PL02.2.A RAPID, COMPREHENSIVE REPORTING OF MOLECULAR DIAGNOSTICS WITH RAPID-CNS2: A PROSPECTIVE VALIDATION COHORT
Bibliographic record
Abstract
Abstract BACKGROUND The WHO classification of CNS tumours 2021 recommends reporting a wide range of molecular alterations for WHO-compatible diagnoses. Conventional molecular diagnostic workflows warrant considerable investment and long turnaround times limited by batching. Nanopore sequencing enables long read sequencing, real-time targeting and simultaneous base modification detection with compact devices. Rapid-CNS2- a rapid, comprehensive adaptive sampling based sequencing pipeline with a turnaround time of 5 days for CNS tumours was previously described. This study presents an optimised Rapid-CNS2 pipeline and prospective validation cohort that successfully reports hard-to-detect variants. MATERIAL AND METHODS We performed targeted adaptive sampling-based sequencing on 156 samples comprising a mix of archival and current diagnostic cases from the Department of Neuropathology, University Hospital Heidelberg. We tested variable conditions including minimum DNA input, target size, flowcell reuse and sequencing times. An analysis pipeline for detection of single nucleotide variants (SNV), indels, copy number alterations (CNA), fusions, complex structural variants (SV), target gene methylation and methylation classification incorporating multi-GPU optimization was deployed. Results were compared to NGS panel sequencing and EPIC array analyses. RESULTS Integrated diagnoses for all cases were in accordance with those issued using molecular alterations reported by conventional analyses. Rapid-CNS2 showed improved resolution over NGS panel sequencing for CNAs. Complete concordance was achieved for MGMT promoter methylation status and methylation classification. High accuracy was obtained for detection of SNV/Indels. Long reads spanning the breakpoint identified clinically relevant fusions. Subclonal SVs including EGFR vIII missed by NGS panel sequencing were detected with high confidence. The optimized workflow shortened the turnaround time to 30 hours from sample receipt to report and minimum input DNA required was reduced to 500ng. Due to the versatility of adaptive sampling, the analyses can readily be limited to a smaller set of targets or methylation and CNAs only, further reducing the turnaround time. CONCLUSION We present a thorough validation and prospective clinical application of Rapid-CNS2 with cases spanning a range of brain tumours and metastases. The improved custom neurooncology adaptive sampling-based sequencing pipeline enables simultaneous copy-number, mutational, structural variant and methylation analysis with flexible target selection, no additional library preparation, competitive costs and next-day reporting of RESULTS
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".