Capturing clinically actionable copy number alterations in Wilms tumor using nanopore sequencing

Capturing clinically actionable copy number alterations in Wilms tumor using nanopore sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Capturing clinically actionable copy number alterations in Wilms tumor using nanopore sequencing Larissa V. Furtado, Carolyn Jablonowski, Pandurang Kolekar, Teresa Santiago, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8206667/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Copy number alterations (CNVs) involving chromosomes 1p, 1q, 16q, and 11p15 are key genomic markers used in the risk stratification of Wilms tumor (WT). These CNVs, when considered alongside disease stage and other clinical features, are associated with an increased risk of relapse. Accordingly, testing for these changes is recommended to guide treatment choices in children with favorable histology disease. Current methods for detecting segmental CNVs in WT, including single nucleotide polymorphism (SNP) arrays and short-read sequencing, require prolonged turnaround times, high cost, and do not capture loss of imprinting (LOI) at 11p15, a key adverse predictor for patients who would otherwise have very low risk disease. In this study, we assessed the feasibility of utilizing nanopore sequencing for CNV and 11p15 LOI analysis in WT. Methods We performed whole-genome sequencing (WGS) using the MinION platform on 15 patient-derived WT xenografts previously characterized by whole exome sequencing (WES) and multiplex ligation-dependent probe amplification (MLPA), applying adaptive sampling in a subset to enrich for clinically relevant regions. End-to-end sequencing analysis was completed within 4 days. Results Despite low sequencing depth (average 6.7x, WGS; 8.5x, adaptive sampling sequencing), nanopore WGS detected 94% (16/17) of the CNVs that are clinically relevant in WT. No false-positive findings were observed. Adaptive sampling showed high concordance with WGS for CNV detection and allowed sample multiplexing. However, the sequencing depth obtained by nanopore WGS and adaptive sampling was insufficient for assessment of methylation status at the Imprinting Control Regions (ICR)1 and ICR2 at 11p15. Conclusions This study demonstrates the feasibility of using nanopore sequencing for the assessment of clinically relevant CNVs in WT and highlights the potential of this technology for the integrated evaluation of copy number variation and 11p15 methylation status with a much shorter turnaround time and lower cost than other conventional methods for CNV testing. Successful clinical integration will require higher throughput nanopore sequencing platforms, as indicated by the current findings. Wilms tumor nanopore ONT sequencing copy number alterations methylation Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Wilms tumor (WT) is the most common kidney cancer of childhood 1 . The goals of treatment are to maximize cure while appropriately risk stratifying patients to minimize long-term toxicity of therapy by selecting intensified treatment for patients with a high risk of relapse and minimizing treatment for patients with excellent prognosis. In combination with disease stage, histology findings, and additional clinical features, the presence of specific somatic molecular alterations, such as loss of heterozygosity (LOH) of 1p and 16q, 11p15.5, and 1q gain is associated with an increased risk of relapse after initial therapy for patients diagnosed with WT 2–6 . Therefore, timely cytogenetic and molecular testing for 1q gain and/or LOH of 1p, 16q, and 11p15.5 is recommended for all children with newly diagnosed favorable histology WT since it would impact therapy decisions 7 . In the current clinical practice, these segmental copy number variants (CNVs) are typically evaluated using single nucleotide polymorphism (SNP) array or short-read sequencing methods (e.g., Illumina sequencers). However, the cost of these tests is high (per sample cost around US $ 2,300 for the SNP array recommended by the Children’s Oncology Group for WT CNV testing and US $ 2,600 for the Illumina WGS sequencing performed in our institution for tumor-only CNV assessment), and the turnaround time is approximately 2–4 weeks. In addition, neither short-read sequencing nor the current SNP array testing recommended by the Children’s Oncology Group (COG) assesses methylation status or loss of imprinting (LOI) at the 11p15.5 locus. Methylation abnormalities at this site have been identified as adverse prognostic markers in young patients with stage I disease 66 . Nanopore sequencing [Oxford Nanopore Technologies (ONT), Oxford, United Kingdom] offers multiple advantages over conventional methods for CNV testing. It provides long- and short-read sequencing information with quick turnaround times (2–5 days), simple sample preparation (direct, PCR-free sequencing of native DNA), genome-wide or targeted methylation profiling data for differential methylation analysis without the need for bisulfite conversion or additional wet laboratory steps, real-time analysis, and lower cost (approximately US $ 800 per sample with the MinION platform). In addition, targeted analysis of clinically relevant genomic regions can be carried out with adaptive sampling 8 without the need for the design and optimization of primers or additional reagents. Adaptive sampling may be particularly advantageous for WT CNV testing, where a limited number of chromosome arm-level CNVs are of clinical interest, and due to the flexibility to include other alterations that may be clinically actionable in the future, such as TP53 mutation or 17p13 LOH 9 , 10 .Ultra-low pass nanopore whole genome sequencing has demonstrated promising results for the detection of arm-level alterations and high-level focal alterations in central nervous system tumor samples 11 , 12 and in cell-free tumor DNA in cerebrospinal fluid of patients with gliomas 13 and lung cancer 14 . In this study, we assessed the feasibility of utilizing nanopore (ONT) sequencing for targeted CNV and 11p15 LOI analysis in WT. METHODS Study Samples The study included 15 WT patient-derived xenograft (PDX) samples which had their copy number profile previously characterized by whole exome sequencing (WES) and multiplex ligation-dependent probe amplification (MLPA) 15 (Table 1). The samples selected for this proof-of-concept study harbored CNVs that are clinically relevant in WT (i.e., 1p deletion, 1q gain, 11p15 deletion, and 16q deletion) as well as other CNV changes to broadly evaluate specificity and performance of the assay for detection of a wide spectrum of CNVs. Samples KT-22, KT-35, and KT-45 did not have any copy number changes relevant for WT risk stratification detected previously, therefore, serving as internal negative controls for this analysis. All samples had greater than 70% tumor purity. DNA isolation DNA was isolated from frozen PDX samples using DNeasy blood and tissue kit (Qiagen, Germantown, MD) according to the manufacturer’s protocol. DNA concentration was determined by Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, MA) and sample purity was assessed by Nanodrop One (Thermo Fisher Scientific). Library Preparation and Nanopore Sequencing Following extraction and DNA quality control, fragmentation was achieved using a g-TUBE (Covaris) aiming for ~ 9kbp fragment length. Fragment size was assessed on a 4150 TapeStation system (Agilent Biotechnologies, Santa Clara, CA) using genomic screentape. Sequencing libraries for individual whole genome analysis were generated using the SQK-LSK114 ligation kit from Oxford Nanopore Technologies (ONT), according to the manufacturer’s instructions. Ligation and barcoding for multiplexed adaptive sampling were generated using the Ligation sequencing gDNA Barcoding Kit 24 V14 (SQK-NBD114.24) from ONT. For DNA sequencing, sample libraries were individually loaded onto MinION Mk1B flow cells (CAT No. FLO-MIN114) following ONT's standard instructions for 72 hours. All samples underwent whole genome sequencing (WGS). Sequencing and data acquisition were performed using ONT's MinKNOW v25.05.12 and Dorado v 7.6.8 for raw signal values using a MacBook Pro with Apple M3 Pro chip 12-core CPU and 18-core GPU, Space Black, 36GB, 4TB Solid-State Drive (SSD), 140 W USB-C Power Adapter (Fig. 1 ). Adaptive Sampling In addition to WGS, we performed one adaptive sampling sequencing run including 5 samples (KT-22, KT-25, KT-47, KT-55, and KT-60, Table 1) (Fig. 1 ). These samples were selected for this analysis because in addition to harboring CNVs that are clinically relevant in WT, some had methylation changes in 11p15 (KT-25, KT-47, and KT-60), as well as copy number changes involving the WT1 and AMER1 genes, which are frequently altered in WT (KT-22, KT-47, KT-55, and KT-60). The objective of this analysis was to determine the extent to which targeted sequencing enhances coverage depth and resolution of methylation status. Adaptative sampling was used to enrich for a) sequences of chromosomes 1, 11, 16, and 17, b) 45 genes that are commonly mutated genes in WT, c) genomic regions that are known not to be typically affected by copy number alterations in this tumor type (copy neutral internal controls), and d) imprinting control regions (ICR) around H19/IGF2 10, 16 . The targeted regions encompassed approximately 506 Mbp, which represents ~ 15.8% of the human genome. Even though the focus of the current study is on the detection of CNVs that are clinically relevant in WT, we decided to include other genomic regions that are associated with this tumor to allow for future analysis. The Browser Extensible Data (BED) file was checked using ONT's Bed Bugs ( https://epi2me.nanoporetech.com/bed-bugs/ , last accessed on 06/20/2025), and a 10kbp buffer was added to each area of interest prior to sequencing. Bioinformatic Analyses The base calling of raw reads generated by the ONT platform was performed with dorado v7.6.8 ( https://github.com/nanoporetech/dorado ) using DNA base calling model [email protected] . All the reads that passed the base calling model were mapped against the reference human genome GRCh38 (hg38) assembly using minimap2 v2.24 16 and depth of coverage was determined using mosdepth v0.3.3 17 . CNVs for ONT WGS long read data were evaluated using QDNAseq v1.34 18 . Modified bases, such as methylation of CpG sites, were called out and summarized using modkit v0.3.3 ( https://github.com/nanoporetech/modkit ). The nextflow based wf-human-variation pipeline v2.6.0 ( https://github.com/epi2me-labs/wf-human-variation ) was used to execute these steps. CNV analysis for short-read WES data that were previously generated for the samples included in this study 15 was carried out using CNVKit v0.9.10 19 . The arm-level CNVs detected using ONT WGS long read data were compared to those detected from short-read WES using their log2 scores visualized using CNVKit heatmap. A cutoff of absolute log2 ratio of 0.4 (corresponding to gain of 0.6 copies) is used in this work. Methylation profiles of ICR around H19/IGF2 (ICR1) and KCNQ1OT1 (ICR2) for the samples reported to show gain in methylation by previous methylation-sensitive MLPA analysis (Table 1) were compared with commercially available male (CAT No. G1471, Promega, Fitchburg, WI) and female (CAT No. G1521, Promega, Fitchburg, WI) control DNA samples. Methylartist v1.4.0 20 and figeno v1.8.1 21 were used to visualize methylation profiles in ICR regions. Mann-Whitney U test was used to assess for statistically significant difference in methylation between different groups of samples. RESULTS Quality Assessment of ONT Sequencing Data All samples (n = 15) sequenced using ONT WGS had genome wide average depth of coverage of at least 5x (range: 5-8x, average: 6.7x, median: 7x). The statistics related to the number, length, N50, yield and mapping rate of long reads are summarized in Table 2. Adaptive sampling provided an approximate 2x unit increase in average depth coverage for the targeted regions (from 6.7x to 8.5x). Other quality metric statistics yielded results similar to those observed with shallow WGS (sWGS) (Table 2). The average time required for wf-human-variation workflow execution was found to be approximately 4 hour 30 minutes, assuming optimal availability for required resources (32 CPUs, 128 GB memory), which can further be improved by appropriately scaling compute resources. End-to-end sequencing analysis was completed within 4 days. Performance of ONT WGS Platform for Detection of Copy Number Changes The standard log2 ratio estimated by QDNASeq was used to determine copy number changes. A log2 ratio of 0 indicates no copy number change, a log2 ratio of ~ 0.5 corresponds to a single copy gain, and a log2 ratio of 1 reflects a two-copy gain, with increasingly higher values indicating additional gains. Conversely, negative log2 ratios represent copy number losses. sWGS detected 94% (16/17) of the CNVs that are clinically relevant in WT (Table 3, Figs. 2 and 3 ). In sample KT-53, the 16q deletion detected by MLPA 15 was not observed with sWGS or WES. Intra-tumor heterogeneity and differences in test methodology between MLPA and sequencing may account for the discordant results. sWGS detected a 1q gain in sample KT-51 that was not identified by WES. Both sWGS and WES concordantly detected a 16q deletion in this sample. As anticipated, no WT-relevant CNVs were identified in samples KT-22, KT-35, and KT-45 by sWGS. We observed > 95% concordance between ONT sWGS-based approach and WES for the additional CNVs detected in this cohort (Fig. 2 ). Some of the notable discrepancies included the detection of a focal loss in chromosome 10p and gain of 10q in sample KT-51 by sWGS, which was not detected by WES. Likewise, focal 17p deletion and 17q gain were detected only by sWGS in sample KT-66. In addition, gain of chromosomes 2, 3, 18, 19, and 20 identified by sWGS in sample KT-55 was not confirmed by WES analysis (Fig. 2 ). Such discrepancies may be attributed to intra-tumor heterogeneity of the xenograft samples sequenced. Performance of ONT WGS Platform for Assessment of 11p15 Methylation Status Methylation profiles of all the xenograft samples in two imprinting control regions (ICRs) located at chromosome 11p15 were compared with those of two control samples (male and female, Human Genomic DNA, Promega, Fitchburg, WI) (Fig. 4 ). As per report from COG AREN0532, 30% to 70% methylation of both H19/ ICR1 (chr11:1998744–2003508; hg38) and KCNQ1OT1/ ICR2 (chr11:2699998–2700998; hg38) was considered to be retention of imprinting (ROI); 80% to 100% methylation of H19 /ICR1 and 30% to 70% methylation of KCNQ1OT1/ ICR2 was considered to be LOI; and 80% to 100% methylation of H19 and 0% to 20% methylation of KCNQ1OT1/ ICR2 was considered to be loss of heterozygosity (LOH) 6 . All the xenograft samples showed expected methylation levels in the KCNQ1OT1/ ICR2 region; however, the H19 /ICR1 region did not show sufficient depth of coverage to determine methylation level (Fig. 4 ). Six WT xenograft samples (KT-25, KT-45, KT-47, KT-48, KT-60 and KT-66) previously reported by MLPA analysis to have undergone LOI showed 30% to 70% methylation of KCNQ1OT1/ ICR2 (green lines in Figure S1). In contrast, samples previously characterized as having copy neutral LOH (KT-31, KT-35, KT-43, KT-51 and KT-53) and loss of maternal copy of chromosome 11p15 (KT-76 and KT-28) showed ~ 0% methylation in this region (magenta and orange lines in Figure S1 respectively). The six samples with LOI (n = 6) showed a statistically significant gain in methylation of KCNQ1OT1/ ICR2 when compared with the xenograft samples with copy neutral LOH (n = 5; two-sided Mann-Whitney U test P = 0.04; Figure S2). The overall sequencing coverage of control samples (< 5x) was not adequate for this comparison. In addition, due to low sequencing coverage at the ICR1 locus, the methylation level in H19/ ICR1 could not be determined by this analysis (Fig. 4 A). Performance of ONT Adaptive Sampling for Detection of Copy Number Changes Results from adaptive sampling and sWGS were 100% concordant for the clinically relevant copy number changes in WT (i.e., 1p deletion, 1q gain, 11p15 deletion, and 16q deletion) (Table 3 and Figure S3). For all other CNVs, slight discordance was observed only in a chromosome 11q segment of sample KT-60, where the size of the 11q deletion was increased by ~ 20 Mbp after adaptive sampling (from chr11: 80,000,001-135,086,622 to chr11: 60,000,001-135,086,622) with a decrease in log2 ratio from − 0.97 to − 0.47. Such discrepancies may be explained by the increased sequencing depth and higher analytical sensitivity of the sequencing analysis using adaptive sampling, whereby the neighboring regions of 11q that did not get sufficiently sequenced by sWGS but were adequately covered after adaptive sampling. These results overall indicate high reproducibility of CNV calls even at shallow depth of sequencing before and after adaptive sampling, suggesting that adaptive sampling can play an important role in determining clinically relevant copy number changes (Figure S3). Performance of ONT Adaptive Sampling for Assessment of 11p15 Methylation Status The trend of methylation profiles in ICR2 region overall remained the same after adaptive sampling with an expected increase in the number of methylated CpG sites for quantification. The ICR1 regions remained under covered, precluding conclusive results (Figure S4). DISCUSSION This is the first study to evaluate the performance of sWGS and adaptive sampling using nanopore technology for the detection of CNVs and 11p15 LOI in WT. Molecular assessment of LOH at chromosomes 1p and/or 16q has been incorporated into COG risk stratification for favorable-histology WT based on prospective findings from NWTS-5 3 and subsequent validation in AREN0532 and AREN0533 22 . In NWTS-5 (> 1,700 patients), LOH at 1p and/or 16q was an independent predictor of relapse and mortality, with the highest risk observed in patients with WT harboring combined 1p/16q LOH. Across both early- and advanced-stage cohorts, patients with WT having 1p/16q LOH had significantly inferior relapse-free survival despite standard therapy, supporting systemic treatment intensification 3 . Building on this, AREN0532 and AREN0533 demonstrated that biomarker-directed augmentation of chemotherapy improved event-free survival 22 . Although LOI of 11p15 is also considered to be a prognostic indicator in stage I WT in young children, it is not incorporated into the ongoing trial study AREN2231 because current testing technologies are not routinely available and do not return results fast enough to be used in upfront clinical decisions at this time 7 . Accurate LOI assessment requires allele-specific methylation resolution, which conventional bisulfite sequencing and methylation arrays are generally unable to provide. Reliable detection of allele-specific methylation necessitates simultaneous profiling of both allele-informative SNPs and DNA methylation marks within the same or directly linked sequencing data. To this end, long-read nanopore sequencing provides direct single-molecule resolution for detection of cytosine modifications in native DNA and because reads span kilobases, enables haplotype-aware, allele-specific methylation analyses required to detect LOI. In addition, compared with traditional short-read sequencing and microarray-based methods, nanopore-based sequencing offers a substantially faster turnaround, delivering results in approximately 5 days instead of the 2–4 weeks typically required by conventional approaches. Current favorable histology WT therapies start with a cycle of vincristine and actinomycin-D to allow for results that affect final risk stratification to return. In the future, shorter turnaround time for molecular results could allow more intense or targeted therapies to be delivered earlier for high-risk groups. Also, diagnostic conundrums regarding the presence of diffuse or focal anaplasia could be informed by earlier reporting of molecular results at the 17p13 ( TP53 ) locus. These capabilities could facilitate a timelier and more comprehensive stratification of patients with favorable histology WT for risk-adapted first-line treatment, making nanopore an attractive option for WT testing. To evaluate the feasibility of CNV and 11p15 LOI detection using nanopore, we tested 15 WT xenograft samples that were previously characterized by WES and MLPA. We opted to use the MinIon platform for this pilot study due to its affordability and ease of use, which could facilitate deployment for single sample testing in our center, as well as its potential application in under-resourced settings. Our results indicate that MinIon sequencing can perform on par with conventional short-read WES for CNVs that are clinically relevant in WT. In our cohort, we detected all WT-relevant CNVs except for one alteration (16q deletion) in one xenograft sample (KT-53). This tumor was found to have diffuse anaplasia, and thus 16q deletion would not have been clinically actionable in this specific case. The analysis did not yield any false positive calls. Nonetheless, the MinION platform has limitations that should be recognized, including its relatively low sequencing throughput (~ 20 Gb per flow cell) compared with other nanopore sequencers (e.g., PromethION, ~ 100–300 Gb per flow cell) and short-read platforms, which limits its achievable sequencing depth. In our study, the read depth obtained by MinION sequencing, even with adaptive sampling, was insufficient to enable effective assessment of methylation status at 11p15, particularly for the H19 / ICR1 locus (Fig. 4 ). Despite the limitation, the WT relevant CNV data obtained by MinION sequencing are comparable in scope to those produced by COG-recommended SNP array testing. CONCLUSIONS In conclusion, this study demonstrates the feasibility of using nanopore sequencing for the assessment of clinically relevant CNVs in WT, and highlights the potential of this technology for the integrated assessment of CNV and 11p15 methylation status in WT. Future work will focus on validating this approach in a prospective patient cohort with the aim of developing a clinical-grade assay capable of providing rapid, targeted detection of clinically actionable chromosomal and epigenetic alterations to inform clinical decision-making. Fresh tumor samples obtained directly at the time of the procedure (nephrectomies or biopsies) represent an optimal substrate for nanopore sequencing because high-quality DNA can be extracted without the degradation or artifacts introduced by formalin fixation or extended storage. Because nanopore library preparation relies on streamlined workflows, tissue collected in the operating room can be transitioned directly to sequencing with minimal processing, allowing timely results and facilitating integration into clinical testing workflows. The current findings underscore that successful clinical integration will require higher-capacity sequencing platforms, with the PromethION representing a particularly suitable option due to its scalability, throughput, and reduced computational demands. Abbreviations BED Browser Extensible Data CNV Copy number alteration COG Children’s Oncology Group ICR Imprinting Control Regions LOH Loss of heterozygosity LOI Loss of imprinting MLPA Multiplex ligation-dependent probe amplification ONT Oxford Nanopore Technologies PDX Patient-derived xenograft sWGS Shallow whole genome sequencing SNP Single nucleotide polymorphism WES Whole exome sequencing WGS Whole genome sequencing WT Wilms tumor Declarations ACKNOWLEDGMENTS The authors thank Dr. Heather Mefford and Dr. Soham Sengupta for providing computational resources for adaptive sampling sequencing. Ethics approval and consent to participate The requirement for informed consent was waived because the study involved retrospective analysis of de-identified data. Ethical approval for this work was obtained from the Institutional Review Board at St. Jude Children’s Research Hospital under project number 24-1767. All procedures complied with the Declaration of Helsinki and institutional guidelines. Consent for publication Not applicable. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Department of Surgery at St. Jude Children’s Research Hospital and by a 5K08CA255569-03 National Institutes of Health (NIH)/National Cancer Institute (NCI) Grant to AJM. Author contributions Conceptualization, L.V.F., T.S., A.M.D., and A.J.M.; methodology, C.J., P.K., X.M., A.J.M. and L.V.F.; wet-laboratory procedures, C.J.; bioinformatics procedures, P.K. and X.M.; data analysis, P.K., C.J., L.V.F, A.J.M., and X.M.; resources, A.J.M., A.M.D., C.L.M, A.W.; writing—original draft preparation, L.V.F., C.J., and P.K.; writing—review and editing, A.J.M., T.S., A.M.D., C.L.M., A.W., and X.M.; funding acquisition, A.M.D. and A.J.M. All authors read and approved the manuscript. L.V.F., C.J., and P.K. contributed equally to this work. References Davidoff AM (2012) Wilms tumor. Adv Pediatr 59(1):247–267. 10.1016/j.yapd.2012.04.001 Evageliou N, Renfro LA, Geller J, Perlman E, Kalapurakal J, Paulino A et al (2024) Prognostic impact of lymph node involvement and loss of heterozygosity of 1p or 16q in stage III favorable histology Wilms tumor: A report from Children's Oncology Group Studies AREN03B2 and AREN0532. Cancer 130(5):792–802. 10.1002/cncr.35084 Grundy PE, Breslow NE, Li S, Perlman E, Beckwith JB, Ritchey ML et al (2005) Loss of heterozygosity for chromosomes 1p and 16q is an adverse prognostic factor in favorable-histology Wilms tumor: a report from the National Wilms Tumor Study Group. J Clin Oncol 23(29):7312–7321. 10.1200/JCO.2005.01.2799 Chagtai T, Zill C, Dainese L, Wegert J, Savola S, Popov S et al (2016) Gain of 1q As a Prognostic Biomarker in Wilms Tumors (WTs) Treated With Preoperative Chemotherapy in the International Society of Paediatric Oncology (SIOP) WT 2001 Trial: A SIOP Renal Tumours Biology Consortium Study. J Clin Oncol 34(26):3195–3203. 10.1200/JCO.2015.66.0001 Gratias EJ, Dome JS, Jennings LJ, Chi YY, Tian J, Anderson J et al (2016) Association of Chromosome 1q Gain With Inferior Survival in Favorable-Histology Wilms Tumor: A Report From the Children's Oncology Group. J Clin Oncol 34(26):3189–3194. 10.1200/JCO.2015.66.1140 Fernandez CV, Perlman EJ, Mullen EA, Chi YY, Hamilton TE, Gow KW et al (2017) Clinical Outcome and Biological Predictors of Relapse After Nephrectomy Only for Very Low-risk Wilms Tumor '. Ann Surg 265(4):835–840. 10.1097/Sla.0000000000001716 Benedetti DJ, Cost NG, Ehrlich PF, Evageliou N, Fialkowski E, Parsons LN et al (2025) Updated favourable-histology Wilms tumour risk stratification: rationale for future Children's Oncology Group clinical trials. Nat Rev Urol . ;10.1038/s41585-025-01055-1 Loose M, Malla S, Stout M (2016) Real-time selective sequencing using nanopore technology. Nat Methods 13(9):751–754. 10.1038/nmeth.3930 Ooms AH, Gadd S, Gerhard DS, Smith MA, Guidry Auvil JM, Meerzaman D et al (2016) Significance of TP53 Mutation in Wilms Tumors with Diffuse Anaplasia: A Report from the Children's Oncology Group. Clin Cancer Res 22(22):5582–5591. 10.1158/1078 – 0432.CCR-16-0985 Gadd S, Huff V, Walz AL, Ooms A, Armstrong AE, Gerhard DS et al (2017) A Children's Oncology Group and TARGET initiative exploring the genetic landscape of Wilms tumor. Nat Genet 49(10):1487–1494. 10.1038/ng.3940 Euskirchen P, Bielle F, Labreche K, Kloosterman WP, Rosenberg S, Daniau M et al (2017) Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing. Acta Neuropathol 134(5):691–703. 10.1007/s00401-017-1743-5 Wongsurawat T, Jenjaroenpun P, Anekwiang P, Arigul T, Thongrattana W, Jamshidi-Parsian A et al (2024) Exploiting nanopore sequencing for characterization and grading of IDH-mutant gliomas. Brain Pathol 34(1):e13203. 10.1111/bpa.13203 Mouliere F, Mair R, Chandrananda D, Marass F, Smith CG, Su J et al (2018) Detection of cell-free DNA fragmentation and copy number alterations in cerebrospinal fluid from glioma patients. Embo Mol Med 10(12):ARTNe9323 15252/emmm.201809323 Martignano F, Munagala U, Crucitta S, Mingrino A, Semeraro R, Del Re M et al (2021) Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients. Mol Cancer 20(1):32. 10.1186/s12943-021-01327-5 Murphy AJ, Chen X, Pinto EM, Williams JS, Clay MR, Pounds SB et al (2019) Forty-five patient-derived xenografts capture the clinical and biological heterogeneity of Wilms tumor. Nat Commun 10(1):5806. 10.1038/s41467-019-13646-9 Li H (2021) New strategies to improve minimap2 alignment accuracy. Bioinformatics 37(23):4572–4574. 10.1093/bioinformatics/btab705 Pedersen BS, Quinlan AR (2018) Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34(5):867–868. 10.1093/bioinformatics/btx699 Scheinin I, Sie D, Bengtsson H, van de Wiel MA, Olshen AB, van Thuijl HF et al (2014) DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res 24(12):2022–2032. 10.1101/gr.175141.114 Talevich E, Shain AH, Botton T, Bastian BC, CNVkit (2016) Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol 12(4):e1004873. 10.1371/journal.pcbi.1004873 Cheetham SW, Kindlova M, Ewing AD (2022) Methylartist: tools for visualizing modified bases from nanopore sequence data. Bioinformatics 38(11):3109–3112. 10.1093/bioinformatics/btac292 Sollier E, Heilmann J, Gerhauser C, Scherer M, Plass C, Lutsik P (2024) Figeno: multi-region genomic figures with long-read support. Bioinformatics 40(6). 10.1093/bioinformatics/btae354 Dix DB, Fernandez CV, Chi YY, Mullen EA, Geller JI, Gratias EJ et al (2019) Augmentation of Therapy for Combined Loss of Heterozygosity 1p and 16q in Favorable Histology Wilms Tumor: A Children's Oncology Group AREN0532 and AREN0533 Study Report. J Clin Oncol 37(30):2769–2777. 10.1200/JCO.18.01972 Tables Tables are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table1.docx Table 1. Characteristics of Wilms tumor xenograft samples included in the study Table2.xlsx Table 2. Coverage QC metrics and summary of variants for samples sequenced using Oxford Nanopore Technology platform Table3.docx Table 3. Comparison of results from copy number alterations that are clinically relevant in Wilms tumor between WES/MLPA and ONT WGS FigureS1.tif Supplemental Figure S1. Methylation profiles of xenograft and control samples around H19 /ICR1 and KCNQ1OT1 /ICR2 regions generated using figeno for ONT data (see Methods). Plots on the left show methylation profiles around H19 /ICR1 whereas those on right show methylation profiles around KCNQ1OT1 /ICR2. Blue shaded regions indicate the ICR region with genomic coordinates (hg38) and ideogram of chromosome 11 at the bottom. Blue bars on ideograms show chromosomal locations of the respective ICRs. Samples are grouped in different categories as indicated by different colored lines as follows - green: LOI/ICR1 gain in methylation, magenta: copy neutral LOH, orange: loss of maternal copy, navy: wild type, and gray: control samples. FigureS2.tif Supplemental Figure S2. Differences in methylation levels of samples in KCNQ1OT1 /ICR2 region. Shown are the boxplots for %methylation values based on ONT data for control samples (n=2), xenograft samples with LOI/IC1 gain in methylation (n=6; green), copy neutral LOH (n=5; magenta), maternal copy number loss (n=2) and wild type (n=2). The P value based on two-sided Mann-Whitney U test is indicated for xenograft samples with LOI/IC1 gain in methylation and those copy neutral LOH (sample size n>3). FigureS3.tif Supplemental Figure S3. Heatmap summarizing results obtained from ONT adaptive sampling and ONT sWGS. Results from adaptive sampling and sWGS were 100% concordant for the clinically relevant copy number changes in WT (i.e., 1p deletion, 1q gain, 11p15 deletion, and 16q deletion). FigureS4.tif Supplemental Figure S4. Changes in methylation and coverage profiles of imprinting control regions H19 /ICR1 and KCNQ1OT1 /ICR2 before and after adaptive sampling (A) Methylation profile around H19 /ICR1 and (B) KCNQ1OT1 /ICR2 generated using methylartist (see Methods). Blue shaded regions indicate the ICR region. The tracks from top to bottom indicate the following - genomic coordinates of the region (hg38), genes in the region, mapped reads (filled and open circles respectively show methylated and unmethylated CpG sites), raw base modification (methylation) scores (modstat), smoothened methylation scores and depth of coverage. The data for five xenograft samples before and after adaptive sampling are shown in different colors as indicated in the legend. SupplementalTable1.xlsx Supplemental Table 1. Genomic regions selected for adaptive sampling Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8206667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550795275,"identity":"a63d3d1c-be57-44ff-83fa-4eacc02ccb7b","order_by":0,"name":"Larissa V. Furtado","email":"data:image/png;base64,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","orcid":"","institution":"Department of Pathology, St. Jude Children’s Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Larissa","middleName":"V.","lastName":"Furtado","suffix":""},{"id":550795276,"identity":"dbe9cd31-8181-4bea-9d02-6c33b7dea1e5","order_by":1,"name":"Carolyn Jablonowski","email":"","orcid":"","institution":"Department of Surgery, St. Jude Children’s Research Hospital, Memphis","correspondingAuthor":false,"prefix":"","firstName":"Carolyn","middleName":"","lastName":"Jablonowski","suffix":""},{"id":550795277,"identity":"416a085f-fa63-4215-863d-4ec20cdcd007","order_by":2,"name":"Pandurang Kolekar","email":"","orcid":"","institution":"Department of Computational Biology, St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Pandurang","middleName":"","lastName":"Kolekar","suffix":""},{"id":550795278,"identity":"16febeef-05a1-4681-88e6-71b588ac4af5","order_by":3,"name":"Teresa Santiago","email":"","orcid":"","institution":"Department of Pathology, St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"Santiago","suffix":""},{"id":550795279,"identity":"8a06f32d-ceb3-43bd-bdc4-32e2d931e721","order_by":4,"name":"Christopher L. Morton","email":"","orcid":"","institution":"Department of Surgery, St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"L.","lastName":"Morton","suffix":""},{"id":550795280,"identity":"e81c4957-581f-442b-ac0c-7f57f07b661d","order_by":5,"name":"Allison Woolard","email":"","orcid":"","institution":"Department of Surgery, St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"","lastName":"Woolard","suffix":""},{"id":550795281,"identity":"d879a5c0-aea5-4ca9-87dc-f52b809cef24","order_by":6,"name":"Andrew M. Davidoff","email":"","orcid":"","institution":"Department of Surgery, St. Jude Children’s Research Hospital; Division of Pediatric Surgery, Department of Surgery, University of Tennessee Health Science Center","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"M.","lastName":"Davidoff","suffix":""},{"id":550795282,"identity":"8fc2a1e9-24e0-41c7-b085-0a6ece0baa7c","order_by":7,"name":"Xiaotu Ma","email":"","orcid":"","institution":"Department of Computational Biology, St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaotu","middleName":"","lastName":"Ma","suffix":""},{"id":550795283,"identity":"e0d6cdf0-563b-4d85-9e86-00043883121b","order_by":8,"name":"Andrew J. Murphy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYHACAxiDjYGhgoGBsQEhQoyWMwwSJGphbGOQQBbBCvhnN2/78LGNIdrg9tljj3nn1dUxz0je+ICh4p5dAw4tEneOFc+c2caQu+FcXrox77bDEowz0ooNGM4UJ+PSwnAjx5iZdxtQyxkeM2nebQeAWnLMJBjbEpJx6ZAHafkL1zKnDqTF/Ac+LQYgLYxwLQ3MYFuA4ZBgh0uL4Y20YsbefxK5M8/wpUnOOXZYsrHnWbFEwpmEBFxa5G4kb2b4ccYmt+8M7zGJNzV1/IbtyRs/fKhIsMelBQpAscHDwMQDsrcBSACtSGwgoAeshfEHkJKHcgnZMgpGwSgYBSMHAADCP1cd3CEEOAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Surgery, St. Jude Children’s Research Hospital; Division of Pediatric Surgery, Department of Surgery, University of Tennessee Health Science Center","correspondingAuthor":true,"prefix":"","firstName":"Andrew","middleName":"J.","lastName":"Murphy","suffix":""}],"badges":[],"createdAt":"2025-11-25 21:04:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8206667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8206667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101205455,"identity":"f80e0247-f49c-418f-8fc2-3b56d57ce8d7","added_by":"auto","created_at":"2026-01-27 09:49:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141199,"visible":true,"origin":"","legend":"","description":"","filename":"WTCNVONTmanuscriptv2112525.docx","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/b5b40c2b701b0aa5a20aba54.docx"},{"id":101067548,"identity":"18202223-27ae-4935-9f93-48acf95f2bdd","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8206667.json","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/97808cca265feba6033ea47e.json"},{"id":101205265,"identity":"12d1da7e-e846-47ff-9f8f-ff563d310c40","added_by":"auto","created_at":"2026-01-27 09:48:46","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84922,"visible":true,"origin":"","legend":"","description":"","filename":"rs82066670enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/d7204f5d66391be1fdbf4cca.xml"},{"id":101067556,"identity":"550b3117-0b34-48be-b06a-dfecc8655fdf","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82661,"visible":true,"origin":"","legend":"","description":"","filename":"rs82066670structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/fd846c3f348ea3be331c1d25.xml"},{"id":101067558,"identity":"ea375147-1d0f-4096-ba0e-a0e2e5f2f67f","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93210,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/7059ab6df1d97803f5a122fa.html"},{"id":101067547,"identity":"01d92920-f8f2-4953-bc31-56e35fa22e95","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":842217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. Overview of the experimental and analytical workflow of the study.\u003c/strong\u003e \u0026nbsp;(\u003cstrong\u003eA\u003c/strong\u003e) Schematic representation of the ONT WGS workflow beginning with nucleic acid extraction from patient-derived xenograft (PDX) specimens, followed by library preparation, sequencing on the MinION platform, and computational analysis. (\u003cstrong\u003eB\u003c/strong\u003e) DNA from the 5 PDX samples subjected to adaptive sampling was individually barcoded during library preparation for a multiplexed sequencing run performed using the MinION system. Created with BioRender.com\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/2b5d9e4974a2b3a99f771397.png"},{"id":101067554,"identity":"0268c0cd-7080-4d1c-abe2-deb36f3961bf","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":506220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap summarizing results obtained from ONT WGS and WES/MLPA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShown are the log2 scores of copy number changes observed in 15 xenograft samples using ONT sWGS and WES data.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/80ec96c63d9bb6a6fec35f4c.png"},{"id":101205565,"identity":"061d05a9-4665-4a63-91e9-0391cf4e0c26","added_by":"auto","created_at":"2026-01-27 09:49:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":894177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComposite image showing 3 ONT WGS CNV plots (samples KT-22, KT-28, and KT-47) side-by-side with H\u0026amp;E images from the corresponding xenografts. \u003c/strong\u003eAll three xenografts demonstrate morphological features of favorable histology Wilms tumors. KT-22 (\u003cstrong\u003eA)\u003c/strong\u003e displays triphasic morphology; KT-28 (\u003cstrong\u003eC\u003c/strong\u003e) is mainly composed of stromal elements, and KT-47 (\u003cstrong\u003eE\u003c/strong\u003e) exhibits early epithelium and blastemal components. Hematoxylin \u0026amp; Eosin, 200x. (\u003cstrong\u003eB\u003c/strong\u003e) ONT WGS did not identify any CNVs that are clinically significant in WT (i.e. 1p deletion, 1q gain, 11p15 deletion, and 16q deletion) in sample KT-22. (\u003cstrong\u003eD\u003c/strong\u003e) Deletions involving 11p and 16q (green, dashed line) were detected in KT-28. (\u003cstrong\u003eF\u003c/strong\u003e) 1q gain (green, dashed line) was found in sample KT-47 by the ONT WGS analysis. Both the WT relevant CNVs (green, dashed line) and the other CNV alterations in these samples—copy gains shown as red horizontal lines and deletions as blue horizontal lines—were confirmed by WES (Table 3).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/21e8692b0bdc587992a35a3d.png"},{"id":101067561,"identity":"dfc8af07-56c3-4d6c-8e61-7bd39bc3281b","added_by":"auto","created_at":"2026-01-25 07:13:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":776824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethylation and coverage profiles of imprinting control regions (ICR) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eH19\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/ICR1 and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKCNQ1OT1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/ICR2. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Methylation profile around \u003cem\u003eH19\u003c/em\u003e/ICR1 and (\u003cstrong\u003eB\u003c/strong\u003e) \u003cem\u003eKCNQ1OT1\u003c/em\u003e/ICR2\u003cem\u003e \u003c/em\u003egenerated using methylartist (see Methods). Blue shaded regions indicate the ICR region. The tracks from top to bottom indicate the following - genomic coordinates of the region (hg38), genes in the region, mapped reads (filled and open circles respectively show methylated and unmethylated CpG sites), raw base modification (methylation) scores (modstat), smoothened methylation scores and depth of coverage. The xenograft (n=15) and control samples (n=2) are shown in different colors as indicated in the legend. Adaptively sequenced samples were used wherever available.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/2de1573f4ac58cff278359f1.png"},{"id":101297077,"identity":"9183ba0e-5d7c-4e94-96fb-f08894c4a001","added_by":"auto","created_at":"2026-01-28 09:24:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3425300,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/465ab763-00c1-4ce7-955e-4815f982e174.pdf"},{"id":101067546,"identity":"ffc3ed9b-2197-4254-b392-001e49e47bc9","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30556,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1. Characteristics of Wilms tumor xenograft samples included in the study\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/7e472eb2a7b54bf3b86aa85b.docx"},{"id":101206045,"identity":"f2ffd9bd-64ac-41e8-87ea-9337aa65d1ec","added_by":"auto","created_at":"2026-01-27 09:51:41","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 2. Coverage QC metrics and summary of variants for samples sequenced using Oxford Nanopore Technology platform\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/5006b323fd71cd24d3e866d3.xlsx"},{"id":101067553,"identity":"2b032dc3-66aa-4888-900d-15492cecdc37","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23184,"visible":true,"origin":"","legend":"\u003cp\u003eTable 3. Comparison of results from copy number alterations that are clinically relevant in Wilms tumor between WES/MLPA and ONT WGS\u003c/p\u003e","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/861a7e8924d6f1be07287b40.docx"},{"id":101067559,"identity":"a5f05089-9cd7-49c1-bd91-31d7e1bd0c86","added_by":"auto","created_at":"2026-01-25 07:13:23","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1612336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S1. Methylation profiles of xenograft and control samples around \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eH19\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/ICR1 and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKCNQ1OT1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/ICR2 regions generated using figeno for ONT data (see Methods). \u003c/strong\u003ePlots on the left show methylation profiles around \u003cem\u003eH19\u003c/em\u003e/ICR1 whereas those on right show methylation profiles around \u003cem\u003eKCNQ1OT1\u003c/em\u003e/ICR2. Blue shaded regions indicate the ICR region with genomic coordinates (hg38) and ideogram of chromosome 11 at the bottom. Blue bars on ideograms show chromosomal locations of the respective ICRs. Samples are grouped in different categories as indicated by different colored lines as follows - green: LOI/ICR1 gain in methylation, magenta: copy neutral LOH, orange: loss of maternal copy, navy: wild type, and gray: control samples.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/de22f98cb9b801b7806399ff.tif"},{"id":101205060,"identity":"7d76621a-6574-4ffb-bb14-c83c2261952f","added_by":"auto","created_at":"2026-01-27 09:46:08","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":185422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S2. Differences in methylation levels of samples in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKCNQ1OT1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/ICR2 region. \u003c/strong\u003eShown are the boxplots for %methylation values based on ONT data for control samples (n=2), xenograft samples with LOI/IC1 gain in methylation (n=6; green), copy neutral LOH (n=5; magenta), maternal copy number loss (n=2) and wild type (n=2). The P value based on two-sided Mann-Whitney U test is indicated for xenograft samples with LOI/IC1 gain in methylation and those copy neutral LOH (sample size n\u0026gt;3).\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/29ce10082cdf2f03ae4807ec.tif"},{"id":101067557,"identity":"456c960a-99c9-456c-96d4-4e05be4d62e2","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":187334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S3. Heatmap summarizing results obtained from ONT adaptive sampling and ONT sWGS. \u003c/strong\u003eResults from adaptive sampling and sWGS were 100% concordant for the clinically relevant copy number changes in WT (i.e., 1p deletion, 1q gain, 11p15 deletion, and 16q deletion).\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/3429ba52fa1337cbb38a3225.tif"},{"id":101067562,"identity":"bbee43c2-755b-4317-892b-71606b07e40f","added_by":"auto","created_at":"2026-01-25 07:13:23","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":5055078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S4. Changes in methylation and coverage profiles of imprinting control regions \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eH19\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/ICR1 and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKCNQ1OT1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/ICR2 before and after adaptive sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Methylation profile around \u003cem\u003eH19\u003c/em\u003e/ICR1 and \u003cstrong\u003e(B\u003c/strong\u003e) \u003cem\u003eKCNQ1OT1\u003c/em\u003e/ICR2\u003cem\u003e \u003c/em\u003egenerated using methylartist (see Methods). Blue shaded regions indicate the ICR region. The tracks from top to bottom indicate the following - genomic coordinates of the region (hg38), genes in the region, mapped reads (filled and open circles respectively show methylated and unmethylated CpG sites), raw base modification (methylation) scores (modstat), smoothened methylation scores and depth of coverage. The data for five xenograft samples before and after adaptive sampling are shown in different colors as indicated in the legend.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/9224da0d5a9440472d766b96.tif"},{"id":101067555,"identity":"89679ace-1d37-49b4-bb79-06765b5eb10b","added_by":"auto","created_at":"2026-01-25 07:13:22","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":21563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table 1. \u003c/strong\u003eGenomic regions selected for adaptive sampling\u003c/p\u003e","description":"","filename":"SupplementalTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8206667/v1/813f931ab5a6177e49429df3.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCapturing clinically actionable copy number alterations in Wilms tumor using nanopore sequencing\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eWilms tumor (WT) is the most common kidney cancer of childhood\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The goals of treatment are to maximize cure while appropriately risk stratifying patients to minimize long-term toxicity of therapy by selecting intensified treatment for patients with a high risk of relapse and minimizing treatment for patients with excellent prognosis. In combination with disease stage, histology findings, and additional clinical features, the presence of specific somatic molecular alterations, such as loss of heterozygosity (LOH) of 1p and 16q, 11p15.5, and 1q gain is associated with an increased risk of relapse after initial therapy for patients diagnosed with WT\u003csup\u003e2\u0026ndash;6\u003c/sup\u003e. Therefore, timely cytogenetic and molecular testing for 1q gain and/or LOH of 1p, 16q, and 11p15.5 is recommended for all children with newly diagnosed favorable histology WT since it would impact therapy decisions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the current clinical practice, these segmental copy number variants (CNVs) are typically evaluated using single nucleotide polymorphism (SNP) array or short-read sequencing methods (e.g., Illumina sequencers). However, the cost of these tests is high (per sample cost around US\u003cspan\u003e$\u003c/span\u003e 2,300 for the SNP array recommended by the Children\u0026rsquo;s Oncology Group for WT CNV testing and US\u003cspan\u003e$\u003c/span\u003e2,600 for the Illumina WGS sequencing performed in our institution for tumor-only CNV assessment), and the turnaround time is approximately 2\u0026ndash;4 weeks. In addition, neither short-read sequencing nor the current SNP array testing recommended by the Children\u0026rsquo;s Oncology Group (COG) assesses methylation status or loss of imprinting (LOI) at the 11p15.5 locus. Methylation abnormalities at this site have been identified as adverse prognostic markers in young patients with stage I disease\u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNanopore sequencing [Oxford Nanopore Technologies (ONT), Oxford, United Kingdom] offers multiple advantages over conventional methods for CNV testing. It provides long- and short-read sequencing information with quick turnaround times (2\u0026ndash;5 days), simple sample preparation (direct, PCR-free sequencing of native DNA), genome-wide or targeted methylation profiling data for differential methylation analysis without the need for bisulfite conversion or additional wet laboratory steps, real-time analysis, and lower cost (approximately US\u003cspan\u003e$\u003c/span\u003e800 per sample with the MinION platform). In addition, targeted analysis of clinically relevant genomic regions can be carried out with adaptive sampling\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e without the need for the design and optimization of primers or additional reagents. Adaptive sampling may be particularly advantageous for WT CNV testing, where a limited number of chromosome arm-level CNVs are of clinical interest, and due to the flexibility to include other alterations that may be clinically actionable in the future, such as \u003cem\u003eTP53\u003c/em\u003e mutation or 17p13 LOH\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.Ultra-low pass nanopore whole genome sequencing has demonstrated promising results for the detection of arm-level alterations and high-level focal alterations in central nervous system tumor samples\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eand in cell-free tumor DNA in cerebrospinal fluid of patients with gliomas\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and lung cancer\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we assessed the feasibility of utilizing nanopore (ONT) sequencing for targeted CNV and 11p15 LOI analysis in WT.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Samples\u003c/h2\u003e\u003cp\u003eThe study included 15 WT patient-derived xenograft (PDX) samples which had their copy number profile previously characterized by whole exome sequencing (WES) and multiplex ligation-dependent probe amplification (MLPA)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;1). The samples selected for this proof-of-concept study harbored CNVs that are clinically relevant in WT (i.e., 1p deletion, 1q gain, 11p15 deletion, and 16q deletion) as well as other CNV changes to broadly evaluate specificity and performance of the assay for detection of a wide spectrum of CNVs. Samples KT-22, KT-35, and KT-45 did not have any copy number changes relevant for WT risk stratification detected previously, therefore, serving as internal negative controls for this analysis. All samples had greater than 70% tumor purity.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA isolation\u003c/h3\u003e\n\u003cp\u003eDNA was isolated from frozen PDX samples using DNeasy blood and tissue kit (Qiagen, Germantown, MD) according to the manufacturer\u0026rsquo;s protocol. DNA concentration was determined by Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, MA) and sample purity was assessed by Nanodrop One (Thermo Fisher Scientific).\u003c/p\u003e\n\u003ch3\u003eLibrary Preparation and Nanopore Sequencing\u003c/h3\u003e\n\u003cp\u003eFollowing extraction and DNA quality control, fragmentation was achieved using a g-TUBE (Covaris) aiming for ~\u0026thinsp;9kbp fragment length. Fragment size was assessed on a 4150 TapeStation system (Agilent Biotechnologies, Santa Clara, CA) using genomic screentape. Sequencing libraries for individual whole genome analysis were generated using the SQK-LSK114 ligation kit from Oxford Nanopore Technologies (ONT), according to the manufacturer\u0026rsquo;s instructions. Ligation and barcoding for multiplexed adaptive sampling were generated using the Ligation sequencing gDNA Barcoding Kit 24 V14 (SQK-NBD114.24) from ONT. For DNA sequencing, sample libraries were individually loaded onto MinION Mk1B flow cells (CAT No. FLO-MIN114) following ONT's standard instructions for 72 hours. All samples underwent whole genome sequencing (WGS).\u003c/p\u003e\u003cp\u003eSequencing and data acquisition were performed using ONT's MinKNOW v25.05.12 and Dorado v 7.6.8 for raw signal values using a MacBook Pro with Apple M3 Pro chip 12-core CPU and 18-core GPU, Space Black, 36GB, 4TB Solid-State Drive (SSD), 140 W USB-C Power Adapter (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eAdaptive Sampling\u003c/h3\u003e\n\u003cp\u003eIn addition to WGS, we performed one adaptive sampling sequencing run including 5 samples (KT-22, KT-25, KT-47, KT-55, and KT-60, Table\u0026nbsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These samples were selected for this analysis because in addition to harboring CNVs that are clinically relevant in WT, some had methylation changes in 11p15 (KT-25, KT-47, and KT-60), as well as copy number changes involving the \u003cem\u003eWT1\u003c/em\u003e and \u003cem\u003eAMER1\u003c/em\u003e genes, which are frequently altered in WT (KT-22, KT-47, KT-55, and KT-60). The objective of this analysis was to determine the extent to which targeted sequencing enhances coverage depth and resolution of methylation status. Adaptative sampling was used to enrich for a) sequences of chromosomes 1, 11, 16, and 17, b) 45 genes that are commonly mutated genes in WT, c) genomic regions that are known not to be typically affected by copy number alterations in this tumor type (copy neutral internal controls), and d) imprinting control regions (ICR) around \u003cem\u003eH19/IGF2\u003c/em\u003e\u003csup\u003e10, 16\u003c/sup\u003e. The targeted regions encompassed approximately 506 Mbp, which represents\u0026thinsp;~\u0026thinsp;15.8% of the human genome. Even though the focus of the current study is on the detection of CNVs that are clinically relevant in WT, we decided to include other genomic regions that are associated with this tumor to allow for future analysis. The Browser Extensible Data (BED) file was checked using ONT's Bed Bugs (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://epi2me.nanoporetech.com/bed-bugs/\u003c/span\u003e\u003cspan address=\"https://epi2me.nanoporetech.com/bed-bugs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, last accessed on 06/20/2025), and a 10kbp buffer was added to each area of interest prior to sequencing.\u003c/p\u003e\n\u003ch3\u003eBioinformatic Analyses\u003c/h3\u003e\n\u003cp\u003eThe base calling of raw reads generated by the ONT platform was performed with dorado v7.6.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nanoporetech/dorado\u003c/span\u003e\u003cspan address=\"https://github.com/nanoporetech/dorado\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using DNA base calling model [email protected]. All the reads that passed the base calling model were mapped against the reference human genome GRCh38 (hg38) assembly using minimap2 v2.24\u003csup\u003e16\u003c/sup\u003e and depth of coverage was determined using mosdepth v0.3.3\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCNVs for ONT WGS long read data were evaluated using QDNAseq v1.34\u003csup\u003e18\u003c/sup\u003e. Modified bases, such as methylation of CpG sites, were called out and summarized using modkit v0.3.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nanoporetech/modkit\u003c/span\u003e\u003cspan address=\"https://github.com/nanoporetech/modkit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The nextflow based wf-human-variation pipeline v2.6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/epi2me-labs/wf-human-variation\u003c/span\u003e\u003cspan address=\"https://github.com/epi2me-labs/wf-human-variation\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to execute these steps.\u003c/p\u003e\u003cp\u003eCNV analysis for short-read WES data that were previously generated for the samples included in this study\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was carried out using CNVKit v0.9.10\u003csup\u003e19\u003c/sup\u003e. The arm-level CNVs detected using ONT WGS long read data were compared to those detected from short-read WES using their log2 scores visualized using CNVKit heatmap. A cutoff of absolute log2 ratio of 0.4 (corresponding to gain of 0.6 copies) is used in this work.\u003c/p\u003e\u003cp\u003eMethylation profiles of ICR around \u003cem\u003eH19/IGF2\u003c/em\u003e (ICR1) and \u003cem\u003eKCNQ1OT1\u003c/em\u003e (ICR2) for the samples reported to show gain in methylation by previous methylation-sensitive MLPA analysis (Table\u0026nbsp;1) were compared with commercially available male (CAT No. G1471, Promega, Fitchburg, WI) and female (CAT No. G1521, Promega, Fitchburg, WI) control DNA samples. Methylartist v1.4.0\u003csup\u003e20\u003c/sup\u003e and figeno v1.8.1\u003csup\u003e21\u003c/sup\u003e were used to visualize methylation profiles in ICR regions. Mann-Whitney U test was used to assess for statistically significant difference in methylation between different groups of samples.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eQuality Assessment of ONT Sequencing Data\u003c/h2\u003e\u003cp\u003eAll samples (n\u0026thinsp;=\u0026thinsp;15) sequenced using ONT WGS had genome wide average depth of coverage of at least 5x (range: 5-8x, average: 6.7x, median: 7x). The statistics related to the number, length, N50, yield and mapping rate of long reads are summarized in Table\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eAdaptive sampling provided an approximate 2x unit increase in average depth coverage for the targeted regions (from 6.7x to 8.5x). Other quality metric statistics yielded results similar to those observed with shallow WGS (sWGS) (Table\u0026nbsp;2). The average time required for wf-human-variation workflow execution was found to be approximately 4 hour 30 minutes, assuming optimal availability for required resources (32 CPUs, 128 GB memory), which can further be improved by appropriately scaling compute resources. End-to-end sequencing analysis was completed within 4 days.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePerformance of ONT WGS Platform for Detection of Copy Number Changes\u003c/h3\u003e\n\u003cp\u003eThe standard log2 ratio estimated by QDNASeq was used to determine copy number changes. A log2 ratio of 0 indicates no copy number change, a log2 ratio of ~\u0026thinsp;0.5 corresponds to a single copy gain, and a log2 ratio of 1 reflects a two-copy gain, with increasingly higher values indicating additional gains. Conversely, negative log2 ratios represent copy number losses.\u003c/p\u003e\u003cp\u003esWGS detected 94% (16/17) of the CNVs that are clinically relevant in WT (Table\u0026nbsp;3, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In sample KT-53, the 16q deletion detected by MLPA\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was not observed with sWGS or WES. Intra-tumor heterogeneity and differences in test methodology between MLPA and sequencing may account for the discordant results. sWGS detected a 1q gain in sample KT-51 that was not identified by WES. Both sWGS and WES concordantly detected a 16q deletion in this sample. As anticipated, no WT-relevant CNVs were identified in samples KT-22, KT-35, and KT-45 by sWGS. We observed\u0026thinsp;\u0026gt;\u0026thinsp;95% concordance between ONT sWGS-based approach and WES for the additional CNVs detected in this cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Some of the notable discrepancies included the detection of a focal loss in chromosome 10p and gain of 10q in sample KT-51 by sWGS, which was not detected by WES. Likewise, focal 17p deletion and 17q gain were detected only by sWGS in sample KT-66. In addition, gain of chromosomes 2, 3, 18, 19, and 20 identified by sWGS in sample KT-55 was not confirmed by WES analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Such discrepancies may be attributed to intra-tumor heterogeneity of the xenograft samples sequenced.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePerformance of ONT WGS Platform for Assessment of 11p15 Methylation Status\u003c/h2\u003e\u003cp\u003eMethylation profiles of all the xenograft samples in two imprinting control regions (ICRs) located at chromosome 11p15 were compared with those of two control samples (male and female, Human Genomic DNA, Promega, Fitchburg, WI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As per report from COG AREN0532, 30% to 70% methylation of both \u003cem\u003eH19/\u003c/em\u003eICR1 (chr11:1998744\u0026ndash;2003508; hg38) and \u003cem\u003eKCNQ1OT1/\u003c/em\u003eICR2 (chr11:2699998\u0026ndash;2700998; hg38) was considered to be retention of imprinting (ROI); 80% to 100% methylation of \u003cem\u003eH19\u003c/em\u003e/ICR1 and 30% to 70% methylation of \u003cem\u003eKCNQ1OT1/\u003c/em\u003eICR2 was considered to be LOI; and 80% to 100% methylation of \u003cem\u003eH19\u003c/em\u003e and 0% to 20% methylation of \u003cem\u003eKCNQ1OT1/\u003c/em\u003eICR2 was considered to be loss of heterozygosity (LOH)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. All the xenograft samples showed expected methylation levels in the \u003cem\u003eKCNQ1OT1/\u003c/em\u003eICR2 region; however, the \u003cem\u003eH19\u003c/em\u003e/ICR1 region did not show sufficient depth of coverage to determine methylation level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Six WT xenograft samples (KT-25, KT-45, KT-47, KT-48, KT-60 and KT-66) previously reported by MLPA analysis to have undergone LOI showed 30% to 70% methylation of \u003cem\u003eKCNQ1OT1/\u003c/em\u003eICR2 (green lines in Figure S1). In contrast, samples previously characterized as having copy neutral LOH (KT-31, KT-35, KT-43, KT-51 and KT-53) and loss of maternal copy of chromosome 11p15 (KT-76 and KT-28) showed\u0026thinsp;~\u0026thinsp;0% methylation in this region (magenta and orange lines in Figure S1 respectively). The six samples with LOI (n\u0026thinsp;=\u0026thinsp;6) showed a statistically significant gain in methylation of \u003cem\u003eKCNQ1OT1/\u003c/em\u003eICR2 when compared with the xenograft samples with copy neutral LOH (n\u0026thinsp;=\u0026thinsp;5; two-sided Mann-Whitney U test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; Figure S2). The overall sequencing coverage of control samples (\u0026lt;\u0026thinsp;5x) was not adequate for this comparison. In addition, due to low sequencing coverage at the ICR1 locus, the methylation level in \u003cem\u003eH19/\u003c/em\u003eICR1 could not be determined by this analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePerformance of ONT Adaptive Sampling for Detection of Copy Number Changes\u003c/h2\u003e\u003cp\u003eResults from adaptive sampling and sWGS were 100% concordant for the clinically relevant copy number changes in WT (i.e., 1p deletion, 1q gain, 11p15 deletion, and 16q deletion) (Table\u0026nbsp;3 and Figure S3). For all other CNVs, slight discordance was observed only in a chromosome 11q segment of sample KT-60, where the size of the 11q deletion was increased by ~\u0026thinsp;20 Mbp after adaptive sampling (from chr11: 80,000,001-135,086,622 to chr11: 60,000,001-135,086,622) with a decrease in log2 ratio from \u0026minus;\u0026thinsp;0.97 to \u0026minus;\u0026thinsp;0.47. Such discrepancies may be explained by the increased sequencing depth and higher analytical sensitivity of the sequencing analysis using adaptive sampling, whereby the neighboring regions of 11q that did not get sufficiently sequenced by sWGS but were adequately covered after adaptive sampling. These results overall indicate high reproducibility of CNV calls even at shallow depth of sequencing before and after adaptive sampling, suggesting that adaptive sampling can play an important role in determining clinically relevant copy number changes (Figure S3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePerformance of ONT Adaptive Sampling for Assessment of 11p15 Methylation Status\u003c/h2\u003e\u003cp\u003eThe trend of methylation profiles in ICR2 region overall remained the same after adaptive sampling with an expected increase in the number of methylated CpG sites for quantification. The ICR1 regions remained under covered, precluding conclusive results (Figure S4).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis is the first study to evaluate the performance of sWGS and adaptive sampling using nanopore technology for the detection of CNVs and 11p15 LOI in WT. Molecular assessment of LOH at chromosomes 1p and/or 16q has been incorporated into COG risk stratification for favorable-histology WT based on prospective findings from NWTS-5\u003csup\u003e3\u003c/sup\u003e and subsequent validation in AREN0532 and AREN0533\u003csup\u003e22\u003c/sup\u003e. In NWTS-5 (\u0026gt;\u0026thinsp;1,700 patients), LOH at 1p and/or 16q was an independent predictor of relapse and mortality, with the highest risk observed in patients with WT harboring combined 1p/16q LOH. Across both early- and advanced-stage cohorts, patients with WT having 1p/16q LOH had significantly inferior relapse-free survival despite standard therapy, supporting systemic treatment intensification\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Building on this, AREN0532 and AREN0533 demonstrated that biomarker-directed augmentation of chemotherapy improved event-free survival\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Although LOI of 11p15 is also considered to be a prognostic indicator in stage I WT in young children, it is not incorporated into the ongoing trial study AREN2231 because current testing technologies are not routinely available and do not return results fast enough to be used in upfront clinical decisions at this time\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Accurate LOI assessment requires allele-specific methylation resolution, which conventional bisulfite sequencing and methylation arrays are generally unable to provide. Reliable detection of allele-specific methylation necessitates simultaneous profiling of both allele-informative SNPs and DNA methylation marks within the same or directly linked sequencing data. To this end, long-read nanopore sequencing provides direct single-molecule resolution for detection of cytosine modifications in native DNA and because reads span kilobases, enables haplotype-aware, allele-specific methylation analyses required to detect LOI. In addition, compared with traditional short-read sequencing and microarray-based methods, nanopore-based sequencing offers a substantially faster turnaround, delivering results in approximately 5 days instead of the 2\u0026ndash;4 weeks typically required by conventional approaches. Current favorable histology WT therapies start with a cycle of vincristine and actinomycin-D to allow for results that affect final risk stratification to return. In the future, shorter turnaround time for molecular results could allow more intense or targeted therapies to be delivered earlier for high-risk groups. Also, diagnostic conundrums regarding the presence of diffuse or focal anaplasia could be informed by earlier reporting of molecular results at the 17p13 (\u003cem\u003eTP53\u003c/em\u003e) locus. These capabilities could facilitate a timelier and more comprehensive stratification of patients with favorable histology WT for risk-adapted first-line treatment, making nanopore an attractive option for WT testing.\u003c/p\u003e\u003cp\u003eTo evaluate the feasibility of CNV and 11p15 LOI detection using nanopore, we tested 15 WT xenograft samples that were previously characterized by WES and MLPA. We opted to use the MinIon platform for this pilot study due to its affordability and ease of use, which could facilitate deployment for single sample testing in our center, as well as its potential application in under-resourced settings. Our results indicate that MinIon sequencing can perform on par with conventional short-read WES for CNVs that are clinically relevant in WT. In our cohort, we detected all WT-relevant CNVs except for one alteration (16q deletion) in one xenograft sample (KT-53). This tumor was found to have diffuse anaplasia, and thus 16q deletion would not have been clinically actionable in this specific case. The analysis did not yield any false positive calls. Nonetheless, the MinION platform has limitations that should be recognized, including its relatively low sequencing throughput (~\u0026thinsp;20 Gb per flow cell) compared with other nanopore sequencers (e.g., PromethION, ~\u0026thinsp;100\u0026ndash;300 Gb per flow cell) and short-read platforms, which limits its achievable sequencing depth. In our study, the read depth obtained by MinION sequencing, even with adaptive sampling, was insufficient to enable effective assessment of methylation status at 11p15, particularly for the \u003cem\u003eH19\u003c/em\u003e/\u003cem\u003eICR1\u003c/em\u003e locus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Despite the limitation, the WT relevant CNV data obtained by MinION sequencing are comparable in scope to those produced by COG-recommended SNP array testing.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, this study demonstrates the feasibility of using nanopore sequencing for the assessment of clinically relevant CNVs in WT, and highlights the potential of this technology for the integrated assessment of CNV and 11p15 methylation status in WT. Future work will focus on validating this approach in a prospective patient cohort with the aim of developing a clinical-grade assay capable of providing rapid, targeted detection of clinically actionable chromosomal and epigenetic alterations to inform clinical decision-making. Fresh tumor samples obtained directly at the time of the procedure (nephrectomies or biopsies) represent an optimal substrate for nanopore sequencing because high-quality DNA can be extracted without the degradation or artifacts introduced by formalin fixation or extended storage. Because nanopore library preparation relies on streamlined workflows, tissue collected in the operating room can be transitioned directly to sequencing with minimal processing, allowing timely results and facilitating integration into clinical testing workflows. The current findings underscore that successful clinical integration will require higher-capacity sequencing platforms, with the PromethION representing a particularly suitable option due to its scalability, throughput, and reduced computational demands.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eBED\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBrowser Extensible Data\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eCNV\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCopy number alteration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChildren\u0026rsquo;s Oncology Group\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eICR\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImprinting Control Regions\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eLOH\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLoss of heterozygosity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eLOI\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLoss of imprinting\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eMLPA\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultiplex ligation-dependent probe amplification\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eONT\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOxford Nanopore Technologies\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003ePDX\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePatient-derived xenograft\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003esWGS\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eShallow whole genome sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eSNP\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eWES\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhole exome sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eWGS\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhole genome sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWilms tumor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr. Heather Mefford and Dr. Soham Sengupta for providing computational resources for adaptive sampling sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement for informed consent was waived because the study involved retrospective analysis of de-identified data. Ethical approval for this work was obtained from the Institutional Review Board at St. Jude Children\u0026rsquo;s Research Hospital under project number 24-1767. All procedures complied with the Declaration of Helsinki and institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Department of Surgery at St. Jude Children\u0026rsquo;s Research Hospital and by a 5K08CA255569-03 National Institutes of Health (NIH)/National Cancer Institute (NCI) Grant to AJM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, L.V.F., T.S., A.M.D., and A.J.M.; methodology, C.J., P.K., X.M., A.J.M. and L.V.F.; wet-laboratory procedures, C.J.; bioinformatics procedures, P.K. and X.M.; data analysis, P.K., C.J., L.V.F, A.J.M., and X.M.; resources, A.J.M., A.M.D., C.L.M, A.W.; writing\u0026mdash;original draft preparation, L.V.F., C.J., and P.K.; writing\u0026mdash;review and editing, A.J.M., T.S., A.M.D., C.L.M., A.W., and X.M.; funding acquisition, A.M.D. and A.J.M. All authors read and approved the manuscript. L.V.F., C.J., and P.K. contributed equally to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavidoff AM (2012) Wilms tumor. Adv Pediatr 59(1):247\u0026ndash;267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.yapd.2012.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.yapd.2012.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvageliou N, Renfro LA, Geller J, Perlman E, Kalapurakal J, Paulino A et al (2024) Prognostic impact of lymph node involvement and loss of heterozygosity of 1p or 16q in stage III favorable histology Wilms tumor: A report from Children's Oncology Group Studies AREN03B2 and AREN0532. Cancer 130(5):792\u0026ndash;802. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.35084\u003c/span\u003e\u003cspan address=\"10.1002/cncr.35084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrundy PE, Breslow NE, Li S, Perlman E, Beckwith JB, Ritchey ML et al (2005) Loss of heterozygosity for chromosomes 1p and 16q is an adverse prognostic factor in favorable-histology Wilms tumor: a report from the National Wilms Tumor Study Group. J Clin Oncol 23(29):7312\u0026ndash;7321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2005.01.2799\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2005.01.2799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChagtai T, Zill C, Dainese L, Wegert J, Savola S, Popov S et al (2016) Gain of 1q As a Prognostic Biomarker in Wilms Tumors (WTs) Treated With Preoperative Chemotherapy in the International Society of Paediatric Oncology (SIOP) WT 2001 Trial: A SIOP Renal Tumours Biology Consortium Study. J Clin Oncol 34(26):3195\u0026ndash;3203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2015.66.0001\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2015.66.0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGratias EJ, Dome JS, Jennings LJ, Chi YY, Tian J, Anderson J et al (2016) Association of Chromosome 1q Gain With Inferior Survival in Favorable-Histology Wilms Tumor: A Report From the Children's Oncology Group. J Clin Oncol 34(26):3189\u0026ndash;3194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2015.66.1140\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2015.66.1140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernandez CV, Perlman EJ, Mullen EA, Chi YY, Hamilton TE, Gow KW et al (2017) Clinical Outcome and Biological Predictors of Relapse After Nephrectomy Only for Very Low-risk Wilms Tumor '. Ann Surg 265(4):835\u0026ndash;840. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/Sla.0000000000001716\u003c/span\u003e\u003cspan address=\"10.1097/Sla.0000000000001716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenedetti DJ, Cost NG, Ehrlich PF, Evageliou N, Fialkowski E, Parsons LN et al (2025) Updated favourable-histology Wilms tumour risk stratification: rationale for future Children's Oncology Group clinical trials. \u003cem\u003eNat Rev Urol\u003c/em\u003e. ;10.1038/s41585-025-01055-1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoose M, Malla S, Stout M (2016) Real-time selective sequencing using nanopore technology. Nat Methods 13(9):751\u0026ndash;754. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.3930\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.3930\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOoms AH, Gadd S, Gerhard DS, Smith MA, Guidry Auvil JM, Meerzaman D et al (2016) Significance of TP53 Mutation in Wilms Tumors with Diffuse Anaplasia: A Report from the Children's Oncology Group. Clin Cancer Res 22(22):5582\u0026ndash;5591. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078\u0026thinsp;\u0026ndash;\u0026thinsp;0432.CCR-16-0985\u003c/span\u003e\u003cspan address=\"10.1158/1078\u0026thinsp;\u0026ndash;\u0026thinsp;0432.CCR-16-0985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGadd S, Huff V, Walz AL, Ooms A, Armstrong AE, Gerhard DS et al (2017) A Children's Oncology Group and TARGET initiative exploring the genetic landscape of Wilms tumor. Nat Genet 49(10):1487\u0026ndash;1494. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.3940\u003c/span\u003e\u003cspan address=\"10.1038/ng.3940\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEuskirchen P, Bielle F, Labreche K, Kloosterman WP, Rosenberg S, Daniau M et al (2017) Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing. Acta Neuropathol 134(5):691\u0026ndash;703. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00401-017-1743-5\u003c/span\u003e\u003cspan address=\"10.1007/s00401-017-1743-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWongsurawat T, Jenjaroenpun P, Anekwiang P, Arigul T, Thongrattana W, Jamshidi-Parsian A et al (2024) Exploiting nanopore sequencing for characterization and grading of IDH-mutant gliomas. Brain Pathol 34(1):e13203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bpa.13203\u003c/span\u003e\u003cspan address=\"10.1111/bpa.13203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMouliere F, Mair R, Chandrananda D, Marass F, Smith CG, Su J et al (2018) Detection of cell-free DNA fragmentation and copy number alterations in cerebrospinal fluid from glioma patients. Embo Mol Med 10(12):ARTNe9323\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e15252/emmm.201809323\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartignano F, Munagala U, Crucitta S, Mingrino A, Semeraro R, Del Re M et al (2021) Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients. Mol Cancer 20(1):32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12943-021-01327-5\u003c/span\u003e\u003cspan address=\"10.1186/s12943-021-01327-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurphy AJ, Chen X, Pinto EM, Williams JS, Clay MR, Pounds SB et al (2019) Forty-five patient-derived xenografts capture the clinical and biological heterogeneity of Wilms tumor. Nat Commun 10(1):5806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-019-13646-9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-13646-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H (2021) New strategies to improve minimap2 alignment accuracy. Bioinformatics 37(23):4572\u0026ndash;4574. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btab705\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btab705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedersen BS, Quinlan AR (2018) Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34(5):867\u0026ndash;868. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btx699\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btx699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScheinin I, Sie D, Bengtsson H, van de Wiel MA, Olshen AB, van Thuijl HF et al (2014) DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res 24(12):2022\u0026ndash;2032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gr.175141.114\u003c/span\u003e\u003cspan address=\"10.1101/gr.175141.114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalevich E, Shain AH, Botton T, Bastian BC, CNVkit (2016) Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol 12(4):e1004873. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pcbi.1004873\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1004873\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheetham SW, Kindlova M, Ewing AD (2022) Methylartist: tools for visualizing modified bases from nanopore sequence data. Bioinformatics 38(11):3109\u0026ndash;3112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btac292\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btac292\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSollier E, Heilmann J, Gerhauser C, Scherer M, Plass C, Lutsik P (2024) Figeno: multi-region genomic figures with long-read support. Bioinformatics 40(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btae354\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btae354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDix DB, Fernandez CV, Chi YY, Mullen EA, Geller JI, Gratias EJ et al (2019) Augmentation of Therapy for Combined Loss of Heterozygosity 1p and 16q in Favorable Histology Wilms Tumor: A Children's Oncology Group AREN0532 and AREN0533 Study Report. J Clin Oncol 37(30):2769\u0026ndash;2777. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.18.01972\u003c/span\u003e\u003cspan address=\"10.1200/JCO.18.01972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"86b530af-723c-4a31-ba55-46b5fb9c23b7","identifier":"10.13039/100000002","name":"National Institutes of Health","awardNumber":"5K08CA255569-03 ","order_by":0}],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"St. Jude Children's Research Hospital","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wilms tumor, nanopore, ONT, sequencing, copy number alterations, methylation","lastPublishedDoi":"10.21203/rs.3.rs-8206667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8206667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCopy number alterations (CNVs) involving chromosomes 1p, 1q, 16q, and 11p15 are key genomic markers used in the risk stratification of Wilms tumor (WT). These CNVs, when considered alongside disease stage and other clinical features, are associated with an increased risk of relapse. Accordingly, testing for these changes is recommended to guide treatment choices in children with favorable histology disease. Current methods for detecting segmental CNVs in WT, including single nucleotide polymorphism (SNP) arrays and short-read sequencing, require prolonged turnaround times, high cost, and do not capture loss of imprinting (LOI) at 11p15, a key adverse predictor for patients who would otherwise have very low risk disease. In this study, we assessed the feasibility of utilizing nanopore sequencing for CNV and 11p15 LOI analysis in WT.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed whole-genome sequencing (WGS) using the MinION platform on 15 patient-derived WT xenografts previously characterized by whole exome sequencing (WES) and multiplex ligation-dependent probe amplification (MLPA), applying adaptive sampling in a subset to enrich for clinically relevant regions. End-to-end sequencing analysis was completed within 4 days.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDespite low sequencing depth (average 6.7x, WGS; 8.5x, adaptive sampling sequencing), nanopore WGS detected 94% (16/17) of the CNVs that are clinically relevant in WT. No false-positive findings were observed. Adaptive sampling showed high concordance with WGS for CNV detection and allowed sample multiplexing. However, the sequencing depth obtained by nanopore WGS and adaptive sampling was insufficient for assessment of methylation status at the Imprinting Control Regions (ICR)1 and ICR2 at 11p15.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study demonstrates the feasibility of using nanopore sequencing for the assessment of clinically relevant CNVs in WT and highlights the potential of this technology for the integrated evaluation of copy number variation and 11p15 methylation status with a much shorter turnaround time and lower cost than other conventional methods for CNV testing. Successful clinical integration will require higher throughput nanopore sequencing platforms, as indicated by the current findings.\u003c/p\u003e","manuscriptTitle":"Capturing clinically actionable copy number alterations in Wilms tumor using nanopore sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-25 07:13:17","doi":"10.21203/rs.3.rs-8206667/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"584bf03a-47ea-483d-88f9-e07bf1f3fce2","owner":[],"postedDate":"January 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-25T07:13:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-25 07:13:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8206667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8206667","identity":"rs-8206667","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}