Cancer is a heterogeneous disease resulting from the accumulation of genetic defects that negatively impact control of cell division, motility, adhesion and apoptosis. Deregulation in signaling along the EgfR-MAPK pathway is common in breast cancer, though the manner in which deregulation occurs varies between both individuals and cancer subtypes.
We were interested in identifying subnetworks within the EgfR-MAPK pathway that are similarly deregulated across subsets of breast cancers. To that end, we mapped genomic, transcriptional and proteomic profiles for 30 breast cancer cell lines onto a curated Pathway Logic symbolic systems model of EgfR-MAPK signaling. This model was composed of 539 molecular states and 396 rules governing signaling between active states. We analyzed these models and identified several subtype-specific subnetworks, including one that suggested Pak1 is particularly important in regulating the MAPK cascade when it is over-expressed. We hypothesized that Pak1 over-expressing cell lines would have increased sensitivity to Mek inhibitors. We tested this experimentally by measuring quantitative responses of 20 breast cancer cell lines to three Mek inhibitors. We found that Pak1 over-expressing luminal breast cancer cell lines are significantly more sensitive to Mek inhibition compared to those that express Pak1 at low levels. This indicates that Pak1 over-expression may be a useful clinical marker to identify patient populations that may be sensitive to Mek inhibitors.
All together, our results support the utility of symbolic system biology models for identification of therapeutic approaches that will be effective against breast cancer subsets.
This work was supported by the Director, Office of Science, Office of Biological and Environmental Research, of the US Department of Energy under Contract No. DE-AC02-05CH11231, and by the National Institutes of Health, National Cancer Institute grants P50 CA 58207 Breast SPORE, the U54 CA 112970 (ICBP), and by the SmithKline Beecham Corporation grant to JWG.
Researchers should cite this work as follows:
Laura M Heiser; Nicholas J Wang; Carolyn L Talcott; Keith R Laderoute; Merrill Knapp; Yinghui Guan; Zhi Hu; Safiyyah Ziyad; Barbara L Weber; Sylvie Laquerre; Jeffrey R Jackson; Richard F Wooster; Wen Lin Kuo; Joe W Gray; Paul T Spellman (2014), "Integrated analysis of breast cancer cell lines reveals unique signaling pathways," https://ncihub.org/resources/524.