UKCTOCS Longitudinal Women's Cohort Pilot
Research type
Research Study
Full title
Feasibility pilot for transforming UKCTOCS Longitudinal Women’s Cohort (UKLWC) into a national cohort for study of chronic disease.
IRAS ID
243957
Contact name
Usha Menon
Contact email
Sponsor organisation
University College London
Clinicaltrials.gov Identifier
Z6364106/2018/04/102, UCL Data Protection Registration Number
Duration of Study in the UK
1 years, 0 months, 0 days
Research summary
Summary of Research
Chronic disease is used to describe a condition lasting 3 months or more which cannot be prevented by vaccines or cured by medication, nor do they just disappear. With the ageing population across the high income countries, chronic diseases present a major public health issue and burden on healthcare. Early recognition of risk of chronic conditions and timely preventative strategies could potentially reduce disease burden and lower healthcare costs.
Data accumulated over 10 years on genetic predisposition to chronic conditions through the Genome Wide Association Studies have impacted on efforts in risk prediction/stratification for many diseases. Such risk stratification based on common low-risk loci (single-nucleotide-polymorphisms, SNPs) is currently being explored including efforts in screening for breast cancer. Furthermore, it is one of the key goals/items on the NHS Agenda with Chief Medical Officer’s 2016 annual report ‘Generation Genome’ calling for such efforts to translate into patient benefit in the future.
Major efforts have been made to combine genetic and epidemiological data through various consortia to refine such risk-based strategies which has mainly been derived by pooling data from a large number of studies. Available population and patient resources with information on genotype /phenotype lack the necessary scale, depth and breadth to allow the cost-effective systematic application of a genomic-led, disease-agnostic strategy for drug-discovery. It is therefore imperative to profile additional large cohorts of individuals at risk to obtain refined estimates on SNP-based risk.
Genome profiling using serum samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), one of the world’s largest randomised controlled trials, would complement this effort. With available comprehensive electronic health record (eHR) linkage which allows exploiting clinical phenotyping and diagnoses made during routine healthcare in the NHS and a unique biorepository suitable for multi-omics analysis, UKCTOCS provides the opportunity to unravel the genome-phenome connections.Summary of Results
Chronic disease is used to describe a condition lasting 3 months or more that cannot be prevented by vaccines or cured by medication, nor do they just disappear. With the ageing population across the high-income countries, chronic diseases present a major public health issue and burden on healthcare. Early recognition of risk of chronic conditions and timely preventative strategies could potentially reduce disease burden and lower healthcare costs. Data accumulated over 10 years on genetic predisposition to chronic conditions have impacted on efforts in risk prediction/stratification for many diseases. Major efforts have been made to combine genetic and epidemiological data through various consortia to refine such risk-based strategies that has mainly been derived by pooling data from a large number of studies. Genome profiling using serum samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), one of the world’s largest randomised controlled trials, would complement this effort. With available comprehensive electronic health record (eHR) linkage which allows exploiting clinical phenotyping and diagnoses made during routine healthcare in the NHS and a unique biorepository suitable for multi-omics analysis, UKCTOCS provides the opportunity to unravel the genome-phenome connections.
As a first step towards the latter, our aim was to perform a feasibility study to optimise the protocol for DNA extraction and genotyping and/or sequencing on a small subset of samples. Depending on the results we would consider a bigger study in the future that would enable us to achieve our ultimate goal. The aim of the optimisation of the workflow was to find the most suitable cost-effective DNA protocol that can achieve (1) high-throughput processing, (2) using minimum serum volume and (3) obtaining high DNA yield/quality (suitable for genotyping and potentially other applications such as sequencing). The core UKCTOCS team identified a set of randomly chosen serum samples collected and consented for use in secondary research. A small set of samples with a sub-aliquot of their baseline serum samples (1.5 ml, 3 straws) were tested with two commercially available DNA bead-based protocols (Promega and Adapted Kleargene method with mag beads on KingFisher 96) against a number of parameters to compare the DNA yield such as total serum volume, number of washing steps, elution volume and quantification methods. The results from the first test clearly showed that elution volume and also the quantification method was key to the yield of DNA and the accuracy of the measured concentration, respectively. The Promega protocol could not be further modified to use a lower elution volume with the robotics as opposed to the Kleargene method that could be further modified if required. In addition Nanodrop and Dropsense are both UV based methodologies and more likely to be influenced by impurities resulting in overestimation of the DNA concentration. Qubit (or Picogreen for high-throughput) is expensive but highly specific for measuring the DNA yield, thus a better methodology to use and our preferred method.
Additional optimisation experiments in a subsequent small set of serum samples were carried out to evaluate further the Adapted Kleargene protocol. The results were encouraging and the extracted DNA from this set was subsequently used for genotyping as per protocol using a commercially available chip/technology (Illumina Infinium Global Screening Array vs2). The GSA has high coverage on clinically relevant markers giving high coverage of genes encoding for a number of difference diseases. This array allows cost effective genotyping in biobank sized cohorts whilst still maximising opportunities for translatable research. The genotyping results from this small set showed that even with an average load of as low as ~65ng total DNA the pass rate of all samples was at 99%. Using this platform (Adapted Kleargene) we carried out further experiments using an additional set of 100 samples to validate the previously established workflow of DNA extraction and genotyping. DNA yield was quantified by Picogreen and we obtained encouraging results with an average DNA concentration of ~11ng/ul and an average of total DNA yield ~400ng. The extracted DNA from this independent set was subsequently also used for genotyping with GSA. The genotyping results from this set showed again that even with relatively low DNA load the pass rate of all samples was at 98%. The experimental work gave us promising results which were also confirmed in a third set of 96 samples.
This work established as originally aimed a sound workflow of DNA extraction from serum and subsequent genotyping with a global array with consistent good results. This work now forms the basis for any future collaborative projects aimed to extract and genotype samples from the bioresource.
REC name
East of England - Cambridgeshire and Hertfordshire Research Ethics Committee
REC reference
18/EE/0168
Date of REC Opinion
18 May 2018
REC opinion
Favourable Opinion