Analysis of signaling pathways in 90 cancer cell lines by protein lysate array

Multiple signal transduction pathways play a crucial role in cancer development, progression, and response to different therapies. An important issue is whether common signal transduction pathways are ubiquitously altered in all cancer types and some unique pathways are involved in different cancer types. Another important issue is whether and how transduction signaling molecules are heterogeneously expressed and activated in different cancer cells within and between cancer cell types.

To gain insight into these issues, we assembled a protein lysate array with 90 different cell lines of 12 different cell types. Each sample is diluted 2-fold six times, and samples from the dilution series were printed three times on the array. We then measured the expression levels and phosphorylation status of 52 different signaling proteins with specific antibodies and carried out statistical hierarchical clustering analysis.

The most significant finding based on the cluster analysis was that the cell lines did not group based on tumor types, suggesting that the signaling pathways studied were commonly activated in most of the tumor types cultured in vitro. As expected, related proteins associated with specific signaling pathways clustered together, and analysis of the 30 most differentially expressed proteins revealed the PI3-K signaling pathway was upregulated in several different tumor types and the VEGF-angiogenesis pathway was downregulated in hematopoetic cancers. Another important observation, with clinical implications was that EGFR was the most heterogeneous among all the cell lines. We also observed signaling pathways unique to specific types of cancers such as the inverse relationship between p16ink and Rb, and the EGFR mediated pathway activation characteristic of pancreatic cancers.

Using reverse phase lysate array analysis in this study, we were able to determine potential relationships and signaling pathways, both common and unique, to different types of cancer using cell lines in vitro. This data could be utilized for mining information related to an individual cancer of interest and combined with morphological and genomic profiles would help in creating a combination of expression markers and/or functional signaling maps for specific cancer diagnosis and therapy.