De novo discovery of a gamma-secretase inhibitor response signature using a novel in vivo breast tumor model.
Notch pathway signaling plays a fundamental role in normal biological processes and is frequently deregulated in many cancers. Although several hypotheses regarding cancer subpopulations most likely to respond to therapies targeting the Notch pathway have been proposed, clinical utility of these predictive markers has not been shown. To understand the molecular basis of gamma-secretase inhibitor (GSI) sensitivity in breast cancer, we undertook an unbiased, de novo responder identification study using a novel genetically engineered in vivo breast cancer model. We show that tumors arising from this model are heterogeneous on the levels of gene expression, histopathology, growth rate, expression of Notch pathway markers, and response to GSI treatment. In addition, GSI treatment of this model was associated with inhibition of Hes1 and proliferation markers, indicating that GSI treatment inhibits Notch signaling. We then identified a pretreatment gene expression signature comprising 768 genes that is significantly associated with in vivo GSI efficacy across 99 tumor lines. Pathway analysis showed that the GSI responder signature is enriched for Notch pathway components and inflammation/immune-related genes. These data show the power of this novel in vivo model system for the discovery of biomarkers predictive of response to targeted therapies, and provide a basis for the identification of human breast cancers most likely to be sensitive to GSI treatment.
Watters
James W
JW
Department of Molecular Profiling, Merck Research Laboratories, North Wales, Pennsylvania 19454, USA. james_watters@merck.com
Cheng
Chun
C
Majumder
Pradip K
PK
Wang
Ruojie
R
Yalavarthi
Sireesha
S
Meeske
Carol
C
Kong
Lingxin
L
Sun
Wenping
W
Lin
Jie
J
Heyer
Joerg
J
Ware
Chris
C
Winter
Christopher
C
Reilly
John F
JF
Demuth
Tim
T
Clark
Steve
S
Chiu
M Isabel
MI
Robinson
Murray O
MO
Kohl
Nancy
N
Kannan
Karuppiah
K
D053829:Amyloid Precursor Protein Secretases / Q000037:antagonists & inhibitors*; D000818:Animals; D048708:Cell Growth Processes / Q000187:drug effects; D003499:Cyclic S-Oxides / Q000008:administration & dosage*; D004334:Drug Administration Schedule; D004791:Enzyme Inhibitors / Q000008:administration & dosage*; D053263:Gene Regulatory Networks; D006801:Humans; D008325:Mammary Neoplasms, Experimental / Q000188:drug therapy* / Q000201:enzymology* / Q000235:genetics / Q000473:pathology; D051379:Mice; D008810:Mice, Inbred C57BL; D008822:Mice, Transgenic; D013830:Thiadiazoles / Q000008:administration & dosage*
D003499:Cyclic S-Oxides; D004791:Enzyme Inhibitors; C523799:MRK 003; D013830:Thiadiazoles; D053829:Amyloid Precursor Protein Secretases