Cancer Development and Progression

  • Mei He
  • Jennifer Rosen
  • David Mangiameli
  • Steven K. Libutti
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 593)

Abstract

Cancer development and progression is a complex process that involves a host of functional and genetic abnormalities. Genomic perturbations and the gene expression they lead to, can now be globally identified with the use of DNA microarray. This relatively new technology has forever changed the scale of biological investigation. The enormous amount of data generated via a single chip has led to major global studies of the cellular processes underlying malignant transformation and progression. The multiplicity of platforms from different proprietors has offered investigators flexibility in their experimental design. Additionally, there are several more recent microarrays whose designs were inspired by the nucleotide-based technology. These include protein, multi-tissue, cell, and interference RNA microarrays. Combinations of microarray and other contemporary scientific methods, such as, laser capture microdissection (LCM), comparative genomic hybridization (CGH), single nude-otide polymorphism analysis (SNP) and chromatin immunoprecipitation (ChIP), have created entirely new fields of interest in the more global quest to better define the molecular basis of malignancy. In addition to basic science applications, many clinical inquiries have been performed. These queries have shown microarray to have clinical utility in cancer diagnosis, risk stratification, and patient management.

References

  1. 1.
    Dean M. Cancer as a complex developmental disorder-nineteenth Cornelius P. Rhoads Memorial Award Lecture. Cancer Res 1998; 58(24):5633–5636.PubMedGoogle Scholar
  2. 2.
    Solomon E, Borrow J, Goddard AD. Chromosome aberrations and cancer. Science 1991; 254(5035):1153–1160.PubMedCrossRefGoogle Scholar
  3. 3.
    Fearon ER, Vogelstein B. A genetic model for colorectal tumorigenesis. Cell 1990; 61(5):759–767.PubMedCrossRefGoogle Scholar
  4. 4.
    Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100(1):57–70.PubMedCrossRefGoogle Scholar
  5. 5.
    Ha PK, Benoit NE, Yochem R et al. A transcriptional progression model for head and neck cancer. Clin Cancer Res 2003; 9(8):3058–3064.PubMedGoogle Scholar
  6. 6.
    Dave SS, Wright G, Tan B et al. Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med 2004; 351(21):2159–2169.PubMedCrossRefGoogle Scholar
  7. 7.
    van de Vijver MJ, He YD, van’t Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347(25):1999–2009.PubMedCrossRefGoogle Scholar
  8. 8.
    Zhu H, Klemic JF, Chang S et al. Analysis of yeast protein kinases using protein chips. Nat Genet 2000; 26(3):283–289.PubMedCrossRefGoogle Scholar
  9. 9.
    Houseman BT, Mrksich M. Carbohydrate arrays for the evaluation of protein binding and enzymatic modification. Chem Biol 2002; 9(4):443–454.PubMedCrossRefGoogle Scholar
  10. 10.
    Kononen J, Bubendorf L, Kallionimeni A et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. 1998; 4(7):844–847, (1998/07//print).Google Scholar
  11. 11.
    Baghdoyan S, Roupioz Y, Pitaval A et al. Quantitative analysis of highly parallel transfection in cell microarrays. Nucleic Acids Res 2004; 32(9):e77.PubMedCrossRefGoogle Scholar
  12. 12.
    Kuruvilla FG, Shamji AF, Sternson SM et al. Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays. Nature 2002; 416(6881):653–657.PubMedCrossRefGoogle Scholar
  13. 13.
    Lockhart DJ, Dong H, Byrne MC et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996; 14(13):1675–1680.PubMedCrossRefGoogle Scholar
  14. 14.
    Pietu G, Alibert O, Guichard V et al. Novel gene transcripts preferentially expressed in human muscles revealed by quantitative hybridization of a high density cDNA array. Genome Res 1996; 6(6):492–503.PubMedCrossRefGoogle Scholar
  15. 15.
    DeRisi J, Penland L, Brown PO et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 1996; 14(4):457–460.PubMedCrossRefGoogle Scholar
  16. 16.
    Perou CM, Sorlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000; 406(6797):747–752.PubMedCrossRefGoogle Scholar
  17. 17.
    van’t Veer LJ, Dai H, van de Vijver MJ et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415(6871):530–536.CrossRefGoogle Scholar
  18. 18.
    Ma XJ, Salunga R, Tuggle JT et al. Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci USA 2003; 100(10):5974–5979.PubMedCrossRefGoogle Scholar
  19. 19.
    Golub TR, Slonim DK, Tamayo P et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999; 286(5439):531–537.PubMedCrossRefGoogle Scholar
  20. 20.
    Alizadeh AA, Eisen MB, Davis RE et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403(6769):503–511.PubMedCrossRefGoogle Scholar
  21. 21.
    Templin MF, Stoll D, Schwenk JM et al. Protein microarrays: Promising tools for proteomic research. Proteomics 2003; 3(11):2155–2166.PubMedCrossRefGoogle Scholar
  22. 22.
    MacBeath G. Protein microarrays and proteomics. Nat Genet 2002; 32(Suppl):526–532.PubMedCrossRefGoogle Scholar
  23. 23.
    Zhu H, Bilgin M, Bangham R et al. Global analysis of protein activities using proteome chips. Science 2001; 293(5537):2101–2105.PubMedCrossRefGoogle Scholar
  24. 24.
    Houseman BT, Huh JH, Kron SJ et al. Peptide chips for the quantitative evaluation of protein kinase activity. Nat Biotechnol 2002; 20(3):270–274.PubMedCrossRefGoogle Scholar
  25. 25.
    Ge H. UPA, a universal protein array system for quantitative detection of protein-protein, protein-DNA, protein-RNA and protein-ligand interactions. Nucleic Acids Res 2000; 28(2):e3.PubMedCrossRefGoogle Scholar
  26. 26.
    Liotta LA, Espina V, Mehta AI et al. Protein microarrays: Meeting analytical challenges for clinical applications. Cancer Cell 2003; 3(4):317–325.PubMedCrossRefGoogle Scholar
  27. 27.
    Hicks DG, Tubbs RR. Assessment of the HER2 status in breast cancer by fluorescence in situ hybridization: A technical review with interpretive guidelines. Hum Pathol 2005; 36(3):250–261.PubMedCrossRefGoogle Scholar
  28. 28.
    Wheeler DB, Bailey SN, Guertin DA et al. RNAi living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells. Nat Methods 2004; 1(2):127–132.PubMedCrossRefGoogle Scholar
  29. 29.
    Emmert-Buck MR, Bonner RF, Smith PD et al. Laser capture microdissection. Science 1996; 274(5289):998–1001.PubMedCrossRefGoogle Scholar
  30. 30.
    Segal JP, Stallings NR, Lee CE et al. Use of laser-capture microdissection for the identification of marker genes for the ventromedial hypothalamic nucleus. J Neurosci 2005; 25(16):4181–4188.PubMedCrossRefGoogle Scholar
  31. 31.
    Wang E, Miller LD, Ohnmacht GA et al. High-fidelity mRNA amplification for gene profiling. Nat Biotechnol 2000; 18(4):457–459.PubMedCrossRefGoogle Scholar
  32. 32.
    Aoyagi K, Tatsuta T, Nishigaki M et al. A faithful method for PCR-mediated global mRNA amplification and its integration into microarray analysis on laser-captured cells. Biochem Biophys Res Commun 2003; 300(4):915–920.PubMedCrossRefGoogle Scholar
  33. 33.
    Polacek DC, Passerini AG, Shi C et al. Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA. Physiol Genomics 2003; 13(2):147–156.PubMedGoogle Scholar
  34. 34.
    Zhao H, Hastie T, Whitfield ML et al. Optimization and evaluation of T7 based RNA linear amplification protocols for cDNA microarray analysis. BMC Genomics 2002; 3(1):31.PubMedCrossRefGoogle Scholar
  35. 35.
    Simone NL, Bonner RF, Gillespie JW et al. Laser-capture microdissection: Opening the microscopic frontier to molecular analysis. Trends Genet 1998; 14(7):272–276.PubMedCrossRefGoogle Scholar
  36. 36.
    Pollack JR, Perou CM, Alizadeh AA et al. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet 1999; 23(1):41–46.PubMedCrossRefGoogle Scholar
  37. 37.
    Zardo G, Tiirikainen MI, Hong C et al. Integrated genomic and epigenomic analyses pinpoint biallelic gene inactivation in tumors. Nat Genet 2002; 32(3):453–458.PubMedCrossRefGoogle Scholar
  38. 38.
    Schwaenen C, Nessling M, Wessendorf S et al. Automated array-based genomic profiling in chronic lymphocytic leukemia: Development of a clinical tool and discovery of recurrent genomic alterations. Proc Natl Acad Sci USA 2004; 101(4):1039–1044.PubMedCrossRefGoogle Scholar
  39. 39.
    Paris PL, Andaya A, Fridlyand J et al. Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors. Hum Mol Genet 2004; 13(13):1303–1313.PubMedCrossRefGoogle Scholar
  40. 40.
    Callagy G, Pharoah P, Chin SF et al. Identification and validation of prognostic markers in breast cancer with the complementary use of array-CGH and tissue microarrays. J Pathol 2005; 205(3):388–396.PubMedCrossRefGoogle Scholar
  41. 41.
    Weiss MM, Kuipers EJ, Postma C et al. Genomic alterations in primary gastric adenocarcinomas correlate with clinicopathological characteristics and survival. Cell Oncol 2004; 26(5–6):307–317.PubMedGoogle Scholar
  42. 42.
    Martinez-Climent JA, Alizadeh AA, Segraves R et al. Transformation of follicular lymphoma to diffuse large cell lymphoma is associated with a heterogeneous set of DNA copy number and gene expression alterations. Blood 2003; 101(8):3109–3H7.PubMedCrossRefGoogle Scholar
  43. 43.
    The International HapMap Project. Nature 2003; 426(6968):789–796.Google Scholar
  44. 44.
    Gabriel SB, SchafTner SF, Nguyen H et al. The structure of haplotype blocks in the human genome. Science 2002; 296(5576):2225–2229.PubMedCrossRefGoogle Scholar
  45. 45.
    Judson R, Salisbury B, Schneider J et al. How many SNPs does a genome-wide haplotype map require? Pharmacogenomics 2002; 3(3):379–391.PubMedCrossRefGoogle Scholar
  46. 46.
    Matsuzaki H, Loi H, Dong S et al. Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonudeotide array. Genome Res 2004; 14(3):414–425.PubMedCrossRefGoogle Scholar
  47. 47.
    Liu S, Li Y, Fu X et al. Analysis of the factors affecting the accuracy of detection for single base alterations by oligonudeotide microarray. Exp Mol Med 2005; 37(2):71–77.PubMedGoogle Scholar
  48. 48.
    Zhou X, Rao NP, Cole SW et al. Progress in concurrent analysis of loss of heterozygosity and comparative genomic hybridization utilizing high density single nucleotide polymorphism arrays. Cancer Genet Cytogenet 2005; 159(1):53–57.PubMedCrossRefGoogle Scholar
  49. 49.
    Irving JA, Bloodworth L, Bown NP et al. Loss of heterozygosity in childhood acute lymphoblastic leukemia detected by genome-wide microarray single nucleotide polymorphism analysis. Cancer Res 2005; 65(8):3053–3058.PubMedGoogle Scholar
  50. 50.
    Evans DM, Cardon LR. Guidelines for genotyping in genomewide linkage studies: Single-nucleotide-polymorphism maps versus microsatellite maps. Am J Hum Genet 2004; 75(4):687–692.PubMedCrossRefGoogle Scholar
  51. 51.
    Middleton FA, Pato MT, Gentile KL et al. Genomewide linkage analysis of bipolar disorder by use of a high-density single-nucleotide-polymorphism (SNP) genotyping assay: A comparison with microsatellite marker assays and finding of significant linkage to chromosome 6q22. Am J Hum Genet 2004; 74(5):886–897.PubMedCrossRefGoogle Scholar
  52. 52.
    Nal B, Mohr E, Ferrier P. Location analysis of DNA-bound proteins at the whole-genome level: Untangling transcriptional regulatory networks. Bioessays 2001; 23(6):473–476.PubMedCrossRefGoogle Scholar
  53. 53.
    Blais A, Dynlacht BD. Devising transcriptional regulatory networks operating during the cell cycle and differentiation using ChlP-on-chip. Chromosome Res 2005; 13(3):275–288.PubMedCrossRefGoogle Scholar
  54. 54.
    Ren B, Robert F, Wyrick JJ et al. Genome-wide location and function of DNA binding proteins. Science 2000; 290(5500):2306–2309.PubMedCrossRefGoogle Scholar
  55. 55.
    Orlando V. Mapping chromosomal proteins in vivo by formaldehyde-crosslinked-chromatin immunoprecipitation. Trends Biochem Sci 2000; 25(3):99–104.PubMedCrossRefGoogle Scholar
  56. 56.
    Darville MI, Terryn S, Eizirik DL. An octamer motif is required for activation of the inducible nitric oxide synthase promoter in pancreatic beta-cells. Endocrinology 2004; 145(3):1130–1136.PubMedCrossRefGoogle Scholar
  57. 57.
    Palmiter RD, Haines ME. Regulation of protein synthesis in chick oviduct. 4th, Role of testosterone. J Biol Chem 1973; 248(6):2107–2116.PubMedGoogle Scholar
  58. 58.
    Tenenbaum SA, Carson CC, Atasoy U et al. Genome-wide regulatory analysis using en masse nuclear run-ons and ribonomic profiling with autoimmune sera. Gene 2003; 317(1–2):79–87.PubMedCrossRefGoogle Scholar
  59. 59.
    Mazzanti CM, Tandle A, Lorang D et al. Early genetic mechanisms underlying the inhibitory effects of endostatin and fumagillin on human endothelial cells. Genome Res 2004; 14(8):1585–1593.PubMedCrossRefGoogle Scholar
  60. 60.
    Feldman AL, Stetler-Stevenson WG, Costouros NG et al. Modulation of tumor-host interactions, angiogenesis, and tumor growth by tissue inhibitor of metalloproteinase 2 via a novel mechanism. Cancer Res 2004; 64(13):4481–4486.PubMedCrossRefGoogle Scholar
  61. 61.
    Mazzanti C, Zeiger MA, Costouros NG et al. Using gene expression profiling to differentiate benign versus malignant thyroid tumors. Cancer Res 2004; 64(8):2898–2903.PubMedCrossRefGoogle Scholar
  62. 62.
    Rosen J, He M, Umbricht C et al. A six gene model for differentiating benign from malignant thyroid tumors based on gene expression. Surgery 2005; 138(6):1050–6.PubMedCrossRefGoogle Scholar
  63. 63.
    Eschrich S, Yang I, Bloom G et al. Molecular staging for survival prediction of colorectal cancer patients. J Clin Oncol 2005; 23(15):3526–3535.PubMedCrossRefGoogle Scholar
  64. 64.
    Glinsky GV, Berezovska O, Glinskii AB. Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer. J Clin Invest 2005; 115(6):1503–1521.PubMedCrossRefGoogle Scholar
  65. 65.
    Sanchez-Carbayo M, Socci ND, Lozano JJ et al. Gene discovery in bladder cancer progression using cDNA microarrays. Am J Pathol 2003; 163(2):505–516.PubMedGoogle Scholar
  66. 66.
    Kim JM, Sohn HY, Yoon SY et al. Identification of gastric cancer-related genes using a cDNA microarray containing novel expressed sequence tags expressed in gastric cancer cells. Clin Cancer Res 2005; 11(2 Pt 1):473–482.PubMedGoogle Scholar
  67. 67.
    Xu L, Tan AC, Naiman DQ et al. Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics 2005; 21(20):3905–3911.PubMedCrossRefGoogle Scholar
  68. 68.
    Rhodes DR, Yu J, Shanker K et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 2004; 101(25):9309–9314.PubMedCrossRefGoogle Scholar
  69. 69.
    Zhong L, Hidalgo GE, Stromberg AJ et al. Using protein microarray as a diagnostic assay for nonsmall cell lung cancer. Am J Respir Crit Care Med 2005; 172(10):1308–1314.PubMedCrossRefGoogle Scholar
  70. 70.
    Kang JY, Dolled-Filhart M, Ocal IT et al. Tissue microarray analysis of hepatocyte growth factor/ Met pathway components reveals a role for Met, matriptase, and hepatocyte growth factor activator inhibitor 1 in the progression of node-negative breast cancer. Cancer Res 2003; 63(5):1101–1105.PubMedGoogle Scholar
  71. 71.
    Bubendorf L, Kononen J, Koivisto P et al. Survey of gene amplifications during prostate cancer progression by high-throughout fluorescence in situ hybridization on tissue microarrays. Cancer Res 1999; 59(4):803–806.PubMedGoogle Scholar
  72. 72.
    Mousses S, Bubendorf L, Wagner U et al. Clinical validation of candidate genes associated with prostate cancer progression in the CWR22 model system using tissue microarrays. Cancer Res 2002; 62(5):1256–1260.PubMedGoogle Scholar

Copyright information

© Landes Bioscience and Springer Science+Business Media 2007

Authors and Affiliations

  • Mei He
    • 1
  • Jennifer Rosen
    • 1
  • David Mangiameli
    • 1
  • Steven K. Libutti
    • 1
  1. 1.Surgery Branch, National Cancer InstituteNational Institutes of HealthBethesdaUSA

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