Integrating multiomics and prior knowledge: a study of the Graphnet penalty impact.

MOTIVATION: In the field of oncology, statistical models are used for the discovery of candidate factors that influence the development of the pathology or its outcome. These statistical models can be designed in a multiblock framework to study the relationship between different multiomic data, and variable selection is often achieved by imposing constraints on the model parameters. A priori graph constraints have been used in the literature as a way to improve feature selection in the model, yielding more interpretability. However, it is still unclear how these graphs interact with the models and how they impact the feature selection. Additionally, with the availability of different graphs encoding different information, one can wonder how the choice of the graph meaningfully impacts the results obtained. RESULTS: We proposed to study the graph penalty impact on a multiblock model. Specifically, we used the SGCCA as the multiblock framework. We studied the effect of the penalty on the model using the TCGA-LGG dataset. Our findings are 3-fold. We showed that the graph penalty increases the number of selected genes from this dataset, while selecting genes already identified in other works as pertinent biomarkers in the pathology. We demonstrated that using different graphs leads to different though consistent results, but that graph density is the main factor influencing the obtained results. Finally, we showed that the graph penalty increases the performance of the survival prediction from the model-derived components and the interpretability of the results. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/neurospin/netSGCCA.

Genetic variations within human gained enhancer elements affect human brain sulcal morphology.

The expansion of the cerebral cortex is one of the most distinctive changes in the evolution of the human brain. Cortical expansion and related increases in cortical folding may have contributed to emergence of our capacities for high-order cognitive abilities. Molecular analysis of humans, archaic hominins, and non-human primates has allowed identification of chromosomal regions showing evolutionary changes at different points of our phylogenetic history. In this study, we assessed the contributions of genomic annotations spanning 30 million years to human sulcal morphology measured via MRI in more than 18,000 participants from the UK Biobank. We found that variation within brain-expressed human gained enhancers, regulatory genetic elements that emerged since our last common ancestor with Old World monkeys, explained more trait heritability than expected for the left and right calloso-marginal posterior fissures and the right central sulcus. Intriguingly, these are sulci that have been previously linked to the evolution of locomotion in primates and later on bipedalism in our hominin ancestors.

Multiway generalized canonical correlation analysis.

Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied, and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using electroencephalography (EEG).

The genetic architecture of language functional connectivity.

Language is a unique trait of the human species, of which the genetic architecture remains largely unknown. Through language disorders studies, many candidate genes were identified. However, such complex and multifactorial trait is unlikely to be driven by only few genes and case-control studies, suffering from a lack of power, struggle to uncover significant variants. In parallel, neuroimaging has significantly contributed to the understanding of structural and functional aspects of language in the human brain and the recent availability of large scale cohorts like UK Biobank have made possible to study language via image-derived endophenotypes in the general population. Because of its strong relationship with task-based fMRI (tbfMRI) activations and its easiness of acquisition, resting-state functional MRI (rsfMRI) have been more popularised, making it a good surrogate of functional neuronal processes. Taking advantage of such a synergistic system by aggregating effects across spatially distributed traits, we performed a multivariate genome-wide association study (mvGWAS) between genetic variations and resting-state functional connectivity (FC) of classical brain language areas in the inferior frontal (pars opercularis, triangularis and orbitalis), temporal and inferior parietal lobes (angular and supramarginal gyri), in 32,186 participants from UK Biobank. Twenty genomic loci were found associated with language FCs, out of which three were replicated in an independent replication sample. A locus in 3p11.1, regulating EPHA3 gene expression, is found associated with FCs of the semantic component of the language network, while a locus in 15q14, regulating THBS1 gene expression is found associated with FCs of the perceptual-motor language processing, bringing novel insights into the neurobiology of language.

Radiogenomics of diffuse intrinsic pontine gliomas (DIPGs): correlation of histological and biological characteristics with multimodal MRI features.

OBJECTIVES: The diffuse intrinsic pontine gliomas (DIPGs) are now defined by the type of histone H3 mutated at lysine 27. We aimed to correlate the multimodal MRI features of DIPGs, H3K27M mutant, with their histological and molecular characteristics. METHODS: Twenty-seven treatment-naïve children with histopathologically confirmed DIPG H3K27M mutant were prospectively included. MRI performed prior to biopsy included multi-b-value diffusion-weighted imaging, ASL, and dynamic susceptibility contrast (DSC) perfusion imaging. The ADC and cerebral blood flow (CBF) and blood volume (CBV) were measured at the biopsy site. We assessed quantitative histological data, including microvascular density, nuclear density, and H3K27M-positive nuclear density. Gene expression profiling was also assessed in the samples. We compared imaging and histopathological data according to histone subgroup. We correlated MRI quantitative data with histological data and gene expression. RESULTS: H3.1K27M mutated tumors showed higher ADC values (median 3151 µm2/s vs 1741 µm2/s, p = 0.003), and lower perfusion values (DSC-rCBF median 0.71 vs 1.43, p = 0.002, and DSC-rCBV median 1.00 vs 1.71, p = 0.02) than H3.3K27M ones. They had similar microvascular and nuclear density, but lower H3K27M-positive nuclear density (p = 0.007). The DSC-rCBV was positively correlated to the H3K27M-positive nuclear density (rho = 0.74, p = 0.02). ADC values were not correlated with nuclear density nor perfusion values with microvascular density. The expression of gated channel activity-related genes tended to be inversely correlated with ADC values and positively correlated with DSC perfusion. CONCLUSIONS: H3.1K27M mutated tumors have higher ADC and lower perfusion values than H3.3K27M ones, without direct correlation with microvascular or nuclear density. This may be due to tissular edema possibly related to gated channel activity-related gene expression. KEY POINTS: • H3.1K27M mutant DIPG had higher apparent diffusion coefficient (p = 0.003), lower α (p = 0.048), and lower relative cerebral blood volume (p = 0.02) than H3.3K27M mutant DIPG at their biopsy sites. • Biopsy samples obtained within the tumor's enhancing portion showed higher microvascular density (p = 0.03) than samples obtained outside the tumor's enhancing portion, but similar H3K27M-positive nuclear density (p = 0.84). • Relative cerebral blood volume measured at the biopsy site was significantly correlated with H3K27M-positive nuclear density (rho = 0.74, p = 0.02).

Enhancer Locus in ch14q23.1 Modulates Brain Asymmetric Temporal Regions Involved in Language Processing.

Identifying the genes that contribute to the variability in brain regions involved in language processing may shed light on the evolution of brain structures essential to the emergence of language in Homo sapiens. The superior temporal asymmetrical pit (STAP), which is not observed in chimpanzees, represents an ideal phenotype to investigate the genetic variations that support human communication. The left STAP depth was significantly associated with a predicted enhancer annotation located in the 14q23.1 locus, between DACT1 and KIAA0586, in the UK Biobank British discovery sample (N = 16 515). This association was replicated in the IMAGEN cohort (N = 1726) and the UK Biobank non-British validation sample (N = 2161). This genomic region was also associated to a lesser extent with the right STAP depth and the formation of sulcal interruptions, "plis de passage," in the bilateral STAP but not with other structural brain MRI phenotypes, highlighting its notable association with the superior temporal regions. Diffusion MRI emphasized an association with the fractional anisotropy of the left auditory fibers of the corpus callosum and with networks involved in linguistic processing in resting-state functional MRI. Overall, this evidence demonstrates a specific relationship between this locus and the establishment of the superior temporal regions that support human communication.

Variable selection for generalized canonical correlation analysis.

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to 3 or more sets of variables. RGCCA is a component-based approach which aims to study the relationships between several sets of variables. The quality and interpretability of the RGCCA components are likely to be affected by the usefulness and relevance of the variables in each block. Therefore, it is an important issue to identify within each block which subsets of significant variables are active in the relationships between blocks. In this paper, RGCCA is extended to address the issue of variable selection. Specifically, sparse generalized canonical correlation analysis (SGCCA) is proposed to combine RGCCA with an [Formula: see text]-penalty in a unified framework. Within this framework, blocks are not necessarily fully connected, which makes SGCCA a flexible method for analyzing a wide variety of practical problems. Finally, the versatility and usefulness of SGCCA are illustrated on a simulated dataset and on a 3-block dataset which combine gene expression, comparative genomic hybridization, and a qualitative phenotype measured on a set of 53 children with glioma. SGCCA is available on CRAN as part of the RGCCA package.

Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes.

Diffuse intrinsic pontine glioma (DIPG) is the most severe paediatric solid tumour, with no significant therapeutic progress made in the past 50 years. Recent studies suggest that diffuse midline glioma, H3-K27M mutant, may comprise more than one biological entity. The aim of the study was to determine the clinical and biological variables that most impact their prognosis. Ninety-one patients with classically defined DIPG underwent a systematic stereotactic biopsy and were included in this observational retrospective study. Histone H3 genes mutations were assessed by immunochemistry and direct sequencing, whilst global gene expression profiling and chromosomal imbalances were determined by microarrays. A full description of the MRI findings at diagnosis and at relapse was integrated with the molecular profiling data and clinical outcome. All DIPG but one were found to harbour either a somatic H3-K27M mutation and/or loss of H3K27 trimethylation. We also discovered a novel K27M mutation in HIST2H3C, and a lysine-to-isoleucine substitution (K27I) in H3F3A, also creating a loss of trimethylation. Patients with tumours harbouring a K27M mutation in H3.3 (H3F3A) did not respond clinically to radiotherapy as well, relapsed significantly earlier and exhibited more metastatic recurrences than those in H3.1 (HIST1H3B/C). H3.3-K27M-mutated DIPG have a proneural/oligodendroglial phenotype and a pro-metastatic gene expression signature with PDGFRA activation, while H3.1-K27M-mutated tumours exhibit a mesenchymal/astrocytic phenotype and a pro-angiogenic/hypoxic signature supported by expression profiling and radiological findings. H3K27 alterations appear as the founding event in DIPG and the mutations in the two main histone H3 variants drive two distinct oncogenic programmes with potential specific therapeutic targets.

Dnmt3b recruitment through E2F6 transcriptional repressor mediates germ-line gene silencing in murine somatic tissues.

Methylation of cytosine residues within the CpG dinucleotide in mammalian cells is an important mediator of gene expression, genome stability, X-chromosome inactivation, genomic imprinting, chromatin structure, and embryonic development. The majority of CpG sites in mammalian cells is methylated in a nonrandom fashion, raising the question of how DNA methylation is distributed along the genome. Here, we focused on the functions of DNA methyltransferase-3b (Dnmt3b), of which deregulated activity is linked to several human pathologies. We generated Dnmt3b hypomorphic mutant mice with reduced catalytic activity, which first revealed a deregulation of Hox genes expression, consistent with the observed homeotic transformations of the posterior axis. In addition, analysis of deregulated expression programs in Dnmt3b mutant embryos, using DNA microarrays, highlighted illegitimate activation of several germ-line genes in somatic tissues that appeared to be linked directly to their hypomethylation in mutant embryos. We provide evidence that these genes are direct targets of Dnmt3b. Moreover, the recruitment of Dnmt3b to their proximal promoter is dependant on the binding of the E2F6 transcriptional repressor, which emerges as a common hallmark in the promoters of genes found to be up-regulated as a consequence of impaired Dnmt3b activity. Therefore, our results unraveled a coordinated regulation of genes involved in meiosis, through E2F6-dependant methylation and transcriptional silencing in somatic tissues.

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities.

Purpose: Predicting H3.1, TP53, and ACVR1 mutations in DIPG could aid in the selection of therapeutic options. The contribution of clinical data and multi-modal MRI were studied for these three predictive tasks. To keep the maximum number of subjects, which is essential for a rare disease, missing data were considered. A multi-modal model was proposed, collecting all available data for each patient, without performing any imputation. Methods: A retrospective cohort of 80 patients with confirmed DIPG and at least one of the four MR modalities (T1w, T1c, T2w, and FLAIR), acquired with two different MR scanners was built. A pipeline including standardization of MR data and extraction of radiomic features within the tumor was applied. The values of radiomic features between the two MR scanners were realigned using the ComBat method. For each prediction task, the most robust features were selected based on a recursive feature elimination with cross-validation. Five different models, one based on clinical data and one per MR modality, were developed using logistic regression classifiers. The prediction of the multi-modal model was defined as the average of all possible prediction results among five for each patient. The performances of the models were compared using a leave-one-out approach. Results: The percentage of missing modalities ranged from 6 to 11% across modalities and tasks. The performance of each individual model was dependent on each specific task, with an AUC of the ROC curve ranging from 0.63 to 0.80. The multi-modal model outperformed the clinical model for each prediction tasks, thus demonstrating the added value of MRI. Furthermore, regardless of performance criteria, the multi-modal model came in the first place or second place (very close to first). In the leave-one-out approach, the prediction of H3.1 (resp. ACVR1 and TP53) mutations achieved a balanced accuracy of 87.8% (resp. 82.1 and 78.3%). Conclusion: Compared with a single modality approach, the multi-modal model combining multiple MRI modalities and clinical features was the most powerful to predict H3.1, ACVR1, and TP53 mutations and provided prediction, even in the case of missing modality. It could be proposed in the absence of a conclusive biopsy.

Critical oncogenic mutations in newly diagnosed pediatric diffuse intrinsic pontine glioma.

Diffuse intrinsic pontine gliomas (DIPG) can not be cured with current treatment modalities. Targeted therapy in this disease would benefit from advanced technologies detecting relevant drugable mutations. Twenty patients with classic newly diagnosed DIPG underwent stereotactic biopsies and were analyzed for the presence of 983 different mutations in 115 oncogenes and tumor-suppressor genes using OncoMap, a mass spectrometric method of allele detection. Our results identified oncogenic mutations in TP53 (40%), PI3KCA (15%), and ATM/MPL (5%) while none were identified in a large number of other genes commonly mutated in malignant gliomas. The identification of oncogenic mutations in the PI3K pathway offers the potential of a therapeutic target at initial diagnosis in this devastating disease.

Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours.

Tumour lesion segmentation is a key step to study and characterise cancer from MR neuroradiological images. Presently, numerous deep learning segmentation architectures have been shown to perform well on the specific tumour type they are trained on (e.g., glioblastoma in brain hemispheres). However, a high performing network heavily trained on a given tumour type may perform poorly on a rare tumour type for which no labelled cases allows training or transfer learning. Yet, because some visual similarities exist nevertheless between common and rare tumours, in the lesion and around it, one may split the problem into two steps: object detection and segmentation. For each step, trained networks on common lesions could be used on rare ones following a domain adaptation scheme without extra fine-tuning. This work proposes a resilient tumour lesion delineation strategy, based on the combination of established elementary networks that achieve detection and segmentation. Our strategy allowed us to achieve robust segmentation inference on a rare tumour located in an unseen tumour context region during training. As an example of a rare tumour, Diffuse Intrinsic Pontine Glioma (DIPG), we achieve an average dice score of 0.62 without further training or network architecture adaptation.

Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects.

Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome-wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a single block, while the last approach considers each haplotype independently. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single-SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.

Cortical signatures in behaviorally clustered autistic traits subgroups: a population-based study.

Extensive heterogeneity in autism spectrum disorder (ASD) has hindered the characterization of consistent biomarkers, which has led to widespread negative results. Isolating homogenized subtypes could provide insight into underlying biological mechanisms and an overall better understanding of ASD. A total of 1093 participants from the population-based "Healthy Brain Network" cohort (Child Mind Institute in the New York City area, USA) were selected based on score availability in behaviors relevant to ASD, aged 6-18 and IQ >= 70. All participants underwent an unsupervised clustering analysis on behavioral dimensions to reveal subgroups with ASD traits, identified by the presence of social deficits. Analysis revealed three socially impaired ASD traits subgroups: (1) high in emotionally dysfunctional traits, (2) high in ADHD-like traits, and (3) high in anxiety and depressive symptoms. 527 subjects had good quality structural MRI T1 data. Site effects on cortical features were adjusted using the ComBat method. Neuroimaging analyses compared cortical thickness, gyrification, and surface area, and were controlled for age, gender, and IQ, and corrected for multiple comparisons. Structural neuroimaging analyses contrasting one combined heterogeneous ASD traits group against controls did not yield any significant differences. Unique cortical signatures, however, were observed within each of the three individual ASD traits subgroups versus controls. These observations provide evidence of ASD traits subtypes, and confirm the necessity of applying dimensional approaches to extract meaningful differences, thus reducing heterogeneity and paving the way to better understanding ASD traits.

Clinical, molecular, and radiomic profile of gliomas with FGFR3-TACC3 fusions.

BACKGROUND: Actionable fibroblast growth factor receptor 3 (FGFR3)-transforming acidic coiled-coil protein 3 fusions (F3T3) are found in approximately 3% of gliomas, but their characteristics and prognostic significance are still poorly defined. Our goal was to characterize the clinical, radiological, and molecular profile of F3T3 positive diffuse gliomas. METHODS: We screened F3T3 fusion by real-time (RT)-PCR and FGFR3 immunohistochemistry in a large series of gliomas, characterized for main genetic alterations, histology, and clinical evolution. We performed a radiological and radiomic case control study, using an exploratory and a validation cohort. RESULTS: We screened 1162 diffuse gliomas (951 unselected cases and 211 preselected for FGFR3 protein immunopositivity), identifying 80 F3T3 positive gliomas. F3T3 was mutually exclusive with IDH mutation (P < 0.001) and EGFR amplification (P = 0.01), defining a distinct molecular cluster associated with CDK4 (P = 0.04) and MDM2 amplification (P = 0.03). F3T3 fusion was associated with longer survival for the whole series and for glioblastomas (median overall survival was 31.1 vs 19.9 mo, P = 0.02) and was an independent predictor of better outcome on multivariate analysis.F3T3 positive gliomas had specific MRI features, affecting preferentially insula and temporal lobe, and with poorly defined tumor margins. F3T3 fusion was correctly predicted by radiomics analysis on both the exploratory (area under the curve [AUC] = 0.87) and the validation MRI (AUC = 0.75) cohort. Using Cox proportional hazards models, radiomics predicted survival with a high C-index (0.75, SD 0.04), while the model combining clinical, genetic, and radiomic data showed the highest C-index (0.81, SD 0.04). CONCLUSION: F3T3 positive gliomas have distinct molecular and radiological features, and better outcome.

eQTL of KCNK2 regionally influences the brain sulcal widening: evidence from 15,597 UK Biobank participants with neuroimaging data.

The grey and white matter volumes are known to reduce with age. This cortical shrinkage is visible on magnetic resonance images and is conveniently identified by the increased volume of cerebrospinal fluid in the sulci between two gyri. Here, we replicated this finding using the UK Biobank dataset and studied the genetic influence on these cortical features of aging. We divided all individuals genetically confirmed of British ancestry into two sub-cohorts (12,162 and 3435 subjects for discovery and replication samples, respectively). We found that the heritability of the sulcal opening ranges from 15 to 45% (SE = 4.8%). We identified 4 new loci that contribute to this opening, including one that also affects the sulci grey matter thickness. We identified the most significant variant (rs864736) on this locus as being an expression quantitative trait locus (eQTL) for the KCNK2 gene. This gene regulates the immune-cell into the central nervous system (CNS) and controls the CNS inflammation, which is implicated in cortical atrophy and cognitive decline. These results expand our knowledge of the genetic contribution to cortical shrinking and promote further investigation into these variants and genes in pathological context such as Alzheimer's disease in which brain shrinkage is a key biomarker.

A genome-wide association study identifies susceptibility loci for primary central nervous system lymphoma at 6p25.3 and 3p22.1: a LOC Network study.

BACKGROUND: Primary central nervous system lymphoma (PCNSL) is a rare form of extra-nodal non-Hodgkin lymphoma. PCNSL is a distinct subtype of non-Hodgkin lymphoma, with over 95% of tumors belonging to the diffuse large B-cell lymphoma (DLBCL) group. We have conducted a genome-wide association study (GWAS) on immunocompetent patients to address the possibility that common genetic variants influence the risk of developing PCNSL. METHODS: We performed a meta-analysis of 2 new GWASs of PCNSL totaling 475 cases and 1134 controls of European ancestry. To increase genomic resolution, we imputed >10 million single nucleotide polymorphisms using the 1000 Genomes Project combined with UK10K as reference. In addition we performed a transcription factor binding disruption analysis and investigated the patterns of local chromatin by Capture Hi-C data. RESULTS: We identified independent risk loci at 3p22.1 (rs41289586, ANO10, P = 2.17 × 10-8) and 6p25.3 near EXOC2 (rs116446171, P = 1.95 x 10-13). In contrast, the lack of an association between rs41289586 and DLBCL suggests distinct germline predisposition to PCNSL and DLBCL. We found looping chromatin interactions between noncoding regions at 6p25.3 (rs11646171) with the IRF4 promoter and at 8q24.21 (rs13254990) with the MYC promoter, both genes with strong relevance to B-cell tumorigenesis. CONCLUSION: To our knowledge this is the first study providing insight into the genetic predisposition to PCNSL. Our findings represent an important step in defining the contribution of common genetic variation to the risk of developing PCNSL.

A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET.

Several reports have shown that radiomic features are affected by acquisition and reconstruction parameters, thus hampering multicenter studies. We propose a method that, by removing the center effect while preserving patient-specific effects, standardizes features measured from PET images obtained using different imaging protocols. Methods: Pretreatment 18F-FDG PET images of patients with breast cancer were included. In one nuclear medicine department (department A), 63 patients were scanned on a time-of-flight PET/CT scanner, and 16 lesions were triple-negative (TN). In another nuclear medicine department (department B), 74 patients underwent PET/CT on a different brand of scanner and a different reconstruction protocol, and 15 lesions were TN. The images from department A were smoothed using a gaussian filter to mimic data from a third department (department A-S). The primary lesion was segmented to obtain a lesion volume of interest (VOI), and a spheric VOI was set in healthy liver tissue. Three SUVs and 6 textural features were computed in all VOIs. A harmonization method initially described for genomic data was used to estimate the department effect based on the observed feature values. Feature distributions in each department were compared before and after harmonization. Results: In healthy liver tissue, the distributions significantly differed for 4 of 9 features between departments A and B and for 6 of 9 between departments A and A-S (P < 0.05, Wilcoxon test). After harmonization, none of the 9 feature distributions significantly differed between 2 departments (P > 0.1). The same trend was observed in lesions, with a realignment of feature distributions between the departments after harmonization. Identification of TN lesions was largely enhanced after harmonization when the cutoffs were determined on data from one department and applied to data from the other department. Conclusion: The proposed harmonization method is efficient at removing the multicenter effect for textural features and SUVs. The method is easy to use, retains biologic variations not related to a center effect, and does not require any feature recalculation. Such harmonization allows for multicenter studies and for external validation of radiomic models or cutoffs and should facilitate the use of radiomic models in clinical practice.

Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma.

Few methodological studies regarding widely used textural indices robustness in MRI have been reported. In this context, this study aims to propose some rules to compute reliable textural indices from multimodal 3D brain MRI. Diagnosis and post-biopsy MR scans including T1, post-contrast T1, T2 and FLAIR images from thirty children with diffuse intrinsic pontine glioma (DIPG) were considered. The hybrid white stripe method was adapted to standardize MR intensities. Sixty textural indices were then computed for each modality in different regions of interest (ROI), including tumor and white matter (WM). Three types of intensity binning were compared [Formula: see text]: constant bin width and relative bounds; [Formula: see text] constant number of bins and relative bounds; [Formula: see text] constant number of bins and absolute bounds. The impact of the volume of the region was also tested within the WM. First, the mean Hellinger distance between patient-based intensity distributions decreased by a factor greater than 10 in WM and greater than 2.5 in gray matter after standardization. Regarding the binning strategy, the ranking of patients was highly correlated for 188/240 features when comparing [Formula: see text] with [Formula: see text], but for only 20 when comparing [Formula: see text] with [Formula: see text], and nine when comparing [Formula: see text] with [Formula: see text]. Furthermore, when using [Formula: see text] or [Formula: see text] texture indices reflected tumor heterogeneity as assessed visually by experts. Last, 41 features presented statistically significant differences between contralateral WM regions when ROI size slightly varies across patients, and none when using ROI of the same size. For regions with similar size, 224 features were significantly different between WM and tumor. Valuable information from texture indices can be biased by methodological choices. Recommendations are to standardize intensities in MR brain volumes, to use intensity binning with constant bin width, and to define regions with the same volumes to get reliable textural indices.

Transcriptomic and epigenetic profiling of 'diffuse midline gliomas, H3 K27M-mutant' discriminate two subgroups based on the type of histone H3 mutated and not supratentorial or infratentorial location.

Diffuse midline glioma (DMG), H3 K27M-mutant, is a new entity in the updated WHO classification grouping together diffuse intrinsic pontine gliomas and infiltrating glial neoplasms of the midline harboring the same canonical mutation at the Lysine 27 of the histones H3 tail.Two hundred and fifteen patients younger than 18 years old with centrally-reviewed pediatric high-grade gliomas (pHGG) were included in this study. Comprehensive transcriptomic (n = 140) and methylation (n = 80) profiling was performed depending on the material available, in order to assess the biological uniqueness of this new entity compared to other midline and hemispheric pHGG.Tumor classification based on gene expression (GE) data highlighted the similarity of K27M DMG independently of their location along the midline. T-distributed Stochastic Neighbor Embedding (tSNE) analysis of methylation profiling confirms the discrimination of DMG from other well defined supratentorial tumor subgroups. Patients with diffuse intrinsic pontine gliomas (DIPG) and thalamic DMG exhibited a similarly poor prognosis (11.1 and 10.8 months median overall survival, respectively). Interestingly, H3.1-K27M and H3.3-K27M primary tumor samples could be distinguished based both on their GE and DNA methylation profiles, suggesting that they might arise from a different precursor or from a different epigenetic reorganization.These differences in DNA methylation profiles were conserved in glioma stem-like cell culture models of DIPG which mimicked their corresponding primary tumor. ChIP-seq profiling of H3K27me3 in these models indicate that H3.3-K27M mutated DIPG stem cells exhibit higher levels of H3K27 trimethylation which are correlated with fewer genes expressed by RNAseq. When considering the global distribution of the H3K27me3 mark, we observed that intergenic regions were more trimethylated in the H3.3-K27M mutated cells compared to the H3.1-K27M mutated ones.H3 K27M-mutant DMG represent a homogenous group of neoplasms compared to other pediatric gliomas that could be further separated based on the type of histone H3 variant mutated and their respective epigenetic landscapes. As these characteristics drive different phenotypes, these findings may have important implication for the design of future trials in these specific types of neoplasms.