IMPROVED BREAST CANCER PROGNOSIS BASED ON A HYBRID MARKER SELECTION APPROACH

L. Hedjazi, M.-V. Le Lann, T. Kempowsky-Hamon, F. Dalenc, G. Favre

Abstract

Clinical factors, such as patient age and histo-pathological state, are still the basis of day-to-day decision for cancer management. However, with the high throughput technology, gene expression profiling and proteomic sequences have known recently a widespread use for cancer and other diseases management. We aim through this work to assess the importance of using both types of data to improve the breast cancer prognosis. Nevertheless, two challenges are faced for the integration of both types of information: high-dimensionality and heterogeneity of data. The first challenge is due to the presence of a large amount of irrelevant genes in microarray data whereas the second is related to the presence of mixed-type data (quantitative, qualitative and interval) in the clinical data. In this paper, an efficient fuzzy feature selection algorithm is used to alleviate simultaneously both challenges. The obtained results prove the effectiveness of the proposed approach.

References

  1. Aguado J. C., and Aguilar-Martin J., 1999. A mixed qualitative-quantitative self-learning classification technique applied to diagnosis, QR'99 The Thirteenth International Workshop on Qualitative Reasoning. Chris Price, 124-128.
  2. Chang H. Y., Nuyten D. S. A., Sneddon J. B., Hastie T., Tibshirani R., et al., 2005. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. PNAS 2005, 102 (10): 3738-3743
  3. Deepa, S., Claudine I., 2005. Utilizing Prognostic and Predictive Factors in Breast Cancer. Current Treatment Options in Oncology 2005, 6:147-159. Current Science Inc
  4. Gevaert O., De Smet F., Timmerman D., Moreau Y., De Moor B., 2006. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian network, Bioinformatics 22 (14), 184-190.
  5. Golub T. , Slonim D., Tamayo P., Huard C., Gaasenbeek M., et al., 1999. Molecular Classification of Cancer Class Discovery and Class Prediction by Gene Expression Monitoring, Science 286 (5439), 531-537.
  6. Guyon I., Elisseeff A., 2003. An introduction to variable and feature selection, J. Mach. Learn. Res, 3, 1157- 1182.
  7. Hedjazi L., Aguilar-Martin J., Le Lann M. V., and Kempowsky T., 2010a, Membership-Margin based Feature Selection for Mixed-Type and HighDimensional Data, Fuzzy Sets and Systems Journal., submitted for publication.
  8. Hedjazi, L., Kempowsky T., Le Lann M. V., AguilarMartin J., 2010b. Prognosis of breast cancer based on a fuzzy classification method. 3rd International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2010); 1st International Conference on Bioinformatics (BIOINFORMATICS 2010). Valence (Spain), 20-23 January 2010, pp.123- 130.
  9. Kohavi R., and John G. H., 1997. Wrapper for feature subset selection, Artificial Intelligence 97, 273-324.
  10. Ramaswamy S., Tamayo P., Rifkin R., Mukherjee S., Yeang C., et al., 2001. MultiClass Cancer Diagnosis Using Tumor Gene Expression Signatures, Proc. Nat'l Acad. Sc. USA 98 ( 26) , 15149-15154.
  11. Straver M. E., Glas A. M., Hannemann J., Wesseling J., van de Vijver M. J., et al.,2009. The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer, Breast Cancer Res. Treat., doi:10.1007/s10549-009-0333-
  12. Sun Y., 2007. Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE TPAMI, 2 (6) , 1035-1051.
  13. Sun Y., Goodison S., Li J., Liu L., Farmerie W., 2007. Improved breast cancer prognosis through the combination of clinical and genetic markers. Bioinformatics, Gene expression. Oxford University Press 23 (1), 30-37.
  14. Van't Veer L.J., Dai H., van de Vijver M. J., et al., 2002. Gene expression profiling predicts clinical outcome of breast cancer, Nature, 415, pp. 530-536.
  15. Van de Vijver M. J., He Y. D., Van't Veer L. J., Dai H., Hart A., et al., 2002. A Gene expression signature as a predictor of survival in breast cancer, N Engl J Med, 347 (25), pp. 1999-2009.
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Paper Citation


in Harvard Style

Hedjazi L., Le Lann M., Kempowsky-Hamon T., Dalenc F. and Favre G. (2011). IMPROVED BREAST CANCER PROGNOSIS BASED ON A HYBRID MARKER SELECTION APPROACH . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011) ISBN 978-989-8425-36-2, pages 159-164. DOI: 10.5220/0003152301590164


in Bibtex Style

@conference{bioinformatics11,
author={L. Hedjazi and M.-V. Le Lann and T. Kempowsky-Hamon and F. Dalenc and G. Favre},
title={IMPROVED BREAST CANCER PROGNOSIS BASED ON A HYBRID MARKER SELECTION APPROACH},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)},
year={2011},
pages={159-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003152301590164},
isbn={978-989-8425-36-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)
TI - IMPROVED BREAST CANCER PROGNOSIS BASED ON A HYBRID MARKER SELECTION APPROACH
SN - 978-989-8425-36-2
AU - Hedjazi L.
AU - Le Lann M.
AU - Kempowsky-Hamon T.
AU - Dalenc F.
AU - Favre G.
PY - 2011
SP - 159
EP - 164
DO - 10.5220/0003152301590164