Gene Expression Microarray Studies
  
  Breast cancer vs. non-breast cancer (data derived from Ellis et al., 2002)
 

Related Microarray Pages (at our site)
Published Breast Cancer Data Sets: Georgetown University Studies/Collaborations
Published Human Breast Cancer Data Sets: Other Studies
Published Studies in Rodents
Selected Gene Expression Microarray Links
Bioinformatics and Biostatistics Working Group 
 
Recent publications: Ellis et al. Gu et al.

Overview

   We have begun to apply the emerging technology of gene expression microarrays to identify molecular profiles associated with specific breast cancer phenotypes and responsiveness/resistance to selected systemic therapies in breast cancer. For example, we have optimized a method for the collection and processing of breast core needle biopsies for gene expression profiling (Ellis et al.) and implicated several genes in acquired antiestrogen resistance (Gu et al.).  

  Working with our colleagues in informatics (Dr. Yue Wang, Computational Bioinformatics and Bioimaging Laboratory (CBIL), Virginia Tech. ), biostatistics (Dr. Aiyi Liu, NICHD; Dr. Edmund Gehan, Dr. Antai Wang, Georgetown University; Dr.Bruce Trock, Johns Hopkins University) and medical oncology (Dr. Minetta Liu, Dr. Claudine Issacs, Georgetown University; Dr. Vered Stearns, Johns Hopkins University; Dr. Dan Hayes, University of Michigan) we have continued to develop and apply novel approaches and algorithms for the visualization and analysis of the complex multidimensional data sets generated by this technology (see also Dr. Wang's website).

Microarray-Based Projects in Breast Cancer
Functional studies of estrogen independence
Estrogen and phytoestrogen regulated gene profiling
Predicting antiestrogen responsiveness
Functional studies of genes associated with antiestrogen resistance
Predicting Taxane responses in xenografts and patients
Predicting recurrence in early stage breast cancers
Algorithm development and application
Predicting retinoid responsiveness
Effect of diet on gene expression and function

Collaborating Institutions
The Catholic University of America, Washington, DC
The University of Edinburgh, Scotland
Virginia Tech, Northern Virginia Campus, VA
The University of Colorado, Denver, CO
The University of Michigan, MI
Oncotech, Inc. Tustin, CA
Finsen Laboratories, Copenhagen Denmark
National Institute of Child Health and Development, NIH, Bethesda, MD

Collaborating Institutions
Gene expression profiling, data visualization, data analysis, and related areas from our Bioinformatics and Biostatistics Working Group 
  1. Wang, Z., Zhang, J., Lu, J., Lee, R., Kung, S.-Y., Clarke R. & Wang Y. Discriminatory mining of gene expression microarray data.J Signal Process Systems, in press. Link to Journal  
  2. Liu, A., Zhang, Y., Gehan, E. & Clarke, R. Block principal components analysis with application to gene microarray data classification. Stat Med, 21: 3465-3474, 2002 Link to Abstract  (at Statistics in Medicine) 
  3. Wang, Y., Zhang, J., Huang, K., Khan, J.  Szabo, Z. Independent component imaging of disease signatures. IEEE Intl Symp Biomed Imag, in press.Link to IEEE Publications  
  4. Zhang, J., Huang, K., Khan, J., Li, K., Bhujwalla, Z., Clarke, R., Gu, Z., Szabo, Z., Xuan, J, & Wang, Y. Computational decomposition of molecular signatures. Proc Intl Conf Diagnostic Imaging Analysis, in press. Link to IEEE Publications  
  5. Ellis, M., Davis, N., Coop, A., Liu, M., Schumaker, L., Lee, R.Y., Srikanchana, R., Russell, C., Singh, B., Miller, W.R., Stearns, V., Pennanen, M., Tsangaris, T., Gallagher, A., Liu, A., Zwart, A., Hayes, D.F., Lippman, M.E., Wang, Y. & Clarke, R. Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses. Clin Cancer Res, 8: 1155-1166, 2002. Link to Abstract (at Clinical Cancer Research). 
  6. Gu, Z., Lee, R.Y., Skaar, T.C., Bouker, K.B., Welch, J.N., Lu, J., Liu, A., Davis, N., Leonessa, F., Brüner, N., Wang, Y. & Clarke, R. Association of interferon regulatory factor-1, nucleophosmin, nuclear factor kappa-B and cAMP response element binding with acquired resistance to Faslodex (ICI 182,780). Cancer Res, 62: 3428-3437, 2002. Link to Abstract (at Cancer Research). 
  7. Wang, Y., Lu, J., Lee, R. & Clarke, R. Iterative normalization of cDNA microarray data. IEEE Trans Inf Technol Biomed, 6: 29-37, 2002. Link to Abstract (PubMed). 
  8. Wang, Y., Lu, J., Lee, R. & Clarke, R. Iterative normalization of cDNA microarray data. IEEE Trans Inf Technol Biomed, 6: 29-37, 2002. Link to Abstract (PubMed). 
  9. Wang, Y., Lu, J., Lee, R. & Clarke, R. Iterative normalization of cDNA microarray data. IEEE Trans Inf Technol Biomed, 6: 29-37, 2002. Link to Abstract (PubMed). 
  10. Wang, Y., Woods, K. & McClain, M. Information-theoretic matching of two point sets. IEEE Trans Image Process, in press, 2002. Link to Journal 
  11. Xuan, J., Adali, T., Wang, Y., Hayes, W., Lynch, J., Freedman, M.T. & Mun, S.K. A computerized simulation system for prostate needle biopsy.Simul Gaming,32: 391-403, 2001. Link to Journal 
  12. Wang, Y., Adali, T., Xuan, J. &. Szabo, Z. Magnetic resonance image analysis by information theoretic criteria and stochastic site models. IEEE Trans Inf Technol Biomed, 5: 150-158, 2001. Link to Abstract (PubMed).  
  13. Lu, J., Wang, Y., Xuan, J., Kung, S.Y., Gu, Z. & Clarke, R. Discriminative analysis of gene microarray data. Proc IEEE Neural Netw Signal Process, 11: 218-227, 2001. Link to IEEE Publications  
  14. Wang, Y., Luo, L., Freedman, M.T. & Kung, S.-Y. Probabilistic principal component subspaces: a hierarchical finite mixture model for data visualization. IEEE Trans Neural Netw, 11:635-646, 2000. Link to Journal 
  15. Xuan J., Adali T., Wang, Y.  & Siegel, E.Automatic detection of foreign objects in computed radiography. J Biomed Opt, 5:425-31, 2000.Link to Abstract (PubMed). 
  16. Wang, Y., Adali, T., Kung, S.-Y. & Szabo, Z. Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach. IEEE Trans Image Process, 7: 1165-1181, 1998. Link to Journal 
  17. Wang, Y., Lin, S.-H., Li, H. & Kung, S.-Y. Data mapping by probabilistic modular networks and information theoretic criteria. IEEE Trans Signal Process, 46: 3378-3397, 1998. Link to Journal 
  18. Skaar, T.C., Prasad, S.C., Sharareh, S., Brüner, N., Lippman, M.E. & Clarke, R. Two-dimensional gel electrophoresis analyses identify nucleophosmin as an estrogen regulated protein associated with acquired estrogen-independence in human breast cancer cells. J Steroid Biochem Mol Biol, 67:391-402, 1998. Link to Abstract (PubMed). 
  19. Wang, Y., Adali, T. & Lo, S.-C.B. Automatic threshold selection using histogram quantification. J Biomed Opt, 2:211-217, 1997. Link to Journal  
 
Other manuscripts submitted and in preparation