From big data analysis to personalized medicine for all: challenges and opportunities

The rich get richer and the poor get poorer

The developing world is home to 84 % of the world’s population, yet accounts for only
12 % of the global spending on health 13]. There is a large disparity between the distribution of people and global health
expenditures across geographical regions (Fig. 1). While public financing of health from domestic sources has increased globally by
100 % from 1995 to 2006, a majority of low and middle-income countries experienced
a reduction of funding during the same time 14]. Several life-threating but easily preventable or treatable diseases are still prevalent
in developing countries (e.g. malaria). Personalized medicine will further increase
these disparities and many low and middle-income countries may miss the train of personalized
medicine 15]–17], unless the international community devotes important efforts towards strengthening
health systems of the most disadvantaged nations.

Fig. 1. Distributions of populations and global health expenditure according to WHO 2012

Systems medicine, the application of systems biology to human diseases 18], requires investments in infrastructures with cutting-edge omics facilities and analytical
tools, advanced digital technologies (high computing performance and storage resources),
and highly-qualified multi-disciplinary teams (clinicians, epidemiologists, biologists,
computer scientists, statisticians and mathematicians) in addition to investments
in security and privacy. On the bright side, technology is evolving quickly and new
developments are producing data more efficiently. A few examples include the development
of high-throughput next generation sequencing and microarrays in genomics and transcriptomics,
mass spectrometry-based flow cytometer in proteomics, real-time medical imaging, and
more recently, lab-on-a-chip technologies 19]. Some predict that a technological plateau may be reached for different reasons (reliability,
cost-effectiveness), but these projections are not validated by historical trends
in science as novel technological developments can always occur 20]. However, there is a consensus that most of the cost in omics studies will come from
data analysis rather than data generation 9].

The economic value of omics networks as personalized tests for future disease onset
or response to specific treatments / interventions remains largely unknown. A recent
study by Philips et al. reflects this issue and highlights a lag between clinical and economical value assessment
of personalized medical tests in current research 21]. Very few studies have incorporated an economic aspect in the evaluation of personalized
tests. These tests range from those available in clinical use or in advanced stage
of development, genetic tests with Food and Drug Administration labels, tests with
demonstrated clinical utility, and tests examining conditions with high mortality
or high health-associated expenditures. Economic evaluations of personalized tests
are needed to guide investments and policy decisions. They are an important pre-requisite
to hasten the transition to personalized medicine. In addition, those few personalized
tests that included economic information were found to be relatively cost-effective,
but only a minority of them were cost-saving, suggesting that better health is not
necessarily associated with lower expenditures 21]. In summary, the costs associated with personalized medicine transition remain unclear,
but personalized medicine may further widen the economic inequality in health systems
between high and low-income countries. This jeopardizes social and political pillars
of stability, and highlights the need for a broader translation-oriented focus across
the globe 22].

Several ideas for stimulating sustainable innovations in developing nations include
micro-grants as proposed by Ozdemir V. et al.23]. Although $1,000 micro-grants are relatively small, they far exceed the annual income
of individuals below the poverty line of $1.25/day as defined by the World Bank. Recipients
of these grants may go a long way in connecting and co-producing knowledge based innovations
to broaden translational efforts. Type 1 micro-grants which are awarded through funding
agencies may support small labs and local scholars to connect personalized medicine
with new models of discovery and translation 23]. Type 2 micro-grants funded by science observatories and/or citizens through crowd-funding
mechanisms may facilitate developments of global health diplomacy to share novel innovations
(i.e. therapeutics, diagnostics) in areas with similar burdens 23]. There is an overall need to support local scholars in promoting knowledge and innovation
within low and middle-income countries 24]. This includes for example, the case of advocating for treatment of persons with
Human Immunodeficiency Virus (HIV) infections where their peers may not recognize
their illness as an endemic that affects society 24]. One successful example of personalized medicine for HIV patients in low and middle-income
countries include personal text messages for improving adherence to antiretroviral
therapy in Kenya and Cameroon 25].

Interdisciplinary programs for global translational science such as the Science Peace
Corps are another promising catalyzing agent for research and developments in low
and middle-income countries (http://www.peacecorps.gov/) 22]. The present Peace Corps program entails volunteer work (6 weeks minimum and up to
2 years) in various regions across the globe to serve as a steady flux of knowledge
for translational research. Junior or senior scientists may cover topics from life
sciences, medicine, surgery, and psychiatry. This program is bi-directional as it
serves both the rich and poor to elucidate the concept of “health” and integrate personalized
medicine within various environments. Lagging developments in low and middle-income
countries are in fact open opportunities with rewards for intellectual individuals
given the simple fact that it is where the majority of the human populations reside.

The “tragedy of the commons” is a conceptual economic problem where the benefits of
common and open resources are jeopardized by individuals’ self-interest to optimize
personal gains 26]. The 2009 Economics Nobel Laureate, Elinor Ostrom, has shown that this issue is not
actually common among humans since individuals work through establishing trust, and
tend to find solutions to common problems themselves 27]. Societies do systematically develop complex sustainable regulations to collectively
benefit each other where assurance is a critical factor for cooperation 28]. There is a need to understand institutional diversity if humans are to act collectively
to benefit each other. Diverse applications of personalized medicine can be envisioned
to cope with the diversity of the world by allowing multi-tier personalized health
care systems at multiple scales and avoiding a single top-tier health care system
that may instead compromise resource management. This also brings about the need for
nested regulation systems for both science and ethics (i.e. ethics-of-ethics) as the
assurance factor for cooperation 29], 30]. Transparency and accountability need to be imposed on all scientists, practitioners,
ethicists, sociologists, and policymakers. No one should be above the fray for accountability
if a sustainable transition towards personalized medicine is to occur.

Omics data: the shifting bottlenecks

In parallel to the gap in health systems between rich and poor countries that personalized
medicine may widen, an increasing lag has been observed in our ability to generate
versus integrate and interpret omics data these last ten years 9]. New technologies and knowledge emerging from the Human Genome Project, fueled by
biotechnology companies, led to the omics revolution in the beginning of the 21
^th
century 31]. Using high-throughput technologies, we are now able to perform an exhaustive number
of measurements over a short period of time giving access to individuals’ DNA (genomics),
transcribed RNA from genes over time (transcriptomics), DNA methylation and protein
profiles of specific tissues and cells (epigenomics and proteomics), metabolites (metabolomics),
among other types of omics data 32]. Even histopathological and radiological images which are traditionally evaluated
and scored by trained experts are now subjected to computational quantifications (i.e.
imaging informatics) 10]–12], 33]. Business models based on returns on investments have driven ongoing technological
developments to accelerate the generation of omics data at increased affordability
in comparison with existing technologies. As a consequence, omics platforms and individual
omics profiles are expected to become fairly affordable and data generation is no
more a bottleneck for most laboratories, at least in the middle and high-income countries
34].

Initially, there were great expectations for omics data to provide clues on the mechanisms
underlying disease initiation and progression as well as new strategies for disease
prediction, prevention and treatment 1]. The idea was to translate omics profiles into subject-specific care based on their
disease networks (Fig. 2). However, our ability to decipher molecular mechanisms that regulate complex relationships
remains limited despite growing access to omics profiles. Biological processes are
very complex, and this coupled with the noisy nature of experimental data (e.g. cellular
heterogeneity) and the limitations of statistical analyses (e.g. false positive associations)
poses many challenges to detecting interactions between “networks” and “networks of
networks”. As an illustration, only a minority of the genetic variants predisposing
to type 2 diabetes have been identified so far, despite large-scale studies involving
up to 150,000 subjects 1], 35]. It becomes more and more obvious that the bottleneck in laboratories has shifted
from data generation to data management and interpretation 36].

Fig. 2. A basic framework of personalized medicine. The integration of omics profiles permit
accurate modeling of complex diseases and opens windows of opportunities for innovative
clinical applications to subsequently benefit the patient

Personalized medicine needs hybrid education

Although solutions for the challenges of big data already exist and are adopted by
companies such as Google, Apple, Amazon, and Facebook to tackle the fairly homogenous
big data (i.e. user data) 37], the heterogeneous nature of omics data presents a new challenge that requires sufficient
understanding of the underlying biological concepts and analysis algorithms to carry
out data integration and interpretation 38]. It is important for the working scientist to understand 1) the underlying problem,
2) the methods of data analysis, and 3) the advantages, and disadvantages of different
computational platforms to carry out explorations and draw inference. Expertise in
biology provides a foundation to contextualize causal effects and guide identification
and interpretation of interaction signals from noise. There is also no uniformly most
powerful method to analyze omics data and the use of various approaches to infer biological
interactions requires modeling expertise 39]. Otherwise, research quality is sacrificed to avoid the logistical challenges of
modeling in exchange for the use of more straightforward approaches 40]. Lastly, computer programing skills are necessary to navigate explorations and analyze
omics data accordingly. There is a need for reliable and maintainable computer codes
through best practices for scientific computing 41]. Approximately 90 % of scientists are self-taught in developing software and one
may lack basic practices such as task automation, code review, unit testing, version
control and issue tracking 42], 43]. Barriers between disciplines still exist between informaticians, mathematicians,
statisticians, biologists, and clinicians due to a too divergent scientific background.
Cutting-edge science is integrative by essence and innovative strategies in universities
to educate and train future researchers at the interface of traditionally partitioned
disciplines is urgently needed for the transition to personalized medicine. Johns
Hopkins University is leading this evolution by changing the teaching plans and establishing
new programs in the school of medicine that integrate the notion of personalized medicine
44]. Although increased knowledge at the population level is a key factor in development
of modern societies, there is an upper limit to the wealth of knowledge and expertise
a single individual can hold 45]. This is the reason why, in addition to multidisciplinary individual training, initiatives
by universities, research funding agencies, and governments are encouraged to connect
researchers from diverse scientific backgrounds on interface topics related to systems
biology and personalized medicine. The recent shift by the Canadian Institutes of
Health Research from distinct discipline (e.g. genetics) to multidisciplinary expert
panels in funding biomedical research is a step in the right direction. The creation
of interdisciplinary research institutes, such as the Steno Diabetes Center in Denmark
that combine clinical, educational and multifaceted research activities to lead translational
research in diabetes care and prevention, is another sensible initiative that could
prefigure what may become personalized medicine institutes in the future.

Management and processing of omics data

Major investments need to be made in bioinformatics, biomathematics, and biostatistics
by the scientific community to accelerate the transition to personalized medicine.
Classic research laboratories do not possess sufficient storage and computational
resources for processing omics data. Laboratory-hosted servers require investments
in informatics support for configuring and using software. Such servers are not only
expensive to setup and maintain, but do not meet the dynamic requirements of different
workflows for processing omics data, leading to either extravagant or sub-optimal
servers. One promising technology to close the gap between generation and handling
of omics data is cloud computing 46], 47]. It is an adaptive storage and computing service that exploits the full potential
of multiple computers together as a virtual resource via the Internet 48]. Examples include the EasyGenomics cloud in Beijing Genomics Institute (BGI), and
“Embassy” clouds as part of ELIXIR project in collaboration with multiple European
countries (UK, Sweden, Switzerland, Czech Republic, Estonia, Norway, the Netherlands,
and Denmark) 49]. The focus is currently placed on developing cloud-based toolkits and workflow platforms
for high-throughput processing and analysis of omics data 50], 51], 49], 52]. More recently, Graphics Processing Units (GPUs) have been proposed for general-purpose
computing in a cloud environment 53]. GPUs provide faster computations as accelerators by one or two orders of magnitudes
compared to general Central Processing Units (CPUs) and have been exploited to cope
with exponentially growing data 54]–56]. MUMmerGPU for example, processes queries in parallel on a graphics card, achieves
more than a 10-fold speedup over a CPU version of the sequence alignment kernel, and
outperforms the CPU version of MUMmer by 3.5-fold in total application time when aligning
reads 57]. However, a significant amount of work will be required for developing parallelization
algorithms considering the heterogeneous framework of omics data that present challenges
in communications and synchronizations 37]. There are tradeoffs between computational cost (floating-point operations), synchronization,
and communications to consider while developing parallelization algorithms 58]. Moreover, developing error-free and secure applications is a challenging and labor-intensive,
yet critically important task. Examples of programming errors and studies outlining
wrongly mapped SNPs in commercial SNP chips have been reported in literature 59]–61]. There is a need to validate the reliability of research platforms before considering
the clinical utility of omics data. For instance, ToolShed, a feature of the Galaxy
project that draws in software developers worldwide to upload and validate software
tools, aims to enhance the reliability of bioinformatics tools. Novel tools and workflows
with demonstrated usefulness and instructions are publically available (http://toolshed.g2.bx.psu.edu/) 62]. Both storage and computing platform such as Bioimbus 63], Bioconductor 64], CytoScape 65], are made available by scientists to exchange algorithms and data. There are many
questions and methodologies that researchers may wish to consider, and this continuously
drives on novel bioinformatics tools. Ultimately, lightweight programing environments
and supporting programs with diverse cloud-based utilities are essential to enable
those without or with limited programing skills to investigate biological networks
66]. Figure 3 illustrates a cloud-based framework that may help to implement personalized medicine.
Much more programing efforts are still needed for the integration and interpretation
of omics data in the transition to personalized medicine. Potential downstream applications
are not always apparent when data are generated, promoting sophisticated flexible
programs that may be regularly updated 67].

Fig. 3. An interdisciplinary cloud-based model to implement personalized medicine. The consecutive
knowledge and service swapping between modeling and software experts in research and
development units is essential for the management, integration, and analysis of omics
data. Thorough software and model development will derive updates upon knowledge bases
for complex diseases, in addition to clinical utilities, commercial applications,
privacy and access control, user-friendly interfaces, and advanced software for fast
computations within the cloud. This translates into personalized medicine via personal clouds that upload wellness indices into personal devices, electronic databases
for health professionals, and innovative medical devices

Integrative methods of omics data

Lastly, the depiction of biological systems through the integration of omics data
requires appropriate mathematical and statistical methodologies to infer and describe
causal links between different subcomponents 40]. The integration of omics data is both a challenge and an opportunity in biostatistics
and biomathematics that is an increasing reality with the decreasing costs of omics
profiles. Aside from the computational complexity of analyzing thousands of measurements,
the extraction of correlations as true and meaningful biological interactions is not
trivial. Biological systems include non-linear interactions and joint effects of multiple
factors that make it difficult to distinguish signals from random errors. Caspase-8
for example, has opposing biological functions as it promotes cell death by triggering
the extrinsic pathway of apoptosis, while having beneficial effects on cell survival
through embryonic development, T-lymphocyte activation, and resistance to necrosis
induced by tumor necrosis factor-? (TNF-?) 68]. Genes may carry out different functions in different cell types / tissues, which
adds to the already substantial inter-individual variability. Biological complexity
presents a challenge in extracting useful information within high-dimensional data
69]. Both computational and experimental methodologies are needed to fully elucidate
biological networks. However, in contrast to experimental assays, computational models
rely on biologically driven variables and have inherent pitfalls of omics data.

Coping with to the curse of dimensionality

High-dimensionality is one of the main challenges that biostatisticians and biomathematicians
face when deciphering omics data. It is the issue of “large p, small n”, where the number of measurements, p, is far greater than the number of independent samples, n69], 33]. The analysis of thousands of measurements often leads to results with poor biological
interpretability and plausibility. The reliability of models decreases with each added
dimension (i.e. increased model complexity) for a fixed sample size (i.e. bias-variance
dilemma, see Fig. 4) 69]. All estimate instability, model overfitting, local convergence, and large standard
errors compromise the prediction advantage provided by multiple measures. A better
understanding of these inherent caveats comes from the key concept behind statistical
inference that is the distribution of repeated identical experiments. This distribution
can be characterized by parameters such as the mean, and variance that quantify the
average value (i.e. effect size), and degree of variability (i.e. biological or experimental
noise). These parameters are estimated from observed data drawn from the true distribution
(i.e. a finite number of independent samples). The reliability of estimates from a
small sample size is low where it is more likely to observe estimates that deviate
from the true distribution parameters. The chance of encountering such deviations
also increases with the number of different measurements in a fixed sample. It is
difficult to reliably estimate many parameters, and correctly infer associations from
multiple hypotheses tested simultaneously. As a result, the analysis of both single
and integrative omics data is prone to high rates of false-positives due to chance
alone. This requires researchers to adjust for multiple testing to control for type
1 error rate using various methods based on the family-wise error rate (e.g. Bonferroni
corrections, Westfall and Young permutation), and the false-positive rate (e.g. Benjamin
and Hochberg) that are under strict assumptions 70]–75]. Another solution to overcome multiple testing issues is to reduce dimensionality
via sparse methods that provide sparse linear combinations from a subset of relevant
variables (i.e. sparse canonical correlation analysis, sparse principal components
analysis, sparse regression) 76], 77]. Both mixOmics and integrOmics are publically available R packages for utilizing sparse methods on omics data 77], 78]. There are several approaches to derive “optimal” tuning parameters to dictate the
number of relevant variables to pursue 79], 80]. However, stochastic processes to select “best” subsets of variables inferred from
a given sample population may not contain the best information on another independent
study, and certainly not at an individual level (i.e. selection-bias) 81], 82]. Reducing dimensionality is problematic as key mechanistic information could be lost.
There is an overall tradeoff between false positive rates and the benefit of identifying
novel associations within biological process that align with that of bias and variance
(Fig. 4) 70].

Fig. 4. The bias-variance tradeoff with increasing model complexity

The multi-level ontology analyses (MONA) is one approach that bypasses the high-dimensionality
as described by Sass et al.83]. This method integrates multiple omics information (DNA sequence, mRNA and protein
expressions, DNA methylation, and other regulation factors) and copes with redundancies
related to multiple testing problems by approximating marginal probabilities using
the expectation propagation algorithm 84]. The MONA approach allows for biological insights to be incorporated into the defined
network as prior knowledge. This can address overfitting or uncertainty issues though
reducing the solutions space to biological meaningful regions 85], 86]. This approach, however, relies on predefined known biological networks (i.e. protein–protein
interactions) or on the accuracy of mechanistic models (i.e. network models). Another
strategy to analyze omics data involves integrating multiple data types into one single
data set that holds maximum information. This reduces the complexity of omics data
to the analysis of a single high-dimensional data set. Co-inertia analysis for example,
has been used to integrate both proteomic and gene expression data to visualize and
identify clusters of networks 87], 88]. It was initially introduced by Culhane et al. to compare gene expression data provided by different platforms, but has been further
generalized to assess similarities between omic data sets 89]. The basic principal is to apply within and between principal component analysis,
correspondence analysis, or multiple correspondence analysis while maximizing the
sum of squares of covariances between variables (i.e. maximizing co-inertia between
hyperspaces). The omicade4 package in R is available for exploring omics data using multiple co-inertia analysis
90]. Other similar, but conceptually different approaches include generalized singular
value decomposition 91], and integrative bioclustering methods 92], 93]. An integrative omics study by Tomescu et al., have utilized all three approaches to characterize networks within Plasmodium faclicparum at different stages of life cycles 94]. Although the basic mathematical assumptions are different, the overlap in their
results was considerable. The relative importance and incremental value of individual
omics data on one another may also be considered when predicting specific outcomes.
For instance, Hamid et al. recently proposed a weighted kernel Fisher discriminant analysis that accounts for
both quality and informativity of each individual omics data to integrate 95]. Significant improvements however, may not occur when data are redundant (i.e. correlated)
or of low quality.

Mixing apples and oranges

Another challenge for integrating omics data lies in deriving meaningful interpretable
correlations. For example, direct correlation analyses between transcriptomics and
proteomics profiles are not valid in eukaryotic organisms. No high correlations between
the two domains were observed as reported by multiple studies, and this was attributed
to post-transcriptional and post-translational regulations 96]–99]. The advantage of integrating transcriptomic and proteomic data may diminish without
accounting for regulation factors as the resulting inflated variability may limit
reliability and reproducibility of findings 100]. Many complex traits are tightly regulated and incorporating regulation factors may
explain a relevant portion of observed variations due to true heterogeneity (i.e.
true differences in effect sizes). Unlike the impact of noise on estimate precision
which could be minimized by increasing the sample size, true heterogeneity may only
be adjusted for during analysis when possible or via standardizations that limit generalizability. True heterogeneity poses a problem
given biological complexity in the pursuit of precise effect size estimations (Fig. 5). Hence, there is a need for network analysis to account for protein-protein and
protein-DNA interactions in the context of integrating transcriptomics and proteomics
data alone. An early study by Hwang et al. utilized network models to identify protein-protein and DNA-protein interactions
with experimental verifications 101].

Fig. 5. Noise and true heterogeneity within complex systems. Source of noise include measurement
error and sampling variability. True heterogeneity however, is the result of true
differences of effect sizes due to 1) the dynamic biological nature which encompasses
feedback loops and temporal associations; and 2) multi-factorial complexity. Increasing
the sample sizes is one solution to bypass noise and attain precise effect sizes,
but true heterogeneity can only be adjusted during analysis when possible and via standardizations and calibrations that limit generalizability of the conclusions

Bayesian networks are graphical models that involve structure and parameter optimization
steps to represent probabilistic dependencies 102]. This modeling strategy that elucidates biological networks has been utilized in
various studies 103], 104]. A seminal example includes the use of dynamic Bayesian networks trained on chromatin
data to identify expressed and non-expressed DNA segments in a myeloid leukemia cell
line 105]. This was done by integrating position of histone modifications, and transcription
factors’ binding sites at multiple intervals. It is however, a computationally demanding
approach that requires advanced computing methods such as parallel computing and acceleration
via GPUs 106]. Network models may serve as meaningful statistical results to be integrated with
the biological domain. It has the potential to generate insight and a number of hypotheses
on biological interactions to be experimentally and/or independently verified through
a follow-up validation set. The ultimate goal is to continuously provide insight into
biological interactions to subsequently build upon.

Separate the wheat from the chaff

It is important to minimize sources of error with omics data as it is challenging
to distinguish between random error and true interaction signals. Hence, it is necessary
to utilize statistical methods to account for sources of error. For example, the quality
of omics data may vary between high-throughput platforms. Hu et al. have proposed quality-adjusted effect size models that were used to integrate multiple
gene-expression microarray data given heterogeneous microarray experimental standards
107]. Omic studies are also prone to errors such as sample swapping and improper data
entry. New methodologies for assessing data quality include Multi-Omics Data Matcher
(MODMatcher) 108]. Moreover, complex diseases are often evaluated using a single phenotype that compromises
statistical analysis by introducing errors such as misclassifications, and/or lack
of accountability for disease severity 109]. Modeling images for example, requires multiple phenotypes to properly capture image
features 110]. Joint modeling of multiple responses to accurately capture complex phenotypes has
been shown to increase power of discovery in genome-wide association studies 111]. There are even novel network methodologies to account for within-disease heterogeneity
112], 113]. Network approaches in modeling complex diseases may provide a map of disease progression
and play a major role in the proactive maintenance of wellness 114]. All reproducibility and validations of complex interaction signals are essential
in the pursuit of personalized medicine. This highlights the growing need for metadata
as the science of hows (i.e. “data about data”) to help harmonize omics studies and
enable proper reproducibility of research results 115]. Examples of a metadata checklist and a metadata publication are available 116], 117]. Metadata may also serve as open innovations for integrative sciences, and may prove
to be valuable for diversifying models of discovery and translation in high, and more
importantly, low and middle-income countries. Altogether, validations on multiple
data sets are required as evidence of stability, and that theoretically sound new
methods outperform existing ones 118]. Both descriptive and mechanistic models for determining relevant biological networks
require handling with care 119]. Software that integrate and interpret omics data are currently developed by competing
companies in the private sector (e.g. Anaxomics, LifeMap), which may rapidly advance
the field in the near future.