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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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Factor – phenotype relationships.ER and PgR factors predict progesterone receptor status: (a) training data set [21]; (b) projected into the Wang data. Outcomes are PgR− (blue, obs = 0) and PgR+ (red, obs = 1). The ER factors (Acidosis 1, Hypoxia 4, Lactic Acidosis 2, or Lactosis 5): (c) training set [21], strongly associated with ER status; (d) projected into the tumor expression data from a completely different study – the Wang data set in this case 25 – are able to predict ER status. Outcomes are ER− (blue, obs = 0) and ER+ (red, obs = 1). (e) p53 status prediction, with outcomes p53 wild type (blues, obs = 0) and mutant (reds, obs = 1) split between training (dark blue and red) and test/validation (light blue and pink) samples.
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pone-0004523-g003: Factor – phenotype relationships.ER and PgR factors predict progesterone receptor status: (a) training data set [21]; (b) projected into the Wang data. Outcomes are PgR− (blue, obs = 0) and PgR+ (red, obs = 1). The ER factors (Acidosis 1, Hypoxia 4, Lactic Acidosis 2, or Lactosis 5): (c) training set [21], strongly associated with ER status; (d) projected into the tumor expression data from a completely different study – the Wang data set in this case 25 – are able to predict ER status. Outcomes are ER− (blue, obs = 0) and ER+ (red, obs = 1). (e) p53 status prediction, with outcomes p53 wild type (blues, obs = 0) and mutant (reds, obs = 1) split between training (dark blue and red) and test/validation (light blue and pink) samples.

Mentions: The analysis indicates that highly scoring regression models for the prediction of ER status utilize one of the factors – Acidosis 1, Hypoxia 4, Lactic Acidosis 2, or Lactosis 5. From Figure 2a, one can see that the correlation between any two of these factors is high, so we will refer to them collectively as the ER factors. Figure 3a demonstrates the ability of this factor to predict ER status on the training set [21] and 3b shows prediction on a distinct and completely unrelated test set [27]. To examine the gene ontology (GO) composition of the list of genes involved in the ER factors, we applied the GATHER analysis [29] and find that GO terms associated with cell cycle, proliferation and and mitosis are greatly enriched in these factors (Table 1), corroborating well-known connection between cell progression and ER. It is also expected that the presence of lactic acid or hypoxia acts to shut down the cell cycle and the ER factor appears to directly link the two processes.


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 – phenotype relationships.ER and PgR factors predict progesterone receptor status: (a) training data set [21]; (b) projected into the Wang data. Outcomes are PgR− (blue, obs = 0) and PgR+ (red, obs = 1). The ER factors (Acidosis 1, Hypoxia 4, Lactic Acidosis 2, or Lactosis 5): (c) training set [21], strongly associated with ER status; (d) projected into the tumor expression data from a completely different study – the Wang data set in this case 25 – are able to predict ER status. Outcomes are ER− (blue, obs = 0) and ER+ (red, obs = 1). (e) p53 status prediction, with outcomes p53 wild type (blues, obs = 0) and mutant (reds, obs = 1) split between training (dark blue and red) and test/validation (light blue and pink) samples.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0004523-g003: Factor – phenotype relationships.ER and PgR factors predict progesterone receptor status: (a) training data set [21]; (b) projected into the Wang data. Outcomes are PgR− (blue, obs = 0) and PgR+ (red, obs = 1). The ER factors (Acidosis 1, Hypoxia 4, Lactic Acidosis 2, or Lactosis 5): (c) training set [21], strongly associated with ER status; (d) projected into the tumor expression data from a completely different study – the Wang data set in this case 25 – are able to predict ER status. Outcomes are ER− (blue, obs = 0) and ER+ (red, obs = 1). (e) p53 status prediction, with outcomes p53 wild type (blues, obs = 0) and mutant (reds, obs = 1) split between training (dark blue and red) and test/validation (light blue and pink) samples.
Mentions: The analysis indicates that highly scoring regression models for the prediction of ER status utilize one of the factors – Acidosis 1, Hypoxia 4, Lactic Acidosis 2, or Lactosis 5. From Figure 2a, one can see that the correlation between any two of these factors is high, so we will refer to them collectively as the ER factors. Figure 3a demonstrates the ability of this factor to predict ER status on the training set [21] and 3b shows prediction on a distinct and completely unrelated test set [27]. To examine the gene ontology (GO) composition of the list of genes involved in the ER factors, we applied the GATHER analysis [29] and find that GO terms associated with cell cycle, proliferation and and mitosis are greatly enriched in these factors (Table 1), corroborating well-known connection between cell progression and ER. It is also expected that the presence of lactic acid or hypoxia acts to shut down the cell cycle and the ER factor appears to directly link the two processes.

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