Artificial Intelligence in Medicine
Volume 43, Issue 2 , Pages 99-111 , June 2008

Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach

  • Lee J. Lancashire

      Affiliations

    • Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, United Kingdom
    • The Nottingham Trent University, School of Biomedical and Natural Sciences, Clifton Campus, Clifton Lane, Nottingham NG11 8NS, United Kingdom
    • Corresponding Author InformationCorresponding author at: Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, United Kingdom. Tel.: +44 161 446 3156; fax: +44 161 446 3109.
    • ,
  • Robert C. Rees

      Affiliations

    • The Nottingham Trent University, School of Biomedical and Natural Sciences, Clifton Campus, Clifton Lane, Nottingham NG11 8NS, United Kingdom
    • ,
  • Graham R. Ball

      Affiliations

    • The Nottingham Trent University, School of Biomedical and Natural Sciences, Clifton Campus, Clifton Lane, Nottingham NG11 8NS, United Kingdom
    • Corresponding Author InformationCorresponding author. Tel.: +44 115 848 3394; fax: +44 115 848 3093.

Received 9 January 2007 ,Revised 29 February 2008 ,Accepted 10 March 2008.

References 

  1. Fidler IJ. The pathogenesis of cancer metastasis: the 'seed and soil’ hypothesis revisited. Nat Rev Cancer. 2003;3:453–458
  2. Gruvberger S, Ringner M, Chen Y, Panavally S, Saal LH, Borg A, et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res. 2001;61:5979–5984
  3. Adib TR, Henderson S, Perrett C, Hewitt D, Bourmpoulia D, Ledermann J, et al. Predicting biomarkers for ovarian cancer using gene-expression microarrays. Br J Cancer. 2004;90:686–692
  4. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002;8:816–824
  5. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001;98:13790–13795
  6. Jiang Y, Harlocker SL, Molesh DA, Dillon DC, Stolk JA, Houghton RL, et al. Discovery of differentially expressed genes in human breast cancer using subtracted cDNA libraries and cDNA microarrays. Oncogene. 2002;21:2270–2282
  7. Lee JS, Chu IS, Mikaelyan A, Calvisi DF, Heo J, Reddy JK, et al. Application of comparative functional genomics to identify best-fit mouse models to study human cancer. Nat Genet. 2004;36:1306–1311
  8. Masters JR, Lakhani SR. How diagnosis with microarrays can help cancer patients. Nature. 2000;404:921
  9. Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005;365:488–492
  10. Mousses S, Bubendorf L, Kononen J, Bittner M, Chen Y, Kolmer M, et al. Identification of genes involved in hormone-independent prostate cancer by cDNA microarrays, followed by in vivo analysis of selected genes using tissue microarray analysis. Nat Genet. 1999;23:64
  11. Porkka K, Saramaki O, Tanner M, Visakorpi T. Amplification and overexpression of Elongin C gene discovered in prostate cancer by cDNA microarrays. Lab Invest. 2002;82:629–637
  12. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med. 2002;346:1937–1947
  13. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536
  14. West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA. 2001;98:11462–11467
  15. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673–679
  16. Nicolau M, Tibshirani R, Borresen-Dale AL, Jeffrey SS. Disease-specific genomic analysis: identifying the signature of pathologic biology. Bioinformatics. 2007;23:957–965
  17. Pelham RJ, Rodgers L, Hall I, Lucito R, Nguyen KCQ, Navin N, et al. Identification of alterations in DNA copy number in host stromal cells during tumor progression. Proc Natl Acad Sci. 2006;103:19848–19853
  18. Bellman RE. Adaptive Control Processes. Princeton, NJ: Princeton University Press; 1961;
  19. Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, et al. Gene expression predictors of breast cancer outcomes. Lancet. 2003;361:1590–1596
  20. Ball G, Mian S, Holding F, Allibone RO, Lowe J, Ali S, et al. An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics. 2002;18:395–404
  21. Lancashire L, Schmid O, Shah H, Ball G. Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis. Bioinformatics. 2005;21:2191–2199
  22. Lancashire LJ, Mian S, Ellis IO, Rees RC, Ball GR. Current developments in the analysis of proteomic data: artificial neural network data mining techniques for the identification of proteomic biomarkers related to breast cancer. Curr Proteomics. 2005;2:15–29
  23. Mian S, Ball G, Hornbuckle J, Holding F, Carmichael J, Ellis I, et al. A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to paclitaxel and doxorubicin under in vitro conditions. Proteomics. 2003;3:1725–1737
  24. Bishop C. Neural networks for pattern recognition. Oxford: Oxford University Press; 1995;
  25. Rumelhart DE, McClelland JL. Parallel distribution processing: explorations in the microstructure of cognition. Cambridge, MA, USA: MIT Press; 1986;
  26. Mitchell TM. Machine learning. USA: McGraw-Hill Education; 1997;
  27. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. 2005;23:7332–7341
  28. Subasi A, Ercelebi E. Classification of EEG signals using neural network and logistic regression. Comput Methods Programs Biomed. 2005;78:87–99
  29. Lancashire L, Ugurel S, Creaser C, Schadendorf D, Rees R, Ball G. Utilizing artificial neural networks to elucidate serum biomarker patterns which discriminate between clinical stages in melanoma. In:  Fogel G editors. Proceedings of the 2nd IEEE symposium on computational intelligence in bioformatics and computational biology. IEEE, La Jolla, CA, USA. 2005;p. 455–460
  30. Lancashire LJ, Mian S, Rees RC, Ball GR. Preliminary artificial neural network analysis of SELDI mass spectrometry data for the classification of melanoma tissue. In:  Al-Dabass D editors. ESM 2003: 17th European simulation multiconference-foundations for successful modelling & simulation. SCS Europe, Nottingham, UK. 2003;p. 131–135
  31. Mian S, Ugurel S, Parkinson E, Schlenzka I, Dryden I, Lancashire L, et al. Serum proteomic fingerprinting discriminates between clinical stages and predicts disease progression in melanoma patients. J Clin Oncol. 2005;23:5088–5093
  32. Iversen C, Lancashire L, Waddington M, Forsythe S, Ball G. Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data. BMC Microbiol. 2006;6:28
  33. Lacroix M, Leclercq G. About GATA3, HNF3A, and XBP1, three genes co-expressed with the oestrogen receptor-alpha gene (ESR1) in breast cancer. Mol Cell Endocrinol. 2004;219:1–7
  34. Hole N, Stern PL. A 72kD trophoblast glycoprotein defined by a monoclonal antibody. Br J Cancer. 1988;57:239–246
  35. Carsberg CJ, Myers KA, Evans GS, Allen TD, Stern PL. Metastasis-associated 5T4 oncofoetal antigen is concentrated at microvillus projections of the plasma membrane. J Cell Sci. 1995;108(Part 8):2905–2916
  36. Starzynska T, Rahi V, Stern PL. The expression of 5T4 antigen in colorectal and gastric carcinoma. Br J Cancer. 1992;66:867–869
  37. Calin G, Herlea V, Barbanti-Brodano G, Negrini M. The coding region of the Bloom syndrome BLM gene and of the CBL proto-oncogene is mutated in genetically unstable sporadic gastrointestinal tumors. Cancer Res. 1998;58:3777–3781
  38. D’Amato RF, Eriquez LA, Tomfohrde KM, Singerman E. Rapid identification of Neisseria gonorrhoeae and Neisseria meningitidis by using enzymatic profiles. J Clin Microbiol. 1978;7:77–81
  39. Kaneko H, Inoue R, Yamada Y, Sukegawa K, Fukao T, Tashita H, et al. Microsatellite instability in B-cell lymphoma originating from Bloom syndrome. Int J Cancer. 1996;69:480–483
  40. Watt PM, Hickson ID. Failure to unwind causes cancer. Genome stability. Curr Biol. 1996;6:265–267
  41. Bieche I, Girault I, Urbain E, Tozlu S, Lidereau R. Relationship between intratumoral expression of genes coding for xenobiotic-metabolizing enzymes and benefit from adjuvant tamoxifen in estrogen receptor alpha-positive postmenopausal breast carcinoma. Breast Cancer Res. 2004;6:R252–R263
  42. Garcia-Closas M, Brinton LA, Lissowska J, Chatterjee N, Peplonska B, Anderson WF, et al. Established breast cancer risk factors by clinically important tumour characteristics. Br J Cancer. 2006;95:123–129
  43. Murphy N, Millar E, Lee CS. Gene expression profiling in breast cancer: towards individualising patient management. Pathology. 2005;37:271–277
  44. Martin M. Molecular biology of breast cancer. Clin Transl Oncol. 2006;8:7–14
  45. Steyerberg EW, Eijkemans MJ, Habbema JD. Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. J Clin Epidemiol. 1999;52:935–942
  46. Ioannidis JP. Microarrays and molecular research: noise discovery?. Lancet. 2005;365:454–455
  47. Domeniconi C, Papadopoulos D, Gunopulos D, Ma S. Subspace clustering of high dimensional data. In:  Berry MW, et al. editor. Proceedings of the 4th SIAM international conference on data mining. SIAM, Lake Buena Vista, FL, USA. 2004;p. 517–521
  48. Greene D, Cunningham P. Producing accurate interpretable clusters from high-dimensional data. In:  Jorge A, et al. editor. Proceedings of the 9th European conference on principles and practice of knowledge discovery in databases (PKDD’05). Springer. 2005;p. 486–494

PII: S0933-3657(08)00029-8

doi: 10.1016/j.artmed.2008.03.001

Artificial Intelligence in Medicine
Volume 43, Issue 2 , Pages 99-111 , June 2008

Article Tools

* Image Usage & Resolution