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Inferring pathway dysregulation in cancers from multiple types of omic data.

MacNeil SM, Johnson WE, Li DY, Piccolo SR, Bild AH - Genome Med (2015)

Bottom Line: Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common.Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level.GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncological Sciences, University of Utah, Salt Lake City, UT USA ; Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT USA.

ABSTRACT
Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common. Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level. GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns. We compare GSOA to alternative methods and demonstrate its ability to identify pathways known to play a role in various cancer phenotypes. Software implementing the GSOA method is freely available from https://bitbucket.org/srp33/gsoa.

No MeSH data available.


Related in: MedlinePlus

AUC scores for each omic type from the HER2 analysis. GSOA was applied to various types of omic data from TCGA. HER2+ breast cancer samples were compared against HER2− samples. Predictions based on RNA-sequencing data attained the highest accuracy
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Fig4: AUC scores for each omic type from the HER2 analysis. GSOA was applied to various types of omic data from TCGA. HER2+ breast cancer samples were compared against HER2− samples. Predictions based on RNA-sequencing data attained the highest accuracy

Mentions: We next applied GSOA to somatic CNV and SNV data for the same samples. RNA-Sequencing data yielded the highest AUC values overall (see Fig. 4). These findings are reasonable because the HER2-positive subtype is driven by ERBB2 amplification, which leads to increased expression of HER2 and likely other interacting molecules [44]. We then compared GSOA predictions from RNA-Sequencing data against predictions for the other data types. The RNA-Sequencing and CNV predictions were modestly correlated (Spearman correlation coefficient = 0.294, Additional file 1: Fig. S7A), while the correlation between RNA-Sequencing and somatic mutation predictions was not significant (see Additional file 1: Fig. S7B). These findings suggest that various types of omic data may provide complementary evidence regarding the factors that influence pathway activity.Fig. 4


Inferring pathway dysregulation in cancers from multiple types of omic data.

MacNeil SM, Johnson WE, Li DY, Piccolo SR, Bild AH - Genome Med (2015)

AUC scores for each omic type from the HER2 analysis. GSOA was applied to various types of omic data from TCGA. HER2+ breast cancer samples were compared against HER2− samples. Predictions based on RNA-sequencing data attained the highest accuracy
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4499940&req=5

Fig4: AUC scores for each omic type from the HER2 analysis. GSOA was applied to various types of omic data from TCGA. HER2+ breast cancer samples were compared against HER2− samples. Predictions based on RNA-sequencing data attained the highest accuracy
Mentions: We next applied GSOA to somatic CNV and SNV data for the same samples. RNA-Sequencing data yielded the highest AUC values overall (see Fig. 4). These findings are reasonable because the HER2-positive subtype is driven by ERBB2 amplification, which leads to increased expression of HER2 and likely other interacting molecules [44]. We then compared GSOA predictions from RNA-Sequencing data against predictions for the other data types. The RNA-Sequencing and CNV predictions were modestly correlated (Spearman correlation coefficient = 0.294, Additional file 1: Fig. S7A), while the correlation between RNA-Sequencing and somatic mutation predictions was not significant (see Additional file 1: Fig. S7B). These findings suggest that various types of omic data may provide complementary evidence regarding the factors that influence pathway activity.Fig. 4

Bottom Line: Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common.Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level.GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncological Sciences, University of Utah, Salt Lake City, UT USA ; Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT USA.

ABSTRACT
Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common. Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level. GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns. We compare GSOA to alternative methods and demonstrate its ability to identify pathways known to play a role in various cancer phenotypes. Software implementing the GSOA method is freely available from https://bitbucket.org/srp33/gsoa.

No MeSH data available.


Related in: MedlinePlus