Teresia Kling

Clinically Significant Copy Number Alterations and Complex Rearrangements of MYB and NFIB in Head and Neck Adenoid Cystic Carcinoma

Adenoid cystic carcinoma (ACC) of the head and neck is a malignant tumor with poor long‐term prognosis. Besides the recently identified MYB–NFIB fusion oncogene generated by a t(6;9) translocation, little is known about other genetic alterations in ACC. Using high‐resolution, array‐based comparative genomic hybridization, and massively paired‐end sequencing, we explored genomic alterations in 40 frozen ACCs. Eighty‐six percent of the tumors expressed MYB–NFIB fusion transcripts and 97% overexpressed MYB mRNA, indicating that MYB activation is a hallmark of ACC. Thirty‐five recurrent copy number alterations (CNAs) were detected, including losses involving 12q, 6q, 9p, 11q, 14q, 1p, and 5q and gains involving 1q, 9p, and 22q. Grade III tumors had on average a significantly higher number of CNAs/tumor compared to Grade I and II tumors (P = 0.007). Losses of 1p, 6q, and 15q were associated with high‐grade tumors, whereas losses of 14q were exclusively seen in Grade I tumors. The t(6;9) rearrangements were associated with a complex pattern of breakpoints, deletions, insertions, inversions, and for 9p also gains. Analyses of fusion‐negative ACCs using high‐resolution arrays and massively paired‐end sequencing revealed that MYB may also be deregulated by other mechanisms in addition to gene fusion. Our studies also identified several down‐regulated candidate tumor suppressor genes (CTNNBIP1, CASP9, PRDM2, and SFN) in 1p36.33‐p35.3 that may be of clinical significance in high‐grade tumors. Further, studies of these and other potential target genes may lead to the identification of novel driver genes in ACC.

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease‐driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long‐ and short‐term survivors. Our method constructs causal network models of gene expression by combining genome‐wide DNA‐ and RNA‐level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease‐relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53‐interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large‐scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

Comparative drug pair screening across multiple glioblastoma cell lines reveals novel drug-drug interactions

BACKGROUND Glioblastoma multiforme (GBM) is the most aggressive brain tumor in adults, and despite state-of-the-art treatment, survival remains poor and novel therapeutics are sorely needed. The aim of the present study was to identify new synergistic drug pairs for GBM. In addition, we aimed to explore differences in drug-drug interactions across multiple GBM-derived cell cultures and predict such differences by use of transcriptional biomarkers. METHODS We performed a screen in which we quantified drug-drug interactions for 465 drug pairs in each of the 5 GBM cell lines U87MG, U343MG, U373MG, A172, and T98G. Selected interactions were further tested using isobole-based analysis and validated in 5 glioma-initiating cell cultures. Furthermore, drug interactions were predicted using microarray-based transcriptional profiling in combination with statistical modeling. RESULTS Of the 5 × 465 drug pairs, we could define a subset of drug pairs with strong interaction in both standard cell lines and glioma-initiating cell cultures. In particular, a subset of pairs involving the pharmaceutical compounds rimcazole, sertraline, pterostilbene, and gefitinib showed a strong interaction in a majority of the cell cultures tested. Statistical modeling of microarray and interaction data using sparse canonical correlation analysis revealed several predictive biomarkers, which we propose could be of importance in regulating drug pair responses. CONCLUSION We identify novel candidate drug pairs for GBM and suggest possibilities to prospectively use transcriptional biomarkers to predict drug interactions in individual cases.

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.

Validation of the MethylationEPIC BeadChip for fresh-frozen and formalin-fixed paraffin-embedded tumours

DNA methylation is the most studied epigenetic modification due to its role in regulating gene expression, and its involvement in the pathogenesis of cancer and several diseases upon aberrations in methylation. The method of choice to evaluate genome-wide methylation has been the Illumina HumanMethylation450 BeadChip (450K), but it was recently replaced with the MethylationEPIC BeadChip (EPIC). We therefore sought to validate the EPIC array in comparison to the 450K array for both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tumours. We also performed analysis on the EPIC array with paired FF and FFPE samples to adapt to a clinical setting where FFPE is routinely used. Further, we compared two restoration methods, REPLI-g and Infinium, for FFPE-derived DNA on the EPIC array.The Pearson correlation of β values for common probes on the 450K and EPIC array was high for both FF (mean: 0.992) and FFPE (mean: 0.984) samples. The β values generated from the EPIC array for FFPE samples correlated well with the paired FF tumours, but varied between 0.901 and 0.987. We did note that sample pairs with lower correlation had less bimodal density distributions of β values and displayed higher noise in the copy number alteration plots (generated from the methylation array data) in the FFPE sample. Both REPLI-g and the Infinium restoration for FFPE samples performed well on the EPIC array and generated equivalent correlation scores to the paired FF sample.

Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma

Glioblastomas are characterized by transcriptionally distinct subtypes, but despite possible clinical relevance, their regulation remains poorly understood. The commonly used molecular classification systems for GBM all identify a subtype with high expression of mesenchymal marker transcripts, strongly associated with invasive growth. We used a comprehensive data-driven network modeling technique (augmented sparse inverse covariance selection, aSICS) to define separate genomic, epigenetic, and transcriptional regulators of glioblastoma subtypes. Our model identified Annexin A2 (ANXA2) as a novel methylation-controlled positive regulator of the mesenchymal subtype. Subsequent evaluation in two independent cohorts established ANXA2 expression as a prognostic factor that is dependent on ANXA2 promoter methylation. ANXA2 knockdown in primary glioblastoma stem cell-like cultures suppressed known mesenchymal master regulators, and abrogated cell proliferation and invasion. Our results place ANXA2 at the apex of a regulatory cascade that determines glioblastoma mesenchymal transformation and validate aSICS as a general methodology to uncover regulators of cancer subtypes.

c-Jun-N-terminal phosphorylation regulates DNMT1 expression and genome wide methylation in gliomas.

High-grade gliomas (HGG) are the most common brain tumors, with an average survival time of 14 months. A glioma-CpG island methylator phenotype (G-CIMP), associated with better clinical outcome, has been described in low and high-grade gliomas. Mutation of IDH1 is known to drive the G-CIMP status. In some cases, however, the hypermethylation phenotype is independent of IDH1 mutation, suggesting the involvement of other mechanisms. Here, we demonstrate that DNMT1 expression is higher in low-grade gliomas compared to glioblastomas and correlates with phosphorylated c-Jun. We show that phospho-c-Jun binds to the DNMT1 promoter and causes DNA hypermethylation. Phospho-c-Jun activation by Anisomycin treatment in primary glioblastoma-derived cells attenuates the aggressive features of mesenchymal glioblastomas and leads to promoter methylation and downregulation of key mesenchymal genes (CD44, MMP9 and CHI3L1). Our findings suggest that phospho-c-Jun activates an important regulatory mechanism to control DNMT1 expression and regulate global DNA methylation in Glioblastoma.

System-Scale Network Modeling of Cancer Using EPoC

One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.

Pediatric brain tumor cells release exosomes with a miRNA repertoire that differs from exosomes secreted by normal cells

High-grade gliomas (HGGs) are very aggressive brain tumors with a cancer stem cell component. Cells, including cancer stem cells, release vesicles called exosomes which contain small non-coding RNAs such as microRNAs (miRNAs). These are thought to play an important role in cell-cell communication. However, we have limited knowledge of the types of exosomal miRNAs released by pediatric HGG stem cells; a prerequisite for exploring their potential roles in HGG biology. Here we isolated exosomes released by pediatric glioma stem cells (GSCs) and compared their repertoire of miRNAs to genetically normal neural stem cells (NSCs) exosomes, as well as their respective cellular miRNA content. Whereas cellular miRNAs are similar, we find that the exosomal miRNA profiles differ between normal and tumor cells, and identify several differentially expressed miRNAs. Of particular interest is miR-1290 and miR-1246, which have previously been linked to ‘stemness’ and invasion in other cancers. We demonstrate that GSC-secreted exosomes influence the gene expression of receiving NSCs, particularly targeting genes with a role in cell fate and tumorigenesis. Thus, our study shows that GSCs and NSCs have similar cellular miRNA profiles, yet differ significantly in the repertoire of exosomal miRNAs and these could influence malignant features of HGG.

Cell line-based xenograft mouse model of paediatric glioma stem cells mirrors the clinical course of the patient

We have established a robust and reliable cell line-based glioma stem cell xenograft mouse model, genetically and epigenetically similar to its originating patient tumours. The survival of patients and injected mice correlate significantly, demonstrating that the transplanted mice mimic the clinical course of the patient.