A DNA Methylation Prognostic Signature of Glioblastoma: Identification of NPTX2-PTEN-NF-Kb Nexus
Total Page:16
File Type:pdf, Size:1020Kb
Published OnlineFirst September 27, 2013; DOI: 10.1158/0008-5472.CAN-13-0298 Cancer Integrated Systems and Technologies Research A DNA Methylation Prognostic Signature of Glioblastoma: Identification of NPTX2-PTEN-NF-kB Nexus Sudhanshu Shukla1, Irene Rosita Pia Patric1, Sivaarumugam Thinagararjan1, Sujaya Srinivasan1, Baisakhi Mondal1, Alangar S. Hegde2, Bangalore A. Chandramouli3, Vani Santosh4, Arimappamagan Arivazhagan3, and Kumaravel Somasundaram1 Abstract Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n ¼ 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kB pathway association with high-risk group. NF-kB inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule- 1 as key NF-kB targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 over- expression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kB activity by inhibiting AKT through a p53-PTEN- dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kB-activated high- risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kB pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. Cancer Res; 73(22); 1–11. Ó2013 AACR. Introduction drogenase 1 (IDH1) mutation status, and Glioma-CpG Island fi The grade IV glioma (glioblastoma; GBM) is the most Methylator Phenotype (G-CIMP) have been identi ed as GBM common, malignant primary brain tumor in adults. The prog- prognostic indicators (1, 8, 9). fi fi nosis remains poor with median survival ranging from 12 to 15 Expression pro ling studies have identi ed mRNA and fi months in spite of improvements in treatment protocol (1). microRNA signatures for classi cation and prognosis in GBM – Genetic heterogeneity in GBM has been proposed to explain (10 14). However, none of the gene signatures have been the limitations in the effectiveness of current therapies, which translated into clinics, suggesting the need for more robust fi necessitates the need for prognostic gene signature (2). Many prognostic gene signature panels. Here, we have identi ed and genetic and epigenetic alterations as well as expression of some validated a 9-gene methylation signature for GBM prognosti- genes have been correlated with poor or better prognosis (3–7). cation. Multivariate analysis with all known prognostic mar- fi In addition, molecular biomarkers like methyl guanine methyl kers and signatures for GBM identi ed our methylation sig- transferase (MGMT) promoter methylation, isocitrate dehy- nature to be an independent GBM prognostic indicator. Fur- thermore, an activated NF-kB pathway was found to be associated with poor prognosis. We also show that neuronal Authors' Affiliations: 1Department of Microbiology and Cell Biology, pentraxin II (NPTX2), a component of methylation signature Indian Institute of Science; 2Sri SatyaSai Institute of Higher Medical with risky methylation, inhibits cell growth by antagonizing 3 4 Sciences; Departments of Neurosurgery and Neuropathology, National NF-kB pathway through p53-PTEN activation, thus connecting Institute of Mental Health and Neuro Sciences, Bangalore, India the methylation signature to NF-kB pathway. Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). Materials and Methods Corresponding Author: Kumaravel Somasundaram, Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore Plasmids and reporter constructs 560012, India. Phone: 91-80-23607171; Fax: 91-80-23602697; E-mail: pCMV-Entry/NPTX2 was obtained from Origene. CAPI3K [email protected] (pCDNA3-CD2p110myc) was described earlier (15, 16). CAAKT doi: 10.1158/0008-5472.CAN-13-0298 (pCDNA MYR HA AKT1) was from Addgene (Plasmid 9008). Ó2013 American Association for Cancer Research. PG13-Luc was described earlier (17). CAIKK, NF-kB-Luc, www.aacrjournals.org OF1 Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2013 American Association for Cancer Research. Published OnlineFirst September 27, 2013; DOI: 10.1158/0008-5472.CAN-13-0298 Shukla et al. PTEN-luc, shATM, and shATR plasmid constructs were target genes were used to generate an interaction network obtained from Profs. I. Verma, K.N. Balaji, Dr. R.C.M. Simmen, using STRING database (19) using "experiment," "textmining," Dr. Y. Shiloh, and Dr. T. De Lange, respectively. and 0.700 confidence level as input option. To make a protein– protein interaction network, STRING curates publically avail- Cell lines and reagents able databases for the information on direct and indirect Temozolomide, adriamycin, 5-aza-20-deoxycytidine, MTT, functional protein interactions. The data file of protein–pro- and EscortIII transfection reagent were purchased from Sigma. tein interaction network generated from STRING was then Glioma cell lines U138, LN18, U343, LN229, U251, U87, T98G, used in Medusa software (20) to construct a network. This U373 (all human glioma derived), 293, and C6 (Rat glioma network was then analyzed by ViaComplex software (21). derived) were obtained from the laboratory of Dr. A. Guha, ViaComplex plots the gene expression activity over the Medusa University of Toronto, Toronto, Canada, and grown in DMEM network topology. ViaCompex overlays expression data with medium. SVG cells were obtained from Dr. P. Seth, National the interaction network and constructs a landscape graph by Brain Research Center, New Delhi, and grown in MEM medi- distributing microarray signal with interaction network coor- um. The medium was supplemented with 10% FBS, penicillin, dinates. For each gene, the average of each group (high-risk and and streptomycin. No information is available about their low-risk) was plotted over the average of the same gene in authentication, although many common known mutations/ normal brain and landscape was constructed. alterations have been verified in our laboratory. Other methods and additional information Patient samples and clinical data The details for additional clinical data, genomic DNA extrac- Tumor samples used for study were obtained from patients tion, sodium bisulfite conversion, methylation array analysis, who were operated at Sri Sathya Sai Institute of Higher Medical bisulfite sequencing, RNA isolation, real-time quantitative Sciences and National Institute of Mental Health and Neuro- RT-PCR analysis, 5-Aza-2'-deoxycytidine treatment, transfec- sciences, Bangalore, India. Normal brain tissue samples (ante- tion, colony formation assays, stable cell line generation, rior temporal lobe) obtained during surgery for intractable proliferation assay, cytotoxicity assay, CHIP assay, and PTEN epilepsy were used as control samples. Tissues were bisected activity ELISA are given in the Supplementary information. and one half was snap-frozen in liquid nitrogen and stored at À80C until DNA/RNA isolation. The other half was fixed in Statistical analysis formalin and processed for paraffin sections. These sections To identify the CpGs whose methylation correlates with were used for the histopathologic grading of tumor and survival, methylation data, available for 44 patients, were immunohistochemical staining. considered for survival analysis. The probes with missing value imputed using k nearest neighbor algorithm using Network analysis Imputation package in R. The assessment of survival correla- To find out the key signaling pathways differently regulat- tion for all 27,578 probes was performed using BRB-Array- ed between low-risk and high-risk patients, we compared gene Tools survival analysis (BRB-ArrayTools is available without expression profiles of The Cancer Genome Atlas (TCGA) data charge for non-commercial applications at http://linus.nci. set (Agilent expression data, 17,814 genes) with gene expression nih.gov/BRB-ArrayTools.html). Firstly, for each CpG probe, differences between low-risk and high-risk groups by conduct- univariate Cox proportional hazard regression was done and ing t-test analysis with FDR correction. We identified 3,127 parametric P value was calculated. The survival time and genes that were significantly differentially regulated between censoring status were randomly assigned to each patient the high-risk and low-risk groups (1,983 genes upregulated and and univariate Cox proportional hazard regression was 1,144 genes were downregulated in high-risk