Stratified Medicine Paediatrics 2 (StratMedPaediatrics2)

Background: Relapsed childhood cancers remain a significant clinical problem, with ~250 deaths per year and few new concepts for treatment.

StratifiedMedicinePaediatrics was the UK’s first national molecular profiling platform, and it successfully provided an infrastructure to identify and report on molecular abnormalities within a clinically meaningful timeframe.

Diagnostics developed by StratMedPaeds were implemented to the NHS Test directory for Standard of Care (SoC) clinical use, representing a major success for children.

Despite these advances, only 20% of children who require experimental therapies worldwide have treatment identified through molecular testing, mandating a renewed effort to diagnose children with greater speed, precision and power.

Aims: StratifiedMedicinePaediatrics2, while continuing to support NHS in the timely implementation of genomic testing, will use our established tissue-referral, analysis and reporting pathways to advance this capability by: 1) identifying with greater precision the critical molecular drivers and potential drug targets enriched in cancer relapse (tissues, blood and CSF), 2) identifying interactions between the tumour, immune and tissue microenvironments that enhance the response to emerging novel therapies, and 3) delivering blood-based cancer tests that will precisely diagnose cancer, be used to guide treatment and rapidly identify emerging drug resistance targets.

Methods: Together with the CRUK Cancer Mapping Grand Challenge, Genomics England, and the GMSA (North London Genomics Hub) we will interrogate the largest yet assembled cohort of childhood cancer biopsies, blood and CSF at whole genome-scale and with unprecedented resolution, integrating nanopore WGS, methylomic, RNASeq, spatial and immunoproteomic data to produce a comprehensive map of the landscape of relapsed tumours and circulating tumour DNA (ctDNA) in pairwise comparison to corresponding maps of newly diagnosed tumours.

An advanced digital computational environment with machine learning capability will be used to interrogate the clinical, molecular and histopathological data emerging from the study.

Use of results: We will use these highly-annotated clinical and molecular data to highlight novel and potentially druggable dependencies that drive childhood cancer relapse, with a focus on therapeutic mechanism that is directly relevant to the use of emerging molecularly targeted drugs and immunotherapies.

In blood, we will identify specific patterns of cancer relapse, highlight mechanisms of resistance to treatment and provide a means by which all children seeking to receive experimental therapy on future clinical trials can be rapidly diagnosed and serially monitored.

The data generated will support the development of next-generation, rapidly adaptive, molecularly-monitored clinical trials that we hope will deliver improved clinical benefit to these patients.