The uncertain road towards genomic medicine
1 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
2 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
3 Institute for Neuroscience and Muscle Research, Children’s Hospital at Westmead, Westmead, NSW 2145, Australia
Cheap, high-throughput approaches to generating biological data are transforming biology into a data-driven science and promise to similarly transform medicine. However, the road to genomic medicine is paved with challenges and uncertainty.
BLISTERING CHANGE IN GENOMIC TECHNOLOGY
The last decade has seen a staggering rate of progress in the technology used by researchers to harvest biological data, a trend that is most starkly illustrated by DNA sequencing technologies. The costs of sequencing DNA have dropped over 10 000-fold in the past decade due to the emergence of so-called ‘next-generation’ sequencing (NGS) platforms, which read genetic information in millions or billions of short fragments analyzed in parallel.
In the past three years the influx of capital and technology into the DNA sequencing arena has led to a chaotic and crowded arms race between both established biotechnology companies and new players. Current platforms differ along four primary axes (Table 1) – cost per base, sequencing throughput per hour, read length, and accuracy – meaning that each is applicable to different analytical niches.
Over the past 12 months there has been increasing excitement about the development of ‘benchtop’ sequencers which forego high yields in exchange for low capital costs, small physical footprints, and more rapid turnaround times, making them far more attractive to smaller biomedical laboratories. Competition in the benchtop sequencing market is currently extremely intense, with no clear winner among the existing platforms , but a tremendous amount of excitement about their potential for driving wider adoption of NGS approaches.
What might the future of sequencing look like? One new – and, importantly, still untested – arrival in the market provides an exciting potential glimpse. The technology announced by UK-based startup company Oxford Nanopore (ONT) at the Advances in Genome Biology and Technology (AGBT) meeting in February will (if the claims of the company are accurate) thoroughly disrupt the current sequencing market, with a combination of extremely low entry and running costs, minimal sample preparation requirements, and exceptionally long reads. The most provocative claim made by ONT was the feasibility of a single-use USB thumb drive-sized instrument, the MinION, which would potentially allow users to sequence minimally purified DNA and perform real-time analysis via direct connection to a laptop computer.
The claims made by ONT remain to be confirmed by independent researchers, and overall it remains unclear which – if any – of the current competitors in the sequencing market will reign supreme as genomics begins to enter the routine clinical setting. However, regardless of the outcome, this technological arms race has clearly resulted in a frenzy of innovation and cost-competitiveness that will ultimately benefit biologists, clinicians, and patients.
NOT BY DNA ALONE
Advances in DNA sequencing approaches alone are not enough, of course: humans are more than just a product of their genomes. True predictive medicine will require integrating risk factors from both genetic and environmental sources. However, unlike genetic risk factors that can be quantitated with increasing precision, measuring the impact of environmental stress on the disease risk of individuals is extremely challenging.
Fortunately, there is a machine that accurately integrates both genetic and environmental risk: the human body itself. All risk factors must by necessity change the internal state of human cells and tissues in ways that ultimately lead to the development of disease. Although understanding the genetic and environmental causes of disease is important, these will always provide (at best) indirect measures of an individual’s true risk. To improve our predictions we must be able to measure accurately the dynamic cellular processes that foretell encroaching pathology.
For many of these processes it is possible, with sufficient ingenuity, to adapt NGS technologies to provide a high-throughput read-out; such approaches have enabled large-scale analyses of RNA, investigations of the 3D structure of DNA, and the identification of regions of the genome bound by proteins affecting the expression of genes (e.g., transcription factors). In other cases, such as the global measurement of protein levels (the proteome) or small molecules (the metabolome), alternative approaches are moving towards maturity, albeit often at a slower rate than in the DNA sequencing arena.
A recent paper by Michael Snyder and colleagues  provides a window into the future of integrated health tracking using high-throughput technologies. Over the 14-month course of the study, data from a wide variety of both ‘omic’ technologies and traditional medical tests were collected at multiple time-points from a single individual: Snyder himself. The resulting battery of information, referred to informally as the Snyderome, was then mined for information about disease risk. The study is notable not so much for its clinical impact as for the sheer diversity of approaches taken. This was personal genomics on an audacious scale.
Of course, establishing genomic medicine as routine will require much more than n = 1 studies of comparatively healthy individuals. However, this study illustrates a crucial point: genomic medicine will not be about predicting disease from genetic variation alone, but instead will require measuring health in a real-time, dynamic, and interactive fashion that integrates many different measurement technologies. In other words, true genomic medicine will require moving beyond the genome.
NOTHING WORTH DOING IS EASY
The dramatic pace of advances in genomic technology provides reasons for optimism about the approaching future of genome-guided medicine. However, we must also be realistic about the hurdles that must be overcome for this vision to become reality.
Managing the large, complex data-sets generated by genomic approaches raises major challenges even for large research institutes with well-funded informatics departments. These challenges will be magnified in a healthcare environment where the desire to protect patient privacy creates a burdensome regulatory environment that complicates all decisions about IT infrastructure and data management.
Even introducing small amounts of genetic information (such as genotypes for specific markers associated with adverse drug reactions, for instance) is problematic. How would the system cope with the influx of information generated by a Snyderome for every outpatient? And how will regulatory agencies deal with the flood of technology-driven discoveries, protecting patients from tests that are either technically inaccurate or have weak predictive power, but without unduly suppressing innovation?
Collecting the massive evidence base required to demonstrate the accuracy of new biomarkers or the efficacy of experimental personalized therapies will become increasingly difficult under the standard clinical trial model. Instead, alternative models in which patients voluntarily contribute longitudinal health data to ongoing projects  and play a more active role in the research process  will need to be tested and refined.
Converting raw genomic data into clinically actionable information will also require developing interfaces for intuitively displaying health-relevant findings to both clinicians and patients. In particular, empowering patients to make changes to prevent the development of diseases such as type 2 diabetes may be best achieved by providing real-time summaries of their health status in a format that is readily interpretable to non-experts.
Any realistic discussion of genomic medicine, especially in the context of the US health system, must be tempered by issues of cost-effectiveness and reimbursement. The opportunities for technological disruption offered by genomics will make little headway if they are opposed wholesale by healthcare providers and payers. Overcoming such reluctance will require solid data demonstrating that genomic approaches can improve health outcomes and reduce overall costs.
Finally, the future of medicine will be dictated by neither technology nor economics alone: patients, thankfully, have voices as well. In the heady world of genomics it is easy to lose track of the realities of chronic disease for the majority of our fellow human beings. If we create shiny new technologies that remain economically out-of-reach for the majority of the population, or that simply make healthcare even more complex and confusing for patients, we will have failed.
THE ROAD AHEAD
Researchers working in translational genomics are mindful of these obstacles, but remain optimistic about the future. Although the challenges above are very real, most of us believe that both the need for change and the power of information-driven approaches are strong enough that all such hurdles will ultimately be overcome.
The short-term path from here is clear: genomic approaches are already transforming medicine in the areas of cancer diagnosis and treatment, the genetic diagnosis of rare diseases, and the prevention of adverse drug reactions. Genomic approaches will soon become routine and pervasive in these areas. As the cost of DNA sequencing drops, the case for universal whole-genome sequencing of cancer samples, patients with rare diseases, and participants in large-scale clinical trials of novel therapeutic approaches will be unambiguous.
Beyond that the future is less certain: certainly a global upheaval in medicine is coming, but the world that will emerge on the other side has not yet been decided, and all of us still have a chance to help determine what it will look like. Academic researchers have a particularly important role to play in shaping this world, although we will need to change our mind-set to do so: our current incentive structure relies far too heavily on the generation of high-impact papers, and far too little on actually improving healthcare.
Generating data on a genomic scale is exciting, and important, but of course we must always remember that sequence data alone cannot cure disease. It is only by engaging more actively with clinicians, and particularly with patients and the broader public, that we will be able to convert these data into systems that genuinely improve lives.
For table 1, references, and more informations please visit the source link
Daniel G. MacArthur, & Monkol Lek (2012). The uncertain road towards genomic medicine Trends in Genetics, Volume 28 (Issue 7), 303-305 DOI: 10.1016/j.tig.2012.05.001
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