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From Big Data Analysis to Personalized Medicine for All: Challenges and Opportunities Akram Alyass1, Michelle Turcotte1 and David Meyre1,2*

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From Big Data Analysis to Personalized Medicine for All: Challenges and Opportunities Akram Alyass1, Michelle Turcotte1 and David Meyre1,2* Alyass et al. BMC Medical Genomics (2015) 8:33 DOI 10.1186/s12920-015-0108-y REVIEW Open Access From big data analysis to personalized medicine for all: challenges and opportunities Akram Alyass1, Michelle Turcotte1 and David Meyre1,2* Abstract Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine. Keywords: Big data, Omics, Personalized medicine, High-throughput technologies, Cloud computing, Integrative methods, High-dimensionality Introduction of biology and medicine [5, 6]. The use of deterministic Access to large omics (genomics, transcriptomics, proteo- networks for normal and abnormal phenotypes are mics, epigenomic, metagenomics, metabolomics, nutrio- thought to allow for the proactive maintenance of wellness mics, etc.) data has revolutionized biology and has led to specific to the individual, that is predictive, preventive, the emergence of systems biology for a better understand- personalized, and participatory medicine (P4, or more ing of biological mechanisms. Systems biology aims to generally speaking, personalized medicine) [1]. model complex biological interactions by integrating in- Many predict the emergence of personalized medicine formation from interdisciplinary fields in a holistic man- in the near future, but it is not likely to come about as ner (holism instead of the more traditional reductionism). quickly as the scientific community and the media may In contrast to treating a mixture of factors as single think [7]. In parallel to an escalating two-tiered health entities leading to an endpoint, systems biology relies on system at the global level, a similar two-tiered phenomenon experimental and computational approaches in order to is observed with regard to our ability to generate and provide mechanistic insights to an endpoint [1]. Trad- analyze omics data that may delay even further the transi- itional observational epidemiology or biology alone are tion to personalized medicine. The generation and manage- not sufficient to fully elucidate multifaceted heterogeneous ment (storage, and computational resources) of omics data disorders and this directly limits all prevention and treat- remain expensive despite technological progress. This im- ment pursuits for such diseases [2, 3]. It is widely recog- plies that personalized medicine could be restricted to the nized that multiple dimensions must be considered wealthier countries [8]. This is mirrored by a growing gap simultaneously to gain understanding of biological sys- in our abilities to generate and interpret omics data. The tems [4]. Systems approaches are driving the leading-edge bottleneck in omics approaches is becoming less and less about data generation and more and more about data man- * Correspondence: [email protected] agement, integration, analysis, and interpretation [9]. There 1Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada is an urgent need to bridge the gap between advances in 2Department of Pathology and Molecular Medicine, McMaster University, high-throughput technologies and our ability to manage, 1280 Main Street West, Hamilton, ON, Canada © 2015 Alyass et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Alyass et al. BMC Medical Genomics (2015) 8:33 Page 2 of 12 integrate, analyze, and interpret omics data [10–12]. This genomics and transcriptomics, mass spectrometry-based review addresses the growing gaps in socioeconomic and flow cytometer in proteomics, real-time medical imaging, scientific progress toward personalized medicine. and more recently, lab-on-a-chip technologies [19]. Some predict that a technological plateau may be reached for Review different reasons (reliability, cost-effectiveness), but these The rich get richer and the poor get poorer projections are not validated by historical trends in science The developing world is home to 84 % of the world’s as novel technological developments can always occur population, yet accounts for only 12 % of the global [20]. However, there is a consensus that most of the cost spending on health [13]. There is a large disparity be- in omics studies will come from data analysis rather than tween the distribution of people and global health ex- data generation [9]. penditures across geographical regions (Fig. 1). While The economic value of omics networks as personalized public financing of health from domestic sources has in- tests for future disease onset or response to specific creased globally by 100 % from 1995 to 2006, a majority treatments / interventions remains largely unknown. A of low and middle-income countries experienced a re- recent study by Philips et al. reflects this issue and high- duction of funding during the same time [14]. Several lights a lag between clinical and economical value life-threating but easily preventable or treatable diseases assessment of personalized medical tests in current re- are still prevalent in developing countries (e.g. malaria). search [21]. Very few studies have incorporated an eco- Personalized medicine will further increase these dispar- nomic aspect in the evaluation of personalized tests. ities and many low and middle-income countries may These tests range from those available in clinical use or miss the train of personalized medicine [15–17], unless in advanced stage of development, genetic tests with the international community devotes important efforts Food and Drug Administration labels, tests with demon- towards strengthening health systems of the most disad- strated clinical utility, and tests examining conditions vantaged nations. with high mortality or high health-associated expendi- Systems medicine, the application of systems biology tures. Economic evaluations of personalized tests are to human diseases [18], requires investments in infra- needed to guide investments and policy decisions. They structures with cutting-edge omics facilities and analyt- are an important pre-requisite to hasten the transition ical tools, advanced digital technologies (high computing to personalized medicine. In addition, those few person- performance and storage resources), and highly-qualified alized tests that included economic information were multi-disciplinary teams (clinicians, epidemiologists, biol- found to be relatively cost-effective, but only a minority ogists, computer scientists, statisticians and mathemati- of them were cost-saving, suggesting that better health is cians) in addition to investments in security and privacy. not necessarily associated with lower expenditures [21]. On the bright side, technology is evolving quickly and In summary, the costs associated with personalized new developments are producing data more efficiently. A medicine transition remain unclear, but personalized few examples include the development of high-through- medicine may further widen the economic inequality in put next generation sequencing and microarrays in health systems between high and low-income countries. Fig. 1 Distributions of populations and global health expenditure according to WHO 2012 Alyass et al. BMC Medical Genomics (2015) 8:33 Page 3 of 12 This jeopardizes social and political pillars of stability, common problems themselves [27]. Societies do systemat- and highlights the need for a broader translation-oriented ically develop complex sustainable regulations to collect- focus across the globe [22]. ively benefit each other where assurance is a critical factor Several ideas for stimulating sustainable innovations in for cooperation [28]. There is a need to understand ins- developing nations include micro-grants as proposed by titutional diversity if humans are to act collectively to Ozdemir V. et al. [23]. Although $1,000 micro-grants benefit each other. Diverse applications of personalized are relatively small, they far exceed the annual income of medicine can be envisioned to cope with the diversity of individuals below the poverty line of $1.25/day as de- the world by allowing multi-tier personalized health care fined by the World Bank. Recipients of these grants may systems at multiple scales and avoiding a single top-tier go a long way in connecting
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