Abstract
At Windber Research Institute we have started research programs that use artificial neural networks (ANNs) in the study of breast cancer in order to identify heterogeneous data predictors of patient disease stages. As an initial effort, we have chosen matrix metalloproteinases (MMPs) as potential biomarker predictors. MMPs have been implicated in the early and late stage development of breast cancer. However, it is unclear whether these proteins hold predictive power for breast disease diagnosis, and we are not aware of any exploratory modeling efforts that address the question. Here we report the development of ANN models employing plasma levels of these proteins for breast disease predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ellsworth, R.E., Ellsworth, D.L., Lubert, S.M., Hooke, J., Somiari, R.I., Shriver, C.D.: High-throughput loss of heterozygosity mapping in 26 commonly deleted regions in breast cancer. Cancer Epidemiol Biomarkers Prev. 12, 915–919 (2003)
Somiari, S.B., Shriver, C.D., He, J., Parikh, K., Jordan, R., Hooke, J., Hu, H., Deyarmin, B., Lubert, S., Malicki, L., et al.: Global search for chromosomal abnormalities in infiltrating ductal carcinoma of the breast using array-comparative genomic hybridization. Cancer Genet. Cytogenet. 155, 108–118 (2004)
Somiari, R.I., Sullivan, A., Russell, S., Somiari, S., Hu, H., Jordan, R., George, A., Katenhusen, R., Buchowiecka, A., Arciero, C., et al.: High-throughput proteomic analysis of human infiltrating ductal carcinoma of the breast. Proteomics 3, 1863–1873 (2003)
Hu, H., Brzeski, H., Hutchins, J., Ramaraj, M., Qu, L., Xiong, R., Kalathil, S., Kato, R., Tenkillaya, S., Carney, J., et al.: Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research. Pharmacogenomics 5, 933–941 (2004)
Somiari, S., Shriver, C.D., Heckman, C., Olsen, C., Hu, H., Jordan, R., Arciero, C., Russell, S., Garguilo, G., Hooke, J., et al.: Plasma concentration and activity of matrix metalloproteinase 2 & 9 in patients with breast cancer and at risk of developing breast cancer. Cancer Letters (2005) (in press)
Ueno, H., Nakamura, H., Inoue, M., Imai, K., Noguchi, M., Sato, H., Seiki, M., Okada, Y.: Expression and tissue localization of membrane-types 1, 2, and 3 matrix metalloproteinases in human invasive breast carcinomas. Cancer Res. 57, 2055–2060 (1997)
Sheen-Chen, S.M., Chen, H.S., Eng, H.L., Sheen, C.C., Chen, W.J.: Serum levels of matrix metalloproteinase 2 in patients with breast cancer. Cancer Lett. 173, 79–82 (2001)
Wu, Y., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A., Metz, C.E.: Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 187, 81–87 (1993)
Goldberg, V., Manduca, A., Ewert, D.L., Gisvold, J.J., Greenleaf, J.F.: Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence. Med. Phys. 19, 1475–1481 (1992)
Astion, M.L., Wilding, P.: Application of neural networks to the interpretation of laboratory data in cancer diagnosis. Clin. Chem. 38, 34–38 (1992)
Markopoulos, C., Karakitsos, P., Botsoli-Stergiou, E., Pouliakis, A., Ioakim-Liossi, A., Kyrkou, K., Gogas, J.: Application of the learning vector quantizer to the classification of breast lesions. Anal. Quant. Cytol. Histol. 19, 453–460 (1997)
Ravdin, P.M., Clark, G.M., Hilsenbeck, S.G., Owens, M.A., Vendely, P., Pandian, M.R., McGuire, W.L.: A demonstration that breast cancer recurrence can be predicted by neural network analysis. Breast. Cancer Res. Treat. 21, 47–53 (1992)
Ripley, R.M., Harris, A.L., Tarassenko, L.: Non-linear survival analysis using neural networks. Stat. Med. 23, 825–842 (2004)
Fahlman, S.E., Lebiere, C.: The Cascade Correlation Learning Architecture. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems, vol. 2, pp. 524–532 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, H. et al. (2005). Can Circulating Matrix Metalloproteinases Be Predictors of Breast Cancer? A Neural Network Modeling Study. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_138
Download citation
DOI: https://doi.org/10.1007/11539087_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)