Genomic profiling of a Hepatocyte growth factor-dependent signature for MET-targeted therapy in glioblastoma

Journal of Translational Medicine

Research

Maria Libera Ascierto¹  Francesco M. Marincola²  Sandeep Mittal³  George F. Vande Woude⁴  Qian Xie⁵  Jennifer Johnson⁶  Liang Kang⁶  Michael E. Berens⁷  David Newsome⁸  Kirk Tanner⁸  Michael Briggs⁹ 

[1] Department of Transfusion Medicine, Infectious Disease and Immunogenetics Section, Clinical Center, Trans-National Institutes of Health Center for Human Immunology, National Institutes of Health, Bethesda, MD, USA;Department of Oncology, Johns Hopkins University, Baltimore, MD, USA;Department of Transfusion Medicine, Infectious Disease and Immunogenetics Section, Clinical Center, Trans-National Institutes of Health Center for Human Immunology, National Institutes of Health, Bethesda, MD, USA;Research Branch, Sidra Medical and Research Center, Doha, Qatar;Departments of Neurosurgery and Oncology, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA;Laboratory of Molecular Oncology, Van Andel Research Institute, Grand Rapids, MI, USA;Molecular Oncogenesis and Targeted Therapy, Laboratory of Molecular Oncology, Van Andel Research Institute, 333 Bostwick AVE NE, 49503, Grand Rapids, MI, USA;Molecular Oncogenesis and Targeted Therapy, Laboratory of Molecular Oncology, Van Andel Research Institute, 333 Bostwick AVE NE, 49503, Grand Rapids, MI, USA;Laboratory of Molecular Oncology, Van Andel Research Institute, Grand Rapids, MI, USA;Translational Genomics Research Institute, Phoenix, AZ, USA;Vertex Pharmaceutical Inc., Boston, MA, USA;Woodland Pharmaceuticals, Shrewsbury, MA, USA;

关键词: Predictive signature;    Hepatocyte growth Factor;    MET;    Glioblastoma;    Targeted therapy;   

DOI  :  10.1186/s12967-015-0667-x

 received in 2015-06-12, accepted in 2015-09-09,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundConstitutive MET signaling promotes invasiveness in most primary and recurrent GBM. However, deployment of available MET-targeting agents is confounded by lack of effective biomarkers for selecting suitable patients for treatment. Because endogenous HGF overexpression often causes autocrine MET activation, and also indicates sensitivity to MET inhibitors, we investigated whether it drives the expression of distinct genes which could serve as a signature indicating vulnerability to MET-targeted therapy in GBM.MethodsInterrogation of genomic data from TCGA GBM (Student’s t test, GBM patients with high and low HGF expression, p ≤ 0.00001) referenced against patient-derived xenograft (PDX) models (Student’s t test, sensitive vs. insensitive models, p ≤ 0.005) was used to identify the HGF-dependent signature. Genomic analysis of GBM xenograft models using both human and mouse gene expression microarrays (Student’s t test, treated vs. vehicle tumors, p ≤ 0.01) were performed to elucidate the tumor and microenvironment cross talk. A PDX model with EGFRamp was tested for MET activation as a mechanism of erlotinib resistance.ResultsWe identified a group of 20 genes highly associated with HGF overexpression in GBM and were up- or down-regulated only in tumors sensitive to MET inhibitor. The MET inhibitors regulate tumor (human) and host (mouse) cells within the tumor via distinct molecular processes, but overall impede tumor growth by inhibiting cell cycle progression. EGFRamp tumors undergo erlotinib resistance responded to a combination of MET and EGFR inhibitors.ConclusionsCombining TCGA primary tumor datasets (human) and xenograft tumor model datasets (human tumor grown in mice) using therapeutic efficacy as an endpoint may serve as a useful approach to discover and develop molecular signatures as therapeutic biomarkers for targeted therapy. The HGF dependent signature may serve as a candidate predictive signature for patient enrollment in clinical trials using MET inhibitors. Human and mouse microarrays maybe used to dissect the tumor-host interactions. Targeting MET in EGFRamp GBM may delay the acquired resistance developed during treatment with erlotinib.

【 授权许可】

CC BY   
© Johnson et al. 2015