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Cross-study projections of genomic biomarkers: an evaluation in cancer genomics.

Lucas JE, Carvalho CM, Chen JL, Chi JT, West M - PLoS ONE (2009)

Bottom Line: We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures.These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology.In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.

View Article: PubMed Central - PubMed

Affiliation: Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America. joe@stat.duke.edu

ABSTRACT
Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies--in cancer and other diseases--have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.

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Related in: MedlinePlus

Factor associations.(a) Connections between genes and the 10 lactic acidosis factors in the statistical factor analysis of the breast cancer data from [21]. The genes include the initial selected signature genes (black) and those added through the iterative enrichment analysis (red), with black or red indicating that a gene (row) is highly associated with a factor (column), and white indicating little or no association. Cross-talk between putative pathway-related factors and genes is evident. (b) Lactic acidosis signature (vertical axis) is predicted by a linear regression fit (horizontal axis) on the seven factors significantly associated with the lactic acidosis signature. (c) Image of thresholded correlations between 67 factors (vertical) and the 10 lactic acidosis factors (horizontal), with black indicating pairs of factors whose pairwise sample correlation exceeds 0.9 in absolute value.
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pone-0004523-g001: Factor associations.(a) Connections between genes and the 10 lactic acidosis factors in the statistical factor analysis of the breast cancer data from [21]. The genes include the initial selected signature genes (black) and those added through the iterative enrichment analysis (red), with black or red indicating that a gene (row) is highly associated with a factor (column), and white indicating little or no association. Cross-talk between putative pathway-related factors and genes is evident. (b) Lactic acidosis signature (vertical axis) is predicted by a linear regression fit (horizontal axis) on the seven factors significantly associated with the lactic acidosis signature. (c) Image of thresholded correlations between 67 factors (vertical) and the 10 lactic acidosis factors (horizontal), with black indicating pairs of factors whose pairwise sample correlation exceeds 0.9 in absolute value.

Mentions: We will focus, for now, on the ten lactic acidosis factors. Examining the genes in each of the factors (Figure 1a) shows that all factors have representatives from the original signature in addition to genes added during the process of fitting the factor model. It is important to be sure that in the discovery of these ten factors, we have not lost our original signature. We check this by regressing the 10 sets of derived factor scores on the lactic acidosis signature scores. (Calculation of a signature score is described in the Methods section.) Witin a single multivariate regression model, we find that 7 of the 10 are significant at the .01 level, and that when we eliminate the remaining three factors from the multivariate regression, those seven remain significant. Thus, at least seven of the factors show a significant association to the original signature.


Cross-study projections of genomic biomarkers: an evaluation in cancer genomics.

Lucas JE, Carvalho CM, Chen JL, Chi JT, West M - PLoS ONE (2009)

Factor associations.(a) Connections between genes and the 10 lactic acidosis factors in the statistical factor analysis of the breast cancer data from [21]. The genes include the initial selected signature genes (black) and those added through the iterative enrichment analysis (red), with black or red indicating that a gene (row) is highly associated with a factor (column), and white indicating little or no association. Cross-talk between putative pathway-related factors and genes is evident. (b) Lactic acidosis signature (vertical axis) is predicted by a linear regression fit (horizontal axis) on the seven factors significantly associated with the lactic acidosis signature. (c) Image of thresholded correlations between 67 factors (vertical) and the 10 lactic acidosis factors (horizontal), with black indicating pairs of factors whose pairwise sample correlation exceeds 0.9 in absolute value.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2638006&req=5

pone-0004523-g001: Factor associations.(a) Connections between genes and the 10 lactic acidosis factors in the statistical factor analysis of the breast cancer data from [21]. The genes include the initial selected signature genes (black) and those added through the iterative enrichment analysis (red), with black or red indicating that a gene (row) is highly associated with a factor (column), and white indicating little or no association. Cross-talk between putative pathway-related factors and genes is evident. (b) Lactic acidosis signature (vertical axis) is predicted by a linear regression fit (horizontal axis) on the seven factors significantly associated with the lactic acidosis signature. (c) Image of thresholded correlations between 67 factors (vertical) and the 10 lactic acidosis factors (horizontal), with black indicating pairs of factors whose pairwise sample correlation exceeds 0.9 in absolute value.
Mentions: We will focus, for now, on the ten lactic acidosis factors. Examining the genes in each of the factors (Figure 1a) shows that all factors have representatives from the original signature in addition to genes added during the process of fitting the factor model. It is important to be sure that in the discovery of these ten factors, we have not lost our original signature. We check this by regressing the 10 sets of derived factor scores on the lactic acidosis signature scores. (Calculation of a signature score is described in the Methods section.) Witin a single multivariate regression model, we find that 7 of the 10 are significant at the .01 level, and that when we eliminate the remaining three factors from the multivariate regression, those seven remain significant. Thus, at least seven of the factors show a significant association to the original signature.

Bottom Line: We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures.These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology.In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.

View Article: PubMed Central - PubMed

Affiliation: Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America. joe@stat.duke.edu

ABSTRACT
Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies--in cancer and other diseases--have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.

Show MeSH
Related in: MedlinePlus