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Genome Med. 2017 Apr 26;9(1):40. doi: 10.1186/s13073-017-0429-x.

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes.

Author information

1
Department of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA.
2
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
3
Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA.
4
Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
5
Department of Biology, Brigham Young University, Provo, UT, USA.
6
Department of Biomedical Engineering, Center for Spatial Systems Biomedicine, Knight Cancer Institute, Oregon Health and Sciences University, Portland, OR, USA.
7
Department of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA. andreab@genetics.utah.edu.
8
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA. andreab@genetics.utah.edu.
9
Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA. andreab@genetics.utah.edu.

Abstract

BACKGROUND:

The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

METHODS:

Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

RESULTS:

Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

CONCLUSIONS:

Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

KEYWORDS:

Breast cancer; Cancer phenotypes; Gene expression signatures; Genomics; Growth factor receptor network; Targeted therapy

PMID:
28446242
PMCID:
PMC5406893
DOI:
10.1186/s13073-017-0429-x
[Indexed for MEDLINE]
Free PMC Article

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