubMedGoogle Scholar
  75. Maathuis MH, Kalisch M, Bühlmann P et al (2009) Estimating high-dimensional intervention effects from observational data. Ann Stat 37(6A):3133–3164CrossRefGoogle Scholar
  76. Marchionni L, Wilson RF, Wolff AC, Marinopoulos S, Parmigiani G, Bass EB, Goodman SN (2008) Systematic review: gene expression profiling assays in early stage breast cancer. Ann Intern Med 148(5):358–369CrossRefPubMedGoogle Scholar
  77. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera RD, Califano A (2006) Aracne: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform 7(Suppl 1):S7CrossRefGoogle Scholar
  78. Mendell JT (2005) Micrornas: critical regulators of development, cellular physiology and malignancy. Cell Cycle 4(9):1179–1184CrossRefPubMedGoogle Scholar
  79. Milne CB, Kim PJ, Eddy JA, Price ND (2009) Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology. Biotechnol J 4(12):1653–1670. doi: 10.1002/biot.200900234 CrossRefPubMedCentralPubMedGoogle Scholar
  80. Neapolitan RE et al (2004) Learning bayesian networks, vol 1. Prentice Hall, Upper Saddle RiverGoogle Scholar
  81. Ng S, Collisson EA, Sokolov A, Goldstein T, Gonzalez-Perez A, Lopez-Bigas N, Benz C, Haussler D, Stuart JM (2012) Paradigm-shift predicts the function of mutations in multiple cancers using pathway impact analysis. Bioinformatics 28(18):i640–i646. doi:  10.1093/bioinformatics/bts40210.1093/bioinformatics/bts402. URL http://www.ncbi.nlm.nih.gov/pubmed/22962493.
  82. Nordling CO (1953) A new theory on cancer-inducing mechanism. Br J Cancer 7(1):68–72 (1953). URL http://www.ncbi.nlm.nih.gov/pubmed/13051507
  83. Nowell PC (1976) The clonal evolution of tumor cell populations. Science 194(4260):23–28. URL http://www.ncbi.nlm.nih.gov/pubmed/959840
  84. Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320. doi: 10.1038/msb.2009.77. URL http://www.ncbi.nlm.nih.gov/pubmed/19888215
  85. Ochs MF, Farrar JE, Considine M, Wei Y, Meshinchi S, Arceci RJ (2014) Outlier analysis and top scoring pair for integrated data analysis and biomarker discovery. IEEE/ACM Trans Comput Biol Bioinform. doi:DBACF900-6B21-49D2-9D30-F333A1E9CED0Google Scholar
  86. Ochs MF, Rink L, Tarn C, Mburu S, Taguchi T, Eisenberg B, Godwin AK (2009) Detection of treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data. Cancer Res 69(23):9125–9132CrossRefPubMedCentralPubMedGoogle Scholar
  87. Omenn G, DeAngelis C, DeMets D, Fleming T, Geller G, Gray J, Hayes D, Henderson C, Kessler L, Lapidus S, Leonard D, Moses H, Pao W, Pentz R, Price ND, Quackenbush J, Railey E, Ransohoff D, Reese E, Witten D (2012) Evolution of translational omics: lessons learned and the path forward. Institute of Medicine ReportGoogle Scholar
  88. Paik S (2011) Is gene array testing to be considered routine now? Breast 20(Suppl 3):S87–S91. doi: 10.1016/S0960-9776(11)70301-0 CrossRefPubMedGoogle Scholar
  89. Pan W, Xie B, Shen X (2010) Incorporating predictor network in penalized regression with application to microarray data. Biometrics 66(2):474–484. doi: 10.1111/j.1541-0420.2009.01296.x CrossRefPubMedCentralPubMedGoogle Scholar
  90. Parsons DW, Jones S, Zhang X, Lin JCH, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu IM, Gallia GL, Olivi A, McLendon R, Rasheed BA, Keir S, Nikolskaya T, Nikolsky Y, Busam DA, Tekleab H, Diaz LA Jr, Hartigan J, Smith DR, Strausberg RL, Marie SKN, Shinjo SMO, Yan H, Riggins GJ, Bigner DD, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu VE, Kinzler KW (2008) An integrated genomic analysis of human glioblastoma multiforme. Science 321(5897):1807–1812. doi: 10.1126/science.1164382 CrossRefPubMedCentralPubMedGoogle Scholar
  91. Patnaik SK, Kannisto E, Knudsen S, Yendamuri S (2010) Evaluation of microrna expression profiles that may predict recurrence of localized stage i non-small cell lung cancer after surgical resection. Cancer Res 70(1):36–45CrossRefPubMedGoogle Scholar
  92. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San MateoGoogle Scholar
  93. Pearl J (2000) Causality: models, reasoning and inference, vol 29. Cambridge University Press, CambridgeGoogle Scholar
  94. Pe’er D, Hacohen N (2011) Principles and strategies for developing network models in cancer. Cell 144(6):864–873CrossRefPubMedCentralPubMedGoogle Scholar
  95. Porzelius C, Johannes M, Binder H, Beissbarth T (2011) Leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients. Biom J 53(2):190–201. doi: 10.1002/bimj.201000155 CrossRefPubMedGoogle Scholar
  96. Price ND, Reed JL, Palsson BØ (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2(11):886–897. doi: 10.1038/nrmicro1023 CrossRefPubMedGoogle Scholar
  97. Price ND, Trent J, El-Naggar AK, Cogdell D, Taylor E, Hunt KK, Pollock RE, Hood L, Shmulevich I, Zhang W (2007) Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas. Proc Natl Acad Sci USA 104(9):3414–3419. doi: 10.1073/Pnas.0611373104 CrossRefPubMedCentralPubMedGoogle Scholar
  98. Raponi M, Lancet JE, Fan H, Dossey L, Lee G, Gojo I, Feldman EJ, Gotlib J, Morris LE, Greenberg PL, Wright JJ, Harousseau JL, Lowenberg B, Stone RM, De Porre P, Wang Y, Karp JE (2008) A 2-gene classifier for predicting response to the farnesyltransferase inhibitor tipifarnib in acute myeloid leukemia. Blood 111(5):2589–2596. doi: 10.1182/blood-2007-09-112730. URL http://www.ncbi.nlm.nih.gov/pubmed/18160667
  99. Rejniak KA, Anderson AR (2012) State of the art in computational modeling of cancer. Math Med Biol 29(1):1–2CrossRefPubMedGoogle Scholar
  100. Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721):523–529CrossRefPubMedGoogle Scholar
  101. Schadt EE, Björkegren JLM (2012) New: network-enabled wisdom in biology, medicine, and health care. Sci Transl Med 4(115):115rv1. doi: 10.1126/scitranslmed.3002132
  102. Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E (2011) Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the warburg effect. PLoS Comput Biol 7(3):e1002018. doi: 10.1371/journal.pcbi.1002018
  103. Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26(9):1003–1010. doi: 10.1038/nbt.1487 CrossRefPubMedGoogle Scholar
  104. Simcha DM, Younes L, Aryee MJ, Geman D (2013) Identification of direction in gene networks from expression and methylation. BMC Syst Biol 7(1):118CrossRefPubMedCentralPubMedGoogle Scholar
  105. Simon R (2006) Development and evaluation of therapeutically relevant predictive classifiers using gene expression profiling. J Natl Cancer Inst 98(17):1169–1171. doi: 10.1093/jnci/djj364 CrossRefPubMedGoogle Scholar
  106. Simon R, Radmacher MD, Dobbin K, McShane LM (2003) Pitfalls in the use of dna microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95(1):14–18CrossRefPubMedGoogle Scholar
  107. Staiger C, Cadot S, Kooter R, Dittrich M, Müller T, Klau GW, Wessels LFA (2012) A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer. PLoS One 7(4):e34796. doi: 10.1371/journal.pone.0034796
  108. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43):15545–15550. doi: 10.1073/pnas.0506580102 CrossRefPubMedCentralPubMedGoogle Scholar
  109. Sung J, Kim PJ, Ma S, Funk CC, Magis AT, Wang Y, Hood L, Geman D, Price ND (2013) Multi-study integration of brain cancer transcriptomes reveals organ-level diagnostic signatures. PLoS Comput Biol 9(7):e1003148Google Scholar
  110. Sung J, Wang Y, Chandrasekaran S, Witten DM, Price ND (2012) Molecular signatures from omics data: from chaos to consensus. Biotechnol J 7(8):946–57. doi: 10.1002/biot.201100305. URL http://www.ncbi.nlm.nih.gov/pubmed/22528809
  111. Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D (2005) Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21(20):3896–3904 (2005). doi: 10.1093/bioinformatics/bti631. URL http://www.ncbi.nlm.nih.gov/pubmed/16105897
  112. Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, Thorleifsson SG, Agren R, Bölling C, Bordel S, Chavali AK, Dobson P, Dunn WB, Endler L, Hala D, Hucka M, Hull D, Jameson D, Jamshidi N, Jonsson JJ, Juty N, Keating S, Nookaew I, Le Novère N, Malys N, Mazein A, Papin JA, Price ND, Selkov E Sr, Sigurdsson MI, Simeonidis E, Sonnenschein N, Smallbone K, Sorokin A, van Beek JHGM, Weichart D, Goryanin I, Nielsen J, Westerhoff HV, Kell DB, Mendes P, Palsson BØ (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31(5):419–425. doi: 10.1038/nbt.2488 CrossRefPubMedGoogle Scholar
  113. Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 99(10):6567–6572CrossRefPubMedCentralPubMedGoogle Scholar
  114. Tomasetti C, Levy D (2010) An elementary approach to modeling drug resistance in cancer. Math Biosci Eng 7(4):905–918. URL http://www.ncbi.nlm.nih.gov/pubmed/21077714
  115. Tomasetti C, Vogelstein B, Parmigiani G (2013) Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor initiation. Proc Natl Acad Sci USA 110(6):1999–2004. doi: 10.1073/pnas.1221068110. URL http://www.ncbi.nlm.nih.gov/pubmed/23345422
  116. Tuncbag N, Braunstein A, Pagnani A, Huang SS, Chayes J, Borgs C, Zecchina R, Fraenkel E (2013) Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. J Comput Biol 20(2):124–36. doi: 10.1089/cmb.2012.0092. URL http://www.ncbi.nlm.nih.gov/pubmed/23383998.
  117. Ulitsky I, Krishnamurthy A, Karp RM, Shamir R (2010) Degas: de novo discovery of dysregulated pathways in human diseases. PLoS One 5(10):e13367Google Scholar
  118. Vandin F, Clay P, Upfal E, Raphael B (2012) Discovery of mutated subnetworks associated with clinical data in cancer. In: Proceedings Pacific symposium biocomputing, pp 55–66Google Scholar
  119. Varadan V, Mittal P, Vaske CJ, Benz SC (2012) The integration of biological pathway knowledge in cancer genomics: a review of existing computational approaches. IEEE Signal Process Mag 29(1):35–50. doi: 10.1109/Msp.2011.943037 CrossRefGoogle Scholar
  120. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu JC, Haussler D, Stuart JM (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26(12):i237–i245. doi: 10.1093/bioinformatics/btq182 CrossRefPubMedCentralPubMedGoogle Scholar
  121. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW (2013) Cancer genome landscapes. Science 339(6127):1546–1558. doi: 10.1126/science.1235122. URL http://www.ncbi.nlm.nih.gov/pubmed/23539594
  122. Wang Y, Eddy JA, Price ND (2012) Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 6(1):153. doi: 10.1186/1752-0509-6-153 CrossRefPubMedCentralPubMedGoogle Scholar
  123. Wei Z, Li H (2007) Non-parametric pathway-based regression models for analysis of genomic data. Biostatistics 8(2):265–284. doi: 10.1093/biostatistics/kxl007 CrossRefPubMedGoogle Scholar
  124. Weichselbaum RR, Ishwaran H, Yoon T, Nuyten DSA, Baker SW, Khodarev N, Su AW, Shaikh AY, Roach P, Kreike B, Roizman B, Bergh J, Pawitan Y, de Vijver MJV, Minn AJ (2008) An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer. Proc Natl Acad Sci USA 105(47):18490–18495. doi: 10.1073/Pnas.0809242105
  125. Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, Shmulevich I, Sander C (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45(10):1113–1120. doi: 10.1038/ng.2764 CrossRefPubMedCentralPubMedGoogle Scholar
  126. Wilson JL, Hemann MT, Fraenkel E, Lauffenburger DA (2013) Integrated network analyses for functional genomic studies in cancer. Semin Cancer Biol 23(4):213–218. doi: 10.1016/j.semcancer.2013.06.004. URL http://www.ncbi.nlm.nih.gov/pubmed/23811269.
  127. Winslow R, Trayanova N, Geman D, Miller M (2012) The emerging discipline of computational medicine. Science Transl Med 4(158):158rv11Google Scholar
  128. Winslow RL, Trayanova N, Geman D, Miller MI (2012) Computational medicine: translating models to clinical care. Sci Transl Med 4(158):158rv11. doi: 10.1126/scitranslmed.3003528
  129. Wynn ML, Ventura AC, Sepulchre JA, García HJ, Merajver SD (2011) Kinase inhibitors can produce off-target effects and activate linked pathways by retroactivity. BMC Syst Biol 5:156. doi: 10.1186/1752-0509-5-156 CrossRefPubMedCentralPubMedGoogle Scholar
  130. Xu L, Tan AC, Naiman DQ, Geman D, Winslow RL (2005) Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics 21(20):3905–3911. doi: 10.1093/bioinformatics/bti647 CrossRefPubMedGoogle Scholar
  131. Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, Kamiyama M, Hruban RH, Eshleman JR, Nowak MA, Velculescu VE, Kinzler KW, Vogelstein B, Iacobuzio-Donahue CA (2010) Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467(7319):1114–1117. doi: 10.1038/nature09515. URL http://www.ncbi.nlm.nih.gov/pubmed/20981102
  132. Yeang CH, Ramaswamy S, Tamayo P, Mukherjee S, Rifkin RM, Angelo M, Reich M, Lander E, Mesirov J, Golub T (2001) Molecular classification of multiple tumor types. Bioinformatics 17(Suppl 1):S316–S322. URL http://www.ncbi.nlm.nih.gov/pubmed/11473023
  133. Yoruk E, Ochs MF, Geman D, Younes L (2011) A comprehensive statistical model for cell signaling. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 8(3):592–606CrossRefGoogle Scholar
  134. Zhang D, Tai LK, Wong LL, Chiu LL, Sethi SK, Koay ES (2005) Proteomic study reveals that proteins involved in metabolic and detoxification pathways are highly expressed in her-2/neu-positive breast cancer*. Mol Cell Proteomics 4(11):1686–1696CrossRefPubMedGoogle Scholar
  135. Zhao H, Logothetis CJ, Gorlov IP (2010) Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression. Prostate Cancer Prostateic Dis 13(3):252–259 (2010). doi: 10.1038/pcan.2010.9. URL http://www.ncbi.nlm.nih.gov/pubmed/20386565
  136. Zhu Y, Shen X, Pan W (2009) Network-based support vector machine for classification of microarray samples. BMC Bioinform 10(Suppl 1):S21. doi: 10.1186/1471-2105-10-S1-S21

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Donald Geman
    • 1
    Email author
  • Michael Ochs
    • 2
  • Nathan D. Price
    • 3
  • Cristian Tomasetti
    • 4
  • Laurent Younes
    • 1
  1. 1.Department of Applied Mathematics and StatisticsJohns Hopkins UniversityBaltimoreUSA
  2. 2.Mathematics and StatisticsThe College of New JerseyEwing TownshipUSA
  3. 3.Institute for Systems BiologySeattleUSA
  4. 4.Division of Biostatistics and Bioinformatics, and Department of BiostatisticsJohns Hopkins UniversityBaltimoreUSA

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