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18 - Signaling network analysis of genomic alterations predicts breast cancer drug targets

from Part IV - Next-generation sequencing technology and pharmaco-genomics

Published online by Cambridge University Press:  18 December 2015

Naif Zaman
Affiliation:
McGill University Center for Bioinformatics
Edwin Wang
Affiliation:
McGill University Center for Bioinformatics
Krishnarao Appasani
Affiliation:
GeneExpression Systems, Inc., Massachusetts
Stephen W. Scherer
Affiliation:
University of Toronto
Peter M. Visscher
Affiliation:
University of Queensland
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Summary

Introduction

Thousands upon thousands of tumors have been sequenced so far, representing over 20 different cancer types. These efforts allowed scientists to take a closer look at the genomic alterations (i.e., mutations and copy number variations) within a tumor's genome, in order to be able to potentially explain the underlying mechanism that drives cancer cell survival and proliferation (Banerji et al., 2012; Cancer Genome Atlas Network, 2012; Stephens et al., 2012). However, extracting useful information from a vast source of various data types to establish a link between genomic alterations and the driving force behind cancer cells remains a challenge (Chin et al., 2011).

Over the past decades of cancer research, scientists have learned that during the evolution of normal cells to cancer cells, different genomic alterations are compiled. These alterations can impact gene expression and protein function to modulate certain fundamental characteristics (i.e., cancer hallmarks) of a cancer cell. Cell survival, proliferation, and apoptosis are among the most primitive cancer hallmarks (Hanahan and Weinberg, 2011). The accumulation of genomic alterations allows cancer cells to reach a neoplastic state that enables them to proliferate indefinitely and become nearly immortal. However, these changes appear to be random, with no patterns that can be used for classifications of patients or identifying drug targets for treatment.

Recent studies (Schlabach et al., 2008; Silva et al., 2008) have gone on to identify genes that are required for cancer cell survival and proliferation (i.e., essential genes). They accomplished this by performing a genome-wide RNAi knockdown screening for different cancer cell lines from three different cancer types, whereupon a gene was considered to be an essential gene if the knockdown of that gene reduced the cell's survival and proliferation based on p-values. A key observation to note in these RNAi knockdown studies is that different cancer cell lines had different sets of essential genes, implying that the cancer hallmark traits, such as survival and proliferation, can be affected by different sets of genes. This was true for cell lines that belong to the same cancer type. Therefore, no two lung cancer cell lines, for example, had identical sets of essential genes, although there was some overlap. In addition, there was no one gene that appeared to be essential across all the different lung cancer cell lines.

Type
Chapter
Information
Genome-Wide Association Studies
From Polymorphism to Personalized Medicine
, pp. 269 - 280
Publisher: Cambridge University Press
Print publication year: 2016

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References

Awan, A., Bari, H., Yan, F., et al. (2007). Regulatory network motifs and hotspots of cancer genes in a mammalian cellular signalling network. IET Syst. Biol., 1, 292–297.CrossRefGoogle Scholar
Banerji, S., Cibulskis, K., Rangel-Escareno, C., et al. (2012). Sequence analysis of mutations and translocations across breast cancer subtypes. Nature, 486, 405–409.CrossRefGoogle ScholarPubMed
Barabasi, A.L., Gulbahce, N. and Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Rev. Genet., 12, 56–68.CrossRefGoogle ScholarPubMed
Cancer Genome Atlas Network. (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490, 61–70.
Chan, K.C., Jiang, P., Zheng, Y.W., et al. (2013). Cancer genome scanning in plasma: detection of tumor-associated copy number aberrations, single-nucleotide variants, and tumoral heterogeneity by massively parallel sequencing. Clin. Chem., 59, 211–224.CrossRefGoogle ScholarPubMed
Chin, L., Hahn, W.C., Getz, G. and Meyerson, M. (2011). Making sense of cancer genomic data. Genes Develop., 25, 534–555.CrossRefGoogle ScholarPubMed
Cui, Q., Ma, Y., Jaramillo, M., et al. (2007). A map of human cancer signaling. Molec. Syst. Biol., 3, 152.CrossRefGoogle ScholarPubMed
Garnett, M.J., Edelman, E.J., Heidorn, S.J., et al. (2012). Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature, 483, 570–575.CrossRefGoogle ScholarPubMed
Gerlinger, M., Rowan, A.J., Horswell, S., et al. (2012). Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New. Engl. J. Med., 366, 883–892.CrossRefGoogle ScholarPubMed
Hanahan, D. and Weinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell, 144, 646–674.CrossRefGoogle ScholarPubMed
Heiser, L.M., Sadanandam, A., Kuo, W.L., et al. (2012). Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc. Natl Acad. Sci. USA, 109, 2724–2729.CrossRefGoogle ScholarPubMed
Leary, R.J., Sausen, M., Kinde, I., et al. (2012). Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing. Sci. Transl. Med., 4, 162ra154.CrossRefGoogle ScholarPubMed
Li, L., Tibiche, C., Fu, C., et al. (2012). The human phosphotyrosine signaling network: evolution and hotspots of hijacking in cancer. Genome Res., 22, 1222–1230.CrossRefGoogle Scholar
Marcotte, R., Brown, K.R., Suarez, F., et al. (2012). Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer Discov., 2, 172–189.CrossRefGoogle ScholarPubMed
Murtaza, M., Dawson, S.J., Tsui, D.W., et al. (2013). Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature, 497, 108–112.CrossRefGoogle ScholarPubMed
Parker, J.S., Mullins, M., Cheang, M.C., et al. (2009). Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol., 27, 1160–1167.CrossRefGoogle ScholarPubMed
Rozenblatt-Rosen, O., Deo, R.C., Padi, M., et al. (2012). Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins. Nature, 487, 491–495.CrossRefGoogle ScholarPubMed
Saito, R., Smoot, M.E., Ono, K., et al. (2012). A travel guide to Cytoscape plugins. Nature Meth., 9, 1069–1076.CrossRefGoogle ScholarPubMed
Schlabach, M.R., Luo, J., Solimini, N.L., et al. (2008). Cancer proliferation gene discovery through functional genomics. Science, 319, 620–624.CrossRefGoogle ScholarPubMed
Silva, J.M., Marran, K., Parker, J.S., et al. (2008). Profiling essential genes in human mammary cells by multiplex RNAi screening. Science, 319, 617–620.CrossRefGoogle ScholarPubMed
Sjoblom, T., Jones, S., Wood, L.D., et al. (2006). The consensus coding sequences of human breast and colorectal cancers. Science, 314, 268–274.CrossRefGoogle ScholarPubMed
Slamon, D.J., Clark, G.M., Wong, S.G., et al. (1987). Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science, 235, 177–182.CrossRefGoogle ScholarPubMed
Stephens, P.J., Tarpey, P.S., Davies, H., et al. (2012). The landscape of cancer genes and mutational processes in breast cancer. Nature, 486, 400–404.CrossRefGoogle ScholarPubMed
van ‘t Veer, L.J., Dai, H., van de Vijver, M.J., et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530–536.Google ScholarPubMed
Wang, E. (2010). Cancer Systems Biology. CRC Press, Boca Raton, FL.CrossRefGoogle Scholar
Wang, E., Zou, J., Zaman, N., et al. (2013a). Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks. Semin. Cancer Biol., 23, 279–285.Google ScholarPubMed
Wang, E., Zou, J., Zaman, N., et al. (2013b). Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance. Semin. Cancer Biol., 23, 286–292.Google ScholarPubMed
Wang, E., Zaman, N., McGee, S., et al. (2015). Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data. Semin. Cancer Biol., 30, 4–12.CrossRefGoogle ScholarPubMed
Zaman, N., Li, L., Jaramillo, M.L., et al. (2013). Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. Cell Rep., 5, 216–223.CrossRefGoogle ScholarPubMed

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