Acute lymphoblastic leukaemia (ALL) is the most common childhood cancer, and advances in treatment have significantly improved survival. However, 5–15% of children still relapse, and relapse remains the leading cause of mortality. Existing risk stratification—largely based on clinical variables and classic genetic abnormalities—has improved outcomes but remains insufficiently precise to identify, at diagnosis, which children are at highest risk.
This project addresses that gap by leveraging a unique, long-standing collaboration with the National University of Singapore (NUS), led by Prof Hany in partnership with Professor Allen Yeoh (NUS). Over two decades, this collaboration has generated a valuable repository of clinically annotated and RNA sequencing data from paediatric B-cell ALL patients.
The core objective is to enhance existing clinical risk stratification by incorporating transcriptomic context, enabling more biologically informed prediction of relapse risk at first presentation. Clinically well-annotated datasets from multiple centres in Malaysia and Singapore are curated and harmonised, integrating clinical variables with high-dimensional RNA sequencing data to support precision modelling.
Given the scale and complexity of these datasets, the project applies tailored analytical strategies, including feature selection using differential gene and pathway-level analyses, alongside multiple modelling approaches such as time-to-event analysis. External cohorts are used to support model validation. In parallel, publications arising from a narrative literature review have played a critical role in contextualising the biological framework underpinning the study, while the final analytical findings are being consolidated in a separate manuscript.
Beyond scientific outputs, the project delivers strong capacity building through structured collaboration with Prof Wong Limsoon, Professor and Chair in the School of Computing at the National University of Singapore (NUS), and mentorship during Dr Oh’s attachment at NUS. Early engagement with collaborators at the University of Cambridge positions the work for extension into single-cell analysis.
Ultimately, the project aims to support earlier, more precise treatment decisions, helping clinicians determine—at diagnosis—whether a child is likely to be cured or at risk of relapse, advancing safer and more personalised therapy for children with ALL.
