Batch Effects in Tumor-Only NGS Panel Sequencing and Implications for CNV Detection.

Detecting CYP2C19 deletions from genotyping array signals using neural networks

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.

Intraoperative copy number profiling from ultra-low coverage long-read sequencing for molecular tumor assessment

Copy number variations (CNVs) can serve as important clinical biomarkers for tumor classification and stratification. However, the utility of these CNV biomarkers for intraoperative tumor assessment within the timeframe of neurosurgical procedures has remained elusive due to the protracted duration of conventional CNV characterization methods. Here, we introduce CNVisor, a statistical framework for reliable and robust CNV detection from long-read sequencing, even under ultra-low coverage. Applied to neurosurgical tumor specimens, the proposed method enabled genome-wide CNV profiling and identified clinically relevant CNVs using roughly 60,000 reads within 20 minutes of sequencing. Integrating CNVisor with methylation-based classifiers can further reduce turnaround time and increase the accuracy of glioma subtype stratification. Together, these findings establish real-time CNV profiling using ultra-low coverage nanopore sequencing as a feasible strategy for intraoperative, genomics-informed assessment of CNS tumors.

Comparison of whole-exome, whole-transcriptome genomic profiling and targeted sequencing with 50-gene panels in advanced solid tumors

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.

Panel-based next-generation sequencing with copy number variant detection pipeline could increase the detection rate of hereditary colorectal cancer syndrome compared with Sanger sequencing

Hereditary colorectal cancer syndromes, such as Lynch syndrome and familial adenomatous polyposis, arise from pathogenic/likely pathogenic (P/LP) germline variants in DNA mismatch repair or tumor suppressor genes. Traditional Sanger sequencing could cover only phenotype-driven genes and cannot detect copy number variants (CNVs). Next-generation sequencing (NGS) enables simultaneous multi-gene analysis and CNV detection. In this study, we compared the results of phenotype-driven Sanger sequencing with those of panel-based NGS in patients suspected of having hereditary colorectal cancer syndromes at a single institution. Patients tested for hereditary colorectal cancer syndromes between 2008 and 2018 (Sanger) and 2019–2022 (NGS) were retrospectively analyzed. The NGS assay targeted 171 cancer predisposition genes, and CNVs were inferred by read-depth analysis and confirmed with multiplex ligation-dependent probe amplification. Detected variants were classified per ACMG/AMP 2015 guidelines. Microsatellite instability (MSI) and mismatch repair (MMR) immunohistochemistry (IHC) results were compared with molecular findings. Among 423 patients (254 Sanger, 169 NGS), the detection rate of pathogenic or likely pathogenic variants was higher in NGS (55.0%) than Sanger sequencing (40.6%). NGS additionally identified 17 CNVs (18.1%) that were undetectable by Sanger sequencing and revealed incidental P/LP variants in other cancer-related genes. MSI and MMR IHC results showed strong concordance with molecular findings. 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. These findings support the clinical utility of comprehensive germline testing for patients suspected of having hereditary colorectal cancer syndromes, particularly when CNV detection or simultaneous multi-gene analysis is required.

Robust and generalizable CNV detection for single-cell sequencing assays

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.

Transforming routine solid tumor profiling with automated next-generation sequencing: experience from a reference general hospital.