A common TCN1 loss-of-function variant is associated with lower vitamin B12 concentration in African Americans

Proteogenomic characterization of cholangiocarcinoma

BACKGROUND AND AIMS

Cholangiocarcinoma (CCA) is a highly heterogeneous cancer with limited understanding and few effective therapeutic approaches. We aimed at providing a proteogenomic CCA characterization to inform biological processes and treatment vulnerabilities.

APPROACH AND RESULTS

Integrative genomic analysis with functional validation uncovered biological perturbations downstream of driver events including DPCR1 , RBM47 mutations, SH3BGRL2 copy number alterations, and FGFR2 fusions in CCA. Proteomic clustering identified three subtypes with distinct clinical outcomes, molecular features, and potential therapeutics. Phosphoproteomics characterized targetable kinases in CCA, suggesting strategies for effective treatment with CDK and MAPK inhibitors. Patients with CCA with HBV infection showed increased antigen processing and presentation (APC) and T cell infiltration, conferring a favorable prognosis compared with those without HBV infection. The characterization of extrahepatic CCA recommended the feasible application of vascular endothelial-derived growth factor inhibitors. Multiomics profiling presented distinctive molecular characteristics of the large bile duct and the small bile duct of intrahepatic CCA. The immune landscape further revealed diverse tumor immune microenvironments, suggesting immune subtypes C1 and C5 might benefit from immune checkpoint therapy. TCN1 was identified as a potential CCA prognostic biomarker, promoting cell growth by enhancing vitamin B12 metabolism.

CONCLUSIONS

We characterized the proteogenomic landscape of 217 CCAs with 197 paired normal adjacent tissues and identified their subtypes and potential therapeutic targets. The multiomics analyses with other databases and some functional validations have indicated strategies regarding the clinical, biological, and therapeutic approaches to the management of CCA.

Utilizing Genomically Targeted Molecular Data to Improve Patient-Specific Outcomes in Autism Spectrum Disorder

Molecular biology combined with genomics can be a powerful tool for developing potential intervention strategies for improving outcomes in children with autism spectrum disorders (ASD). Monogenic etiologies rarely cause autism. Instead, ASD is more frequently due to many polygenic contributing factors interacting with each other, combined with the epigenetic effects of diet, lifestyle, and environment. One limitation of genomics has been identifying ways of responding to each identified gene variant to translate the information to something clinically useful. This paper will illustrate how understanding the function of a gene and the effects of a reported variant on a molecular level can be used to develop actionable and targeted potential interventions for a gene variant or combinations of variants. For illustrative purposes, this communication highlights a specific genomic variant, SHANK3. The steps involved in developing molecularly genomically targeted actionable interventions will be demonstrated. Cases will be shared to support the efficacy of this strategy and to show how clinicians utilized these targeted interventions to improve ASD-related symptoms significantly. The presented approach demonstrates the utility of genomics as a part of clinical decision-making.

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

B Vitamins and One-Carbon Metabolism: Implications in Human Health and Disease

Vitamins B9 (folate) and B12 are essential water-soluble vitamins that play a crucial role in the maintenance of one-carbon metabolism: a set of interconnected biochemical pathways driven by folate and methionine to generate methyl groups for use in DNA synthesis, amino acid homeostasis, antioxidant generation, and epigenetic regulation. Dietary deficiencies in B9 and B12, or genetic polymorphisms that influence the activity of enzymes involved in the folate or methionine cycles, are known to cause developmental defects, impair cognitive function, or block normal blood production. Nutritional deficiencies have historically been treated with dietary supplementation or high-dose parenteral administration that can reverse symptoms in the majority of cases. Elevated levels of these vitamins have more recently been shown to correlate with immune dysfunction, cancer, and increased mortality. Therapies that specifically target one-carbon metabolism are therefore currently being explored for the treatment of immune disorders and cancer. In this review, we will highlight recent studies aimed at elucidating the role of folate, B12, and methionine in one-carbon metabolism during normal cellular processes and in the context of disease progression.

PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection

Germline disease-causing variants are generally more spatially clustered in protein 3-dimensional structures than benign variants. Motivated by this tendency, we develop a fast and powerful protein-structure-based scan (PSCAN) approach for evaluating gene-level associations with complex disease and detecting signal variants. We validate PSCAN's performance on synthetic data and two real data sets for lipid traits and Alzheimer's disease. Our results demonstrate that PSCAN performs competitively with existing gene-level tests while increasing power and identifying more specific signal variant sets. Furthermore, PSCAN enables generation of hypotheses about the molecular basis for the associations in the context of protein structures and functional domains.

Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.