An integrated model of the transcriptome of HER2-positive breast cancer.
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.
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.
Krishna R
KR
Kalari
Brian M
BM
Necela
Xiaojia
X
Tang
Kevin J
KJ
Thompson
Melissa
M
Lau
Jeanette E
JE
Eckel-Passow
Jennifer M
JM
Kachergus
S Keith
SK
Anderson
Zhifu
Z
Sun
Saurabh
S
Baheti
Jennifer M
JM
Carr
Tiffany R
TR
Baker
Poulami
P
Barman
Derek C
DC
Radisky
Richard W
RW
Joseph
Sarah A
SA
McLaughlin
High-seng
HS
Chai
Stephan
S
Camille
David
D
Rossell
Yan W
YW
Asmann
E Aubrey
EA
Thompson
Edith A
EA
Perez
D001483:Base Sequence; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000378:metabolism* / Q000473:pathology; D045744:Cell Line, Tumor; D023281:Genomics; D006801:Humans; D008954:Models, Biological; D058990:Molecular Targeted Therapy; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism*; D059467:Transcriptome
D018719:Erb-b2 Receptor Tyrosine Kinases