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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

Summary of gene expression and somatic mutation differences between uterine serous (USC) and uterine endometrioid carcinomas (UEC) for the MYC pathway. Genes upregulated in USC were MYC (P = 3.3 × 10−8), TRRAP (P = 3.8 × 10−4), PI3KCA (P = 1.4 × 10−10), MAPK3 (P = 5.8 × 10−5), CAD (P = 6.9 × 10−9), ODC1 (P = 1.9 × 10−11), CKS1B (P = 9.2 × 10−14), SKP2 (P = 3.0 × 10−3), CDKN1A (P = 6.8 × 10−20), CCNE1 (P = 2.5 × 10−18), and CDK2 (P = 2.0 × 10−5). TP53 (P = 2.5 × 10−3) and FBXW7 (P = 3.8 × 10−4) were upregulated and somatically mutated. CDKN1A (P = 6.8 × 10−20) and TAF9 (5.9 × 10−13) were down in USC. This suggests upregulation of the MYC pathway in USC. A Wilcoxon rank test was used for RNA-Sequencing, and a Fisher’s Exact test for somatic mutations
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Fig5: Summary of gene expression and somatic mutation differences between uterine serous (USC) and uterine endometrioid carcinomas (UEC) for the MYC pathway. Genes upregulated in USC were MYC (P = 3.3 × 10−8), TRRAP (P = 3.8 × 10−4), PI3KCA (P = 1.4 × 10−10), MAPK3 (P = 5.8 × 10−5), CAD (P = 6.9 × 10−9), ODC1 (P = 1.9 × 10−11), CKS1B (P = 9.2 × 10−14), SKP2 (P = 3.0 × 10−3), CDKN1A (P = 6.8 × 10−20), CCNE1 (P = 2.5 × 10−18), and CDK2 (P = 2.0 × 10−5). TP53 (P = 2.5 × 10−3) and FBXW7 (P = 3.8 × 10−4) were upregulated and somatically mutated. CDKN1A (P = 6.8 × 10−20) and TAF9 (5.9 × 10−13) were down in USC. This suggests upregulation of the MYC pathway in USC. A Wilcoxon rank test was used for RNA-Sequencing, and a Fisher’s Exact test for somatic mutations

Mentions: To better understand why the MYC pathway was prioritized in our GSOA analyses, we investigated individual genes within this pathway as well as up- and downstream pathways. We compared gene expression levels and somatic mutation data for USC and UEC tumors and used the Wilcoxon rank test and Fisher’s exact test, respectively, to look for significant differences at the individual gene level (Additional file 1: Table S7). The modes of MYC dysregulation are highlighted in Fig. 5. Expression of MYC was elevated in USC (P = 3.3 × 10−8). MYC binding partner TAF9 (P = 5.9 × 10−13) was down, and TRRAP (P = 3.8 × 10−4) was up. Downregulation of TAF9 was unexpected, and may be worth further exploration. The MEK-ERK and PI3K pathways can induce MYC expression [59], and the PIK3CA (P = 1.4 × 10−10) and MAPK3 (P = 5.8 × 10−5) genes were upregulated in USC, which we also saw in our GSOA analyses. Furthermore, we saw somatic mutations and downregulation of genes that negatively regulate MYC in USC, including TP53 [63] (P = 2.5 × 10−3) and FBXW7 (P = 3.8 × 10−4), which aids in MYC regulation via ubiquitination [64]. FBXW7 mutations are common in USC [54], and also have been shown to increase MYC signaling in gastric cancers [65].Fig. 5


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

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

Summary of gene expression and somatic mutation differences between uterine serous (USC) and uterine endometrioid carcinomas (UEC) for the MYC pathway. Genes upregulated in USC were MYC (P = 3.3 × 10−8), TRRAP (P = 3.8 × 10−4), PI3KCA (P = 1.4 × 10−10), MAPK3 (P = 5.8 × 10−5), CAD (P = 6.9 × 10−9), ODC1 (P = 1.9 × 10−11), CKS1B (P = 9.2 × 10−14), SKP2 (P = 3.0 × 10−3), CDKN1A (P = 6.8 × 10−20), CCNE1 (P = 2.5 × 10−18), and CDK2 (P = 2.0 × 10−5). TP53 (P = 2.5 × 10−3) and FBXW7 (P = 3.8 × 10−4) were upregulated and somatically mutated. CDKN1A (P = 6.8 × 10−20) and TAF9 (5.9 × 10−13) were down in USC. This suggests upregulation of the MYC pathway in USC. A Wilcoxon rank test was used for RNA-Sequencing, and a Fisher’s Exact test for somatic mutations
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Summary of gene expression and somatic mutation differences between uterine serous (USC) and uterine endometrioid carcinomas (UEC) for the MYC pathway. Genes upregulated in USC were MYC (P = 3.3 × 10−8), TRRAP (P = 3.8 × 10−4), PI3KCA (P = 1.4 × 10−10), MAPK3 (P = 5.8 × 10−5), CAD (P = 6.9 × 10−9), ODC1 (P = 1.9 × 10−11), CKS1B (P = 9.2 × 10−14), SKP2 (P = 3.0 × 10−3), CDKN1A (P = 6.8 × 10−20), CCNE1 (P = 2.5 × 10−18), and CDK2 (P = 2.0 × 10−5). TP53 (P = 2.5 × 10−3) and FBXW7 (P = 3.8 × 10−4) were upregulated and somatically mutated. CDKN1A (P = 6.8 × 10−20) and TAF9 (5.9 × 10−13) were down in USC. This suggests upregulation of the MYC pathway in USC. A Wilcoxon rank test was used for RNA-Sequencing, and a Fisher’s Exact test for somatic mutations
Mentions: To better understand why the MYC pathway was prioritized in our GSOA analyses, we investigated individual genes within this pathway as well as up- and downstream pathways. We compared gene expression levels and somatic mutation data for USC and UEC tumors and used the Wilcoxon rank test and Fisher’s exact test, respectively, to look for significant differences at the individual gene level (Additional file 1: Table S7). The modes of MYC dysregulation are highlighted in Fig. 5. Expression of MYC was elevated in USC (P = 3.3 × 10−8). MYC binding partner TAF9 (P = 5.9 × 10−13) was down, and TRRAP (P = 3.8 × 10−4) was up. Downregulation of TAF9 was unexpected, and may be worth further exploration. The MEK-ERK and PI3K pathways can induce MYC expression [59], and the PIK3CA (P = 1.4 × 10−10) and MAPK3 (P = 5.8 × 10−5) genes were upregulated in USC, which we also saw in our GSOA analyses. Furthermore, we saw somatic mutations and downregulation of genes that negatively regulate MYC in USC, including TP53 [63] (P = 2.5 × 10−3) and FBXW7 (P = 3.8 × 10−4), which aids in MYC regulation via ubiquitination [64]. FBXW7 mutations are common in USC [54], and also have been shown to increase MYC signaling in gastric cancers [65].Fig. 5

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