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Integration of network biology and imaging to study cancer phenotypes and responses

Editor: Ying Xu Authors:

Olga C. Rodriguez,

Emanuel Petricoin,

Maria Avantaggiati,

Chris AlbaneseAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 11, Issue 6

Pages 1009 - 1019

https://doi.org/10.1109/TCBB.2014.2338304

Published: 01 November 2014 Publication History

Abstract

Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.

References

[1]

J. J. Tyson, W. T. Baumann, C. Chen, A. Verdugo, I. Tavassoly, Y. Wang, L. M. Weiner, and R. Clarke, "Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells," Nat. Rev. Cancer, vol. 11, no. 7, pp. 523-32, Jul. 2011.

[2]

P. Creixell, E. M. Schoof, J. T. Erler, and R. Linding, "Navigating cancer network attractors for tumor-specific therapy," Nat. Biotechnol., vol. 30, no. 9, pp. 842-848, Sep. 2012.

[3]

A. L. Barabasi, N. Gulbahce, and J. Loscalzo, "Network medicine: A network-based approach to human disease," Nat. Rev. Genet., vol. 12, no. 1, pp. 56-68, Jan. 2011.

[4]

B. Zhang, H. Li, R. B. Riggins, M. Zhan, J. Xuan, Z. Zhang, E. P. Hoffman, R. Clarke, and Y. Wang, "Differential dependency network analysis to identify condition-specific topological changes in biological networks," Bioinformatics, vol. 25, no. 4, pp. 526-32, Feb. 15, 2009.

[5]

B. Zhang, Y. Tian, L. Jin, H. Li, I.-M. Shih, S. Madhavan, R. Clarke, E. Hoffman, J. Xuan, L. Hilakivi-Clarke, and Y. Wang, "DDN: A caBIG analytical tool for differential network analysis," Bioinformatics, vol. 27, no. 7, pp. 1036-1038, 2011.

[6]

T. Ideker and N. J. Krogan, "Differential network biology," Mol. Syst. Biol., vol. 8, p. 565, 2012.

[7]

N. J. Hudson, B. P. Dalrymple, and A. Reverter, "Beyond differential expression: The quest for causal mutations and effector molecules," BMC Genomics, vol. 13, p. 356, 2012.

[8]

N. J. Hudson, A. Reverter, and B. P. Dalrymple, "A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation," PLoS Comput. Biol., vol. 5, no. 5, p. e1000382, May 2009.

[9]

A. de la Fuente, "From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases," Trends Genet., vol. 26, no. 7, pp. 326-33, Jul. 2010.

[10]

A. Reverter, N. J. Hudson, S. H. Nagaraj, M. Perez-Enciso, and B. P. Dalrymple, "Regulatory impact factors: Unraveling the transcriptional regulation of complex traits from expression data," Bioinformatics, vol. 26, no. 7, pp. 896-904, Apr. 1, 2010.

[11]

M. J. Lee, A. S. Ye, A. K. Gardino, A. M. Heijink, P. K. Sorger, G. MacBeath, and M. B. Yaffe, "Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks," Cell, vol. 149, no. 4, pp. 780-794, May 11, 2012.

[12]

J. M. Skerker, B. S. Perchuk, A. Siryaporn, E. A. Lubin, O. Ashenberg, M. Goulian, and M. T. Laub, "Rewiring the specificity of two-component signal transduction systems," Cell, vol. 133, no. 6, pp. 1043-1054, Jun. 13, 2008.

[13]

R. Weissleder, "Molecular imaging in cancer," Science, vol. 312, no. 5777, pp. 1168-1171, May 26, 2006.

[14]

H. Peng, A. Bateman, A. Valencia, and J. D. Wren, "Bioimage informatics: A new category in Bioinformatics," Bioinformatics, vol. 28, no. 8, p. 1057, Apr. 15, 2012.

[15]

L. Chen, P. L. Choyke, T. H. Chan, C. Y. Chi, G. Wang, and Y. Wang, "Tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors," IEEE Trans. Med. Imag., vol. 30, no. 12, pp. 2044-2058, Dec. 2011.

[16]

L. Chen, T. H. Chan, P. L. Choyke, E. M. Hillman, C. Y. Chi, Z. M. Bhujwalla, G. Wang, S. S. Wang, Z. Szabo, and Y. Wang, "CAM-CM: A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues," Bioinformatics, vol. 27, no. 18, pp. 2607-2609, Sep. 15, 2011.

[17]

Y. Yuan, H. Failmezger, O. M. Rueda, H. R. Ali, S. Graf, S. F. Chin, R. F. Schwarz, C. Curtis, M. J. Dunning, H. Bardwell, N. Johnson, S. Doyle, G. Turashvili, E. Provenzano, S. Aparicio, C. Caldas, and F. Markowetz, "Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling," Sci. Transl. Med., vol. 4, no. 157, p. 157ra143, Oct. 24, 2012.

[18]

B. Zhao, G. R. Oxnard, C. S. Moskowitz, M. G. Kris, W. Pao, P. Guo, V. M. Rusch, M. Ladanyi, N. A. Rizvi, and L. H. Schwartz, "A pilot study of volume measurement as a method of tumor response evaluation to aid biomarker development," Clin. Cancer Res., vol. 16, no. 18, pp. 4647-4653, Sep. 15, 2010.

[19]

C. Albanese, O. C. Rodriguez, J. VanMeter, S. T. Fricke, B. R. Rood, Y. Lee, S. S. Wang, S. Madhavan, Y. Gusev, E. F. Petricoin III, and Y. Wang, "Preclinical magnetic resonance imaging and systems biology in cancer research: Current applications and challenges," Am. J. Pathol., vol. 182, no. 2, pp. 312-318, 2013.

[20]

P. Sirajuddin, S. Das, L. Ringer, O. C. Rodriguez, A. Sivakumar, Y.-C. Lee, A. Üren, S. T. Fricke, B. Rood, A. Ozcan, S. S. Wang, S. Karam, V. Yenugonda, P. Salinas, E. Petricoin, M. Pishvaian, M. P. Lisanti, Y. Wang, R. Schlegel, B. Moasser, and C. Albanese, "Quantifying the CDK inhibitor VMY-1-103's activity and tissue levels in an in vivo tumor model by LC-MS/MS and by MRI," Cell Cycle, vol. 11, no. 20, pp. 3801-3809, 2012.

[21]

Y. Wang, T. Adali, J. Xuan, and Z. Szabo, "Magnetic resonance image analysis by information theoretic criteria and stochastic site models," IEEE Trans. Inf. Technol. Biomed., vol. 5, no. 5, pp. 150-158, Jun. 2001.

[22]

M. Pierobon, A. J. VanMeter, N.Moroni, F. Galdi, and E. F. Petricoin, "Reverse-phase protein microarrays," in Molecular Profiling, Methods in Molecular Biology. New York, NY, USA: Springer, 2012, pp. 215-235.

[23]

E. M. Beauchamp, L. Ringer, G. Bulut, K. P. Sajwan, M. D. Hall, Y. C. Lee, D. Peaceman, M. Ozdemirli, O. Rodriguez, T. J. Macdonald, C. Albanese, J. A. Toretsky, and A. Uren, "Arsenic trioxide inhibits human cancer cell growth and tumor development in mice by blocking hedgehog/GLI pathway," J. Clin. Invest., vol. 121, no. 1, pp. 148-160, Jan. 4, 2011.

[24]

E. Segal, C. B. Sirlin, C. Ooi, A. S. Adler, J. Gollub, X. Chen, B. K. Chan, G. R. Matcuk, C. T. Barry, H. Y. Chang, and M. D. Kuo, "Decoding global gene expression programs in liver cancer by noninvasive imaging," Nat. Biotechnol., vol. 25, no. 6, pp. 675-680, Jun. 2007.

[25]

S. Huang, J. Li, K. Chen, T. Wu, J. Ye, X. Wu, and L. Yao, "A transfer learning approach for network modeling," IIE Trans., vol. 44, no. 11, pp. 915-931, Nov. 1, 2012.

[26]

R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Statist. Soc., Series B, vol. 58, pp. 267-288, 1996.

[27]

B. Zhang and Y. Wang, "Learning structural changes of gaussian graphical models in controlled experiments," in Proc. Conf. Uncertainty Artif. Intell., 2010, pp. 701-708.

[28]

H. Li, J. Xuan, Y. Wang, and M. Zhan, "Inferring regulatory networks," Front. Biosci., vol. 13, pp. 263-275, 2008.

[29]

Y. Tian, B. Zhang, I.-M. Shih, and Y. Wang, "Knowledge-guided differential dependency network learning for detecting structural changes in biological networks," in Proc. 2nd ACM Conf. Bioinformat., Comput. Biol. Biomed., Chicago, IL, USA, 2011, pp. 254-263.

[30]

S. Bandyopadhyay, M. Mehta, D. Kuo, M. K. Sung, R. Chuang, E. J. Jaehnig, B. Bodenmiller, K. Licon, W. Copeland, M. Shales, D. Fiedler, J. Dutkowski, A. Guenole, H. van Attikum, K. M. Shokat, R. D. Kolodner, W. K. Huh, R. Aebersold, M. C. Keogh, N. J. Krogan, and T. Ideker, "Rewiring of genetic networks in response to DNA damage," Science, vol. 330, no. 6009, pp. 1385-1389, Dec. 3, 2010.

[31]

M. Kanehisa and S. Goto, "KEGG: Kyoto encyclopedia of genes and genomes," Nucleic Acids Res., vol. 28, no. 1, pp. 27-30, Jan. 1, 2000.

[32]

I. CVX Research. (2012, Sep.). CVX: Matlab software for disciplined convex programming, version 2.0 beta [Online]. Available: http://cvxr.com/cvx

[33]

M. Grant and S. Boyd, "Graph implementations for nonsmooth convex programs, recent advances in learning and control (a tribute to M. Vidyasagar)," in Lecture Notes in Control and Information Sciences, V. Blondel, S. Boyd, and H. Kimura, Eds. New York, NY, USA: Springer, 2008, pp. 95-110.

[34]

X. Yuan, G. Yu, X. Hou, M. Shih Ie, R. Clarke, J. Zhang, E. P. Hoffman, R. R. Wang, Z. Zhang, and Y. Wang, "Genome-wide identification of significant aberrations in cancer genome," BMC Genomics, vol. 13, p. 342, 2012.

[35]

G. Yu, B. Zhang, G. S. Bova, J. Xu, M. Shih Ie, and Y. Wang, "BACOM: In silico detection of genomic deletion types and correction of normal cell contamination in copy number data," Bioinformatics, vol. 27, no. 11, pp. 1473-1480, Jun. 1, 2011.

[36]

D. Pinkel and D. G. Albertson, "Array comparative genomic hybridization and its applications in cancer," Nat. Genet., vol. 37, pp. S11-S17, Jun. 2005.

[37]

E. Kuhn, R. C. Wu, B. Guan, G. Wu, J. Zhang, Y. Wang, L. Song, X. Yuan, L. Wei, R. B. Roden, K. T. Kuo, K. Nakayama, B. Clarke, P. Shaw, N. Olvera, R. J. Kurman, D. A. Levine, T. L. Wang, and M. Shih Ie, "Identification of molecular pathway aberrations in uterine serous carcinoma by genome-wide analyses," J. Nat. Cancer Inst., vol. 104, no. 19, pp. 1503-1513, Oct. 3, 2012.

[38]

Y. Wang, T. Adali, S. Y. Kung, and Z. Szabo, "Quantification and segmentation of brain tissues from MR images: A probabilistic neural network approach," IEEE Trans. Image Process., vol. 7, no. 8, pp. 1165-1181, Aug. 1998.

[39]

T. Van den Bulcke, K. Van Leemput, B. Naudts, P. van Remortel, H. Ma, A. Verschoren, B. De Moor, and K. Marchal, "SynTReN: A generator of synthetic gene expression data for design and analysis of structure learning algorithms," BMC Bioinformat., vol. 7, no. 1, p. 43, 2006.

[40]

S. Loi, B. Haibe-Kains, C. Desmedt, F. Lallemand, A. M. Tutt, C. Gillet, P. Ellis, A. Harris, J. Bergh, J. A. Foekens, J. G. M. Klijn, D. Larsimont, M. Buyse, G. Bontempi, M. Delorenzi, M. J. Piccart, and C. Sotiriou, "Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade," J. Clinical Oncol., vol. 25, no. 10, pp. 1239-1246, Apr. 1, 2007.

[41]

A. Bergamaschi and B. S. Katzenellenbogen, "Tamoxifen downregulation of miR-451 increases 14-3-3[zeta] and promotes breast cancer cell survival and endocrine resistance," Oncogene, vol. 31, no. 1, pp. 39-47, 2012.

[42]

A. Bergamaschi, B. Christensen, and B. Katzenellenbogen, "Reversal of endocrine resistance in breast cancer: interrelationships among 14-3-3zeta, FOXM1, and a gene signature associated with mitosis," Breast Cancer Res., vol. 13,no. 3, p.R70, 2011.

[43]

M. A. Nowak, N. L. Komarova, A. Sengupta, P. V. Jallepalli, I.-M. Shih, B. Vogelstein, and C. Lengauer, "The role of chromosomal instability in tumor initiation," in Proc. Nat. Academy Sci., vol. 99, no. 25, pp. 16226-16231, Dec. 10, 2002.

[44]

R. M. Ricke, J. H. van Ree, and J. M. van Deursen, "Whole chromosome instability and cancer: a complex relationship," Trends Genetics: TIG, vol. 24, no. 9, pp. 457-466, 2008.

[45]

D. M. Berman, S. S. Karhadkar, A. R. Hallahan, J. I. Pritchard, C. G. Eberhart, D. N. Watkins, J. K. Chen, M. K. Cooper, J. Taipale, J. M. Olson, and P. A. Beachy, "Medulloblastoma growth inhibition by hedgehog pathway blockade," Science, vol. 297, no. 5586, pp. 1559-1561, Aug. 30, 2002.

[46]

J. M. de Bont, R. J. Packer, E. M. Michiels, M. L. D. Boer, and R. Pieters, "Biological background of pediatric medulloblastoma and ependymoma: A review from a translational research perspective," Neuro-Oncology , vol. 10, no. 6, pp. 1040-1060, Dec. 2008.

[47]

T. Ideker, O. Ozier, B. Schwikowski, and A. F. Siegel, "Discovering regulatory and signalling circuits in molecular interaction networks," Bioinformat., vol. 18, Suppl. 1, pp. S233-S240, 2002.

Cited By

Huang YChen YQian X(2014)Selected articles from the 2012 IEEE international workshop on genomic signal processing and statistics (GENSIPS 2012)IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2014.235321811:6(981-983)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1109/TCBB.2014.2353218

Index Terms

Integration of network biology and imaging to study cancer phenotypes and responses

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Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 11, Issue 6

November/December 2014

290 pages

ISSN:1545-5963

Editor:
Ying Xu
Department of Biochemistry, Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 November 2014

Accepted: 15 June 2014

Revised: 29 May 2014

Received: 18 April 2014

Published in TCBB Volume 11, Issue 6

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Cited By

Huang YChen YQian X(2014)Selected articles from the 2012 IEEE international workshop on genomic signal processing and statistics (GENSIPS 2012)IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2014.235321811:6(981-983)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1109/TCBB.2014.2353218

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