Integrated analysis of breast cancer cell lines reveals unique signaling pathways

Laura 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 Kuo, Joe Gray, Paul Spellman

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

Background: 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. Results: We were interested in identifying subnetworkswithin 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. Conclusions: 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.

Original languageEnglish (US)
Article numberR31
JournalGenome Biology
Volume10
Issue number3
DOIs
StatePublished - Mar 25 2009
Externally publishedYes

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breast neoplasms
cancer
cell lines
Breast Neoplasms
Cell Line
inhibitor
deregulation
neoplasms
Systems Biology
genetic disorders
Cell Division
Proteomics
proteomics
Cell Movement
adhesion
neoplasm cells
analysis
cell division
Neoplasms
apoptosis

ASJC Scopus subject areas

  • Genetics
  • Cell Biology
  • Ecology, Evolution, Behavior and Systematics

Cite this

Integrated analysis of breast cancer cell lines reveals unique signaling pathways. / Heiser, Laura; Wang, Nicholas J.; Talcott, Carolyn L.; Laderoute, Keith R.; Knapp, Merrill; Guan, Yinghui; Hu, Zhi; Ziyad, Safiyyah; Weber, Barbara L.; Laquerre, Sylvie; Jackson, Jeffrey R.; Wooster, Richard F.; Kuo, Wen; Gray, Joe; Spellman, Paul.

In: Genome Biology, Vol. 10, No. 3, R31, 25.03.2009.

Research output: Contribution to journalArticle

Heiser, L, Wang, NJ, Talcott, CL, Laderoute, KR, Knapp, M, Guan, Y, Hu, Z, Ziyad, S, Weber, BL, Laquerre, S, Jackson, JR, Wooster, RF, Kuo, W, Gray, J & Spellman, P 2009, 'Integrated analysis of breast cancer cell lines reveals unique signaling pathways', Genome Biology, vol. 10, no. 3, R31. https://doi.org/10.1186/gb-2009-10-3-r31
Heiser, Laura ; Wang, Nicholas J. ; Talcott, Carolyn L. ; Laderoute, Keith R. ; Knapp, Merrill ; Guan, Yinghui ; Hu, Zhi ; Ziyad, Safiyyah ; Weber, Barbara L. ; Laquerre, Sylvie ; Jackson, Jeffrey R. ; Wooster, Richard F. ; Kuo, Wen ; Gray, Joe ; Spellman, Paul. / Integrated analysis of breast cancer cell lines reveals unique signaling pathways. In: Genome Biology. 2009 ; Vol. 10, No. 3.
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