An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer.
BACKGROUND
Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation.


METHODS
We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies.


RESULTS
We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient.


CONCLUSIONS
Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.
Bild
Andrea H
AH
Department of Pharmacology and Toxicology, University of Utah, 112 Skaggs Hall, Salt Lake City, UT 84112, USA. andreab@genetics.utah.edu
Parker
Joel S
JS
Gustafson
Adam M
AM
Acharya
Chaitanya R
CR
Hoadley
Katherine A
KA
Anders
Carey
C
Marcom
P Kelly
PK
Carey
Lisa A
LA
Potti
Anil
A
Nevins
Joseph R
JR
Perou
Charles M
CM
D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use; D014408:Biomarkers, Tumor / Q000096:biosynthesis / Q000235:genetics*; D001943:Breast Neoplasms / Q000145:classification / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D003603:Cytotoxins / Q000494:pharmacology / Q000627:therapeutic use; D016208:Databases, Factual; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks / Q000235:genetics; D018740:Genetic Heterogeneity; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics; D009363:Neoplasm Proteins / Q000096:biosynthesis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D009857:Oncogenes; D010641:Phenotype; D015398:Signal Transduction / Q000235:genetics
D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor; D003603:Cytotoxins; D009363:Neoplasm Proteins