Breast cancer is a heterogeneous disease. A significant challenge in the development and testing of new therapies is determining which tumors a drug will target. Initial studies with a novel class of small molecule inhibitors of epigenetic pathways show therapeutic responsiveness in some breast cancers. Here, we propose to investigate the epigenetic changes that underlie diverse molecular phenotypes of breast cancer whose expression patterns have been previously considered on a global scale. We hope to identify which molecular phenotypes of breast cancer will be targeted by specific epigenetic pathway inhibitors. In this proposal, we will activate different epigenetic pathways in normal human mammary epithelial cells using adenoviruses carrying six different histone deacetylase (HDAC) or DNA methyltransferase (DNMT) family member genes to define six pathway expression signatures, characterized by system-wide patterns of activated and silenced genes. A set of over 1800 breast cancers, grouped by molecular phenotype (e.g. basal, luminal), will be queried for each of the six epigenetic pathway signatures to discover the specific epigenetic pathways that are deregulated in each phenotype. Next, we will determine which drugs best treat different phenotypes of breast cancer. Cell lines representing different molecular phenotypes of breast cancer will be treated with six different HDAC and DNMT inhibitors. The association of drug response to molecular phenotype in vitro will be used to predict tumor response in vivo. To do this, we will use fresh human tumors, classified by microarray analysis and orthotopically implanted in immunocompromised mice, to create a panel of tumor-specific xenograft models that represent the most aggressive molecular phenotypes of breast cancer: basal, ERBB2-overexpressing, and highly proliferating luminals. We will test the same six HDAC and DNMT inhibitors on the mouse models and measure how well they and a conventional chemotherapeutic agent (doxorubicin) inhibit tumor growth, verifying the in vitro predictions in an in vivo model system. We hope that our results will provide preclinical evidence for the initiation of a human trial that employs a more focused approach to the use of small molecule inhibitors of epigenetic processes by defining specific phenotypes of breast cancer that a given drug should target rather than testing that drug on all breast cancers. This proposal should demonstrate how tumor biology and genomic signatures can be translated into strategies for personalized cancer treatment. ? ? ?

Public Health Relevance

We introduce a new approach for personalizing therapy of breast cancer. It employs drug assays in cell cultures, tumor-specific mouse models, and novel gene signatures in a patient's tumor to predict the optimal therapy for an individual's tumor.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM085601-01
Application #
7515872
Study Section
Special Emphasis Panel (ZGM1-GDB-2 (CP))
Program Officer
Anderson, Richard A
Project Start
2008-08-01
Project End
2012-07-31
Budget Start
2008-08-01
Budget End
2009-07-31
Support Year
1
Fiscal Year
2008
Total Cost
$401,548
Indirect Cost
Name
University of Utah
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
009095365
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112
Piccolo, Stephen R; Hoffman, Laura M; Conner, Thomas et al. (2016) Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility. Mol Syst Biol 12:860
Piccolo, Stephen R; Andrulis, Irene L; Cohen, Adam L et al. (2015) Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility. BMC Med Genomics 8:72
Zhang, Haiyu; Cohen, Adam L; Krishnakumar, Sujatha et al. (2014) Patient-derived xenografts of triple-negative breast cancer reproduce molecular features of patient tumors and respond to mTOR inhibition. Breast Cancer Res 16:R36
Deng, Glenn; Krishnakumar, Sujatha; Powell, Ashley A et al. (2014) Single cell mutational analysis of PIK3CA in circulating tumor cells and metastases in breast cancer reveals heterogeneity, discordance, and mutation persistence in cultured disseminated tumor cells from bone marrow. BMC Cancer 14:456
El-Chaar, Nader N; Piccolo, Stephen R; Boucher, Kenneth M et al. (2014) Genomic classification of the RAS network identifies a personalized treatment strategy for lung cancer. Mol Oncol 8:1339-54
Cohen, Adam L; Piccolo, Stephen R; Cheng, Luis et al. (2013) Genomic pathway analysis reveals that EZH2 and HDAC4 represent mutually exclusive epigenetic pathways across human cancers. BMC Med Genomics 6:35
Piccolo, Stephen R; Withers, Michelle R; Francis, Owen E et al. (2013) Multiplatform single-sample estimates of transcriptional activation. Proc Natl Acad Sci U S A 110:17778-83
Soldi, R; Cohen, A L; Cheng, L et al. (2013) A genomic approach to predict synergistic combinations for breast cancer treatment. Pharmacogenomics J 13:94-104
Steiling, Katrina; van den Berge, Maarten; Hijazi, Kahkeshan et al. (2013) A dynamic bronchial airway gene expression signature of chronic obstructive pulmonary disease and lung function impairment. Am J Respir Crit Care Med 187:933-42
Powell, Ashley A; Talasaz, Amirali H; Zhang, Haiyu et al. (2012) Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS One 7:e33788

Showing the most recent 10 out of 15 publications