Martin Sill

Hotspot Mutations in H3F3A and IDH1 Define Distinct Epigenetic and Biological Subgroups of Glioblastoma

Glioblastoma (GBM) is a brain tumor that carries a dismal prognosis and displays considerable heterogeneity. We have recently identified recurrent H3F3A mutations affecting two critical amino acids (K27 and G34) of histone H3.3 in one-third of pediatric GBM. Here, we show that each H3F3A mutation defines an epigenetic subgroup of GBM with a distinct global methylation pattern, and that they are mutually exclusive with IDH1 mutations, which characterize a third mutation-defined subgroup. Three further epigenetic subgroups were enriched for hallmark genetic events of adult GBM and/or established transcriptomic signatures. We also demonstrate that the two H3F3A mutations give rise to GBMs in separate anatomic compartments, with differential regulation of transcription factors OLIG1, OLIG2, and FOXG1, possibly reflecting different cellular origins.

Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma.

Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients.

Epigenomic alterations define lethal CIMP-positive ependymomas of infancy.

Ependymomas are common childhood brain tumours that occur throughout the nervous system, but are most common in the paediatric hindbrain. Current standard therapy comprises surgery and radiation, but not cytotoxic chemotherapy as it does not further increase survival. Whole-genome and whole-exome sequencing of 47 hindbrain ependymomas reveals an extremely low mutation rate, and zero significant recurrent somatic single nucleotide variants. Although devoid of recurrent single nucleotide variants and focal copy number aberrations, poor-prognosis hindbrain ependymomas exhibit a CpG island methylator phenotype. Transcriptional silencing driven by CpG methylation converges exclusively on targets of the Polycomb repressive complex 2 which represses expression of differentiation genes through trimethylation of H3K27. CpG island methylator phenotype-positive hindbrain ependymomas are responsive to clinical drugs that target either DNA or H3K27 methylation both in vitro and in vivo. We conclude that epigenetic modifiers are the first rational therapeutic candidates for this deadly malignancy, which is epigenetically deregulated but genetically bland.

Dual-color Proteomic Profiling of Complex Samples with a Microarray of 810 Cancer-related Antibodies

Antibody microarrays have the potential to enable comprehensive proteomic analysis of small amounts of sample material. Here, protocols are presented for the production, quality assessment, and reproducible application of antibody microarrays in a two-color mode with an array of 1,800 features, representing 810 antibodies that were directed at 741 cancer-related proteins. In addition to measures of array quality, we implemented indicators for the accuracy and significance of dual-color detection. Dual-color measurements outperform a single-color approach concerning assay reproducibility and discriminative power. In the analysis of serum samples, depletion of high-abundance proteins did not improve technical assay quality. On the contrary, depletion introduced a strong bias in protein representation. In an initial study, we demonstrated the applicability of the protocols to proteins derived from urine samples. We identified differences between urine samples from pancreatic cancer patients and healthy subjects and between sexes. This study demonstrates that biomedically relevant data can be produced. As demonstrated by the thorough quality analysis, the dual-color antibody array approach proved to be competitive with other proteomic techniques and comparable in performance to transcriptional microarray analyses.

DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis

BACKGROUND The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups. METHODS In this multicentre, retrospective analysis, we investigated genome-wide DNA methylation patterns of meningiomas from ten European academic neuro-oncology centres to identify distinct methylation classes of meningiomas. The methylation classes were further characterised by DNA copy number analysis, mutational profiling, and RNA sequencing. Methylation classes were analysed for progression-free survival outcomes by the Kaplan-Meier method. The DNA methylation-based and WHO classification schema were compared using the Brier prediction score, analysed in an independent cohort with WHO grading, progression-free survival, and disease-specific survival data available, collected at the Medical University Vienna (Vienna, Austria), assessing methylation patterns with an alternative methylation chip. FINDINGS We retrospectively collected 497 meningiomas along with 309 samples of other extra-axial skull tumours that might histologically mimic meningioma variants. Unsupervised clustering of DNA methylation data clearly segregated all meningiomas from other skull tumours. We generated genome-wide DNA methylation profiles from all 497 meningioma samples. DNA methylation profiling distinguished six distinct clinically relevant methylation classes associated with typical mutational, cytogenetic, and gene expression patterns. Compared with WHO grading, classification by individual and combined methylation classes more accurately identifies patients at high risk of disease progression in tumours with WHO grade I histology, and patients at lower risk of recurrence among WHO grade II tumours (p=0·0096) from the Brier prediction test). We validated this finding in our independent cohort of 140 patients with meningioma. INTERPRETATION DNA methylation-based meningioma classification captures clinically more homogenous groups and has a higher power for predicting tumour recurrence and prognosis than the WHO classification. The approach presented here is potentially very useful for stratifying meningioma patients to observation-only or adjuvant treatment groups. We consider methylation-based tumour classification highly relevant for the future diagnosis and treatment of meningioma. FUNDING German Cancer Aid, Else Kröner-Fresenius Foundation, and DKFZ/Heidelberg Institute of Personalized Oncology/Precision Oncology Program.

Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.

Purpose: Antiangiogenic treatment with bevacizumab, a mAb to the VEGF, is the single most widely used therapeutic agent for patients with recurrent glioblastoma. A major challenge is that there are currently no validated biomarkers that can predict treatment outcome. Here we analyze the potential of radiomics, an emerging field of research that aims to utilize the full potential of medical imaging. Experimental Design: A total of 4,842 quantitative MRI features were automatically extracted and analyzed from the multiparametric tumor of 172 patients (allocated to a discovery and validation set with a 2:1 ratio) with recurrent glioblastoma prior to bevacizumab treatment. Leveraging a high-throughput approach, radiomic features of patients in the discovery set were subjected to a supervised principal component (superpc) analysis to generate a prediction model for stratifying treatment outcome to antiangiogenic therapy by means of both progression-free and overall survival (PFS and OS). Results: The superpc predictor stratified patients in the discovery set into a low or high risk group for PFS (HR = 1.60; P = 0.017) and OS (HR = 2.14; P < 0.001) and was successfully validated for patients in the validation set (HR = 1.85, P = 0.030 for PFS; HR = 2.60, P = 0.001 for OS). Conclusions: Our radiomic-based superpc signature emerges as a putative imaging biomarker for the identification of patients who may derive the most benefit from antiangiogenic therapy, advances the knowledge in the noninvasive characterization of brain tumors, and stresses the role of radiomics as a novel tool for improving decision support in cancer treatment at low cost. Clin Cancer Res; 22(23); 5765–71. ©2016 AACR.

Robust biclustering by sparse singular value decomposition incorporating stability selection

MOTIVATION Over the past decade, several biclustering approaches have been published in the field of gene expression data analysis. Despite of huge diversity regarding the mathematical concepts of the different biclustering methods, many of them can be related to the singular value decomposition (SVD). Recently, a sparse SVD approach (SSVD) has been proposed to reveal biclusters in gene expression data. In this article, we propose to incorporate stability selection to improve this method. Stability selection is a subsampling-based variable selection that allows to control Type I error rates. The here proposed S4VD algorithm incorporates this subsampling approach to find stable biclusters, and to estimate the selection probabilities of genes and samples to belong to the biclusters. RESULTS So far, the S4VD method is the first biclustering approach that takes the cluster stability regarding perturbations of the data into account. Application of the S4VD algorithm to a lung cancer microarray dataset revealed biclusters that correspond to coregulated genes associated with cancer subtypes. Marker genes for different lung cancer subtypes showed high selection probabilities to belong to the corresponding biclusters. Moreover, the genes associated with the biclusters belong to significantly enriched cancer-related Gene Ontology categories. In a simulation study, the S4VD algorithm outperformed the SSVD algorithm and two other SVD-related biclustering methods in recovering artificial biclusters and in being robust to noisy data. AVAILABILITY R-Code of the S4VD algorithm as well as a documentation can be found at http://s4vd.r-forge.r-project.org/.

Assessing CpG island methylator phenotype, 1p/19q codeletion, and MGMT promoter methylation from epigenome-wide data in the biomarker cohort of the NOA-04 trial

BACKGROUND Molecular biomarkers including isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation, 1p/19q codeletion, and O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter methylation may improve prognostication and guide treatment decisions for patients with World Health Organization (WHO) anaplastic gliomas. At present, each marker is individually tested by distinct assays. Illumina Infinium HumanMethylation450 BeadChip arrays (HM450) enable the determination of large-scale methylation profiles and genome-wide DNA copy number changes. Algorithms have been developed to detect the glioma CpG island methylator phenotype (G-CIMP) associated with IDH1/2 mutation, 1p/19q codeletion, and MGMT promoter methylation using a single assay. METHODS Here, we retrospectively investigated the diagnostic and prognostic performance of these algorithms in comparison to individual marker testing and patient outcome in the biomarker cohort (n = 115 patients) of the NOA-04 trial. RESULTS Concordance for IDH and 1p/19q status was very high: In 92% of samples, the HM450 and reference data agreed. In discordant samples, survival analysis by Kaplan-Meier and Cox regression analyses suggested a more accurate assessment of biological phenotype by the HM450 analysis. The HM450-derived MGMT-STP27 model to calculate MGMT promoter methylation probability revealed this aberration in a significantly higher fraction of samples than conventional methylation-specific PCR, with 87 of 91 G-CIMP tumors predicted as MGMT promoter-methylated. Pyrosequencing of discordant samples confirmed the HM450 assessment in 14 of 17 cases. CONCLUSIONS G-CIMP and 1p/19q codeletion are reliably detectable by HM450 analysis and are associated with prognosis in the NOA-04 trial. For MGMT, HM450 suggests promoter methylation in the vast majority of G-CIMP tumors, which is supported by pyrosequencing.

Targeted resequencing for analysis of clonal composition of recurrent gene mutations in chronic lymphocytic leukaemia

Recurrent gene mutations contribute to the pathogenesis of chronic lymphocytic leukaemia (CLL). We developed a next‐generation sequencing (NGS) platform to determine the genetic profile, intratumoural heterogeneity, and clonal structure of two independent CLL cohorts. TP53, SF3B1, and NOTCH1 were most frequently mutated (16·3%, 16·9%, 10·7%). We found evidence for subclonal mutations in 67·5% of CLL cases with mutations of cancer consensus genes. We observed selection of subclones and found initial evidence for convergent mutations in CLL. Our data suggest that assessment of (sub)clonal structure may need to be integrated into analysis of the mutational profile in CLL.

SEURAT: visual analytics for the integrated analysis of microarray data.

BackgroundIn translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required.ResultsWe have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms.ConclusionsThe SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.