Abstract B002: Rapid and accurate diagnosis of childhood cancers using long-read nanopore RNA sequencing
Bibliographic record
Abstract
Abstract The SickKids Cancer Sequencing (KiCS) program at the Hospital for Sick Children performs DNA and RNA sequencing of the tumors of all pediatric patients diagnosed with hard-to-cure cancers (metastatic, refractory, relapsed, or others with predicted survival <50%) to determine clinically actionable mutations that would be of immediate benefit to the patient while also building a large dataset of childhood cancer genomes and transcriptomes to guide future study. RNA-seq at SickKids is performed using short-read sequencing; while very powerful, this technique can miss important mutations and typically has a long turnaround time. In a pilot study, we examined the feasibility of long-read nanopore RNA-seq to improve turnaround time (from biopsy to diagnosis) and detection of rare mutations and fusions compared to traditional short-read RNA-seq. Long-read RNA-seq of KiCS tumors with matched short-read RNA-seq and a pathologist-confirmed diagnosis showed high diagnostic concordance between sequencing methods using OTTER, our in-house machine-learning diagnostic classifier. Strikingly, downsampling analyses showed that only 500,000 long reads were necessary for accurate OTTER classification, corresponding to 1-2 hours of sequencing on a nanopore device compared to 24-26 hours on a short-read sequencer. Furthermore, cDNA-PCR long-read libraries require only 3 hours of preparation when starting with extracted total RNA, compared to at least 7 hours when using SickKids’ clinically validated short-read protocol, showing that same-day diagnosis is feasible when using long-read RNA-seq. Analyses are currently underway using prospectively collected samples through the KiCS program. Ultimately, long-read nanopore RNA-seq holds great promise in improving the speed and accuracy of pediatric diagnostics. Citation Format: Matt Hudson, Sandy Fong, Pedro Lemos Ballester, Resel Pereira, Srdjana Filipovic, Reem Khan, Johann Hitzler, Sarah Cohen-Gogo, Anita Villani, David Malkin, Adam Shlien. Rapid and accurate diagnosis of childhood cancers using long-read nanopore RNA sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr B002.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.003 |
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".