IDBS Data Management Blog

The recent news of an NHS driven DNA database for 100,000 subjects in the UK is a major statement of intent from the government. Following up on announcements regarding access to patient data via the Clinical Practice Research Database (CPRD) and calls for the NHS to lead the world in genetic medicine last year, this is a further step towards improving patient care with better knowledge of our genomic characteristics.

With 60 million people under one health provider, the NHS has the capacity to curate world-leading data and insight into the best patient treatments and associated genetic characteristics. If successful, this will also be a highly valuable resource to attract drug companies to perform precision clinical trials and studies in the UK, and provide invaluable patient data to newcomers in the market such as nutriceuticals, health app designers and food & drink producers. This resource could drive an ecosystem of small & medium enterprise (SME) and start-ups to build health-based products built on a detailed understanding of patient population and individual subject characteristics.

Key to the success of the project will be:

• Quality of the clinical and genomic data that is collected and stored
• Reproducibility and accuracy of genomic sequencing
• Data analysis and presentation to support research and clinical decision-making, which is critical to ensuring the information is usable by different communities

Genomic sequencing techniques are still rapidly evolving, resulting in challenges in preparing samples for analysis and in consistent data analysis. Having a single/small number of sites will help this tremendously, but end to end data management (from sample to report) is key to reproducibility, audit trails and data provenance.

The ability to stratify patient populations enables pharma companies to run smaller, more efficacious trials because the predicted outcome is so much more impactful. Profiling large patient populations for clinical characteristics and molecular markers will be a huge benefit to pharma and UK-based trials, allowing populations to be stratified to a strong responding set of target patients, which will often be smaller than a standard trial.

£100m shows a strong commitment to create a knowledge resource to be reckoned with, although realistically this budget will quickly be eaten up over the course of five years.

IDBS is supporting this field in a number of major UK hospitals, many of whom recently spoke at the IDBS Translational Medicine Symposium in London. Quality of clinical data, standardization of genetic analysis and challenges in combining clinical and genomic data were all key topics.

2012 may not be the year that genetics is routinely used in making clinical decisions in every hospital but it promises to be a pivotal year for personalized medicine. The rapidly dropping cost of genome sequencing, now around $4000 per person and the growing availability of electronic patient data is providing a huge opportunity to improve patient outcomes and reduce the incidence of adverse drug events. In Minnesota, US, the Mayo Clinic has begun a study to systematically sequence every patient, with a parallel study testing genetic variants associated with drug metabolism. Both studies will be used to drive the routine use of genetic data in clinical decision-making and will enable a better understanding of the impact on cost and effectiveness of care.

Major initiatives in the UK, such as the Cancer Research UK Stratified Medicine Initiative, are routinely collecting patient and genetic data and the Technology Strategy Board is funding development of low cost genetic screens to be used by the NHS, as well as funding work like IDBS’ Acropolis project that will enable cloud-based collaborative research projects to be run on genetic and patient data.

We await the outcome of these projects with much excitement and, based on the growing number of requests to support clinical use of genetic data in addition to our translational research capabilities, we expect to be very busy in the next 12 months.

I read with interest yesterday’s report in the Lancet highlighting the spiralling global costs of cancer treatment, with 12 million people diagnosed with cancer worldwide costing £185bn ($295bn) per year. The report goes on to say that most developed countries are spending between 4% and 7% of their healthcare budgets just on treating cancer.

The complexity of the market is partially to blame for this growing expense – there were 35 approved cancer drugs in 1970, now there are nearly 100, not to mention the additional imaging and biomarker options that are increasingly available to better diagnose disease and monitor treatment efficacy.

We all know that successful, affordable treatment of cancer requires analysis of patient data to compare treatments, side effects and outcomes. What this takes is access to high quality data about a patient’s medical history, treatments and lab results. This is a significant data integration challenge requiring data extraction from multiple systems and clinically orientated integration of that data. Importantly appropriate pseudonymisation of patient records is also vital in using such information for research purposes.

This is still a major problem for Healthcare organisations, governments, payers and providers. Here’s a typical question we need to be able to answer today:

“How many patients with Triple Negative Breast Cancer were treated with Cytoxan and Taxol and what are their outcomes, ethnicity, exercise, drinking, smoking, and dietary profiles? And then let’s look at their genomic profiles.” 

Access to this type of data and analytics, such as Kaplan Meier survival curves, is essential to improved outcomes for cancer patients.

Too often though this is just too hard, because it is a task of epic proportions, pulling data together from files and basic spreadsheets.

We can all celebrate the success of HER2 tests for Herceptin and KRAS for Vectibix, in fact the report mentions a Japanese KRAS study that showed a £32m ($51m) per year saving for treatment of colorectal cancer using this test. However, Personalised Medicine is still a long way off, and while IDBS is helping advance these capabilities, we are also able to support more immediate needs to improve treatment selection and outcomes.

Here at IDBS we are actively supporting the integration and analysis of patient data with many of the leaders in this field of study, including King’s Health Partners in the UK and Windber Medical Center in the USA. Our systems enable clinicians and clinical researchers to quickly select groups (or cohorts) of patients and see a timeline of their diagnosis, treatment, risks and outcomes. This is enabling technology for clinicians worldwide and more importantly for today’s and tomorrow’s cancer patients.

NGS (next gen sequencing) can help in the delivery of personalised medicine and the understanding of disease – no doubt in my mind. But like all the things that have gone before – HTS (high throughput screening), Combinatorial Chemistry, molecular modelling – it is not a silver bullet – but a tool that can be used to aid the cause. Alone it cannot solve the problem.

We have seen it over and over again – a new tech / method / thingamabob that will solve all problems. In the Wild West they called it snake oil and we know what that means!

To properly leverage technologies such as NGS – they need to be “tamed”. By that I mean used effectively and in a manner that will provide consistent, reproducible context-rich results. To do this the results have to be generated in a fashion similar to all other “analytical methods”, with good control of all the variables that can affect results – thus enabling comparison and use of the results in decision-making.

The goal for NGS is obviously to get into the clinical diagnostics area – and this will be a huge step forward for the provision of personalised medicines. However, to do that, the technologies must be used in conjunction with informatics that can provide this extra layer of control and context of the results obtained … I am obviously talking about ELNs and good scientific data management.

There are also many similarities between the front end of the lab process in all these “analytical areas” and the requirements for lab asset handling, traceability of information and “exception reporting” which suggests that NGS labs will benefit from the same tools and informatics systems: i.e. ELNs, LIMS and data management.

Of course I may be a little biased, but I think our data solutions are light years ahead of the rest.

Perhaps it’s time to have a look at why they are causing such a buzz in the market?

I’ve really enjoyed seeing the Cancer Research UK Stratified Medicine Programme getting so much coverage around the globe. This initiative will blaze the trail for the wider adoption of genetic testing to support diagnosis and treatment of various cancers including breast, colorectal, lung, prostate, ovarian and skin cancer.

The 2 year program will see 9,000 samples and associated clinical data systematically captured and genetically tested for known cancer variants with a view to building a comprehensive warehouse of cancer data. This will then form a research resource to better understand the genetic basis for diagnosis and disease treatment, and in the future support clinical decision-making.

The Cancer Research UK project is part of a larger Stratified Medicines Innovation Platform funded by the Technology Strategy Board and other bodies to advance personalised medicine in the UK.

We at IDBS are proud to be leading one of these projects, designed to support industry and academic collaboration in stratified medicines based on high quality, longitudinal patient information and associated genetics. The Acropolis project will focus on the secure infrastructure to analyse and share this data, leading to improved disease understanding and patient outcomes.

We wish the project team well because this is by no means a trivial undertaking and highlights many of the difficulties faced by academic medical centres around the world in bringing together critical data to support translational medicine research.

The project team face many challenges in bringing these disparate data sets together, not least of which is capturing patient data from the clinical systems it is stored in. This information is frequently distributed across electronic medical records, laboratory systems, PACs and cancer information systems requiring sophisticated informatics to extract the source data and format it consistently. Information governance and protected health information compliance are also key areas to address as is integration with biobanks and incorporation of genetic tests results from standardised procedures.

We will be cheering our sister project on and continuing to provide support to the Cancer Research UK team as we begin the journey towards personalised medicine in the UK.