Molecular subclassification of medulloblastoma and its utility for disease prognostication

Medulloblastoma is the most common malignant brain tumour of childhood. Transcriptomic classification of the disease has indicated the existence of discrete molecular subgroups of medulloblastoma, although the precise number, nature and clinical significance of these subgroups remains unclear. Two groups, characterised by activation of the WNT and SHH signalling pathways, are common to all published studies. An assay for the rapid diagnosis of medulloblastoma subgroups was therefore designed, using transcriptomic gene signatures of pathway activation for the WNT and SHH signalling pathways. The successful validation of these gene signatures in vitro and in silico enabled a meta-analysis of 173 new and published cases to be performed, which defined the molecular and clinico-pathological correlates of the disease subgroups more precisely. WNT subgroup cases were associated with CTNNB1 mutation, chromosome 6 loss and classic histology and were diagnosed > 5 years of age. SHH cases predominated in infants and showed an age-dependent relationship to desmoplastic / nodular histology. WNT / SHH independent tumours showed all histologies, peaked at 3 to 6 years and were associated with chromosome 17p loss. A novel DNA methylation array-based approach was next applied to disease subclassification. Using consensus clustering, based on non-negative matrix factorisation, four methylomic subgroups were identified in a training cohort (n = 100), which were robustly validated in a test cohort (n = 130). The subgroups were characterised by significant relationships to specific clinico-pathological and molecular markers. Two subgroups were characterised by activation of the WNT and SHH signalling pathways and showed equivalent clinico-pathological and molecular characteristics to the previously defined transcriptomic subgroups. For the WNT / SHH independent subgroups, group I was associated with a loss of chromosome 17p, whereas group II was enriched for large cell / anaplastic (LCA) histology. The WNT subgroup was associated with a favourable prognosis, while no survival differences were apparent between the remaining subgroups (SHH, group I, group II). Specific methylation biomarkers were identified for the discrimination of all subgroups. Assays of DNA methylation status were robust in derivatives of FFPE tissues, enabling testing in routinely-collected clinical material. Finally, the prognostic potential of methylomic biomarkers was investigated in a large clinical trials-based cohort (n = 191), with particular focus on the non-WNT subgroups (n = 163), where subgroup membership was not prognostic. Using the Cox Boost algorithm, which adds high dimensional data to mandatory clinical covariates to form cross-validated prognostic Cox survival models, the methylation status of MXI1 and IL8 were each identified as independent prognostic markers. These were incorporated into a novel risk stratification scheme, based on the cumulative assessment of disease risk using clinical (metastatic disease; poor prognosis), pathological (LCA pathology, poor prognosis) and methylomic variables (WNT subgroup, favourable prognosis; MXI1 and IL8 status). Importantly, this scheme assigns 46% of cases to a low risk group of patients (>90% survival) who could potentially be treated less intensively, with the aim of reducing therapy-associated late effects. This model out-performed the current clinical and other state-of-the-art medulloblastoma risk classification schemes. These data provide clear precedent for the utility of DNA methylation biomarkers for disease subclassification and prognostication in medulloblastoma, and their clinical application in diagnostic tumour biopsies.EThOS - Electronic Theses Online ServiceKatie TrustGBUnited Kingdo

The impact of maceration on the ‘Osteo-ome’; a pilot investigation

The bone proteome, i.e., the ‘osteo-ome’, is a rich source of information for forensic studies. There have been advances in the study of biomolecule biomarkers for age-at-death (AAD) and post-mortem interval (PMI) estimations, by looking at changes in protein abundance and post-translational modifications (PTMs) at the peptide level. However, the extent to which other post-mortem factors alter the proteome, including ‘maceration’ procedures adopted in human taphonomy facilities (HTFs) to clean bones for osteological collections, is poorly understood. This pilot study aimed to characterise the impact of these ‘cleaning’ methods for de-fleshing skeletons on bone biomolecules, and therefore, what further impact this may have on putative biomarkers in future investigations. Three specific maceration procedures, varying in submersion time (one week or two days) and water temperature (55 °C or 87 °C) were conducted on six bovid tibiae from three individual bovines; the proteome of fresh and macerated bones of each individual was compared. The maceration at 87 °C for two days had the greatest proteomic impact, decreasing protein relative abundances and inducing specific PTMs. Overall, these results suggest that routinely-employed maceration procedures are harsh, variable and potentially threaten the viability of discovering new forensic biomarkers in macerated skeletal remains

Epigenetic modifiers DNMT3A and BCOR are recurrently mutated in CYLD cutaneous syndrome

Abstract: Patients with CYLD cutaneous syndrome (CCS; syn. Brooke-Spiegler syndrome) carry germline mutations in the tumor suppressor CYLD and develop multiple skin tumors with diverse histophenotypes. Here, we comprehensively profile the genomic landscape of 42 benign and malignant tumors across 13 individuals from four multigenerational families and discover recurrent mutations in epigenetic modifiers DNMT3A and BCOR in 29% of benign tumors. Multi-level and microdissected sampling strikingly reveal that many clones with different DNMT3A mutations exist in these benign tumors, suggesting that intra-tumor heterogeneity is common. Integrated genomic, methylation and transcriptomic profiling in selected tumors suggest that isoform-specific DNMT3A2 mutations are associated with dysregulated methylation. Phylogenetic and mutational signature analyses confirm cylindroma pulmonary metastases from primary skin tumors. These findings contribute to existing paradigms of cutaneous tumorigenesis and metastasis

Integration of genome-level data to allow identification of subtype-specific vulnerability genes as novel therapeutic targets

The identification of cancer-specific vulnerability genes is one of the most promising approaches for developing more effective and less toxic cancer treatments. Cancer genomes exhibit thousands of changes in DNA methylation and gene expression, with the vast majority likely to be passenger changes. We hypothesised that, through integration of genome-wide DNA methylation/expression data, we could exploit this inherent variability to identify cancer subtype-specific vulnerability genes that would represent novel therapeutic targets that could allow cancer-specific cell killing. We developed a bioinformatics pipeline integrating genome-wide DNA methylation/gene expression data to identify candidate subtype-specific vulnerability partner genes for the genetic drivers of individual genetic/molecular subtypes. Using acute lymphoblastic leukaemia as an initial model, 21 candidate subtype-specific vulnerability genes were identified across the five common genetic subtypes, with at least one per subtype. To confirm the approach was applicable across cancer types, we also assessed medulloblastoma, identifying 15 candidate subtype-specific vulnerability genes across three of four established subtypes. Almost all identified genes had not previously been implicated in these diseases. Functional analysis of seven candidate subtype-specific vulnerability genes across the two tumour types confirmed that siRNA-mediated knockdown induced significant inhibition of proliferation/induction of apoptosis, which was specific to the cancer subtype in which the gene was predicted to be specifically lethal. Thus, we present a novel approach that integrates genome-wide DNA methylation/expression data to identify cancer subtype-specific vulnerability genes as novel therapeutic targets. We demonstrate this approach is applicable to multiple cancer types and identifies true functional subtype-specific vulnerability genes with high efficiency

Bone Proteomics Method Optimization for Forensic Investigations

The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC–MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications

HF-VHF帯における生体等価ファントムを用いた電磁波と人体相互作用の検証

研究科: 千葉大学大学院工学研究科学位:千大院工博甲第工185号博士(工学)千葉大

Pediatric pan-central nervous system tumor analysis of immune-cell infiltration identifies correlates of antitumor immunity

Here, using methylCIBERSORT, the authors characterize the tumour-immune microenvironment of paediatric central nervous system (CNS) tumours and its association with tumour type and prognosis. These findings suggest that immuno-methylomic profiling may inform immunotherapy approaches in paediatric patients with CNS tumour

Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups

BackgroundThe malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS).MethodsMetabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival.FindingsGroup-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025).InterpretationTissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis

Handbook of the Social Psychology of Inequality

XXV, 749 p. 15 illus.online resource