Aug 2026· Journal of Molecular Diagnostics· 0 citations· 26 references
Medicine
TL;DR
A strategy is presented that calculates the median and median absolute deviation of gene-level fold changes across all samples within each sequencing batch and incorporates these measures into the result interpretation, providing batch-level reference metrics and supporting more reliable interpretation in comprehensive genomic profiling.
Abstract
Next generation sequencing (NGS) is routinely performed in clinical practice to detect various types of mutations for targeted therapy, diagnosis, and prognosis. Actionable alterations detected by NGS include not only non-synonymous mutations that lead to functional or structural changes of proteins but also copy number variants (CNV) that affect gene dosage, such as gene copy gains, amplifications or deletions. Among tumor-only CNV detection methods, the use of a Panel of Normals (PoN) for relative comparison has become a common practice, largely due to the lack of matched normal samples. It was therefore hypothesized, once established, a PoN and CNV caller may not fully compensate for all experimental variations - such as differences in probe efficiency across reagent lots. To investigate this, 12,104 clinical sequencing datasets from 1,454 sequencing batches were analyzed over a four-year period. This analysis revealed batch-associated fluctuation patterns in gene-level fold changes that could potentially lead to misinterpretation, such as the incorrect classification of gene copy deletions or gains. In this study, a strategy is presented that calculates the median and median absolute deviation of gene-level fold changes across all samples within each sequencing batch and incorporates these measures into the result interpretation. By providing batch-level reference metrics, putative batch-driven artifacts can be identified, reducing false-positive CNV calls and supporting more reliable interpretation in comprehensive genomic profiling.
Since copy number variations (CNVs) in pharmacogenes can cause significant alterations in drug metabolism, their reliable detection is of high importance both for large-scale studies and personalized medicine. Whole-genome sequencing, and specifically long-read sequencing, is the gold standard for CNV detection. Despite increasing availability of these technologies, genotyping arrays are still widely used as cost-effective alternatives in biobank and clinical settings, yet calling CNVs based on array intensity signals is challenging due to low base pair resolution. In this work, we developed a neural network model, nnCNV, to predict deletions in the CYP2C19 pharmacogene region from array intensity signals. We compared our method to the most widely used algorithm, PennCNV, and demonstrated better performance reaching 100% accuracy in the test dataset. Furthermore, we predicted probe-by-probe CYP2C19 deletion coordinates for all Estonian Biobank samples using nnCNV and PennCNV, and validated these predictions using an identity-by-descent (IBD) sharing method, which also demonstrated superior nnCNV performance. For the deletion samples with conflicting PennCNV and nnCNV predictions, we performed PCR analysis for validation, which showed 97% precision for nnCNV compared to 23% for PennCNV. Finally, we assessed the gradient-based feature importance maps and showed that nnCNV utilizes signal intensity information not only from deletion probes, but also from probes in flanking regions. Our results demonstrate that long-range information, which cannot be utilized by hidden Markov models, can improve CNV calling.
Burak Yelmen, R. Hofmeister, Viido Kaur Lutsar et al.· bioRxiv· 0 citations
CNVisor is introduced, a statistical framework for reliable and robust CNV detection from long-read sequencing, even under ultra-low coverage, and established real-time CNV profiling using ultra-low coverage nanopore sequencing as a feasible strategy for intraoperative, genomics-informed assessment of CNS tumors.
G. Wang, C. Kubelt, R. Smicius et al.· medRxiv· 0 citations
There are a growing number of options for therapies matched to molecular biomarkers in solid tumors. This study evaluated the value of genomic profiling in advanced solid tumors using whole-exome, whole-transcriptome sequencing (WES/WTS) in comparison to 50-gene panels. We retrospectively reviewed clinical reports from tumor samples tested with a WES/WTS assay to determine the proportion of samples across 11 tumor types that would have had a matched therapy identified using each of four 50-gene panels and performed these analyses across different classifications of biomarker actionability. We found that, depending on the panel, between 4.2% and 9.1% of the 6943 samples had at least one alteration associated with an on-label FDA-approved therapy identified by WES/WTS but no such alteration identified by the 50-gene panel. Most had high tumor mutational burden (TMB) and/or high microsatellite instability (MSI), or homologous recombination repair (HRR) gene alterations. When considering other opportunities for matched therapy, up to 17.7% of samples had potentially actionable alterations identified by WES/WTS but none identified by the 50-gene panel. This increased to as high as 32.6% when we included alterations associated with clinical trials. Our findings highlight the value of comprehensive WES/WTS-based genomic profiling to identify potential matched therapies, thereby informing clinical decision-making and potentially improving outcomes for more patients.
J. De La O, David W Hall, Jess R. Hoag et al.· Scientific Reports· 0 citations
Panel-based NGS with CNV analysis was associated with a higher detection rate of clinically relevant variants than phenotype-driven Sanger sequencing in this single-institution cohort, and support the clinical utility of comprehensive germline testing for patients suspected of having hereditary colorectal cancer syndromes.
Joonsang Yu, Jaeyeon Ryu, Sollip Kim et al.· Hereditary Cancer in Clinica...· 0 citations
Abstract Copy number variations (CNVs) are genomic structural variants that are strongly linked to cancer progression and genetic disorders. CNVs can be highly heterogeneous at population and tissue scale; thus, single-cell resolution detection holds great promise for studying clonal evolution and CNV-driven changes. Despite advanced sc-RNA-seq CNV detection methods, accurate methods for epigenomic single-cell modalities lag behind. We developed RIDDLER; a robust, unsupervised method that uses outlier-aware statistical modeling to detect CNVs across multiple single-cell modalities and assays. RIDDLER utilizes a robust regression framework to model the expected distribution of reads genome-wide by accounting for assay-specific biases, identifying CNVs as outliers from that distribution. This versatile framing allows deployment of RIDDLER in multiple modalities with appropriate bias features. We demonstrate the accuracy of RIDDLER in calling single-cell CNVs and dissecting clonal heterogeneity in sc-ATAC-seq and sc-methylation. RIDDLER is more accurate and more robust to data sparsity than competing methods. We illustrate useful applications of RIDDLER for dissection of clonal structure, identification of subclonal accessibility peaks, and multimodal integration from CNV structure. RIDDLER stands out as a scalable, generalizable multi-modal method for accurate CNV detection, empowering studies aiming to link CNV dynamics to epigenetic alterations within the same cell.
Travis W Moore, H. Mohammed, Andrew C. Adey et al.· Nucleic Acids Research· 0 citations
Screening tumor types for which a first-line, integrated DNA-RNA NGS strategy provides a valuable advantage for rapid therapeutic decisions demonstrates that integrated DNA-RNA high-throughput NGS enables timely, precise molecular profiling for personalized therapy in solid tumors.
A. Destro, Federica Panebianco, Cecília Durães et al.· Virchows Archiv· 0 citations