Early Detection with Urine and Cognitive Assessment Multiplex (The EDUCAM study)

Background: Cancer screening has demonstrated efficacy in reducing cancer burden.

However, methods used are unsuitable for brain cancer due to its low incidence, the risks (e.g radiation)/costs associated with repeated imaging and the indistinct signal obtained with cell free DNA (cfDNA) mutational analysis.

Recent molecular analysis of IDH wild-type low grade gliomas as an ‘early detected’ glioblastoma with an improved prognosis (versus glioblastoma) has renewed interest in the efficacy of brain cancer screening.

Up to 20% of patients with systemic cancers will develop brain metastasis, for which the incidence is increasing, and no screening test is currently available. Recent studies have identified cognitive deficits in newly diagnosed brain and breast cancer patients. Moreover, we have demonstrated efficacy in urine cfDNA identification of brain cancer.

We propose that by combining these techniques we can improve detection sensitivity and specificity.

Our truly non-invasive approach will make multi-test paradigms more feasible and genome wide DNA assessment, incorporating multiple disease-specific signatures (e.g. urine cfDNA epigenome analysis) will further boost statistical power and therefore improve sensitivity and specificity.

Aims: 1) Multi-modal assessment (cognition assessment and urine cfDNA analysis) for early detection of brain cancer in the ACED cohort. 2) As above but for systemic cancer 3) As above for neurological disease e.g stroke Methods: We will recruit all participants (~1000) to the ACED clinic Cambridge during their baseline visit to the EDUCAM study.

Participants will undergo a 5 minute cognitive test using a tablet computer based application. Subsequently a sample of urine (~100mls) will be collected.

The cognitive data metrics will be used to stratify participants into groups from higher to lower risk of cancer on the basis of performance.

Urine samples will be batched, DNA extracted and fragmentation, mC/hmC pulldown signatures and mutant allele fraction obtained.

Epigenomic/mutational signatures and cognitive data will be used for machine learning modelling to identify whether and how these non-invasive sources can be used additively to predict cancer diagnosis.

Confirmation through the collection of ‘ground truth’ follow up data for the duration of the ACED clinic recruitment will be utilised.

Use: We will have identified whether cognitive tests and urine cfDNA are useful for early cancer/neurological disease detection. We plan to follow up ACED participants and obtain subsequent samples/information on future diagnoses.

This data will inform a larger clinical trial comparing these techniques with other early detection paradigms guiding subsequent broader implementation across the NHS.