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Integrated analysis of breast cancer cell lines reveals unique signaling pathways.

Heiser LM, Wang NJ, Talcott CL, Laderoute KR, Knapp M, Guan Y, Hu Z, Ziyad S, Weber BL, Laquerre S, Jackson JR, Wooster RF, Kuo WL, Gray JW, Spellman PT - Genome Biol. (2009)

Bottom Line: We hypothesized that Pak1 over-expressing cell lines would have increased sensitivity to 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. lmheiser@lbl.gov

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 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.

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Related in: MedlinePlus

The network models cluster into basal, luminal and mixed groups of cell lines. Heatmap shows the network features that varied across the cell line network models. Each column represents data from one network model; each row represents data for one network feature (component in the initial state, rule or component that underwent a state-change). Red indicates the component is present in the cell line; green indicates it is absent. Hierarchical clustering along the vertical dimension reveals that the networks form basal, luminal and mixed clusters. Hierarchical clustering along the horizontal dimension yields 30 signaling modules, each of which represents a small subnetwork. Signaling modules of particular interest, along with the key components in the initial state, are noted along the right side.
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Figure 4: The network models cluster into basal, luminal and mixed groups of cell lines. Heatmap shows the network features that varied across the cell line network models. Each column represents data from one network model; each row represents data for one network feature (component in the initial state, rule or component that underwent a state-change). Red indicates the component is present in the cell line; green indicates it is absent. Hierarchical clustering along the vertical dimension reveals that the networks form basal, luminal and mixed clusters. Hierarchical clustering along the horizontal dimension yields 30 signaling modules, each of which represents a small subnetwork. Signaling modules of particular interest, along with the key components in the initial state, are noted along the right side.

Mentions: We were interested in whether the cell line models could be grouped by their network properties. We addressed this by performing an unsupervised hierarchical clustering of the network features (that is, the components in the initial state, rules, and components that underwent state changes) that differed across the cell lines. This clustering resulted in three major groups for the cell line models: basal, luminal and a third group composed of both basal and luminal cell lines (Figure 4). The observation that there is a mixed group of basal and luminal networks indicates that the cell lines may be segmented by their signaling pathways, rather than by site of origin alone.


Integrated analysis of breast cancer cell lines reveals unique signaling pathways.

Heiser LM, Wang NJ, Talcott CL, Laderoute KR, Knapp M, Guan Y, Hu Z, Ziyad S, Weber BL, Laquerre S, Jackson JR, Wooster RF, Kuo WL, Gray JW, Spellman PT - Genome Biol. (2009)

The network models cluster into basal, luminal and mixed groups of cell lines. Heatmap shows the network features that varied across the cell line network models. Each column represents data from one network model; each row represents data for one network feature (component in the initial state, rule or component that underwent a state-change). Red indicates the component is present in the cell line; green indicates it is absent. Hierarchical clustering along the vertical dimension reveals that the networks form basal, luminal and mixed clusters. Hierarchical clustering along the horizontal dimension yields 30 signaling modules, each of which represents a small subnetwork. Signaling modules of particular interest, along with the key components in the initial state, are noted along the right side.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2691002&req=5

Figure 4: The network models cluster into basal, luminal and mixed groups of cell lines. Heatmap shows the network features that varied across the cell line network models. Each column represents data from one network model; each row represents data for one network feature (component in the initial state, rule or component that underwent a state-change). Red indicates the component is present in the cell line; green indicates it is absent. Hierarchical clustering along the vertical dimension reveals that the networks form basal, luminal and mixed clusters. Hierarchical clustering along the horizontal dimension yields 30 signaling modules, each of which represents a small subnetwork. Signaling modules of particular interest, along with the key components in the initial state, are noted along the right side.
Mentions: We were interested in whether the cell line models could be grouped by their network properties. We addressed this by performing an unsupervised hierarchical clustering of the network features (that is, the components in the initial state, rules, and components that underwent state changes) that differed across the cell lines. This clustering resulted in three major groups for the cell line models: basal, luminal and a third group composed of both basal and luminal cell lines (Figure 4). The observation that there is a mixed group of basal and luminal networks indicates that the cell lines may be segmented by their signaling pathways, rather than by site of origin alone.

Bottom Line: We hypothesized that Pak1 over-expressing cell lines would have increased sensitivity to 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. lmheiser@lbl.gov

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 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.

Show MeSH
Related in: MedlinePlus