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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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Schematic of analytical approach.Computational approach to identify and characterize genomic features associated with HER2-positive tumors.
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pone-0079298-g003: Schematic of analytical approach.Computational approach to identify and characterize genomic features associated with HER2-positive tumors.

Mentions: The analytical plan, outlined in Figure 3, began with paired end (2x50nt) RNA sequence (RNA-Seq) analysis of polyA+ RNA from a survey panel of samples that consisted of 8 benign breast tumor samples plus 8 each ER+, HER2-positive, and triple negative (TN) primary invasive breast tumors. Sequence alignment data are summarized in Table S2; but, briefly, percent aligned reads ranged from 73-86%, with GeneCount-ReadStart aligned ranging from 22M to 68M read pairs per sample. (Note that for paired end sequencing, total aligned 50nt tags is equal to twice the number of read pairs.) Mode normalization of gene counts was carried out as described previously [25]. As shown in Figure S1, density plots indicated that genes with <16 total aligned tags were near or below the limits of detection; and these were eliminated from the analyses. Transcripts with >16 tags aligned ranged from 14-17K per sample, with the exception of one outlier, a TN tumor with only 11,230 transcripts (ID - BR73 in Table S2). Principle components analysis indicated that this tumor did not cluster with the TN tumors, or any of the other tumor cohorts (not shown); this sample was excluded from all subsequent analyses.


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)

Schematic of analytical approach.Computational approach to identify and characterize genomic features associated with HER2-positive tumors.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0079298-g003: Schematic of analytical approach.Computational approach to identify and characterize genomic features associated with HER2-positive tumors.
Mentions: The analytical plan, outlined in Figure 3, began with paired end (2x50nt) RNA sequence (RNA-Seq) analysis of polyA+ RNA from a survey panel of samples that consisted of 8 benign breast tumor samples plus 8 each ER+, HER2-positive, and triple negative (TN) primary invasive breast tumors. Sequence alignment data are summarized in Table S2; but, briefly, percent aligned reads ranged from 73-86%, with GeneCount-ReadStart aligned ranging from 22M to 68M read pairs per sample. (Note that for paired end sequencing, total aligned 50nt tags is equal to twice the number of read pairs.) Mode normalization of gene counts was carried out as described previously [25]. As shown in Figure S1, density plots indicated that genes with <16 total aligned tags were near or below the limits of detection; and these were eliminated from the analyses. Transcripts with >16 tags aligned ranged from 14-17K per sample, with the exception of one outlier, a TN tumor with only 11,230 transcripts (ID - BR73 in Table S2). Principle components analysis indicated that this tumor did not cluster with the TN tumors, or any of the other tumor cohorts (not shown); this sample was excluded from all subsequent analyses.

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