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Protein expression based multimarker analysis of breast cancer samples.

Presson AP, Yoon NK, Bagryanova L, Mah V, Alavi M, Maresh EL, Rajasekaran AK, Goodglick L, Chia D, Horvath S - BMC Cancer (2011)

Bottom Line: We compare the results of this correlation network analysis with results from a standard Cox regression analysis.We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, UCLA, Los Angeles, CA 90095, USA. apresson@ucla.edu

ABSTRACT

Background: Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.

Methods: We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.

Results: We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.

Conclusions: We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.

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Related in: MedlinePlus

Overview for conducting a Weighted Correlation Network Analyses (WGCNA) of patient TMA data (Steps 1-4) and follow up analyses (Steps 5-7). Steps 1-4 are numbered to correspond with the WGCNA methods section in the text. After defining WGCNA and WGCNA* patient groups, we compare these results to a more conventional variable selection approach (Steps 5-6). Finally, we validate the WGCNA* and conventional results in independent Affymetrix gene expression data sets (Step 7).
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Figure 1: Overview for conducting a Weighted Correlation Network Analyses (WGCNA) of patient TMA data (Steps 1-4) and follow up analyses (Steps 5-7). Steps 1-4 are numbered to correspond with the WGCNA methods section in the text. After defining WGCNA and WGCNA* patient groups, we compare these results to a more conventional variable selection approach (Steps 5-6). Finally, we validate the WGCNA* and conventional results in independent Affymetrix gene expression data sets (Step 7).

Mentions: In the following, we outline the analysis steps for conducting a WGCNA of the TMA patient data. An overview diagram is provided in Figure 1. R software for WGCNA and accompanying software tutorials are freely available at: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/.


Protein expression based multimarker analysis of breast cancer samples.

Presson AP, Yoon NK, Bagryanova L, Mah V, Alavi M, Maresh EL, Rajasekaran AK, Goodglick L, Chia D, Horvath S - BMC Cancer (2011)

Overview for conducting a Weighted Correlation Network Analyses (WGCNA) of patient TMA data (Steps 1-4) and follow up analyses (Steps 5-7). Steps 1-4 are numbered to correspond with the WGCNA methods section in the text. After defining WGCNA and WGCNA* patient groups, we compare these results to a more conventional variable selection approach (Steps 5-6). Finally, we validate the WGCNA* and conventional results in independent Affymetrix gene expression data sets (Step 7).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3142534&req=5

Figure 1: Overview for conducting a Weighted Correlation Network Analyses (WGCNA) of patient TMA data (Steps 1-4) and follow up analyses (Steps 5-7). Steps 1-4 are numbered to correspond with the WGCNA methods section in the text. After defining WGCNA and WGCNA* patient groups, we compare these results to a more conventional variable selection approach (Steps 5-6). Finally, we validate the WGCNA* and conventional results in independent Affymetrix gene expression data sets (Step 7).
Mentions: In the following, we outline the analysis steps for conducting a WGCNA of the TMA patient data. An overview diagram is provided in Figure 1. R software for WGCNA and accompanying software tutorials are freely available at: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/.

Bottom Line: We compare the results of this correlation network analysis with results from a standard Cox regression analysis.We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, UCLA, Los Angeles, CA 90095, USA. apresson@ucla.edu

ABSTRACT

Background: Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.

Methods: We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.

Results: We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.

Conclusions: We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.

Show MeSH
Related in: MedlinePlus