Search PubMedSearch

PubMed · 40035215

Distinct rates of VUS reclassification are observed when subclassifying VUS by evidence level.

Abstract

PURPOSE: Genetic testing commonly yields a plethora of variants of uncertain significance (VUS) that can lead to ongoing uncertainty for patients and their caregivers. Although all VUS hold uncertainty, some VUS have more evidence in support of pathogenicity, whereas others have more evidence of a benign role. Sharing these nuances can help guide the investment in follow-up clinical and research investigations and may, at times, influence medical decision making despite appreciated uncertainty. METHODS: Four clinical laboratories have been subclassifying VUS to help prioritize investigation and guide reporting decisions. Each laboratory developed a distinct approach for how these subclasses are used in their laboratories and, in some cases, displayed on reports. We examined the composition of each laboratory's VUS subclasses and the likelihood variants from each subclass were reclassified toward pathogenic or benign. RESULTS: We found that variants in the lowest subclass of VUS were never reclassified as likely pathogenic or pathogenic, whereas those in the highest subclass were much more likely to be reclassified as pathogenic or likely pathogenic. CONCLUSION: Given that forthcoming professional guidance in variant classification will advise the use of VUS subclasses, the experience of our laboratories in using VUS subclasses can inform future practices.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gwendolyn Bennett, Izabela Karbassi, Wenjie Chen, Steven M Harrison, Matthew S Lebo, Linyan Meng, Narasimhan Nagan, Robert Rigobello, Heidi L Rehm. 2025-02-28. Distinct rates of VUS reclassification are observed when subclassifying VUS by evidence level.. https://doi.org/10.1016/j.gim.2025.101400

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans