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Integrated analysis of breast cancer cell lines reveals unique signaling pathways
Laura M Heiser1, Nicholas J Wang, Carolyn L Talcott
1Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. lmheiser@lbl.gov
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 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.
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.
Insights
Pak1 over-expression in luminal breast cancer indicates increased sensitivity to Mek inhibitors. This finding suggests Pak1 could be a clinical marker for identifying patients who may benefit from Mek inhibitor therapy.
Area of Science:
- Systems biology
- Cancer research
- Molecular signaling
Background:
- Breast cancer is a complex disease driven by genetic alterations affecting cell growth and survival.
- The Epidermal Growth Factor Receptor-Mitogen-Activated Protein Kinase (Egfr-MAPK) pathway is frequently deregulated in breast cancer, with variations across subtypes.
- Understanding pathway deregulation is crucial for targeted therapies.
Purpose of the Study:
- To identify specific subnetworks within the Egfr-MAPK pathway deregulated across breast cancer subsets.
- To investigate the role of Pak1 in MAPK signaling and its potential as a predictive marker for Mek inhibitor response.
Main Methods:
- Integrated genomic, transcriptomic, and proteomic data from 30 breast cancer cell lines.
- Utilized a curated Pathway Logic symbolic systems model of Egfr-MAPK signaling (539 states, 396 rules).
- Performed experimental validation using Mek inhibitors on 20 breast cancer cell lines, assessing Pak1 expression levels.
Main Results:
- Identified subtype-specific subnetworks within the Egfr-MAPK pathway.
- Pak1 was highlighted as important in MAPK cascade regulation when over-expressed.
- Pak1 over-expressing luminal breast cancer cell lines showed significantly higher sensitivity to Mek inhibitors.
Conclusions:
- Symbolic systems biology models are valuable for discovering targeted therapeutic strategies for breast cancer subsets.
- Pak1 over-expression may serve as a predictive biomarker for identifying patients responsive to Mek inhibitors.
- This research supports personalized medicine approaches in breast cancer treatment.
