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[Cancer Research 65, 3081-3091, April 15, 2005]
© 2005 American Association for Cancer Research


Molecular Biology, Pathobiology, and Genetics

Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Biaoyang Lin1, James T. White1, Wei Lu1, Tao Xie1, Angelita G. Utleg1, Xiaowei Yan1, Eugene C. Yi1, Paul Shannon1, Irina Khrebtukova3, Paul H. Lange2, David R. Goodlett1, Daixing Zhou3, Thomas J. Vasicek3 and Leroy Hood1

1 Institute for Systems Biology; 2 Department of Urology, University of Washington, Seattle, Washington; and 3 Lynx Therapeutics, Inc., Hayward, California

Requests for reprints: Biaoyang Lin, Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103. Phone: 206-732-1297; Fax: 206-732-1299; E-mail: blin{at}systemsbiology.org.

Prostate cancer is initially responsive to androgen ablation therapy and progresses to androgen-unresponsive states that are refractory to treatment. The mechanism of this transition is unknown. A systems approach to disease begins with the quantitative delineation of the informational elements (mRNAs and proteins) in various disease states. We employed two recently developed high-throughput technologies, massively parallel signature sequencing (MPSS) and isotope-coded affinity tag, to gain a comprehensive picture of the changes in mRNA levels and more restricted analysis of protein levels, respectively, during the transition from androgen-dependent LNCaP (model for early-stage prostate cancer) to androgen-independent CL1 cells (model for late-stage prostate cancer). We sequenced >5 million MPSS signatures, obtained >142,000 tandem mass spectra, and built comprehensive MPSS and proteomic databases. The integrated mRNA and protein expression data revealed underlying functional differences between androgen-dependent and androgen-independent prostate cancer cells. The high sensitivity of MPSS enabled us to identify virtually all of the expressed transcripts and to quantify the changes in gene expression between these two cell states, including functionally important low-abundance mRNAs, such as those encoding transcription factors and signal transduction molecules. These data enable us to map the differences onto extant physiologic networks, creating perturbation networks that reflect prostate cancer progression. We found 37 BioCarta and 14 Kyoto Encyclopedia of Genes and Genomes pathways that are up-regulated and 23 BioCarta and 22 Kyoto Encyclopedia of Genes and Genomes pathways that are down-regulated in LNCaP cells versus CL1 cells. Our efforts represent a significant step toward a systems approach to understanding prostate cancer progression.




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Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
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Copyright © 2005 by the American Association for Cancer Research.