Prospective, multicenter validation of a platform for rapid molecular profiling of central nervous system tumors

Abstract

Molecular data integration plays a central role in central nervous system (CNS) tumor diagnostics but currently used assays pose limitations due to technical complexity, equipment and reagent costs, as well as lengthy turnaround times. We previously reported the development of Rapid-CNS², an adaptive-sampling-based nanopore sequencing workflow. Here we comprehensively validated and further developed Rapid-CNS² for intraoperative use. It now offers real-time methylation classification and DNA copy number information within a 30-min intraoperative window, followed by comprehensive molecular profiling within 24 h, covering the complete spectrum of diagnostically and therapeutically relevant information for the respective entity. We validated Rapid-CNS² in a multicenter setting on 301 archival and prospective samples including 18 samples sequenced intraoperatively. To broaden the utility of methylation-based CNS tumor classification, we developed MNP-Flex, a platform-agnostic methylation classifier encompassing 184 classes. MNP-Flex achieved 99.6% accuracy for methylation families and 99.2% accuracy for methylation classes with clinically applicable thresholds across a global validation cohort of more than 78,000 frozen and formalin-fixed paraffin-embedded samples spanning five different technologies. Integration of these tools has the potential to advance CNS tumor diagnostics by providing broad access to rapid, actionable molecular insights crucial for personalized treatment strategies.

Fig. 1. Overview of the Rapid-CNS² workflow and validation of MNP-Flex.

a, Streamlined workflow that starts with intraoperative sequencing to report broad methylation classification and arm-level copy number alterations followed by postoperative sequencing to report comprehensive molecular markers and fine-grained methylation classification. Lines below indicate the timeline for Rapid-CNS² versus classical methods. b, Sources and types of sequencing data used for validation of MNP-Flex. Credits: Brain outline in b adapted from SVG Repo (https://www.svgrepo.com) under a Creative Commons license CC BY 4.0; DNA helix adapted from SVG Repo (https://www.svgrepo.com) under a Creative Commons license CC BY 4.0. Signal and classification tree in the MNP-Flex logo in b was created using BioRender.com.

Fig. 2. Overview of concordance for the Rapid-CNS² cohort.

a, Archival samples. b, Diagnostic samples. Bars indicate on-target coverage, sequencing time and the percentage of SNVs that were recovered in Rapid-CNS² data from their corresponding NGS data. Site and device blocks indicate site of sequencing (University Hospital Heidelberg or University of Nottingham) and the device that sequencing was run on. NGS and methylation array blocks indicate the availability of corresponding conventional data. Following blocks indicate concordance with available matched conventional data. Methylation classification displays concordance levels of the Rapid-CNS² ad hoc classifier with corresponding methylation array-based classification. Methylation class indicates the ‘ground truth’ or inferred methylation class. MGMTp shows concordance of MGMT promoter methylation status with matched conventional data.

Fig. 3. Evaluation of molecular markers reported by Rapid-CNS².

a, Violin plot showing the percentage of SNVs recovered by Rapid-CNS² libraries compared with their corresponding NGS panel sequencing libraries (n = 103), with the width proportional to the frequency of values at each level. The overlaid box plot indicates the median (horizontal line), the 25th and 75th percentiles (edges of the box) and whiskers extending up to 1.5× the interquartile range from the box boundaries. Data points lying beyond these whisker bounds are shown individually as outliers. b, Methylation values of methylated and unmethylated glioblastoma samples over the MGMT promoter region (highlighted in blue). The panels show (top to bottom) aligned reads colored by sample with CpG sites marked as closed (methylated) or open (unmethylated) dots followed by smoothed methylation profiles for each sample. The smoothed profiles show a clear difference in overall methylation between methylated and unmethylated samples. c, CNV profile generated using EPIC array data (left) and CNV profile generated by Rapid-CNS² (right) for the same sample; focal alterations detected in both are highlighted with ovals. d, Sankey plot comparing predictions from the MNP v.11 classifier for methylation array data with the predictions by Rapid-CNS² (calibrated score >30%) for the same cases. Source data

Fig. 4. MNP-Flex validation.

a, F₁ scores for the methylation array dataset comprising 78,833 samples covering 184 methylation classes. We considered the MNP random forest classifier as ground truth for comparison. All samples with an MNP-RF score >0.7 that were not included in the training set were used for validation. b, Sankey plot showing comparison of methylation array-based MNP-RF predictions with corresponding MNP-Flex predictions over the nonarray cohort. Only samples with an MNP-Flex prediction score >0.3 are shown. c, Comparison of MNP-Flex performance over different technologies. Bar plots indicate accuracy and error bars represent the 95% CIs calculated using a binomial proportion confidence interval via the binom.confint function in R. Samples were processed using WGBS (n = 80), Twist methylation panels (n = 27), nanopore adaptive-sampling-based Rapid-CNS² in Nottingham (n = 41) and Heidelberg (n = 194), ONT-WGS (n = 40) and the conventional methylation array (n = 78,833) dataset. Solid colored bars indicate subclass-level accuracy and bars with increased alpha indicate family-level accuracy. ONT-WGS samples did not have matched array predictions; thus, family-level predictions were inferred from histological and molecular findings. Density plots indicate scores for subclass prediction. Box plots denote percentage of missing CpG sites in each dataset. They include the median (horizontal line), with the box boundaries representing the 25th and 75th percentiles (interquartile range). Whiskers extend to 1.5× the interquartile range beyond the box limits. Data points beyond these whisker boundaries are plotted individually as outliers. Source data

Fig. 5. Intraoperative reporting.

a, Box plots indicate calibrated scores for each time point for correct (green) and incorrect (orange) predictions for simulated retrospective samples. Box plots display the median (horizontal line) and the box boundaries represent the 25th and 75th percentiles (interquartile range). Whiskers extend to 1.5× the interquartile range beyond the box limits. Data points beyond the boundaries are plotted individually as outliers. The number of samples represented per box plot is equal to the number of samples indicated in the bar plots below. Bar plots indicate per-sample methylation class prediction concordance over time. Reads generated within the indicated sequencing time were used for analysis (top). Each rectangle on the bar plot indicates individual samples colored by ground truth methylation class from corresponding methylation array profiles. The x axis indicates time in minutes from beginning of sequencing. A positive y axis indicates correct predictions and a negative y axis indicates incorrect predictions (bottom). b, Prediction score versus time for intraoperative samples run in Heidelberg and Nottingham using a P2 Solo; color indicates class; size of dots indicates number of CpG sites. Source data

Fig. 6. Integrated intraoperative and postoperative classification using Rapid-CNS² and MNP-Flex.

a, Example of the end-to-end workflow combining intraoperative and postoperative analysis using Rapid-CNS² and methylation classification by MNP-Flex. b, For cases outside the scope of the Rapid-CNS² methylation classifier, additional layers of information like mutations, fusions and CNVs were used to provide an integrated diagnosis. Reclassification with MNP-Flex classified the cases as new entities only present in the MNP v.12 classifier. This resulted in accurate classifications for 9 of 12 cases (outlined in green). Two discrepant cases (outlined in orange) had low tumor content on histology inspection and were appropriately classified as ‘control’ classes. One diffuse leptomeningeal glioneuronal tumor (DLGNT) was predicted as pilocytic astrocytoma (outlined in orange), both of which are MAPK-activated low-grade glial and/or glioneuronal tumors. Credits: Nanopore schematic as well as signal and classification tree in the MNP-Flex logo in a was created using BioRender.com. Brain outline in a adapted from SVG Repo (https://www.svgrepo.com) under a Creative Commons license CC BY 4.0; DNA helix adapted from SVG Repo (https://www.svgrepo.com) under a Creative Commons license CC BY 4.0. DD, differential diagnosis; DMG, diffuse midline glioma; LOH, loss of heterozygosity; NOS, not otherwise specified; mut, mutation; wt, wild type.

Extended Data Fig. 1. Summary figure illustrating concordance over levels of evaluation.

“Methylation classification concordance” is the concordance for methylation classes predicted by Rapid-CNS2. “Implication of additional layers” denotes how information like mutations, CNVs, and fusions were used. “Integrated diagnosis concordance” indicates concordance of integrated diagnosis by Rapid-CNS² derived data to that obtained using conventional methods. “Summary” is the summary of concordance.

Extended Data Fig. 2. Rapid molecular reclassification of a suspected glioma.

Representative regions from A) Smear H&E stain and B) frozen H&E section for Study ID 212. H&E slides were inspected by two pathologists independently and both suspected the sample to be a glioma in the intraoperative frozen section diagnosis. C) Rapid-CNS² predicted it to be a EFT_CIC (CIC altered Ewing family tumour) within 30 minutes of sequencing and after 24 h of sequencing. D) MNP-Flex methylation classification after 24 h of sequencing indicated it to be a CIC-rearranged sarcoma. E) Rapid-CNS² and F) EPIC array copy number profiles after 24 h of sequencing.

Extended Data Fig. 3. Intraoperative sequencing and classification reporting.

A) Rapid-CNS² methylation class predictions over 1 hour of sequencing time for 16 classifiable samples from the 18 intraoperative runs. Boxplots (top) indicate calibrated scores for correct (green) and incorrect (orange) predictions. Box plots show the median (horizontal line) and the box boundaries correspond to the 25th and 75th percentiles (interquartile range). Whiskers extend up to 1.5 times the interquartile range beyond these boundaries. Any data points outside this range are displayed individually as outliers. The number of samples represented in each box plot matches the sample counts shown in the accompanying bar plots. (bottom) Each rectangle on the bar plot indicates individual samples coloured by true methylation class or inferred methylation family (GBM). X axis indicates time in minutes. Positive Y-axis indicates correct predictions, negative Y-axis indicates incorrect predictions for the prediction. B) Prediction over time for two unclassifiable samples, not included in A). (left) did not have a true class represented in the classifier and (right) had low ( < 0.3) scores for the corresponding methylation array. Both were predicted with low confidence ( < 25%). Source data

Extended Data Fig. 4. CNV profiles resolve cases with unclear methylation classification.

A) Rapid-CNS² methylation class predictions over 1 hour of sequencing time for intraoperative runs. X axis indicates time in minutes and Y-axis indicates score (%) for the prediction. B) CNV profiles from 5 to 20 min of sequencing for the samples. Study ID 288 was not correctly predicted by methylation, but displayed a clear chr7 gain / chr10 loss in the intraoperative CNV profiles.