Rational Identification of Prognostic Markers of Breast Cancer

Maysson Al-Haj Ibrahim, Joanne L Selway, Kian Chin, Sabah Jassim, Michael A. Cawthorne, Kenneth Langlands


Accurate prognostication is central to the management of breast cancer, and traditional clinical and histochemical-based assessments are increasingly augmented by genetic tests. In particular, the use of microarray data has allowed the creation of molecular disease signatures for the early identification of individuals at elevated risk of relapse. However, tailoring therapy on the basis of a molecular assay is only recommended in certain cases, and the identification of a minimal set of genes whose expression allows informed decision-making in a broader spectrum of disease remains challenging. Finding an optimal solution is, however, an intractable computational task (i.e. retrieving the smallest group of genes with the greatest prognostic power). Our solution was to reduce the genetic search-space by using two filtering steps that enriched by biological function those genes whose expression discriminated disease states. In this way, we were able to identify a new molecular signature, the expression characteristics of which facilitated the classification of intermediate risk disease. We went on to create a statistical test that confirmed the relevance of our approach by comparing the performance of our signature to that of 1000 random signatures.


  1. Chin, K., DeVries, S., Fridlyand, J., Spellman, P.T., Roydasgupta, R., Kuo, W.-L., Lapuk, A., Neve, R.M., Qian, Z., Ryder, T., Chen, F., Feiler, H., Tokuyasu, T., Kingsley, C., Dairkee, S., Meng, Z., Chew, K., Pinkel, D., Jain, A., Ljung, B. M., Esserman, L., Albertson, D. G., Waldman, F. M., Gray, J. W., 2006. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10, 529-541.
  2. Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine learning 20, 273-297.
  3. Efron, B., Tibshirani, R., 1995. Cross-validation and the bootstrap: Estimating the error rate of a prediction rule. Division of Biostatistics.
  4. Galea, M. H., Balmey, R. W., Elston, C. E., Ellis, I. O., 1992. The Nottingham Prognostic Index in primary breast cancer. Breast cancer research and treatment 22, 207-219.
  5. Guo, Z., Zhang, T., Li, X., Wang, Q., Xu, J., Yu, H., Zhu, J., Wang, H., Wang, C., Topol, E., others, 2005. Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 6, 58.
  6. Ibrahim, M., Jassim, S., Cawthorne, M.A., Langlands, K., 2012. Integrating pathway enrichment and gene network analysis provides accurate disease classification. The International Conference on Bioinformatics Models, Methods and Algorithms. 156- 163.
  7. Khunlertgit, N., Yoon, B.-J., 2013. Identification of robust pathway markers for cancer through rank-based pathway activity inference. Advances in Bioinformatics 2013, 1-8.
  8. Loi, S., Haibe-Kains, B., Desmedt, C., Lallemand, F., Tutt, A. M., Gillet, C., Ellis, P., Harris, A., Bergh, J., Foekens, J. A., Klijn, J. G. M., Larsimont, D., Buyse, M., Bontempi, G., Delorenzi, M., Piccart, M. J., Sotiriou, C., 2007. Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. Journal of Clinical Oncology 25, 1239-1246.
  9. Nacu, S., Critchley-Thorne, R., Lee, P., Holmes, S., 2007. Gene expression network analysis and applications to immunology. Bioinformatics 23, 850-858.
  10. Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F. L., Walker, M. G., Watson, D., Park, T., 2004. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. New England Journal of Medicine 351, 2817-2826.
  11. Van Vliet, M. H., Reyal, F., Horlings, H. M., Van de Vijver, M. J., Reinders, M. J., Wessels, L. F., 2008. Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability. BMC Genomics 9, 375.
  12. Van't Veer, L. J., Dai, H., Van De Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., Van Der Kooy, K., Marton, M. J., Witteveen, A. T., others, 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-536.
  13. Venet, D., Dumont, J. E., Detours, V., 2011. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Computational Biology 7, e1002240.
  14. Wang, Y. C., Chen, B. S., 2011. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer. BMC medical genomics 4, 2.
  15. Wesolowski, R., 2011. Gene expression profiling: changing face of breast cancer classification and management. Gene expression 15, 105-115.

Paper Citation

in Harvard Style

Al-Haj Ibrahim M., L Selway J., Chin K., Jassim S., A. Cawthorne M. and Langlands K. (2014). Rational Identification of Prognostic Markers of Breast Cancer . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 265-270. DOI: 10.5220/0004915202650270

in Bibtex Style

author={Maysson Al-Haj Ibrahim and Joanne L Selway and Kian Chin and Sabah Jassim and Michael A. Cawthorne and Kenneth Langlands},
title={Rational Identification of Prognostic Markers of Breast Cancer},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},

in EndNote Style

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - Rational Identification of Prognostic Markers of Breast Cancer
SN - 978-989-758-012-3
AU - Al-Haj Ibrahim M.
AU - L Selway J.
AU - Chin K.
AU - Jassim S.
AU - A. Cawthorne M.
AU - Langlands K.
PY - 2014
SP - 265
EP - 270
DO - 10.5220/0004915202650270