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Detection of gene pathways with predictive power for breast cancer prognosis.

Ma S, Kosorok MR - BMC Bioinformatics (2010)

Bottom Line: Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis.Gene profiling studies have been conducted to search for predictive genomic measurements.Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu

ABSTRACT

Background: Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.

Results: The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified.

Conclusions: The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.

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Related in: MedlinePlus

Densities of OPI and PPI. Left panel: the Dentatorubropallidoluysian atrophy pathway, which has predictive power; Right panel: the Thyroid cancer pathway, which does not have predictive power. Black line: density of OPI; Blue line: density of PPI. Data from [17].
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Figure 1: Densities of OPI and PPI. Left panel: the Dentatorubropallidoluysian atrophy pathway, which has predictive power; Right panel: the Thyroid cancer pathway, which does not have predictive power. Black line: density of OPI; Blue line: density of PPI. Data from [17].

Mentions: With the proposed method, we use the separation of OPI and PPI to measure the predictive power. To gain more insight, we show representative plots of the OPI and PPI in Figure 1. For the dataset described in [17], we select two pathways - the Dentatorubropallidoluysian atrophy pathway which contains 5 genes and is identified as predictive, and the Thyroid cancer pathway which also contains 5 genes and is not predictive. For a better visualization, we plot the estimated densities, rather than histograms, in Figure 1. We can see that for the predictive pathway (left panel), the OPI and PPI are well separated. However, for the pathway without predictive power (right panel), the OPI and PPI are almost completely overlapped.


Detection of gene pathways with predictive power for breast cancer prognosis.

Ma S, Kosorok MR - BMC Bioinformatics (2010)

Densities of OPI and PPI. Left panel: the Dentatorubropallidoluysian atrophy pathway, which has predictive power; Right panel: the Thyroid cancer pathway, which does not have predictive power. Black line: density of OPI; Blue line: density of PPI. Data from [17].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2837025&req=5

Figure 1: Densities of OPI and PPI. Left panel: the Dentatorubropallidoluysian atrophy pathway, which has predictive power; Right panel: the Thyroid cancer pathway, which does not have predictive power. Black line: density of OPI; Blue line: density of PPI. Data from [17].
Mentions: With the proposed method, we use the separation of OPI and PPI to measure the predictive power. To gain more insight, we show representative plots of the OPI and PPI in Figure 1. For the dataset described in [17], we select two pathways - the Dentatorubropallidoluysian atrophy pathway which contains 5 genes and is identified as predictive, and the Thyroid cancer pathway which also contains 5 genes and is not predictive. For a better visualization, we plot the estimated densities, rather than histograms, in Figure 1. We can see that for the predictive pathway (left panel), the OPI and PPI are well separated. However, for the pathway without predictive power (right panel), the OPI and PPI are almost completely overlapped.

Bottom Line: Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis.Gene profiling studies have been conducted to search for predictive genomic measurements.Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu

ABSTRACT

Background: Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.

Results: The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified.

Conclusions: The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.

Show MeSH
Related in: MedlinePlus