Optimization and Validation of Nanopore Based Sequencing Method for Molecular Testing of CNS Tumours
Bibliographic record
Abstract
Background The World Health Organization (WHO) introduced molecular identifiers for the diagnosis and prognosis of CNS tumors including the mutational status of isocitrate dehydrogenase or IDH genes in glial tumors. Currently used immunohistochemistry (IHC) is not capable of detecting the non‐canonical mutations, and sequencing is often required as a follow‐up. Current next‐generation sequencing (NGS) technologies used in tumor molecular marker detection introduce key challenges including high capital cost, complex infrastructure requirements, and long turnaround times. These challenges considerably limit the ability to perform NGS testing in many pathology laboratories. In this study, we aimed to use third generation nanopore sequencing technology to resolve these limitations. The Oxford Nanopore MinION, a pocket‐sized nanopore sequencing device, has minimal capital costs and infrastructural requirements, and shorter turnaround times. However, the nanopore technology has not been validated in clinical practice and has not been optimized on formalin‐fixed paraffin‐embedded (FFPE) tissue. Methods DNA extraction of selective tumor areas was performed from the corresponding FFPE tissue blocks from a cohort of gliomas with confirmed IDH1 and IDH2 gene statuses (n=65). A PCR amplicon‐based approach was used to amplify hot spots of the IDH1 and IDH2 genes, starting with 30ng DNA material. The amplicon libraries were sequenced for 2 hours in multiplex on the MinION device and IDH SNPs were called with the Nanopolish software. ASIP Abstract Mashiat Mimosa Results 26 IDH mutant samples were identified: 21 IDH1 R132H, 2 IDH1 R132G, 2 IDH2 R172G, and 1 IDH2 D177H. All cases showed concordant IDH mutational status when compared to the reference methods (IHC or NGS) and both analytical sensitivity and specificity were 100%. Precision analysis of variant allele frequency (VAF) showed the coefficient of variation was less than 5% (both inter and intra runs), and the limit of detection for VAF was 3%. The range of read depth obtained was 882X to 43,000x with an average of 20,000x. This assay revealed a $50‐$100 material cost per sample, and the time taken from extracted nucleic acid to final result generation was 1‐2 business days. Conclusion This project is the first to optimize and validate an approach to detect SNP mutations in FFPE samples using nanopore technology. It has demonstrated the feasibility and efficacy of the nanopore amplicon sequencing method in cancer FFPE tissue with excellent test performance characteristics, significantly shorter turnaround times at considerably lower costs and without any infrastructural needs. Thus, it can be used to circumvent challenges to current NGS testing platforms and can be the milestone that would make cancer NGS testing available for every laboratory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".