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An integrated model of the transcriptome of HER2-positive breast cancer.

Kalari KR, Necela BM, Tang X, Thompson KJ, Lau M, Eckel-Passow JE, Kachergus JM, Anderson SK, Sun Z, Baheti S, Carr JM, Baker TR, Barman P, Radisky DC, Joseph RW, McLaughlin SA, Chai HS, Camille S, Rossell D, Asmann YW, Thompson EA, Perez EA - PLoS ONE (2013)

Bottom Line: We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel.These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set.These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

View Article: PubMed Central - PubMed

Affiliation: Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida, United States of America ; Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America.

ABSTRACT
Our goal in these analyses was to use genomic features from a test set of primary breast tumors to build an integrated transcriptome landscape model that makes relevant hypothetical predictions about the biological and/or clinical behavior of HER2-positive breast cancer. We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel. These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set. The generality of the model was confirmed by the observation that several key pathways were enriched in HER2-positive TCGA breast tumors. The ability of this model to make relevant predictions about the biology of breast cancer cells was established by the observation that integrin signaling was linked to lapatinib sensitivity in vitro and strongly associated with risk of relapse in the NCCTG N9831 adjuvant trastuzumab clinical trial dataset. Additional modules from the HER2 transcriptome model, including ubiquitin-mediated proteolysis, TGF-beta signaling, RHO-family GTPase signaling, and M-phase progression, were linked to response to lapatinib and paclitaxel in vitro and/or risk of relapse in the N9831 dataset. These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

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Methods workflow for HER2 transcriptomic network.High level analytical approach to build HER2-positive transcriptomic landscape from paired-end RNA-Seq data analysis of breast tumor subtypes.
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pone-0079298-g001: Methods workflow for HER2 transcriptomic network.High level analytical approach to build HER2-positive transcriptomic landscape from paired-end RNA-Seq data analysis of breast tumor subtypes.

Mentions: Figure 1 summarizes the approach to identification of HER2-associated genomic features in our test set of tumor samples. Gene counts for samples were summarized and annotated using our in-house scripts developed using BamToBed utility and UCSC refFlat annotations for further analysis. The read counts for genes were obtained for downstream differential gene expression analysis. There were a total of 22,323 genes with gene count data for normalization. Individual gene count data were normalized using mode normalization method as previously described [25]. Genes that had a median read count >24 (16 reads) in at least one of the four groups were used for gene expression analysis. After removing the genes with low expression, there were 16,195 genes considered for differential gene expression analysis. The Dunnett-Tukey-Kramer (DTK) package in R programming language was used for pairwise multiple comparison tests for unequal variance and unequal sample sizes [26]. Genes for which the HER2 tumor group had a mean log (read+1) significantly different from the means of the other tumor and normal groups were obtained. A HER2-positive gene list (p<0.05) was obtained after filtering multiple comparison values from HER2+, ER+, TN, and benign groups.


An integrated model of the transcriptome of HER2-positive breast cancer.

Kalari KR, Necela BM, Tang X, Thompson KJ, Lau M, Eckel-Passow JE, Kachergus JM, Anderson SK, Sun Z, Baheti S, Carr JM, Baker TR, Barman P, Radisky DC, Joseph RW, McLaughlin SA, Chai HS, Camille S, Rossell D, Asmann YW, Thompson EA, Perez EA - PLoS ONE (2013)

Methods workflow for HER2 transcriptomic network.High level analytical approach to build HER2-positive transcriptomic landscape from paired-end RNA-Seq data analysis of breast tumor subtypes.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3815156&req=5

pone-0079298-g001: Methods workflow for HER2 transcriptomic network.High level analytical approach to build HER2-positive transcriptomic landscape from paired-end RNA-Seq data analysis of breast tumor subtypes.
Mentions: Figure 1 summarizes the approach to identification of HER2-associated genomic features in our test set of tumor samples. Gene counts for samples were summarized and annotated using our in-house scripts developed using BamToBed utility and UCSC refFlat annotations for further analysis. The read counts for genes were obtained for downstream differential gene expression analysis. There were a total of 22,323 genes with gene count data for normalization. Individual gene count data were normalized using mode normalization method as previously described [25]. Genes that had a median read count >24 (16 reads) in at least one of the four groups were used for gene expression analysis. After removing the genes with low expression, there were 16,195 genes considered for differential gene expression analysis. The Dunnett-Tukey-Kramer (DTK) package in R programming language was used for pairwise multiple comparison tests for unequal variance and unequal sample sizes [26]. Genes for which the HER2 tumor group had a mean log (read+1) significantly different from the means of the other tumor and normal groups were obtained. A HER2-positive gene list (p<0.05) was obtained after filtering multiple comparison values from HER2+, ER+, TN, and benign groups.

Bottom Line: We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel.These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set.These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

View Article: PubMed Central - PubMed

Affiliation: Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida, United States of America ; Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America.

ABSTRACT
Our goal in these analyses was to use genomic features from a test set of primary breast tumors to build an integrated transcriptome landscape model that makes relevant hypothetical predictions about the biological and/or clinical behavior of HER2-positive breast cancer. We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel. These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set. The generality of the model was confirmed by the observation that several key pathways were enriched in HER2-positive TCGA breast tumors. The ability of this model to make relevant predictions about the biology of breast cancer cells was established by the observation that integrin signaling was linked to lapatinib sensitivity in vitro and strongly associated with risk of relapse in the NCCTG N9831 adjuvant trastuzumab clinical trial dataset. Additional modules from the HER2 transcriptome model, including ubiquitin-mediated proteolysis, TGF-beta signaling, RHO-family GTPase signaling, and M-phase progression, were linked to response to lapatinib and paclitaxel in vitro and/or risk of relapse in the N9831 dataset. These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

Show MeSH
Related in: MedlinePlus