Keywords Translational bioinformatics - genomic medicine - genome sequencing - precision medicine
Introduction
There has been a remarkable shift over the last decade in the costs of generating
molecular measurements – most notably DNA sequencing but also transcriptomes, proteomes,
and metabolomes [[1 ], [2 ]]. Most impacted by this change is the area of genomic medicine where it is now possible
to move from generating reference or population level data to producing data from
individuals. Concurrently, there have also been advances in the development of new
algorithms and tools to integrate and interpret this data.
Bioinformatics is classically defined as the storage, analysis, and interpretation
of biological data [3D]. In the 2016 edition of the IMIA Yearbook of Medical Informatics,
Russ Altman summarized the evolution of the term Translational BioInformatics (TBI)
from 2004, when the term ‘biomedical informatics’ was used. This term has come to
describe the creation of informatics methods that may include the biological world
(including DNA, RNA, proteins, small molecules, cells), and the clinical world (including
patients, diagnoses, signs, symptoms) [[4 ]], while TBI is defined as the translation of basic capabilities and discoveries
provided by informatics methods into clinically useful tools.
Previous reviews have classified translational bioinformatics into four themes: (i)
linkage of Electronic Health Records (EHRs) to biobanks for genomic discovery, (ii)
adoption of genomics and pharmacogenomics into routine clinical care, (iii) use of
genomics in drug discovery and drug repositioning, and (iv) personal genomic testing
[[5 ], [6 ]]. Over the last three years the translational bioinformatics global landscape has
substantially changed and it is both interesting and exciting to see these themes
have merged with genomic medicine very much at the fore of national policies and investment.
The National Institute of Health (NIH) makes the difference between genomic and precision
medicine by defining the broad term genomic medicine as “an emerging medical discipline
that involves using genomic information about individuals as part of their clinical
care (e.g., for diagnostic or therapeutic decision-making) and the health outcomes
and policy implications of that clinical use” and precision medicine as the approach
for disease treatment and prevention that takes into account individual variability
in genes, environment, and lifestyle.
This review outlines some of the most influential research publications and resources
that have been developed between 2014 and 2016 and the national initiatives that have
started to capitalize on these developments to routinely embed genomic medicine into
healthcare. The field is broad and thus this review can neither cover all the aspects
of TBI nor all the significant publications and outputs.
The translation of genomic data into clinically actionable knowledge is one of the
key challenges of TBI. Even rare monogenic disorders can be influenced by a large
number of different genes and biological pathways, as well as by environmental factors
that are difficult to assess. Patients will also vary in how they present symptoms
and in disease severity. Common medical problems such as heart disease, diabetes,
and obesity do not have a single genetic cause—they are likely associated with the
effects of multiple genes in combination with lifestyle and environmental factors.
In addition, complex or multifactorial disorders do not have a clear-cut pattern of
inheritance. This makes it difficult to determine a person’s risk of inheriting or
passing on these disorders. Complex disorders are also difficult to study and treat
because the specific genetic and environmental factors leading to most of these disorders
have not yet been identified.
The genetic developments of the monogenic disorder cystic fibrosis exemplify some
of the challenges of applying genomic information to the clinic. Although the genetic
basis of cystic fibrosis was well established to be mutations in the cystic fibrosis
transmembrane conductance regulator (CFTR) gene in 1989 [[7 ]], it has taken many years to understand and categorize the 2000 variants within
the CFTR gene into roughly six different functional classes and then stratify patients
for appropriate treatments [[8 ]]. The mutations observed have a wide variety of effects on the CFTR protein: some
disrupt the function of the chloride channel at the apical surface of epithelial cells;
some affect the intracellular processing of CFTR reducing expression levels; and others
alter the transcription of CFTR. The successful development of the drug ivacaftor
in 2012, a potentiator of CFTR function, was the first targeted therapy for patients
with cystic fibrosis caused by specific genotypes [[6 ]]. In 2015, the approval of the combination therapy of a potentiator and a corrector
(ivacaftor and lumacaftor) offered tailored treatments to people with cystic fibrosis
caused by the most common CFTR mutation, Phe508del [[9 ]]. This treatment is a powerful example of precision medicine.
One of the key challenges in the application of genomic medicine in healthcare is
the necessity to protect patient privacy and security of data whilst making genomics
diagnoses and discoveries. What makes this difficult are the wide differences in the
way research and clinical practice are performed. Scientific research has in the last
century been a global effort with English as the practicing language, similar systems
for sharing results, open access to data via publications or other means, as well
as funding via grants or awards. Healthcare practice, on the other hand, is primarily
a national endeavor practiced under complex national legislation in the native language(s)
of the country and is often contractually-funded. Hospital information systems also
vary within and from country to country, with most of patient-specific data being
private to patient and associated clinicians. Thus, when it comes to the application
of bioinformatics methods into clinical studies a whole new paradigm needs to be created
in order to be successful.
Advances in Genomic Medicine Discovery
Advances in Genomic Medicine Discovery
Much has been done to advance and develop the capabilities provided by informatics
methods for genomic medicine discovery. These range from the development of various
sequencing technologies to new models for data storage and data sharing. Below, we
review some of these advances and their contribution to the progress made by various
national genome sequencing initiatives.
Sequencing Technology
Over the past decade, technological improvements in high-throughput sequencing technologies
have resulted in a growing worldwide capacity to easily generate nucleotide sequences
[[2 ]]. The most common current technology, implemented in instruments made by Illumina,
uses the sequencing-by-synthesis method and is able to sequence as many as 18,000
whole human genomes a year. Large-scale initiatives such as the UK 100,000 Genome
Project or the Human Longevity Inc. initiative to build a facility scaling up to 100,000
genomes a year [[10 ]] primarily use this robust technology.
Two other approaches generate longer read lengths than the sequencing-by-synthesis
method, making them well-suited for unsolved problems in genome, transcriptome, and
epigenetics research. The first is nanopore sequencing, in which a single DNA molecule
is guided through a barrier with pores that allow only a single nucleotide to pass
through at a time, the electrical charge of which is then measured and recorded. The
MinION handheld device released by Oxford Nanopore Technologies in mid-2014 is the
first and only nanopore sequencer on the market and has been since deployed widely.
Its applications range from the metagenomic detection of Ebola viral pathogens from
clinical samples in the field during the 2014–2015 epidemic, with an unprecedented
less-than six hours sample-to-answer turnaround time [[11 ]]; to the rapid detection of antimicrobial resistance in outbreak situations where
strain identification can be obtained within 30 minutes of sequencing; and, using
about 500 reads, initial drugresistance profiles within two hours and complete resistance
profiles within 10 hours [[12 ]]. A second long read length method is the Single-Molecule Real-Time sequencing (SMRT)
technology used in Pacific Biosciences machines [[13 ]]. These can generate reads greater than 10,000 bases with over 99.999% accuracy,
enabling the production of finished bacterial genomes, the resolution of structural
variations, and a better resolution of single nucleotide variants.
Reference Data
The generation of the reference datasets enables comparative analyses with parallel
data from disease-centric studies to identify variants and processes that are linked
to disease. These result in a better understanding of the underlying mechanisms by
which various diseases occur, an improved ability to predict which treatments will
work best for specific patients, and improved approaches such as genome-based strategies
for the early detection, diagnosis, and treatment of disease.
Since the availability of the first complete draft of the human genome, many large
experimental studies and associated computational resources have been published in
efforts to further basic biological knowledge. These include the ENCODE project to
understand the function of genes and the elements that regulate genes throughout the
genome [[14 ]], the 1000 Genomes Project to generate comprehensive genetic variation maps of individuals
from multiple populations [[15 ]], advances in mass spectroscopy and electron microscopy which generate 3-dimensional
structures of proteins and enable the identification of functions, protein capture
experiments which explore how DNA and proteins interact with one another and with
the environment to create complex living systems, and large-scale epigenome maps from
healthy and diseased human cells [[16 ]].
The Genome Reference Consortium that coordinates, builds, and manages reference genomes
for several model organisms, develops novel methods such as graph-based algorithms
to represent complex allelic diversity. Significant effort is required to maintain
up to date reference genome sequences, represent alternative loci, and effectively
resolve difficult regions of the genome [[17 ]].
Functional and Clinical Interpretation
Functional and Clinical Interpretation
When identifying causative gene mutations, the first step is for rare diseases to
catalogue all the nucleotide differences or variations in a patient’s genome compared
to a reference genome, and for cancers to catalogue the differences between the healthy
and tumor genomes. The more complex, and naturally the more valuable, next step is
to understand the clinical significance of the variants, their inheritance patterns,
and the strength of their association to the disease or phenotype [[18 ]].
Understanding the clinical and functional significance of each variant requires complex
bioinformatics analyses and the integration of numerous other data types:
Data such as gene structure information is necessary to determine whether the variant
lies in the coding or non-coding portion of the genome;
Coding variant, protein structure, and functional data are needed to determine the
impact of the mutation on protein function;
Transcriptomics and proteomics data are required to determine cell and tissue expression
profiles;
Mutation experimental data from human cell or model organisms and disease variation
information are needed to understand linked phenotypes;
Protein interaction network and biological pathway knowledge are required to learn
more about the function and relationship with other proteins;
Data from clinical trials and pharmaceutical agents are also needed to know if there
have been medicines developed that target this protein or biological pathway;
If available, longitudinal phenotypic information at the individual and population
levels is also needed.
All of the above require the ready availability of curated, structured reference information.
Much of the data required to determine clinical significance is deposited and curated
in bespoke biological repositories such as those hosted by the European Bioinformatics
Institute (EMBL-EBI) [[19 ]] or the NIH National Center for Biotechnology Information (NCBI). However, data
integration for genome-wide bioinformatics analyses and conversion of data to knowledge
rely on the continuous development of analytical pipelines and systems. Tools such
as the Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation,
and prioritization of genomic variants in coding and non-coding regions, providing
access to an extensive collection of genomic annotations [[20 ]].
Clinical Data Environment
Clinical Data Environment
The integration of EHRs in patient care is also needed to link molecular and clinical
data. One important component required for data integration is the careful curation
and mapping of data to controlled vocabularies or ontologies. For genomic data integration
with clinical information, data from primary care, hospitals, outcomes, registries,
and social care records should be first recorded using controlled clinical terminologies,
such as SNOMED Clinical Terms and the Human Phenotype Ontology [[21 ]]. Ontologies as such are not ever complete and end-users such as clinicians will
need to work with ontology developers to continuously improve the precision and accuracy
of terminologies [[22 ]].
Using standard terminologies for recording clinical data is however just the primary
step. Clinical data is usually generated and held across a wide variety of point of
care settings such as acute hospitals, general practitioners, community hospitals,
mental health, and social care. Integration of health data from different sources
can facilitate the efficient and timely use of clinical information gathered throughout
a patient’s journey, and hence should minimise duplication and enable greater continuity
of care. The implementation of EHRs such as EPIC within hospitals has driven greater
standardisation and efficiency. However there were significant challenges to overcome
in the deployment and operationalisation of such a system [[23 ]]. For research, organisations like CDISC work to create standards in order to support
the acquisition, sharing, submission, and archiving of clinical research data.
The end of patent battles on genetic tests by commercial companies, such as Myriad
Genetics and others, over BRCA1 and BRCA2 mutation detection[1 ], coupled with cheaper high-throughput sequencing and high profile celebrity activities
(e.g. the Angelina Jolie effect [[24 ]]), have resulted in an increased demand for new approaches to diagnostic tests.
These include developments in cost-effective accurate mutation detection strategies
and a standardized, systematic approach, to the reporting of test results, especially
in cancer [[25 ]]. Rahman and colleagues have developed a rapid, robust, large-scale, cost-effective
testing pathway that can be utilized within the hospital framework and easily adapted
across other healthcare systems [[26 ]].
As the volume of genomic data grows with associated clinical data, it is also useful
to note that aggregation and reanalysis of such data will result in a new and improved
understanding of clinical value over time [[27 ]]. For example, a novel variant discovered in a patient today may have little or
no information associated with it. However as genomic data grows and this variant
is analyzed in conjunction with other similar variants, more statistically significant
results can result in greater confidence of this variant being associated, or not,
with a disease.
Data Storage and Sharing
New models for data storage and sharing have also emerged so that experts across the
continuum from molecular biology to medicine can access, use, and deposit data. A
pilot project in rare disease, the Deciphering Developmental Disorders (DDD) study,
aimed to determine the feasibility of translating new high-throughput genomic technologies
into clinical practice, and of elucidating the underlying genetic architecture of
developmental disorders. This study, which utilized whole exome sequencing to diagnose
27% of 1,133 previously investigated yet undiagnosed children with developmental disorders
[[28 ]], also established a unique database model in the DECIPHER[2 ] database.
Contributing to the DECIPHER database is an international community of academic departments
of clinical genetics and rare disease genomics that now numbers more than 250 centers
who have uploaded more than 18,000 cases. Each center maintains control of its own
patient data (which are password protected within the center’s own DECIPHER project)
until consent is given to share the data with chosen parties in a collaborative group
or to allow anonymous genomic and phenotypic data to become freely viewable within
genome browsers. Once data is shared, consortium members are able to gain access to
the patient report and to contact other members to discuss patients of mutual interest.
After data analysis, pertinent genomic variants are returned to individual research
participants via their local clinical genetics team. The DDD study also demonstrated
that the systematic recording of relevant clinical data, curation of a gene–phenotype
knowledge base, and development of clinical decision support software were crucial
for scalable prioritization and review of possible diagnostic variants [[29 ]]. Most of the diagnostic variants identified in known genes were novel and not present
in current databases of known disease variations.
The Global Alliance for Global Health (GA4GH) established in 2013 is developing a
common framework of approaches for adoption in order to accelerate progress in human
health, drive efficiencies, and lower costs. The goal of the Alliance is to create
data standards and strategies for storage and analysis of medically relevant genomic
data, and to catalyze the creation of data sharing standards and methods to ensure
worldwide interoperability of medical genomics data [[30 ]]. GA4GH includes institutions like EMBL-EBI that play a key role in facilitating
the transfer of knowledge and expertise in data management and analysis of big data
projects. Currently 452 institutions and companies across 42 countries participate
to the Alliance, which emphasizes the global interest, demand for standardization,
and need for joint working solutions[3 ]. The strategic goal of GA4GH is to build a system of servers, to create standard
markup languages, and to develop resources and applications similar to the implementation
of the World Wide Web for users to access genomics information. A schematic view of
how this may look in practice is shown in [Figure 1 ].
Fig. 1 National and International Data Sharing Mechanisms. Data from national genome sequencing
initiatives and related clinical data will naturally need to be stored within centralized
national storage systems. Anonymized data from the analyses of these projects can
be made available for research use, either via controlled-access data repositories
or open, public data repositories, depending on consent and data category. Products
and Standards developed by GA4GH can be applied by the three different resource types
to ensure interoperability of medical genomics data.
National Sequencing Initiatives: Approach and Progress
National Sequencing Initiatives: Approach and Progress
The use of genomics as a healthcare diagnostic tool is becoming increasingly more
common due to the desperate need to understand the underlying causes of diseases and
to provide more efficacious medicines to patients. The disease areas that are likely
to significantly benefit from the use of genomic diagnostics are those where the identification
of causative gene mutations is more straightforward, such as in rare diseases, cancers,
and infectious diseases, as well as the detection of chromosomal abnormalities in
non-invasive prenatal testing. Over the last few years, several national initiatives
have been launched with the strong support of the leaders of respective countries.
A complete list can be found in [Table 1 ]. The scope, approach, and goals of each of these are quite different, some of which
are described below.
The UK 100,000 Genomes Project was formally announced in December 2012 by the Prime
Minister as part of the UK Government’s Life Sciences Strategy[4 ]. The delivery of the project was charged to a newly established limited company
owned by the UK Department of Health, Genomics, England, with rare diseases, cancers,
and infections chosen as the disease areas to focus on. Since then, the program has
developed and executed an aggressive delivery plan including the setup of several
genomic medicine centers across the country, a data infrastructure, a panel of annotation
suppliers, a clinical interpretation partnership with researchers, relationships with
the pharmaceutical industry and biotechnology companies, as well as a skills and education
program for national health service employees. With over 18,000 genomes sequenced
to date, several cases of rare disease diagnoses of previously undiagnosable genetic
conditions are coming to light[5 ] such as a rare mutation in the SLC2A1 gene which caused a patient’s Glut1 deficiency
syndrome. This mutation was narrowed down from the 6,414,934 variants initially observed
when compared to the reference sequence. Besides clinical interpretation, the project
is also addressing technical and logistical challenges, such as obtaining tumor DNA
of sufficient quality and quantity to meet healthcare pathology test standards, and
the mapping of clinical data to standard ontologies using robust and user-enabled
approaches.
Table 1
Key aspects of national genomics healthcare initiatives
Program Name
Start Date
Number of genomes
Disease areas
Investment
Estonian Genome Project
2001
1 million individuals over 5 years
Random selection
1.5 billion Estonian kroons
Genome Denmark
March 2011
Cancer, pathogen, reference genome
80 million DKK
Iceland (deCode Genetics)
-
10,000
All disease areas
-
UK 100,000 Genomes Project
December 2012
100,000
Rare disease, cancer, infection
£300 million
US Precision Medicine Cohort
January 2015
>1 million volunteers
All disease areas
$215 million
Scottish Genomes Partnership
January 2015
> 3000 citizens
Cancer, childhood illnesses, rare genetic diseases, disorders of the central nervous
system and population studies
£21 million
Genomic Medicine France 2025
April 2015
235,000 genomes annually by 2020
Rare disease, cancer, diabetes, other common diseases, reference genome
€1 billion
Finland
July 2015
-
All disease areas
€50 million
China Precision Medicine Initiative
March 2016
100 million genomes over 15 years
All disease areas
US$9.2 billion
Germany
Planned
€360 million
In January 2015, U.S. President Obama announced the Precision Medicine Initiative
(PMI), a national, large-scale, research enterprise with one million or more volunteers
from diverse social, racial/ethnic, ancestral, geographic, and economic backgrounds,
from all age groups and health statuses as well as a dedicated cohort of oncology
patients. June 2016 saw the kick-off of this initiative with the establishment of
six recruiting centers that aimed to enroll 10,000 participants in the first year,
starting in November, then 35,000 a year through 2020 to reach a total of 150,000[6 ]. The goals of the program included developing criteria and standards for the incorporation
of rapidly evolving technology and mobile health technologies into cohort design,
both for baseline and ongoing data collection, as well as interoperable and standardized
EHRs plus various genomics and imaging approaches[7 ].
The substantial learnings from previously established programs, such as the Electronic
Medical Record and Genomics (eMERGE) network on approaches to integrate genomic variant
information within electronic medical records, will be incorporated into PMI protocols
[[31 ], [32 ]]. Researchers are also working on integrative models based on current knowledge
of genomics and epigenomics and the relevant biochemistry and cellular-tissue physiology
to predict how to obtain data from these very large cohorts. Such predictions could
specify which clinical parameters to measure and at what intervals [[33 ]]. eMERGE is also studying the ethical, legal, and social issues involved in the
use of EHRs for genomics research, such as privacy, confidentiality, and communications
to the public, as well as the return of actionable genomic test results to EHRs for
use in clinical care.
The French national sequencing initiative, France Médecine Génomique 2025, has an
investment of €670 million over the first five years, accompanied by commercial contributions
worth another €230 million, to sequence 235,000 genomes annually by 2020. Launched
in April 2015, the French project will begin by sequencing genomes not only from patients
with rare diseases and cancers, but also from some forms of diabetes, which is noted
as an urgent priority for research to help develop better targeted treatments.
Applications of the Secondary Use of Patient Data
Applications of the Secondary Use of Patient Data
The launch of several national sequencing initiatives will create a ‘longitudinal
life course of electronic health’ database of all participants, based upon a flow
of electronic health data from primary care, hospitals, outcomes, registries, and
social care records. These extensive records will provide the opportunity to evaluate
genomics in the context of rich and extended phenotypes including biochemical parameters,
health outcomes, mortality data, and pharmacogenomics. Analysis of these data beyond
the purpose of primary diagnosis will allow researchers to move past the primary phenotype
of the disease that led to the patient’s enrolment to evaluate genomics in the context
of other continuous traits, diseases, and response to therapy.
The use of genomic data in secondary research has advanced the development of new
tools and algorithms such as those used to model the genetic diversity and evolutionary
patterns of individual cancers [[34 ]]. Studies that aim to use biobanks and integrate different data types have successfully
stratified patients and identified potential biomarkers of drug response. In a recent
study by Folkersen et al. in Rheumatoid Arthritis (RA), a biobank was used to test
the claim that the current state-of-the-art precision medicine will benefit RA patients.
High-throughput RNA sequencing, DNA genotyping, extensive proteomics, and flow cytometry
measurements, as well as comprehensive clinical phenotyping, led to the identification
of a small set of biomarkers available in peripheral blood that predict clinical response
to tumor necrosis factor (TNF) blockade [[35 ]].
Genomic data has also enabled very large-scale projects, such as the Pan-Cancer Analysis
of Whole Genomes (PCAWG) study, which is an international collaboration to identify
common patterns of mutation in more than 2,800 cancer whole genomes from the International
Cancer Genome Consortium. PCAWG aims to generate genomic, transcriptomic, and epigenomic
changes in 50 different tumor types and/ or subtypes. Such analyses have shown the
value of integrating genomics data from the same patient, will lead to novel targets
and disease mechanisms, and should in turn drive enhanced diagnostic and therapeutic
yields for individual patient benefit [[36 ]–[38 ]].
As well as research applications, the development of new or repurposed drugs stands
to substantially benefit from more disease-associated genomic data. One of the main
reasons for the high rate of attrition in late-stage clinical trials is thought to
be the lack of drug efficacy [[39 ]]. Often the incorrect gene or protein is selected as the drug target in early drug
development, where the premise is that perturbation of this protein by a compound
will significantly change the course of disease [[40 ]]. Recent publications have shown that genetic data that link a target to a phenotype
or disease have higher success rates in the clinic [[41 ]].
A study by Bagley et al. combined data from electronic medical systems with disease-associated
genetic variants data to study the relationship between disease co-occurrence and
commonly shared genetic architectures of disease. The study looked at 35 disorders,
medical records for over 1.2 million patients, and variants from over 17,000 publications,
and found specific shared genes between disease classes that were not previously thought
to be related, such as autoimmune and neuropsychiatric disorders [[42 ]].
It is clear that current definitions and categorization of diseases and phenotypes
do not necessarily reflect true molecular relationships and underlying biological
relationships. Public-private initiatives such as Open Targets, a collaboration between
Biogen, the EMBL-European Bioinformatics Institute, GlaxoSmithKline, and the Wellcome
Trust Sanger Institute (http://www.opentargets.org ), aim to provide comprehensive and up-to-date relevant genetics and high-throughput
genomics data for drug target selection and validation [[43 ], [44 ]]. Chen and Butte have provided a comprehensive review on the availability of public
data and the analytical tools across various data types for target selection and drug
discovery [[45 ]].
Processed data from genome sequencing projects and the knowledge extracted from these
can also be integrated into existing reference data in a manner that enables further
analyses while protecting the privacy of patients within each country or system.
Data that can be captured include:
Molecular profiles that characterize differences between diseased and normal states,
or that provide sub-classification of a disease;
Annotation of variants of clinical importance in different diseases;
Annotation of variants and genes, and their association with drugs;
Biomarkers (e.g. protein, RNA, metabolites, and complete metabolomes) for diagnosis
and disease monitoring;
Reference images that can link molecular data to disease phenotypes;
Human pathogen data and their virulence components.
Such data will provide broaden references dataset for further clinical research as
well as develop resources that are fundamentally about understanding biology.
Ongoing Challenges: Technological, Societal, and Economic
Ongoing Challenges: Technological, Societal, and Economic
Bioinformatics analysis leading to clinical interpretation is an expensive part of
the pipeline. The importance of data sharing and common standards is emphasized by
Muir et al. [[46 ]] who highlight that storage and computation costs have not decreased as quickly
as sequencing costs. They concluded that “if the sequence data generated by individual
labs is not processed uniformly and sequence databases are not made easily accessible
and searchable, then the analysis of aggregated datasets will be challenging”. Reducing
costs will require simultaneous improvements in data sharing and use of common standards.
We describe below some of the technological, economic, and societal challenges that
need resolution to fulfil this vision.
Standardization
If we look back at the historic peaks and troughs of molecular biology, major technological
innovations have preceded moments of great discovery. For example, the invention of
high-throughput Sanger sequencing technology enabled the completion of the human genome
[[47 ]]. Innovation in data generation is always followed by a surge of data analysis methods
and tools. At the moment, the bottlenecks in genomic medicine lie at the data analysis
and interpretation end of the pipeline. Interpretation in this scenario is a critical
step since a patient’s diagnosis status and potential treatment options are crucially
dependent on interpretation, and not on the raw or processed sequence data.
Efforts to identify gold standard methods and evaluate the performance of data analytical
methods are currently emerging. These include work by Tokheim and colleagues who compared
eight different algorithms that attempted to identify, from variation data, which
gene variants drove cancer driver genes and which were simply passenger mutations
[[48 ]]. The analysis found that most approaches had a high rate of false positives and
more work was needed to develop a gold standard method.
Electronic Medical Records (EMR) represent a convenient source of coded medical data,
but the lack of standards and the variation among the different systems can introduce
inaccuracies and biases when this data is used for analyses such as calculating disease
prevalence, incidence, age of onset, or disease comorbidity [[49 ]]. There is a further need for standardisation around clinical data capture and communication,
which addresses the quality, completeness, and adoption of standards. Analysis would
also improve if there was standardization in the way data are collected from participants
across hospitals and clinics. Far more can be interpreted from a genome sequence when
an accurate patient record is available. The challenges lie in being able to collect
this information from busy clinicians, and the data also needs to be integrated across
the various points of patient care. Clinical decision support systems that have been
approved by regulatory bodies and in which significance and confidence levels around
genetic findings are systematically inferred and reported [[50 ]] are also needed.
Data Storage and Sharing
Currently, much of the human genomic data generated so far is deposited into public
databases for broad research reuse. Human genomic and phenotypic data from clinical
or research studies which would require a researcher to have a bespoke signed agreement
with the originating body (via a Data Access Committee or other mechanism) are largely
stored in controlled-access repositories such as the European Genome-Phenome Archive
(EGA), the NIH database of Genotypes and Phenotypes (dbGaP), or held by the originating
body. However, given the differing ethical and legal systems of each country, these
systems are not scalable nor are they appropriate for the growing volume of genomic
data from national health studies.
Managed storage systems which follow national legislation and which allow access to
data for research purposes are essential. Researcher access to genomic databases is
necessary to create a research community that will be connected and may contribute
directly to national health services and patient care systems. Analyses that utilize
the aggregated data from hundreds of thousands or millions of patients from multiple
healthcare systems will add much more to our knowledge of the genetic basis of disease
than multiple individual studies using small sample cohorts from individual healthcare
systems.
Large-scale cohorts are particularly important for very rare diseases for which patient
numbers in any one country may be too small to provide adequate data to identify causative
genes. For rare diseases, the sample size needed to infer whether observed variants
that are associated with a disease are causative and statistically significant will
often require combining data from patients in multiple countries: cross-border sharing
of data is essential so that virtual or physical data aggregation and sufficiently
powered analyses can be performed.
Researcher Access to Data
Researcher Access to Data
New data sharing mechanisms are also needed to minimize the movement of large volumes
of data and allow instead for analyses to take place at the point where data are stored.
Cloud computing frameworks allow remote storage, with analysis scripts uploaded to
the cloud and analysis performed remotely on virtual machines physically located at
the remote site “next to” the data. This greatly reduces data transfer requirements
since only the scripts and analytical results are transferred to and from the analysts’
institution or desktop: data resides permanently in the cloud [[51 ]]. The cost structure for computational and analysis resource in genomics points
towards the efficiency of developing a single, large center for data analysis and
processing. Such a resource would, through virtual access, be combined with a more
widely distributed network of expertise [[22 ]]. Initiatives such as the European Open Science Cloud will further the creation
of infrastructures to enable data sharing and service provision across borders and
disciplines[8 ].
With health data for large numbers of people, it will be critical to find ways to
protect individuals’ privacy and the confidentiality of their health information.
However, if data is to be shared by various communities, the correct legal and ethical
frameworks must be in place. It is critical to find ways to protect participants’
privacy and the confidentiality of their health information while simultaneously enabling
research to take place. Current practices for researcher access to data that include
paper-based agreements between users, institutions, and data access committees must
be replaced by electronic mechanisms. These processes, at the interface between basic
research and clinical research, should be strengthened and explicitly funded.
Individuals will also need to understand the risks and benefits of participating in
genomics diagnostic and research. Understanding what data is collected and generated
is also important. Much like the legislation needed to protect consumer data after
the advent of web-based purchases and mobile technology, patients should be made aware
of the use and implications of generating personal genomics information. Requirements
to collect consent for research should be more harmonized and regulatory tools developed
as described in the recently published code of practice [[52 ]]. The new EU data protection framework, in the form of the European General Data
Protection Regulation (GDPR), will place a number of direct obligations on data controllers,
which will drive better forms of consent collection and withdrawal.
Biomedical Informatics Coordination
Biomedical Informatics Coordination
We believe the overall ideal endpoint at the national level is the development of
a ‘Biomedical Informatics Institute’ to act as a driver and coordinating center for
health and biomedical informatics research in each country. This center should seek
to act in conjunction with existing medical research and informatics organizations
to form a seamless and integrated network with hospitals, research organizations,
and local and international health initiatives to maximize the utility of genomics
and electronic health data. In bigger nations, this institute would itself likely
be a network, but with a center of gravity, or hub, at or within one institute.
Such centers would be the natural partners for research bioinformatics organizations
such as EMBL-EBI or NCBI. In European countries, the development of biomedical informatics
institutes or networks may be coordinated through an ELIXIR[9 ] Node: ELIXIR is the European life-science infrastructure for biological data. Research
bioinformatics institutes can then be responsible for handling and providing both
open (public) and controlled access data and bioinformatics services that are shared
between researchers (including clinical researchers), whereas the national biomedical
informatics institute can be responsible for data and services that need to stay within
the national framework (see [Figure 1 ]).
Much research and development is needed in areas such as: the development of analytical
methods, tools, and standards to link and extract value from increasingly complex,
disparate, diverse, and numerous data sets; the development of secure interoperable
research environments and data flows to provide a technology framework to federate
existing platforms that will connect diverse health and biomedical data assets; the
development of partnerships with owners and controllers of data, regional and national
health and social care partners, academia and industry; and the development of skills
and capacity in the discipline of medical informatics, training researchers with interdisciplinary
skills in core data science and medical research. Alongside these, the economic cost
and impact of delivering precision medicine in an effective and affordable ways also
need to be considered.
Future Landscape
It is difficult to imagine exactly what the biomedical industry will look like in
a few years’ time, but it is certain that the surge in biological data flows will
continue. This increase in data will be from large innovative research projects, such
as the International Human Cell Atlas Initiative[10 ] that aims to create comprehensive reference maps of all human cells, and also from
devices, apps, wearables, and implantable technologies. Translational BioInformatics
methods, tools, and resources will need to evolve to include algorithms for streaming
data capture, real-time data aggregation, machine learning, predictive analytics,
and visualization solutions in order to integrate health monitoring data with EMRs
and genomics data [[53 ]].
If genomics medicine approaches are to become part of routine healthcare, doctors
and other healthcare providers will require better grounding in molecular genetics
and biochemistry. They will increasingly find themselves needing to interpret the
results of genetic tests, understand how that information is relevant to treatment
or prevention approaches, and convey this knowledge to patients. Education and skills
in the data sciences is much needed [[54 ]]. Programs to ensure the long-term generation of proficient investigators who understand
the multi-disciplinary nature of genomics in clinical practice and research, should
be established, and will perhaps even form a new medical discipline.
Open data that allows data reuse and data integration has made possible great advances
in molecular biology over the last few decades. These advances range from recombinant
DNA drugs, animal cloning, gene therapy, and forensic science to stem cell therapy.
Although the primary objective of genomic data generated for healthcare purposes is
for disease diagnoses, treatment, and prevention, the availability of these data for
use in secondary research can result in a better understanding of disease mechanisms
and will lead to improvements in treatment strategies. Moreover, the crossover of
bioinformatics into healthcare will further enable fundamental discoveries about the
big questions of biology.
It is worth saying that we are only at the very start of this health revolution brought
about by genome sequencing. If we compare the time the human genome was sequenced
in 2001 [[47 ]] following the first bacterial genome sequence in 1995 [[55 ]], to today, it is not a stretch of the imagination to envision human genome sequencing
as a part of standard care pathways and real-time biomedical and health care analytics
in the clinical setting. The systems and processes we put in place today must support
the future and not just represent our present reality.
