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Cancer Bioinformatics pp 65–87Cite as

Cancer Classification and Molecular Signature Identification

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Abstract

Cancer is a family of diseases that share a common set of characteristics such as reprogrammed energy metabolism, uncontrolled cell growth, tumor angiogenesis and avoidance of immune destruction, referred to as cancer hallmarks, as introduced in Chap. 1. Based on their original cell types, cancers are classified into five classes: (1) carcinoma, which begins in epithelial cells and represents the majority of the human cancer cases; (2) sarcoma, derived from mesenchymal cells, e.g., connective tissue cells such as fibroblasts; (3) lymphoma, leukemia and myeloma, originating in hematopoietic or blood-forming cells; (4) germ cell tumors, developing, as the name implies, from germ cells; and (5) neuroblastoma, glioma, glioblastoma and others derived from cells of the central and peripheral nervous system and denoted as neuroectodermal tumors because of their beginning in the early embryo. Each class may consist of cancers of different types. For example, carcinoma comprises adenocarcinoma, basal-cell carcinoma, small-cell carcinoma and squamous cell carcinoma, independent of their underlying tissue types. Cancers of the same type and developing in the same tissue may have distinct properties in terms of their growth patterns, malignance levels, survival rates and possibly even different underlying mechanisms. They may respond differently to the same drug treatment and hence have different mortality rates. As of now, over 200 types of human cancers have been identified and characterized (Stewart and Kleihues 2003), the majority of which are determined based on the location, the originating cell type and cell morphology. It is now becoming evident that this type of classification, in large part subjective, is not adequate for developing personalized treatment plans, which are becoming increasingly desirable and clearly represents the future of cancer medicine.

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References

  • Albain KS, Barlow WE, Shak S et al. (2010) Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol 11: 55-65

    Article  Google Scholar 

  • Arranz EE, Vara JA, Gamez-Pozo A et al. (2012) Gene signatures in breast cancer: current and future uses. Transl Oncol 5: 398-403

    Article  Google Scholar 

  • Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. Journal of computational biology : a journal of computational molecular cell biology 6: 281-297

    Article  Google Scholar 

  • Beutler E (2001) The treatment of acute leukemia: past, present, and future. Leukemia 15: 658-661

    Article  Google Scholar 

  • Bloom HJ, Richardson WW (1957) Histological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years. Br J Cancer 11: 359-377

    Article  Google Scholar 

  • Chen JJ, Knudsen S, Mazin W et al. (2012) A 71-gene signature of TRAIL sensitivity in cancer cells. Mol Cancer Ther 11: 34-44

    Article  Google Scholar 

  • Cho SH, Jeon J, Kim SI (2012) Personalized medicine in breast cancer: a systematic review. J Breast Cancer 15: 265-272

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20: 273-297

    MATH  Google Scholar 

  • Cui J, Chen Y, Chou WC et al. (2011a) An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer. Nucleic acids research 39: 1197-1207

    Article  Google Scholar 

  • Cui J, Li F, Wang G et al. (2011b) Gene-expression signatures can distinguish gastric cancer grades and stages. PLoS One 6: e17819

    Article  Google Scholar 

  • D’haeseleer P (2005) How does gene expression clustering work? Nature Biotechnology 23: 1499-1501

    Article  Google Scholar 

  • Duan KB, Keerthi SS (2005) Which is the best multiclass SVM method? An empirical study. Multiple Classifier Systems 3541: 278-285

    Article  Google Scholar 

  • Eddy JA, Sung J, Geman D et al. (2010) Relative expression analysis for molecular cancer diagnosis and prognosis. Technol Cancer Res Treat 9: 149-159

    Google Scholar 

  • Erten S, Chowdhury SA, Guan X et al. (2012) Identifying stage-specific protein subnetworks for colorectal cancer. BMC Proc 6 Suppl 7: S1

    Google Scholar 

  • Fuhrman SA, Lasky LC, Limas C (1982) Prognostic significance of morphologic parameters in renal cell carcinoma. Am J Surg Pathol 6: 655-663

    Article  Google Scholar 

  • Gleason DF (1966) Classification of prostatic carcinomas. Cancer Chemother Rep 50: 125-128

    Google Scholar 

  • Gleason DF, Mellinger GT (1974) Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J Urol 111: 58-64

    Google Scholar 

  • Golub TR, Slonim DK, Tamayo P et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531-537

    Article  Google Scholar 

  • Goodison S, Sun Y, Urquidi V (2010) Derivation of cancer diagnostic and prognostic signatures from gene expression data. Bioanalysis 2: 855-862

    Article  Google Scholar 

  • Goseki N, Takizawa T, Koike M (1992) Differences in the mode of the extension of gastric cancer classified by histological type: new histological classification of gastric carcinoma. Gut 33: 606-612

    Article  Google Scholar 

  • Guyon I, Weston J, Barnhill S et al. (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46: 389-422

    Article  MATH  Google Scholar 

  • Inza I, Larranaga P, Blanco R et al. (2004) Filter versus wrapper gene selection approaches in DNA microarray domains. Artif Intell Med 31: 91-103

    Article  Google Scholar 

  • Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8: 118-127

    Article  MATH  Google Scholar 

  • Kim WJ, Kim SK, Jeong P et al. (2011) A four-gene signature predicts disease progression in muscle invasive bladder cancer. Mol Med 17: 478-485

    Google Scholar 

  • Lauren P (1965) The Two Histological Main Types of Gastric Carcinoma: Diffuse and So-Called Intestinal-Type Carcinoma. An Attempt at a Histo-Clinical Classification. Acta pathologica et microbiologica Scandinavica 64: 31-49

    Google Scholar 

  • Li G, Ma Q, Tang H et al. (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic acids research 37: e101

    Article  Google Scholar 

  • Liong ML, Lim CR, Yang H et al. (2012) Blood-based biomarkers of aggressive prostate cancer. PLoS One 7: e45802

    Article  Google Scholar 

  • Livasy CA, Karaca G, Nanda R et al. (2006) Phenotypic evaluation of the basal-like subtype of invasive breast carcinoma. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 19: 264-271

    Article  Google Scholar 

  • Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform 1: 24-45

    Article  Google Scholar 

  • Marisa L, de Reynies A, Duval A et al. (2013) Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med 10: e1001453

    Article  Google Scholar 

  • Mroz EA, Rocco JW (2012) Gene expression analysis as a tool in early-stage oral cancer management. J Clin Oncol 30: 4053-4055

    Article  Google Scholar 

  • Mukherjee S (2010) The emperor of all maladies: a biography of cancer. Scribner,

    Google Scholar 

  • Ramaswamy S, Tamayo P, Rifkin R et al. (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences of the United States of America 98: 15149-15154

    Article  Google Scholar 

  • Rebhan M, ChalifaCaspi V, Prilusky J et al. (1997) GeneCards: Integrating information about genes, proteins and diseases. Trends Genet 13: 163-163

    Article  Google Scholar 

  • Reis-Filho JS, Pusztai L (2011) Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 378: 1812-1823

    Article  Google Scholar 

  • Rodenhiser DI, Andrews JD, Vandenberg TA et al. (2011) Gene signatures of breast cancer progression and metastasis. Breast Cancer Res 13: 201

    Article  Google Scholar 

  • Shah MA, Khanin R, Tang L et al. (2011) Molecular classification of gastric cancer: a new paradigm. Clin Cancer Res 17: 2693-2701

    Article  Google Scholar 

  • Shedden K, Taylor JM, Enkemann SA et al. (2008) Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 14: 822-827

    Article  Google Scholar 

  • Simpson JF, Gray R, Dressler LG et al. (2000) Prognostic value of histologic grade and proliferative activity in axillary node-positive breast cancer: results from the Eastern Cooperative Oncology Group Companion Study, EST 4189. J Clin Oncol 18: 2059-2069

    Google Scholar 

  • Slodkowska EA, Ross JS (2009) MammaPrint 70-gene signature: another milestone in personalized medical care for breast cancer patients. Expert Rev Mol Diagn 9: 417-422

    Article  Google Scholar 

  • Starmans MH, Krishnapuram B, Steck H et al. (2008) Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types. Br J Cancer 99: 1884-1890

    Article  Google Scholar 

  • Stewart BW, Kleihues P (2003) World cancer report. IARC Press,

    Google Scholar 

  • Tibshirani R, Hastie T, Narasimhan B et al. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America 99: 6567-6572

    Article  Google Scholar 

  • Van Mechelen I, Bock HH, De Boeck P (2004) Two-mode clustering methods: a structured overview. Stat Methods Med Res 13: 363-394

    Article  MATH  MathSciNet  Google Scholar 

  • Warburg O (1956) On the origin of cancer cells. Science 123: 309-314

    Article  Google Scholar 

  • Weigelt B, Baehner FL, Reis-Filho JS (2010) The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. The Journal of pathology 220: 263-280

    Article  Google Scholar 

  • Wu S, Liew AW, Yan H et al. (2004) Cluster analysis of gene expression data based on self-splitting and merging competitive learning. IEEE Trans Inf Technol Biomed 8: 5-15

    Article  Google Scholar 

  • Yamagiwa K, Ichikawa K (1918) Experimental study of the pathogenesis of carcinoma. The Journal of Cancer Research 3: 1-29

    Google Scholar 

  • Yeoh EJ, Ross ME, Shurtleff SA et al. (2002) Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1: 133-143

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA

    Ying Xu & David Puett

  2. Department of Computer Science and Engineering, University of Nebraska, Lincoln, NE, USA

    Juan Cui

Authors
  1. Ying Xu
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  2. Juan Cui
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  3. David Puett
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Appendix

Appendix

Table 3.2 Patient data used in the analysis in Sect. 3.2
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Xu, Y., Cui, J., Puett, D. (2014). Cancer Classification and Molecular Signature Identification. In: Cancer Bioinformatics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1381-7_3

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  • DOI: https://doi.org/10.1007/978-1-4939-1381-7_3

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  • Print ISBN: 978-1-4939-1380-0

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