Limits...
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.

Show MeSH

Related in: MedlinePlus

The signaling networks include several hundred components, all connected in a discrete manner. (a) Example network. Each circle represents a component in the network; lines represent connections between them (that is, rules). Key signaling components are noted. (b) A small subnetwork. (c-e) Examples of data used to populate the model. Each histogram shows the distribution of expression values across the complete panel of cell lines. Data for each component in the model were clustered individually to determine whether or not the component should be included in the initial state. Components that clustered into two groups were present in the initial states of some cell lines and absent from others. (c) Raf1 transcript data yields a single group. (d) ErbB4 protein data yields two groups. (e) EsR1 yields three groups.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2691002&req=5

Figure 1: The signaling networks include several hundred components, all connected in a discrete manner. (a) Example network. Each circle represents a component in the network; lines represent connections between them (that is, rules). Key signaling components are noted. (b) A small subnetwork. (c-e) Examples of data used to populate the model. Each histogram shows the distribution of expression values across the complete panel of cell lines. Data for each component in the model were clustered individually to determine whether or not the component should be included in the initial state. Components that clustered into two groups were present in the initial states of some cell lines and absent from others. (c) Raf1 transcript data yields a single group. (d) ErbB4 protein data yields two groups. (e) EsR1 yields three groups.

Mentions: We created our network models with Pathway Logic [35-38], a system designed to build discrete, logical (rule-based) models of signal transduction pathways [39]. Logical models are directly related to the canonical schematic diagrams ('cartoons') commonly used to show functional relationships among proteins, and, as such, are easily interpretable in the context of biological systems (Figure 1b) [40]. The two critical elements of a Pathway Logic model are a rule set and an initial state. The rules represent biochemical reactions, and the initial state is a representation of all proteins present in a particular cell line. Our model contains a rich rule set: the interactions between proteins have all been individually curated from primary literature sources and, therefore represent well-characterized signaling biology. In short, we used our collection of molecular data to identify active states in each cell line, and the rules to define signaling between these active states. The resultant networks are static coarse graphical representations of signaling that can be used to generate hypotheses about key signaling events in subsets of the cell lines.


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 signaling networks include several hundred components, all connected in a discrete manner. (a) Example network. Each circle represents a component in the network; lines represent connections between them (that is, rules). Key signaling components are noted. (b) A small subnetwork. (c-e) Examples of data used to populate the model. Each histogram shows the distribution of expression values across the complete panel of cell lines. Data for each component in the model were clustered individually to determine whether or not the component should be included in the initial state. Components that clustered into two groups were present in the initial states of some cell lines and absent from others. (c) Raf1 transcript data yields a single group. (d) ErbB4 protein data yields two groups. (e) EsR1 yields three groups.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The signaling networks include several hundred components, all connected in a discrete manner. (a) Example network. Each circle represents a component in the network; lines represent connections between them (that is, rules). Key signaling components are noted. (b) A small subnetwork. (c-e) Examples of data used to populate the model. Each histogram shows the distribution of expression values across the complete panel of cell lines. Data for each component in the model were clustered individually to determine whether or not the component should be included in the initial state. Components that clustered into two groups were present in the initial states of some cell lines and absent from others. (c) Raf1 transcript data yields a single group. (d) ErbB4 protein data yields two groups. (e) EsR1 yields three groups.
Mentions: We created our network models with Pathway Logic [35-38], a system designed to build discrete, logical (rule-based) models of signal transduction pathways [39]. Logical models are directly related to the canonical schematic diagrams ('cartoons') commonly used to show functional relationships among proteins, and, as such, are easily interpretable in the context of biological systems (Figure 1b) [40]. The two critical elements of a Pathway Logic model are a rule set and an initial state. The rules represent biochemical reactions, and the initial state is a representation of all proteins present in a particular cell line. Our model contains a rich rule set: the interactions between proteins have all been individually curated from primary literature sources and, therefore represent well-characterized signaling biology. In short, we used our collection of molecular data to identify active states in each cell line, and the rules to define signaling between these active states. The resultant networks are static coarse graphical representations of signaling that can be used to generate hypotheses about key signaling events in subsets of the cell lines.

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