Opinion: Personalised medicine and a European data-driven economy

By Mario Romao, digital health policy manager, Intel; Sarnunas Narbutas, president of Lithuanian Cancer Patient Coalition, Richard Torbett, chief economist, EFPIA; Ernst Hafen, Institute for Molecular Systems Biology (IMSB) ETH, Zürich

Nobody likes getting sick. But when we do fall ill, it is vital that our doctors have access to the best information and diagnostic techniques available. Thankfully, emerging technologies, such as analytics tools for ‘Big Data’ can help health care professionals improve diagnoses and reshape the way medicine is practiced.

Using these data to first understand the cause of disease, the medical profession can then develop new drugs and therapies to find cures or treatments as well as other health interventions targeting the individual. The personalized, individual approach requires advanced technologies and processes to collect, manage and analyze the information and, even more importantly, to contextualize it, integrate it, interpret it and provide rapid and precise decision support in a clinical and public health context.

Getting a 'Data Strategy for Personalised Medicine' right in Europe would yield multiple benefits. Not only would it accelerate the development of more effective treatments and potentially help with the management of healthcare resources, it would also act as a foundation for private sector investment and EU jobs in R&D. Global developments in approaches to Big Data in healthcare are of major importance to the future of several industries.

The ability to cost-effectively sequence a whole genome and the value this would bring to a patient’s care will soon bring genomic data into routine practice. This information, which is becoming less expensive all the time, will also be integrated into electronic health records. With this in mind, several countries have already embarked on government-sponsored genome sequencing programmes.

There are, of course, issues surrounding Big Data – not only its collection, storage, dissemination and standards that need to be applied. There are also huge ethical questions surrounding its usage and its ownership.

Clearly there is a need for safeguards to ensure that the data remains anonymous, that it cannot be shared without the patient’s express permission, that it cannot be sold, for example to insurance companies without that permission, that the patient must have ownership of - and complete access to - his or her data, and that all this should be enshrined in ethics-based laws.

One suggestion that is gaining support is the use of data co-operatives in which every patient and data donor controls when, where and how his or her data can be used. This should overcome the fact that patients may be reluctant to hand over sensitive personal information which could be vital for research.

Given the above, and because personalised medicine requires, in most instances, personalised data, it is important to navigate the complex regulatory landscape of data protection and, in many instances, clarify the boundaries of what is and is not possible.

A basic tenet should be ‘liberate the data but do no harm’. This would allow the best and fastest possible outcomes to be achieved. Scientists need to be able to work with and test on large data sets, which are currently not available. Of course, then there are questions about how best to link these results to clinically meaningful and actionable information, and how to create tailored responses to them. These factors represent further challenges.

The aforementioned issues need to be addressed quickly, as Big Data clearly cannot be un-invented and it will certainly not go away. However, by ensuring the right regulatory frameworks that cover these data protection issues, researchers would potentially be able to access millions of genetic markers. In turn this would accelerate science towards better understating of diseases in specific patients. This data will be commonly leveraged directly in patient care to guide choice of therapy, prevention and screening programmes, increasing overall healthcare efficiency and patient outcomes.

The European Alliance for Personalised Medicine (EAPM) believes that, by 2020, the EU should endeavour to achieve widespread benefits for citizens and patients from personalised healthcare by defining, in 2015, and subsequently executing a Data Strategy for Personalised Medicine

One suggestion is to launch an EU data initiative that considers a 360-degree view of policy enablers. This would ensure a comprehensive analysis of all interrelating decisive factors for the development and adoption of Big Data in respect of personalised medicine in Europe. Through Member States and multi-stakeholder collaboration this would drive policy, regulatory, research and innovation activities to establish a Europe–wide data eco-system in this arena.

In respect of Big Data the future is already here. The EU needs a sense of urgency in order to bring the advantages of these data to the bedside for the benefit of its 500 million citizens. The challenge is to integrate the new data into advanced clinical support systems connected with electronic health records with the clinical training required and bring this science into daily clinical operations.

Research into new statistical and other analytical techniques is urgently needed. And even with existing techniques training is a big issue. In the last decade, next-generation sequencing alongside information and communications technology (ICT) has changed the way biologists and geneticists ‘do’ science. The data produced with these machines requires sophisticated computational skills to be analysed. Importantly, while bioinformatics is now a well-developed discipline, a new set of skills is required now to navigate the plethora of data generated.

We are talking here about ‘Big Data scientists’, who utilise data mining, statistics, and domain knowledge (in this case biomedical knowledge), to interpret data and derive solutions from large datasets. The vast amounts of data (volume), the pace at which it is created and at which updates and new findings are released (velocity), and the diversity of such data (variety) cannot be dealt with without ICT infrastructures and solutions. These include high-performance and ‘Cloud’ computing, plus machine learning and analytics.

In addition, more collaboration between the ICT and life science industries is required to create solutions that biologists and scientists can use, rather than expecting them to adapt to the solution. In this Big Data era, personalised medicine will deliver its benefits through greater involvement of patients in treatment decision-making and health management. Equally, healthcare professionals cannot be expected to adapt to new ways of approaching patients and coping with new technologies unless they are suitably trained.

The European Alliance for Personalised Medicine (EAPM) has developed an ongoing campaign called STEPs (Specialised Treatment for Europe’s Patients) and is working hard to promote dialogue and find solutions, while calling for action at EU level. Big Data, among many other topics, will be discussed at its annual conference on 9-10 September at the Solvay Library in Brussels. This will bring together all stakeholders, including new MEPs, and is timed to precede the appointment of the incoming European Commission. There is a job to be done, so let’s start to do it.

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