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BioMed Research International
Volume 2014 (2014), Article ID 459203, 15 pages
http://dx.doi.org/10.1155/2014/459203
Research Article

Breast Cancer Prognosis Risk Estimation Using Integrated Gene Expression and Clinical Data

School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia

Received 2 November 2013; Revised 11 January 2014; Accepted 2 March 2014; Published 14 May 2014

Academic Editor: Brian Oliver

Copyright © 2014 Ashish Saini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. Weigelt, J. L. Peterse, and L. J. Van't Veer, “Breast cancer metastasis: markers and models,” Nature Reviews Cancer, vol. 5, no. 8, pp. 591–602, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. J. A. Bowersox, “National institutes of health consensus development conference statement: adjuvant therapy for breast cancer, November 1–3, 2000,” Journal of the National Cancer Institute, vol. 93, no. 13, pp. 979–989, 2001. View at Google Scholar · View at Scopus
  3. M. J. Van De Vijver, Y. D. He, L. J. Van 'T Veer et al., “A gene-expression signature as a predictor of survival in breast cancer,” The New England Journal of Medicine, vol. 347, no. 25, pp. 1999–2009, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Paik, S. Shak, G. Tang et al., “A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer,” The New England Journal of Medicine, vol. 351, no. 27, pp. 2817–2826, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Wang, J. G. M. Klijn, Y. Zhang et al., “Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer,” The Lancet, vol. 365, no. 9460, pp. 671–679, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Sotiriou, P. Wirapati, S. Loi et al., “Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis,” Journal of the National Cancer Institute, vol. 98, no. 4, pp. 262–272, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. H.-Y. Chuang, E. Lee, Y.-T. Liu, D. Lee, and T. Ideker, “Network-based classification of breast cancer metastasis,” Molecular Systems Biology, vol. 3, article 140, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Liu, X. Wang, G. Y. Chen et al., “The prognostic role of a gene signature from tumorigenic breast-cancer cells,” The New England Journal of Medicine, vol. 356, no. 3, pp. 217–226, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Xu, A. C. Tan, R. L. Winslow, and D. Geman, “Merging microarray data from separate breast cancer studies provides a robust prognostic test,” BMC Bioinformatics, vol. 9, article 125, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Sotiriou and L. Pusztai, “Gene-expression signatures in breast cancer,” The New England Journal of Medicine, vol. 360, no. 8, pp. 752–800, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Chibon, P. Lagarde, S. Salas et al., “Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity,” Nature Medicine, vol. 16, no. 7, pp. 781–787, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Abraham, A. Kowalczyk, S. Loi, I. Haviv, and J. Zobel, “Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context,” BMC Bioinformatics, vol. 11, article 277, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Alexe, J. Monaco, S. Doyle et al., “Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging,” Experimental Biology and Medicine, vol. 234, no. 8, pp. 860–879, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. H. A. Azim Jr., S. Michiels, P. L. Bedard et al., “Elucidating prognosis and biology of breast cancer arising in young women using gene expression profiling,” Clinical Cancer Research, vol. 18, no. 5, pp. 1341–1351, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Xie, H. Yang, S. Wang et al., “A logistic regression model for predicting axillary lymph node metastases in early breast carcinoma patients,” Sensors, vol. 12, pp. 9936–9950, 2012. View at Publisher · View at Google Scholar
  16. M. Garcia, R. Millat-carus, F. Bertucci, P. Finetti, D. Birnbaum, and G. Bidaut, “Interactome-transcriptome integration for predicting distant metastasis in breast cancer,” Bioinformatics, vol. 28, no. 5, pp. 672–678, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Goldhirsch, J. H. Glick, R. D. Gelber, A. S. Coates, B. Thurlimann, and H.-J. Senn, “Meeting highlights: international expert consensus on the primary therapy of Early Breast Cancer 2005,” Breast, vol. 14, no. 6, p. 643, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Ein-Dor, O. Zuk, and E. Domany, “Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 15, pp. 5923–5928, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Shi and B. Zhang, “Semi-supervised learning improves gene expression-based prediction of cancer recurrence,” Bioinformatics, vol. 27, no. 21, pp. 3017–3023, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Shen, D. Ghosh, and A. M. Chinnaiyan, “Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data,” BMC Genomics, vol. 5, no. 1, article 94, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. A. E. Teschendorff, A. Naderi, N. L. Barbosa-Morais et al., “A consensus-prognostic gene expression classifier for ER positive breast cancer,” Genome Biology, vol. 7, no. 10, article 101, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Desmedt, B. Haibe-Kains, P. Wirapati et al., “Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes,” Clinical Cancer Research, vol. 14, no. 16, pp. 5158–5165, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. F. Reyal, N. Stransky, I. Bernard-Pierrot et al., “Visualizing chromosomes as transcriptome correlation maps: evidence of chromosomal domains containing co-expressed genes—a study of 130 invasive ductal breast carcinomas,” Cancer Research, vol. 65, no. 4, pp. 1376–1383, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Sun, S. Goodison, J. Li, L. Liu, and W. Farmerie, “Improved breast cancer prognosis through the combination of clinical and genetic markers,” Bioinformatics, vol. 23, no. 1, pp. 30–37, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. D. L. Wheeler, T. Barrett, D. A. Benson et al., “Database resources of the National Center for Biotechnology Information,” Nucleic Acids Research, vol. 33, pp. D39–D45, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Nam, B. C. Chung, Y. Kim, K. Lee, and D. Lee, “Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification,” Bioinformatics, vol. 25, no. 23, pp. 3151–3157, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. K. Chin, S. DeVries, J. Fridlyand et al., “Genomic and transcriptional aberrations linked to breast cancer pathophysiologies,” Cancer Cell, vol. 10, no. 6, pp. 529–541, 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Desmedt, F. Piette, S. Loi et al., “Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series,” Clinical Cancer Research, vol. 13, no. 11, pp. 3207–3214, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. R. Edgar, M. Domrachev, and A. E. Lash, “Gene Expression Omnibus: NCBI gene expression and hybridization array data repository,” Nucleic Acids Research, vol. 30, no. 1, pp. 207–210, 2002. View at Google Scholar · View at Scopus
  30. I. Fishel, A. Kaufman, and E. Ruppin, “Meta-analysis of gene expression data: a predictor-based approach,” Bioinformatics, vol. 23, no. 13, pp. 1599–1606, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Greenwood, G. Metodieva, K. Al-Janabi et al., “Stat1 and CD74 overexpression is co-dependent and linked to increased invasion and lymph node metastasis in triple-negative breast cancer,” Journal of Proteomics, vol. 75, no. 10, pp. 3031–3040, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. B. Haibe-Kains, C. Desmedt, C. Sotiriou, and G. Bontempi, “A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?” Bioinformatics, vol. 24, no. 19, pp. 2200–2208, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. P. D. Bos, X. H.-F. Zhang, C. Nadal et al., “Genes that mediate breast cancer metastasis to the brain,” Nature, vol. 459, no. 7249, pp. 1005–1009, 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. D. Venet, J. E. Dumont, and V. Detours, “Most random gene expression signatures are significantly associated with breast cancer outcome,” PLoS Computational Biology, vol. 7, no. 10, article e1002240, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M. Tanabe, “KEGG for integration and interpretation of large-scale molecular data sets,” Nucleic Acids Research, vol. 40, pp. D109–D114, 2012. View at Publisher · View at Google Scholar
  38. L. D. Miller, J. Smeds, J. George et al., “An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 38, pp. 13550–13555, 2005. View at Publisher · View at Google Scholar · View at Scopus
  39. A. J. Minn, G. P. Gupta, P. M. Siegel et al., “Genes that mediate breast cancer metastasis to lung,” Nature, vol. 436, no. 7050, pp. 518–524, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. A. J. Minn, G. P. Gupta, D. Padua et al., “Lung metastasis genes couple breast tumor size and metastatic spread,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 16, pp. 6740–6745, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. “McGill: The CUX1 Expression Signature (C1S) As a Predictor of Breast Cancer outcome,” http://www.mcgill.ca/research/sites/mcgill.ca.research/.