Computational analysis of the mesenchymal signature landscape in gliomas

Where is genetic medicine headed? Exploring the perspectives of Canadian genetic professionals on future trends using the Delphi method

Driven by technological and scientific advances, the landscape of genetic medicine is rapidly changing, which complicates strategic planning and decision-making in this area. To address this uncertainty, we sought to understand genetic professionals' opinions about the future of clinical genetic and genomic services in Canada. We used the Delphi method to survey Canadian genetic professionals about their perspectives on whether scenarios about changes in service delivery and the use of genomic testing would be broadly implemented in their jurisdiction by 2030. We conducted two survey rounds; the response rates were 32% (27/84) and 67% (18/27), respectively. The most likely scenario was the universal use of noninvasive prenatal screening. The least likely scenarios involved population-based genome-wide sequencing for unaffected individuals. Overall, the scenarios perceived as most likely were those that have existing evidence about their benefit and potential medical necessity, whereas scenarios were seen as unlikely if they involved emerging technologies. Participants expected that the need for genetic healthcare services would increase by 2030 owing to changes in clinical guidelines and increased use of genome-wide sequencing. This study highlights the uncertainty in the future of genetic and genomic service provision and contributes evidence that could be used to inform strategic planning in clinical genetics.

Cilium Expression Score Predicts Glioma Survival

The accurate classification, prognostication, and treatment of gliomas has been hindered by an existing cellular, genomic, and transcriptomic heterogeneity within individual tumors and their microenvironments. Traditional clustering is limited in its ability to distinguish heterogeneity in gliomas because the clusters are required to be exclusive and exhaustive. In contrast, biclustering can identify groups of co-regulated genes with respect to a subset of samples and vice versa. In this study, we analyzed 1,798 normal and tumor brain samples using an unsupervised biclustering approach. We identified co-regulated gene expression profiles that were linked to proximally located brain regions and detected upregulated genes in subsets of gliomas, associated with their histologic grade and clinical outcome. In particular, we present a cilium-associated signature that when upregulated in tumors is predictive of poor survival. We also introduce a risk score based on expression of 12 cilium-associated genes which is reproducibly informative of survival independent of other prognostic biomarkers. These results highlight the role of cilia in development and progression of gliomas and suggest potential therapeutic vulnerabilities for these highly aggressive tumors.

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.