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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.

Supplementary Figure 1 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Supplementary Table S3 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Supplementary Table S7 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Supplementary Table S4 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Supplementary Table S2 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Supplementary Table S5 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Supplementary Table S1 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

Supplementary Table S6 from Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease