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.