Rapid and accurate childhood cancer diagnosis with nanopore long-read RNA sequencing | LC26

Abstract
Precision Child Health (PCH) is an institute-wide program at SickKids that brings efforts together to diagnose faster, treat smarter, and predict better for each child. In childhood cancer care, timely and accurate diagnosis is a critical first step, particularly when molecular diagnostics directly inform treatment decisions and prognosis. However, in current practice, cancers such as acute leukaemia and brain tumours often require multiple sequential assays to confirm a molecular subtype, resulting in prolonged turnaround times and occasional diagnostic uncertainty. To accelerate PCH goals, we have developed OTTER, a machine-learning pan-cancer transcriptome classifier trained on in-house RACCOON cancer atlas of >13,000 short-read RNA-Seq samples. By combining the dynamic nature of tumour-derived RNA with the real-time and long-read capabilities of Oxford Nanopore Technologies (ONT) sequencing, we aim to establish a rapid RNA-based diagnostic platform to support PCH. Initial evaluations show that OTTER integrates seamlessly with ONT RNA sequencing data, achieving >90% concordance relative to short-read RNA-Seq and pathologist-confirmed diagnoses. In a pilot study of 50 samples, this workflow enabled robust cancer classification, with disease group assignment within minutes and cancer subtype classification within 1–2 hours of sequencing. Clinically relevant fusions were detected in 100% of known cases, with additional oncogenic variants identified early during analysis. Together, these results substantially reduced the overall turnaround time from biopsy to molecular diagnosis from several weeks to under one week. Beyond local implementation, we are extending this workflow through collaborative projects in Nairobi, Kenya. In an initial cohort of 10 leukaemia cases, where only broad disease groups and flow cytometry results were available, our approach provided additional genetic subtyping using MinION RNA sequencing alone. By combining ONT RNA sequencing with our pan-cancer classifier in a single, streamlined workflow, we demonstrated the potential to support more accessible and timely cancer diagnostics across diverse clinical environments.

Biography
Sandy Fong is a senior bioinformatician at the Shlien Lab, SickKids Research Institute. She leads informatics efforts across national and international collaborative projects, focusing on the development of rapid, RNA-based molecular diagnostics for paediatric cancer through the integration of long-read nanopore sequencing and in-house machine-learning approaches.