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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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Visualization of single nucleotide variant validation.Sanger sequencing validation of MPG eSNV in HER2 tumor. RNA-Seq reads shown over Sanger sequence tracing with mutation indicated by an arrow.
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pone-0079298-g010: Visualization of single nucleotide variant validation.Sanger sequencing validation of MPG eSNV in HER2 tumor. RNA-Seq reads shown over Sanger sequence tracing with mutation indicated by an arrow.

Mentions: It should be emphasized that although the genomic features described above are confined to the HER2-positive tumors within our survey panel, we do not wish to imply that these features are HER2-specific in any general sense or that they may have applicability as biomarkers of HER2-positive tumors. Rather, our goal was to define a set of genomic features that were unique to a test set of HER2-positive tumors, to use these features to develop a model of the genomic architecture of the tumors within that test set, and then to test the ability of that model to make predictions about the biological and/or clinical behavior of HER2-positive tumors. Our analyses have identified 685 genes that are differentially expressed in a pattern that is unique to the HER2-positive tumors in our test set of samples. Likewise, we identified 102 genes that are alternatively spliced and 303 genes that contain eSNVs that are uniquely expressed in this panel of HER2-positive tumors. Moreover, our data indicate that there is limited overlap between these genomic features, as illustrated by the VENN diagram in Figure 9. Only 8 of these genes were differentially expressed (DE in Figure 9) and alternatively spliced (AS), whereas 20 of the genes that contain eSNVs (SNV in Figure 9) were also differentially expressed. A single gene, MPG (N-methylpurine-DNA glycosylase; EC3.2.2.21), was differentially expressed, alternatively spiced, and contained a non-synonymous somatic mutation. Considering the uniqueness of MPG, we elected to independently validate both the splice variants and the eSNV. RNA-Seq analysis nominated a G to A eSNV (chr16:133064) with 14 reads supporting the alternate allele (A) and 11 reads supporting the reference allele (G). Sanger sequencing confirmed that this is a somatic R105Q (G314A) mutation that appears to be heterozygous in the tumor genomic DNA (Figure 10).


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)

Visualization of single nucleotide variant validation.Sanger sequencing validation of MPG eSNV in HER2 tumor. RNA-Seq reads shown over Sanger sequence tracing with mutation indicated by an arrow.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0079298-g010: Visualization of single nucleotide variant validation.Sanger sequencing validation of MPG eSNV in HER2 tumor. RNA-Seq reads shown over Sanger sequence tracing with mutation indicated by an arrow.
Mentions: It should be emphasized that although the genomic features described above are confined to the HER2-positive tumors within our survey panel, we do not wish to imply that these features are HER2-specific in any general sense or that they may have applicability as biomarkers of HER2-positive tumors. Rather, our goal was to define a set of genomic features that were unique to a test set of HER2-positive tumors, to use these features to develop a model of the genomic architecture of the tumors within that test set, and then to test the ability of that model to make predictions about the biological and/or clinical behavior of HER2-positive tumors. Our analyses have identified 685 genes that are differentially expressed in a pattern that is unique to the HER2-positive tumors in our test set of samples. Likewise, we identified 102 genes that are alternatively spliced and 303 genes that contain eSNVs that are uniquely expressed in this panel of HER2-positive tumors. Moreover, our data indicate that there is limited overlap between these genomic features, as illustrated by the VENN diagram in Figure 9. Only 8 of these genes were differentially expressed (DE in Figure 9) and alternatively spliced (AS), whereas 20 of the genes that contain eSNVs (SNV in Figure 9) were also differentially expressed. A single gene, MPG (N-methylpurine-DNA glycosylase; EC3.2.2.21), was differentially expressed, alternatively spiced, and contained a non-synonymous somatic mutation. Considering the uniqueness of MPG, we elected to independently validate both the splice variants and the eSNV. RNA-Seq analysis nominated a G to A eSNV (chr16:133064) with 14 reads supporting the alternate allele (A) and 11 reads supporting the reference allele (G). Sanger sequencing confirmed that this is a somatic R105Q (G314A) mutation that appears to be heterozygous in the tumor genomic DNA (Figure 10).

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