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Background Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. Results The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p -value). Statistical interaction p -values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. Conclusions IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

Background Recent studies have suggested that combinations of multiple epigenetic modifications are essential for controlling gene expression. Despite numerous computational approaches have been developed to decipher the combinatorial epigenetic patterns or “epigenetic code”, none of them has explicitly addressed the relationship between a specific transcription factor (TF) and the patterns. Methods Here, we developed a novel computational method, T-cep, for annotating chromatin states associated with a specific TF. T-cep is composed of three key consecutive modules: (i) Data preprocessing, (ii) HMM training, and (iii) Potential TF-states calling. Results We evaluated T-cep on a TCF7L2-omics data. Unexpectedly, our method has uncovered a novel set of TCF7L2-regulated intragenic enhancers missed by other software tools, where the associated genes exert the highest gene expression. We further used siRNA knockdown, Co-transfection, RT-qPCR and Luciferase Reporter Assay not only to validate the accuracy and efficiency of prediction by T-cep, but also to confirm the functionality of TCF7L2-regulated enhancers in both MCF7 and PANC1 cells respectively. Conclusions Our study for the first time at a genome-wide scale reveals the enhanced transcriptional activity of cell-type-specific TCF7L2 intragenic enhancers in regulating gene expression.

Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing ∼100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution.

Cancers begin through acquisition of genomic alterations in normal cells, leading to uncontrolled cell division. However, cancer cells continue to accumulate alterations throughout the disease course. Therefore cancer in a patient is a continually changing entity subject to microevolution. This leads to tumor heterogeneity, or genetic diversity within a cancer case. Heterogeneity significantly complicates cancer diagnosis and treatment, as different cancer cells in the same patient may behave and respond differently. Current research and clinical paradigms of cancer inspect the disease at one or a few fixed time points, for instance at a patient's oncologist visits. Furthermore, most clinical and research assays of cancer in patients analyze only the small subsets of cancer cells obtained through singular biopsies. Through studying mutation and heterogeneity, we can formulate models of cancer as the dynamic process that it is, and explore the temporal and spatial gaps in between the currently studied snapshots of cancer. In this work, we first investigate microsatellite instability (MSI), a prolific mutational pattern present in subsets of human cancers. We introduce a new software algorithm, MANTIS, which uses next-generation sequencing data to quantify the accumulation of MSI rather than its mere presence or absence. Applying this to a large cohort of 11,139 cancers from 39 types, we identify MSI in 3.8% of cases, including adrenocortical carcinoma where MSI had not previously been characterized. These findings have direct clinical applicability, as tumors with MSI are frequently sensitive to checkpoint inhibitor immunotherapy. Our analysis of such a diverse cohort highlights the potential of large-scale sequencing to identify and characterize rare phenotypes which may benefit subsets of patients. Another means to assess changes in tumor heterogeneity over time is through subclonal modeling. Subclones provide a theoretical framework to approximate tumor heterogeneity and cancer microevolution through gradual accumulation of mutations. Here we infer tumor subclones and estimate their phylogeny in patients with cholangiocarcinoma, small-cell lung cancer, interdigitating dendritic cell sarcoma, and cancers with MSI. We leverage rapid research autopsy to obtain multiple high-quality tumor samples, and formulate subclonal models which identify shifts in cancer cell populations in response to treatment and at various metastatic sites. Furthermore, we provide extensions of the subclonal inference tool Canopy which provide ordering of mutations within phylogenetic branches, as well as quantification of MSI accumulation and microsatellite length distributions within tumor subclones. These findings demonstrate the power of subclonal modeling to provide insights into the timing and distribution of mutational events leading to tumor heterogeneity, and their contributions to treatment resistance and metastasis. Taken together, these studies suggest large-cohort next-generation sequencing and refining subclonal analysis algorithms as future pathways for expanding the \"predictability horizon\" of cancer. Further understanding of the dynamics of cancer mutation, especially intratumor and interpatient heterogeneity, may permit for instance elucidation of the effects of treatments on tumor genetic composition, and prediction of genetic distinctions between primary and metastatic sites of disease. Such a view of cancer as a process hence has the potential to improve precision medicine care of this disease.

BackgroundThe fibroblast growth factor receptor (FGFR) signaling pathway is activated in multiple tumor types through gene amplifications, single base substitutions, or gene fusions. Multiple small molecule kinase inhibitors targeting FGFR are currently being evaluated in clinical trials for patients with FGFR chromosomal translocations. Patients with novel gene fusions involving FGFR may represent candidates for kinase inhibitors.MethodsA targeted RNA-sequencing assay identified a KLK2-FGFR2 fusion gene in two patients with metastatic prostate cancer. NIH3T3 cells were transduced to express the KLK2-FGFR2 fusion. Migration assays, Western blots, and drug sensitivity assays were performed to functionally characterize the fusion.ResultsExpression of the KLK2-FGFR2 fusion protein in NIH3T3 cells induced a profound morphological change promoting enhanced migration and activation of downstream proteins in FGFR signaling pathways. The KLK2-FGFR2 fusion protein was determined to be highly sensitive to the selective FGFR inhibitors AZD-4547, BGJ398, JNJ-42756943, the irreversible inhibitor TAS-120, and the non-selective inhibitor Ponatinib. The KLK2-FGFR2 fusion did not exhibit sensitivity to the non-selective inhibitor Dovitinib.ConclusionsImportantly, the KLK2-FGFR2 fusion represents a novel target for precision therapies and should be screened for in men with prostate cancer.

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large cohorts, driving a need for the development of novel methods for their integration. Of note, clinical/translational studies typically provide snapshot gene and metabolite profiles and, oftentimes, most metabolites are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of gene-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture phenotype-specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via an interaction gene:phenotype p-value). Interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are clustered by the directionality of associations. We implemented our approach as an R package, IntLIM, which includes a user-friendly Shiny app. We applied IntLIM to two published datasets, collected in NCI-60 cell lines and in human breast tumor and non-tumor tissue. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in cancer-related pathways. and also uncover novel relationships that could be tested experimentally. The IntLIM R package is publicly available in GitHub (https://github.com/mathelab/IntLIM).

Current guidelines recommend brain magnetic resonance imaging (MRI) for clinical management of patients with severe herpes simplex encephalitis (HSE). However, the prognostic value of brain imaging has not been demonstrated in this setting. To investigate the association between early brain MRI data and functional outcomes of patients with HSE at 90 days after intensive care unit (ICU) admission. This multicenter cohort study was conducted in 34 ICUs in France from 2007 to 2019 and recruited all patients who received a clinical diagnosis of encephalitis and exhibited cerebrospinal fluid positivity for herpes simplex virus DNA in the polymerase chain reaction analysis. Data analysis was performed from January to April 2020. All patients underwent a standard brain MRI during the first 30 days after ICU admission. MRI acquisitions were analyzed by radiologists blinded to patients' outcomes, using a predefined score. Multivariable logistic regression and supervised hierarchical classifiers methods were used to identify factors associated with poor outcome at 90 days, defined by a score of 3 to 6 (indicating moderate-to-severe disability or death) on the Modified Rankin Scale. Overall, 138 patients (median [interquartile range {IQR}] age, 62.6 [54.0-72.0] years; 75 men [54.3%]) with an admission median (IQR) Glasgow Coma Scale score of 9 (6-12) were studied. The median (IQR) delay between ICU admission and MRI was 1 (1-7) days. At 90 days, 95 patients (68.8%) had a poor outcome, including 16 deaths (11.6%). The presence of fluid-attenuated inversion recovery MRI signal abnormalities in more than 3 brain lobes (odds ratio [OR], 25.71; 95% CI, 1.21-554.42), age older than 60 years (OR, 7.62; 95% CI, 2.02-28.91), and the presence of diffusion-weighted MRI signal abnormalities in the left thalamus (OR, 6.90; 95% CI, 1.12-43.00) were independently associated with poor outcome. Machine learning models identified bilateral diffusion abnormalities as an additional factor associated with poor outcome (34 of 39 patients [87.2%] with bilateral abnormalities had poor outcomes) and confirmed the functional burden of left thalamic lesions, particularly in older patients (all 11 patients aged >60 years had left thalamic lesions). These findings suggest that in adult patients with HSE requiring ICU admission, extensive MRI changes in the brain are independently associated with poor functional outcome at 90 days. Thalamic diffusion signal changes were frequently observed and were associated with poor prognosis, mainly in older patients.

IntroductionEndovascular thrombectomy is the standard of care for anterior circulation acute ischaemic stroke (AIS) secondary to emergent large vessel occlusion in patients who qualify. General anaesthesia (GA) or conscious sedation (CS) is usually required to ensure patient comfort and avoid agitation and movement during thrombectomy. However, the question of whether the use of GA or CS might influence functional outcome remains debated. Indeed, conflicting results exist between observational studies with better outcomes associated with CS and small monocentric randomised controlled trials favouring GA. Therefore, we aim to evaluate the effect of CS versus GA on functional outcome and periprocedural complications in endovascular mechanical thrombectomy for anterior circulation AIS.Methods and analysisAnesthesia Management in Endovascular Therapy for Ischemic Stroke (AMETIS) trial is an investigator initiated, multicentre, prospective, randomised controlled, two-arm trial. AMETIS trial will randomise 270 patients with anterior circulation AIS in a 1:1 ratio, stratified by centre, National Institutes of Health Stroke Scale (≤15 or >15) and association of intravenous thrombolysis or not to receive either CS or GA. The primary outcome is a composite of functional independence at 3 months and absence of perioperative complication occurring by day 7 after endovascular therapy for anterior circulation AIS. Functional independence is defined as a modified Rankin Scale score of 0–2 by day 90. Perioperative complications are defined as intervention-associated arterial perforation or dissection, pneumonia or myocardial infarction or cardiogenic acute pulmonary oedema or malignant stroke evolution occurring by day 7.Ethics and disseminationThe AMETIS trial was approved by an independent ethics committee. Study began in august 2017. Results will be published in an international peer-reviewed medical journal.Trial registration number NCT03229148.