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2
Why Now?
The rise of data-intensive biology, advances in information technology,
and changes in the way health care is delivered have created a compelling
opportunity to improve the diagnosis and treatment of disease by developing
a Knowledge Network, and associated New Taxonomy, that would integrate
biological, patient, and outcomes data on a scale hitherto beyond our reach.
Key enablers of this opportunity include:
• New capabilities to compile molecular data on patients on a scale that
was unimaginable 20 years ago.
• Increasing success in utilizing molecular information to improve the
diagnosis and treatment of disease.
• Advances in information technology, such as the advent of electronic
health records, that make it possible to acquire detailed clinical infor-
mation about large numbers of individual patients and to search for
unexpected correlations within enormous datasets.
• A “perfect storm” among stakeholders that has increased receptivity to
fundamental changes throughout the biomedical research and health-
care-delivery systems.
• Shifting public attitudes toward molecular data and privacy of health-
care information.
Scientific research, information technology, medicine, and public attitudes
are all undergoing unprecedented changes. Biology has acquired the capacity to
systematically compile molecular data on a scale that was unimaginable 20 years
ago. Diverse technological advances make it possible to gather, integrate, ana -
lyze, and disseminate health-related biological data in ways that could greatly
21
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22 TOWARD PRECISION MEDICINE
advance both biomedical research and clinical care. Meanwhile, the magnitude
of the challenges posed by the sheer scientific complexity of the molecular influ -
ences on health and disease are becoming apparent and suggest the need for
powerful new research resources. All these changes provide an opportunity for
the biomedical science and clinical communities to come together to improve
both the discovery of new knowledge and health-care delivery. As discussed
in this chapter, the Committee concluded that this opportunity could best be
exploited through a major, long-term commitment to create an Information
Commons, a Knowledge Network of Disease, and a New Taxonomy.
BIOLOGY HAS BECOME A DATA-INTENSIVE SCIENCE
Advances in DNA-sequencing technology powerfully illustrate biology’s
conversion to a data-intensive science. The first papers describing practical
methods of DNA sequencing were published in 1977 (Maxam and Gilbert
1977; Sanger et al. 1977). These methods required radioisotopic labeling of
DNA, hand-crafting of large electrophoretic gels, and considerable expertise
with biochemical and recombinant-DNA techniques. Although the impact of
these early DNA-sequencing methods on biological discovery was profound,
the total amount of sequence deposited in GenBank, the central depository for
such data, did not pass one billion base pairs (one-third of the length of a single
human genome) until 1997 (NCBI 2011a), and it only reached this landmark
after a first generation of automated instruments came into widespread use
(Favello et al. 1995). Since then > 300 billion base pairs (Benson et al. 2011)
have been deposited, illustrating the still ongoing explosion of genomic data
in the last 20 years.
The National Human Genome Research Institute estimated that the total
cost of obtaining a single human-genome sequence in 2001 was $95 million
(Wetterstrand 2011; see Figure 2-1). Costs subsequently dropped exponentially
following a trajectory described in electronics as Moore’s Law, connoting a re -
duction of cost by 50 percent every two years, until the spring of 2007, at which
point the estimated cost of a single human-genome sequence was still nearly
$10 million. At that point, introduction of a second generation of automated
DNA-sequencing instruments, based on massively parallel, miniaturized analy -
sis, led to a collapse in costs far faster than the Moore’s Law projection. The
most recent update, in January 2011, estimates the cost of a complete-genome
sequence at $21,000, and the cost is still dropping rapidly, with a “$1000 ge -
nome” becoming a realistic target within a few years. (Wolinsky 2007; MITRE
Corporation 2010; Mardis 2011)While whole-genome sequencing remains ex -
pensive by the standards of most clinical laboratory tests, the trend-line leaves
little doubt that costs will drop into the range of many routine clinical tests
within a few years. Whole-genome sequencing will soon become cheaper than
many of the specific genetic tests that are widely ordered today and ultimately
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23
WHY NOW?
FIGURE 2-1 The plummeting cost of complete genome sequencing.
The cost of complete genome sequencing is falling faster than Moore’s Law. The cost is
still dropping rapidly, with a “$1000 genome” becoming a realistic target within a few
Figure 2-1
years.
Bitmapped
SOURCE: Wetterstrand 2011.
will likely become trivial compared to the cost of routine medical care. Hence,
the costs of DNA sequencing will soon cease to be a limiting factor (MITRE
Corporation 2010). Instead, the clinical utility of genome sequences and public
acceptance of their use will drive future developments.
Admittedly, the cost trajectory of DNA sequencing is an unusual success
story, even by high-tech standards. However, it is by no means unique: parallel
developments in other areas of molecular analysis, such as the analysis of large
numbers of small-molecule metabolites and proteins, and the detection of single
molecules, are likely to sweep away purely economic barriers to the diffusion of
many data-intensive molecular methods into biomedical research and clinical
medicine. These technologies will make it possible to monitor and ultimately
to understand and predict the functioning of complex molecular networks in
health and disease.
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24 TOWARD PRECISION MEDICINE
THE OPPORTUNITY TO INTEGRATE
DATA-INTENSIVE BIOLOGY WITH MEDICINE
Human physiology is far more complex than any known machine. The
molecular idiosyncrasies of each human being underlie both the exhilarating
potential and daunting challenges associated with “personalized medicine”.
Individual humans typically differ from each other at millions of sites in their
genomes (Ng et al. 2009). More than ten thousand of these differences are
known to have the potential to alter physiology, and this estimate is certain
to grow as our understanding of the genome expands. All of this new genetic
information could potentially improve diagnosis and treatment of diseases by
taking into account individual differences among patients. We now have the
technology to identify these genetic differences—and, in some instances, infer
their consequences for disease risk and treatment response. Some successes
along these lines have already occurred; however, the scale of these efforts is
currently limited by the lack of the infrastructure that would be required to
integrate molecular information with electronic medical records during the
ordinary course of health care.
The human microbiome project represents an additional opportunity to
inform human health care. The microorganisms that live inside and on humans
are estimated to outnumber human somatic cells by a factor of ten. “If humans
are thought of as a composite of microbial and human cells”, then “the hu -
man genetic landscape is an aggregate of the genes in the human genome and
the microbiome, and human metabolic features” are “a blend of human and
microbial traits” (Turnbaugh et al. 2007). A growing list of diseases, including
obesity, inflammatory bowel disease, gastrointestinal cancers, eczema, and pso -
riasis, have been associated with changes in the structure or function of human
microbiota. The ultimate goal of studying the human microbiome is to better
understand the impact of microbial variation across individuals and populations
and to use this information to target the human microbiome with antibiotics,
probiotics, and prebiotics as therapies for specific disorders. While this field is
in its infancy, growing knowledge of the human microbiome and its function
will enable disease classification and medicine to encompass both humans and
their resident microbes.
There are already compelling examples of improvements in patient care
that have emerged from studies of the human genome and human microbiome:
• Some patients with high cholesterol are heterozygous for a non-func-
tional variant of the low-density-lipoprotein-receptor gene, a genotype
found in one out of every 500 individuals. Lifestyle interventions alone
are ineffective in these individuals at reducing the likelihood of early-
onset cardiovascular disease (Huijgen et al. 2008). Consequently, the
ability to identify the patients who carry the non-functional receptor
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WHY NOW?
makes it possible to proceed directly to the use of statin drugs at an
early age, rather than first attempting to control cholesterol with diet
and exercise. There is strong evidence that the early use of statin drugs
in these individuals can provide a therapeutic benefit.
• In the United States it is estimated that 0.06 percent of the population
carries mutations in the tumor suppressor BRCA1 and 0.4 percent of
people carry mutations in BRCA2 (Malone et al. 2006). These muta-
). muta-
tions predispose to cancer, particularly breast and ovarian cancer (King
et al. 2003). Women who carry these mutations can reduce their risk
of death from cancer through increased cancer screening or through
prophylactic surgeries to remove their breasts or ovaries (Roukos and
Briasoulis 2007); until these mutations were identified it was not pos -
sible to determine who carried the mutations or to take proactive steps
to manage risk.
• Lung cancer patients can now be separated by the genetic profiles of
their cancers into distinct groups that benefit from different treatments
(see Box 2-1).
• It is now clear that most cases of stomach ulcers, once thought to be
caused by stress and other non-infectious factors, are due to coloni -
zation of the stomach lining with the Helicobacter pylori bacterium,
which is very common in human populations (Atherton 2006). This
finding has radically changed the treatment of this disorder. H. pylori
infection also is thought to predispose to the development of stom-
ach cancer, suggesting that treatment of this infection can both help
cure gastric ulcers and also may reduce the development of cancer
of the stomach. In addition, epidemiological studies and other data
have raised the possibility that H. pylori infection may reduce the
individual’s likelihood of developing allergic diseases or even obesity
(Blaser and Falkow 2009), suggesting that the full complexity of the
relationship between infection with this organism and human health
and disease remains to be determined.
• In the last decade, genetic analyses have allowed a more precise diag-
nostic classification of type 2 diabetes. Children and young adults with
mild glucose intolerance and often a strong family history of diabetes
were previously categorized as having “Maturity Onset Diabetes of the
Young (MODY)”. MODY is now understood to represent a series of
specific genetic variants that affect pancreatic beta cell function (Fajans
et al. 2001) such that the American Diabetes Association classification
of type 2 diabetes has replaced the descriptive term MODY with the
specific genetic defects (e.g., chromosome 7, glucokinase; chromosome
12, hepatic nuclear factor 1—alpha; etc.) A dynamic, continuously
evolving Knowledge Network of Disease will be needed to accommo-
date future additions to this list of specific genetic predispositions to
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Box 2-1
Distinguishing Types of Lung Cancer
Lung cancer is the leading cause of cancer-related death in the United States
as well as worldwide, causing more than one million total deaths annually (ACS
2011). Traditionally, lung cancers have been divided into two main types based
on the tumors’ histological appearance: small-cell lung cancer and non-small-cell
lung cancer. Non-small-cell lung cancer is comprised of three subgroups, each of
them defined by histology, including adenocarcinoma, squamous-cell carcinoma,
and large-cell carcinoma.
Since 2004, knowledge of the molecular drivers of non-small-cell lung cancer
has exploded (Figure 2-2). Drivers are mutations in genes that contribute to inap-
propriate cellular proliferation. These driver mutations are necessary for tumor
formation and tumor maintenance. If the inappropriate function of the mutant
protein is shut down, dramatic anti-tumor effects can ensue.
In 2004 two drugs were in development, Gefitinib and Erlotinib, which inhib-
ited the function of certain receptor tyrosine kinases, including epidermal growth
factor receptor (EGFR). These receptors were known to send signals that promote
cellular proliferation and survival, and increased signaling was thought to contrib-
ute to some cancers. In early trials, the drugs were shown to produce dramatic
anti-tumor effects in about 10 percent of patients with non-small-cell lung cancer
(MSKCC 2005). Other patients did not appear to respond at all. However, the
dramatic tumor shrinkage in some patients was enough for U.S. Food and Drug
Administration approval in 2003, even though the molecular basis for the response
was then unknown. Without the ability to recognize the responding patients as a
biologically distinct subset, these agents were tried unsuccessfully on a broad
range of lung-cancer patients, doing nothing for most patients other than increas-
ing costs and side effects. In retrospect, some clinical trials with these agents
probably failed because the actual responders represented too small a proportion
of the patients in the trials (Pao and Miller 2005).
Subsequently, it was discovered that the responding patients carried muta-
tions that activated EGFR in their cancers (Kris et al. 2003; Lynch et al. 2004;
Paez et al. 2004; Pao et al. 2004). This made it possible to predict which patients
would respond to the therapy and to administer the therapy only to this subset
of patients. This led to the design of much more effective clinical trials as well as
reduced treatment costs and increased treatment effectiveness.
Since then, many studies have further divided lung cancers into subsets that
can be defined by driver mutations. Not all of these driver mutations can currently
be targeted with drugs and cancer cells are quick to develop resistance to targeted
drugs even when they are available. Nonetheless, this recent information makes
it possible to develop new targeted therapies that can extend and improve the
quality of life for cancer patients.
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WHY NOW?
FIGURE 2-2 Knowledge of non-small-cell lung cancer has evolved substantially
in recent decades. Figure 2-2
The traditional characterization of lung cancers based on histology has been re-
Bitmapped
placed over the past 20 years by classifications based on driver mutations. In 1987,
this classification was rudimentary as only one driver mutation had been identified,
KRAS. However, the sophistication of this system for molecular classification has
improved with the advent of more genetic information and the identification of
many more driver mutations. Similar approaches could improve the diagnosis, clas-
sification, and treatment of many other diseases.
SOURCE: Pao and Girard 2011.
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28 TOWARD PRECISION MEDICINE
in type 2 diabetes (with the vision of adding clarity to the diagnosis of
Patient 2).
The human genome and microbiome projects are only two examples of
emerging biological information that has the potential to inform health care. It
is similarly likely that other molecular data (such as epigenetic or metabolomic
data), information on the patient’s history of exposure to environmental agents,
and psychosocial or behavioral information will all need to be incorporated into
a Knowledge Network and New Taxonomy that would enhance the diagnosis
and treatment of disease.
THE URGENT NEED TO BETTER UNDERSTAND
PHENOTYPE-GENOTYPE CORRELATIONS
While dramatic progress in understanding the relationship between mo -
lecular features and phenotype is being made, there is an urgent need to under-
stand these links better and to develop strategies to deal with their implications
for the individual patient. BRCA1 and BRCA2 genes are a good example. The
discovery of mutations in BRCA1 and BRCA2 in some people made it possible
to identify individuals at increased risk of cancer, allowing them to manage
their risk with increased cancer screening and prophylactic surgeries. A data -
base cataloging BRCA1 mutations recently listed 2,136 distinct variants of the
gene (NHGRI 2011). Of these, 1,167 were judged by the database’s curators
as likely to be clinically significant, while most of the rest were categorized as
of “unknown” clinical significance. Among the mutations that are believed to
be clinically significant, some are thought to confer a higher risk of cancer than
others (Gayther et al. 1995) but there remains uncertainty about the extent to
which most mutations increase cancer risk (Fackenthal and Olopade 2007).
As a consequence, people with BRCA1 and BRCA2 mutations are forced
to make life-altering treatment decisions with incomplete information. To what
extent does their mutation increase their risks of breast and ovarian cancer and
how do these risks change with age? Should they have prophylactic mastecto-
mies or oophorectomies, and if so, when? Should they wait until after child-
bearing and how would that affect their risks? All of these real-life decisions
carry heavy personal consequences as well as implications for health-care costs.
These treatment decisions do not need to be made based on such frag-
mentary information. If all patients who test positive for BRCA1 or BRCA2
mutations were tracked by their health-care providers long term, it would be
possible to determine what percentage of patients with each mutation develop
cancer, and which cancers. It would be possible to assess the extent to which
prophylactic surgeries reduced risk. It would be possible to assess the effective-
ness of increased cancer screening, the best ways to screen these patients, and
the complications that arise from the inevitable false-positive results that come
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WHY NOW?
from increased screening. Efforts along these lines have so far been based on
modest numbers of patients or cohorts that are not fully representative of the
larger population because it has not been practical to integrate genetic informa-
tion, treatment decisions, and outcomes data for large numbers of unselected
patients. However, recent advances in genomic and information technologies
now make it possible to address systematically these issues by integrating large
datasets that already exist (see Box 2-2).
BRCA1 and BRCA2 are only two of many genes in which differences be-
tween individuals have significant implications for disease risk and treatment
decisions. We have approximately 20,000 genes in our genome and many of
these genes may have many disease-relevant alleles, just like BRCA1. Even if
only a subset of this variation has significant implications for disease risk or
treatment response we have the potential to improve the detection, diagnosis,
and treatment of disease dramatically by large-scale efforts to assess phenotype-
genotype correlations. By integrating patient genotype with health information
and outcomes data a New Taxonomy could identify many new genetic variants
with significant implications for health care.
There is every reason to expect that the genetic influences on most common
diseases will be complex. In each patient, variants in multiple genes will affect
disease onset, progression, and response to treatment, and long-term environ -
mental modulation of these processes will be the rule rather than the exception.
While recent breakthroughs have focused on genomics as a consequence of the
rapid development of technology in that area, the future may see comparable
advances in our ability to understand epigenetic, environmental, microbial, and
social contributions to disease risk and progression. Under these circumstances,
there is an obvious need to categorize diseases with finer granularity, greater
reference to the underlying biology, and in the context of a dynamic Knowledge
Network that has the capacity to integrate the new information on many levels.
Unraveling these diverse influences on human diseases will be a major scientific
challenge of the 21st century.
DRAMATIC ADVANCES IN INFORMATION
TECHNOLOGY ARE DRIVING SYSTEMIC CHANGE
The United States and other countries are currently making multibillion-
dollar investments to implement electronic health records (EHRs) to improve
clinical care. The development of such records creates several new opportuni -
ties to integrate health-care information and biological data and to search for
new links between clinical test results, patient data, and outcomes.
• The increased functionality of EHRs and the improved performance
of search tools open the door to conduct large cohort studies on a
wide range of diseases. Patients with characteristics of interest—for
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Box 2-2
Prospective Cohort Studies—A Special Role
Much of our knowledge about “risk factors” that predispose to complex
diseases comes from observational epidemiological studies, either case-control
studies in which aspects of the life experience of a series of cases are compared
with those of appropriate controls, or prospective cohort studies in which large
numbers of people are followed over time and the life experience of those who
develop a specific disease are compared with those of the much larger number
who have not.
For example, much of what is known about the predictive value of biochemi-
cal factors that are measured in plasma or serum, such as the relation of cho-
lesterol or other lipid with risk of heart attack, is derived from prospective cohort
studies such as the Framingham Heart Study (FHS). Prospective studies are
particularly valuable because the occurrence or treatment of disease may alter
the levels of the biochemical factors so that inference based on levels measured
in a series of already diagnosed cases may be biased. These biomarkers can be
combined with information on lifestyle risk factors such as smoking and body mass
index, and measurements that may also change after diagnosis such as blood
pressure, to create a risk score such as the Framingham Risk Score, that is widely
used to predict the 10-year risk of heart attack (Anderson et al. 1991). The Risk
Score was based on data from slightly more than 5,500 subjects, among whom
several hundred coronary heart disease (CHD) events occurred. A study of this
size is adequate for relatively common disease events such as CHD, or quantita-
tive traits such as blood pressure or bone density for which every participant has
example, those with rheumatoid arthritis or lacking response to antide-
pressants—could be selected via EHRs. Patients in these groups could
then be recruited to provide samples or have their discarded clinical
samples analyzed for research.
• EHRs could be used to provide additional clinical characterization or
to help fill in missing details on subjects studied in a cohort or bio -
bank. In either case, the result would be a rich clinical characterization
of patients at low cost and with linkages to corresponding biological
samples that can be used for molecular studies. Research questions
could be addressed faster and at lower cost as compared to the cur-
rent standard practice of designing large, labor-intensive prospective
studies.
• EHRs permit longitudinal analyses of data from millions of people.
When linked with genomic information—yielding what has been
called EHR genomic research—EHRs could provide the large num-
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WHY NOW?
a value, but too small to provide enough cases of less common diseases such as
site-specific cancers. Larger prospective cohort studies such as the Nurses’ Health
Study (Missmer et al. 2004), and the European Prospective Investigation into Can-
cer (EPIC) (Kaaks et al. 2005), have explored the relationship between circulating
steroid hormone levels and risk of breast cancer in cohorts of tens of thousands
of women, with many hundreds of cases, but the number of cases occurring in the
FHS is still relatively small due to the smaller size of the cohort. For risk factors
with small effects, or to study the interactions between multiple risk factors, even
the largest cohort studies may have too few cases to generate statistical power,
and consortia such as the NCI Breast & Prostate Cohort Consortium (Campa et
al. 2005) are needed to generate the thousands of cases necessary for adequate
power. For less common diseases, consortia are again needed as no single study
will have enough cases.
Some countries have established very large prospective cohort studies such
as the UK Biobank (about 500,000 persons) (Palmer 2007), and some have ad-
vocated for a similar study in the United States (Collins 2004). However, the cost
of enrolling half a million or more persons in a such a research study in the US,
tracking them over decades, and obtaining information on medical diagnoses for a
research database are estimated to be several billion dollars (Willett et al. 2007),
even if the many feasibility issues can be overcome.
An Information Commons and Knowledge Network with appropriate infor-
matic and consent mechanisms could generate similar large longitudinal sample
sets and data through the provision of regular medical care, rather than consider-
ing these as research studies external to the health systems.
bers of subjects and detailed information needed to resolve many of
the questions that smaller cohorts cannot address.
While the use of data from EHRs faces many difficulties, none of these
difficulties is insurmountable. The cost advantages and potential to advance
clinical care make expanding and accelerating studies using EHRs a high prior-
ity. Several health-care systems in the United States have started accruing large
EHR databases linked to clinical biosamples. Notable among the U.S. efforts
are the Harvard University/Partners Healthcare i2b2 effort, the Vanderbilt
BioVu effort (Roden et al. 2008), the UCSF-Kaiser collaboration (discussed in
Chapter 4) and the multi-center eMERGE Network (McCarty et al. 2011) (see
Box 2-3).
Since EHR systems cover a broad swath of human illness, they provide a
unique research opportunity to explore genotype-phenotype associations across
many diseases. As articulated by Jones et al. (2005) and implemented by Denny
et al. (2010), all diseases can be scanned using EHRs for significant associations
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with any genetic variant or set of variants. For example, a single genetic variant
(e.g., a single nucleotide polymorphism or SNP) associated with diabetes in a
standard genetics study can be promptly assessed for correlation with every
EHR-derived phenotype such as obesity, heart disease, smoking history, and hy-
pothyroidism. This approach has been colloquially termed PheWAS (Phenome-
Wide Association Study), in contrast with the more widely developed approach
Box 2-3
The eMERGE Consortium
The Electronic Medical Records and Genomics (eMERGE) Network (www.
gwas.org) is an NIH-funded consortium of five institutions with DNA data linked
to electronic medical records. (All of the institutions agreed to contribute their
genomic association results to dbGAP at the National Library of Medicine.) The
goal of the consortium is to assess the utility of electronic medical records (EMRs)
as resources for genomic science. The project includes an ethics component,
community engagement, and the use of natural language processingto interpret
EMRs. Each institution individually had proposed a genome-wide association
study (GWAS) of about 3,000 subjects with a particular phenotype of interest
(e.g. type 2 diabetes, cataracts, dementia, heart disease, and peripheral vascular
disease) and an associated comparison group.
Several important lessons have been learned from the consortium’s experi-
ence. First, patient data, obtained during the normal course of clinical care, has
proven to be a valid source for replicating genome-phenome associations that
previously had been reported only in carefully qualified research cohorts. Second,
although the individual institutions initially thought that they had large enough
effect sizes and odds ratios to be adequately powered, in most cases, the entire
network was needed to determine genome-wide association. Third, high-quality
EMR-derived phenotypes require four elements: codes (including ICD codes,
though codes have to be repeated multiple times to gain validity), laboratory-
medicine results, medication histories, and natural language processing of physi-
cian comments. The ability to extract high-quality phenotypes from narrative text
is essential along with codes, laboratory results, and medication histories to get
high predictive values. Fourth, although the five electronic medical systems have
widely varying structures, coding systems, user interfaces, and users, once vali-
dated at one site, the information transported across the network with almost no
degradation of its specificity and precision.
Another lesson of critical importance was that the major impediments that
the eMERGE Consortium has had to address are policy related, rather than
technical. For instance, a particular challenge has been to achieve both meaning-
ful data sharing and respect for patient privacy concerns, while adhering to ap-
plicable regulations and laws (Kho et al. 2011; Masys 2011; McGuire et al. 2011)
(eMERGE has addressed this issue, in part, by developing a simplified Data Use
Agreement—see Appendix D.)
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WHY NOW?
called GWAS (Genome-Wide Association Study). Such a scan may show that
the original association is either an epiphenomenon of another pathology or
part of a broader pathotype (Loscalzo et al. 2007). This approach provides an
opportunity to explore this broader range of pathological mechanisms across a
variety of disease types, which is not possible in single phenotype studies. The
power of such association studies to detect relationships between genotype and
disease is limited by the granularity and precision of the current taxonomic
system for disease. A knowledge-network-derived taxonomy that distinguishes
diseases with different biological drivers would enhance the power of associa -
tion studies to uncover new insights.
GATHERING INFORMATION FROM INFORMAL DATA SOURCES
The explosive growth of social networks, particularly in the context of
healthcare issues, may also serve as a novel source of data on health and disease.
Evidence is already accumulating that these alternative and “informal” sources
of health-care data, including information shared by individuals from ubiqui -
tous technologies such as smart phones and social networks, can contribute
significantly to collecting disease and health data (Brownstein et al. 2008, 2009,
2010a,b).
Many data sources exist outside of traditional health-care records that
could be extremely useful in biomedical research and medical practice. Infor-
mal reports from large groups of people (also known as “crowd sourcing”),
when properly filtered and refined, can produce data complementary to infor-
mation from traditional sources. One example is the use of information from
the web to detect the spread of disease in a population. In one instance, a
system called HealthMap, which crawls about 50,000 websites each hour using
a fully automated process, was able to detect an unusual respiratory illness in
Veracruz, Mexico, weeks before traditional public-health agencies (Brownstein
et al. 2009). It also was able to track the progression and spread of H1N1 on
a global scale when no particular public-health agency or health-care resource
could produce that kind of a picture.
The use of mobile phones also has tremendous potential, especially with
developers building apps that engage patient populations. For example, a re -
cent app called Outbreaks Near Me allows people to use their cell phones to
learn about all the disease events in their neighborhood. People also can report
back to the system, putting their own health information into the system.
Many of the social networking sites built around medical conditions are
patient specific and allow individuals to share unstructured information about
health outcomes. Mining that information within proper ethical guidelines
provides a novel opportunity to monitor health outcomes. For example, Google
has mined de-identified search data to build a picture of flu trends. The advent
of these inexpensive ways of collecting health information creates new oppor-
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tunities to integrate information that will enhance the diagnosis and treatment
of disease.
INTEGRATING CLINICAL MEDICINE AND BASIC SCIENCE
Traditionally, a physician’s office or clinic has had few direct connections
with academic research laboratories. In this environment, patient-oriented
research—particularly if it involved studying patients or patient-derived
samples with state-of-the-art scientific techniques and experimental designs—
required a major division of labor between the research and clinical settings.
Typically, researchers have used informal referral networks to make contact
with physicians caring for patients with diseases of special interest to the re -
searchers. Once enrolled in a research study, the patient—or, in some cases,
simply a tissue sample and a little clinical information—passed into a research
setting that maintained its own infrastructure, including Institutional Review
Boards (IRBs), patient coordinators, clinical evaluation centers, instrumenta -
tion, laboratory facilities, and data analysis centers. This approach often yielded
descriptive and anecdotal results of uncertain relevance to larger (and more
diverse) patient populations. Moreover, the patients who contributed are un -
likely to remain connected to the research process or be aware of outcomes. 1
This research model is ill-suited to long-term follow-up of patients since it was
never designed for this purpose.
Although remarkably successful in addressing its original goals of testing
clearly defined hypotheses, this traditional approach to clinical research is
poorly suited to answering current questions about human health that are often
more open-ended and larger in scope than those typically addressed in the past.
Based on committee experience and the input from multiple stakeholders dur-
ing the course of this study, including the two-day workshop, the Committee
identified several reasons that current study designs are mismatched to current
needs. Traditional designs:
• Require very large sample sizes—hence most studies are inevitably
under-powered. As emphasized above, the number and complexity
of questions inherent in genotype-phenotype correlations is virtually
unbounded. Patients with particularly informative genotypes and phe-
notypes—often difficult or impossible to recognize in advance—will
typically be rare. Identification and recruitment of such patients in suf -
ficient numbers to acquire clinically actionable information about their
1 There are notable exceptions such as the Framingham Heart Study and Nurses’ Health Study,
which were designed from the outset to follow a cohort of patients over an extended period of
time. See Box 2-2: Prospective Cohort Studies—A Special Role.
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WHY NOW?
diseases will be possible only if molecular and clinical information can
be combined in huge patient cohorts.
• Involve high costs that are largely unnecessary because of increas-
ing redundancy between the infrastructure present in research and
clinical settings. Most of what is needed to carry out data-intensive
molecular studies of huge patient populations already exists in the
health-care system or, increasingly, will exist as large coordinated
health-care organizations absorb increasing portions of the patient
population, EHRs are more widely implemented, medical decisions are
increasingly driven by molecular analyses (particularly in the realm of
oncology, but increasingly in other subspecialties as well), and consent
standards for treatment converge with those for both outcomes and
molecular research.
• Encourage building closed rather than open research systems. Re-
searchers who devote their careers to building the research infra-
structure described above, cultivating the physician networks, and
navigating the IRB process have little incentive to share patient sam -
ples and data widely. Indeed, the suite of obstacles that a young inves-
tigator must overcome to penetrate this system are a major disincentive
for involvement in patient-oriented research. In addition, the many
talented biomedical researchers who choose to focus their work on
model organisms (such as flies, worms, and mice) have little opportu -
nity to share insights or collaborate with clinical researchers.
• Leave most researchers and physicians living in separate, largely
disconnected communities. The current biomedical training system
separates researchers and physicians from the earliest stages of their
education and creates silos of specialized, but limited knowledge. The
insular nature of the current biomedical system does not encourage
interdisciplinary collaborations and has significant negative effects on
training, study design, prioritization of research efforts, and translation
of new research findings.
• Are poorly suited to long-term follow-up of patients. Long-term
follow-up was not required to conduct the first generation of genotype-
phenotype studies. The questions under investigation were typically of
the nature “Do all cystic fibrosis patients have loss-of-function muta-
tions in the CFTR gene?” Therefore, researchers who sought to estab-
lish the causal role of genotypes in particular phenotypes only needed
confidence that patients had been correctly diagnosed. However, ques-
tions such as “Do cystic fibrosis patients with particular genotypes do
better over a period of decades with particular treatments?” require
long-term follow-up.
• May not provide feedback on clinically relevant results for integra-
tion into a patient’s clinical care. To the extent that inherited germline
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36 TOWARD PRECISION MEDICINE
variation and/or somatic genomic patterns are predictive of prognosis
or response, the feedback of results to the clinical care of the indi -
vidual research subjects may or may not occur according to a complex
mixture of factors including the original informed-consent documents,
the logistics of re-contacting subjects, the perceived validity of the sci -
entific results (which may change over time), the time that has elapsed
between when a sample was taken and the results were generated, and
whether the laboratory work was performed under protocols that per-
mit results feedback. These limiting factors mean that most research
results are not integrated into clinical care. Expert opinion on the
“duty to inform” research participants of clinically relevant results vary
widely. Indeed, many researchers are reluctant to contribute data to a
common resource as it may expose them to questions about whether
feedback to participants is necessary or desirable.
For these, and many other reasons, the project of developing an Informa -
tion Commons, a Knowledge Network of disease, and a New Taxonomy re-
quires a long-term perspective. In a sense, this challenge has parallels with the
building of Europe’s great cathedrals–studies started by one generation will be
completed by another, and plans will change over time as new techniques are
developed and knowledge evolves. As costs in the health-care system are in -
creasingly dominated by the health problems of a long-lived, aging population,
one can imagine that studies that last 5, 10, or even 50 years can answer many
of the key questions on which clinicians will look to researchers for guidance.
Many patients are already put on powerful drugs in their 40s, 50s, and 60s that
they will take for the rest of their lives. The very success of some cancer treat-
ments is shifting attention from short-term survival to the long-term sequelae
of treatment. For all these reasons, the era during which a genetic researcher
simply needed a blood sample and a reliable diagnosis is passing.
Outcomes research is also creating new opportunities for a close integra -
tion of medicine and data-intensive biology. Cost constraints on health-care
services—as well as an increasing appreciation of how often conventional medi-
cal wisdom is wrong—has led to a growing outcomes-research enterprise that
barely existed a few decades ago. The requirements of outcomes researchers for
access to uniform medical records of large patient populations are remarkably
similar to those of molecularly oriented researchers.
MULTIPLE STAKEHOLDERS ARE READY FOR CHANGE
The tremendous recent progress in genetics, molecular biology, and infor-
mation technology has been projected to lead to novel therapeutics and im -
proved health-care outcomes with reduced overall health-care costs. However,
there is little evidence that these benefits are accruing in mainstream medicine
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37
WHY NOW?
(OECD 2011). Instead, health-care costs have steadily increased, and these
increased costs have not necessarily translated into significantly better clinical
outcomes (OECD 2011). This situation has created a “perfect storm” for a
wide variety of stakeholders, including health-care providers, payers, regulators,
patients, and drug developers. The economics of the present situation are not
sustainable and demand change.
Clinical and basic researchers have learned that for their collective efforts
to provide affordable improvements in health care, increased collaboration
and coordination are required. Public–private collaborations are needed to
combine longitudinal health outcomes data with new advances in technology
and basic research. Such initiatives are essential to gain and apply the specific
biological knowledge required to develop new approaches to treat and prevent
disease. A dynamically evolving Knowledge Network of Disease would provide
a framework in which a closer, more effective, relationship between clinical and
basic researchers could thrive.
Nowhere is the need for change more evident and urgent than in the
pharmaceutical and biotechnology industries. Despite a massive increase in the
amount of genomic and molecular information available over the past decade,
the number of effective new therapies developed each year has remained stable,
while the cost of developing each successful therapy has increased dramatically
(Munos 2009). While the new molecular technologies have identified a large
number of novel drug targets, an inadequate biological understanding of these
targets has resulted in an ever-increasing failure rate of expensive clinical tri -
als (Arrowsmith 2011a,b). The present situation in drug development is not
sustainable. The pharmaceutical and biotechnology industries are now lead -
ing proponents for developing public–private collaborations and consortia in
which longitudinal clinical outcomes data can be combined with new molecular
technology to develop the deep biological understanding needed to re-define
disease based on biological mechanisms. Given the time scale on which private
entities must seek return on investment, there is an increased willingness to
regard much of this information as precompetitive. Hence, the information
itself, and the costs of acquiring it, must be widely shared.
A major beneficiary of the proposed Knowledge Network of Disease and
New Taxonomy would be what has been termed “precision medicine.” In pre -
cision medicine, the ultimate end point is the selection of a subset of patients,
with a common biological basis of disease, who are most likely to benefit from
a drug or other treatment, such as a particular surgical procedure. Today, re-
searchers look for relatively small differences between treated and untreated pa-
tients in trials that involve unselected patients, with little insight into the biological
heterogeneity among the patients or their diseases. This approach requires a much
larger number of patients, more time, and greater costs to assess the effectiveness
of new therapies than would more targeted study designs. By using a precision-
medicine approach to focus on those patients early in the drug-development
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38 TOWARD PRECISION MEDICINE
Box 2-4
Precision Medicine for Drug Development
A successful example of the precision medicine approach to drug develop-
ment involves the drug Crizotinib, an inhibitor of the MET and ALK kinases, which
began clinical development in a broad population of patients with lung cancer
(Kwak et al. 2010). During the early stages of the initial Crizotinib clinical trial con-
ducted by pharmaceutical industry scientists, an independent group of academic
scientists published their discovery that a particular chromosomal translocation
involving the gene encoding ALK drives tumor growth in a subset of non-small
cell lung cancer patients (Soda et al. 2007). Access to this knowledge allowed the
pharmaceutical industry scientists to modify their clinical trial to look specifically
at a cohort of patients with this translocation, and the results were dramatic. For
those patients who had the translocation, the median disease-free survival with
Crizotinib was a year, compared to just a few months with the standard of care.
Thus, even in a trial that involved only a small number of patients that were com-
pared to historical controls, it was obvious that the drug was active. In contrast, in
an unselected patient population, most patients did not benefit from this drug and
it was unclear whether the drug had any activity.
(Crizotinib is expected to receive regulatory approval for treatment of ALK
translocation-positive lung cancer within the next year.)
process who are most likely to be helped, fewer side effects and reduced costs are
likely to ensue. In such studies, compliance will likely be better, treatment dura-
tion longer, and therapeutic benefits more obvious than is the case with traditional
designs. Greater therapeutic differences could also result in more efficient regula-
tory approval, and faster adoption by physicians and payers.
As illustrated in Box 2-4, data sharing is essential to the development of
precision medicine. Data sharing needs to occur across companies and across
academic institutions to ensure that everyone benefits from fundamental biolog-
ical knowledge. Institutions need to be convinced that they gain from openness.
Broad engagement of a vast array of public and private stakeholders, includ -
ing university scientists, regulators, health-care providers, payers, government,
and perhaps most importantly the public at large, will be required to support
and sustain the changes required for development of innovative new therapies
that improve health outcomes based on the proposed Knowledge Network of
Disease and associated New Taxonomy.
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WHY NOW?
PUBLIC ATTITUDES TOWARD INFORMATION
AND PRIVACY ARE IN FLUX
Genetic privacy was a central preoccupation during the early years of ge -
nomics, which led to implementation of stringent regulatory procedures to limit
the use of genetic data in patient-oriented research (Andrews and Jaeger 1991).
Many privacy-related issues—ranging from insurance coverage to employment
discrimination, social stigmatization, and the simple desire to be left alone—are
by no means resolved, although passage of the Genetic Information Nondis -
crimination Act (GINA) alleviates many concerns about such discrimination
(Hudson et al. 2008). During the ensuing years, the diffusion of the internet
into every corner of our lives is driving massive changes in public attitudes
toward privacy. Research studies of public attitudes reveal deep ambivalence
about informational privacy. In the particular arena of genetic information and
health records, members of focus groups typically grasp the broad social ben -
efits of sharing data. A consistent theme is that people who contribute their own
information to public databases want to be asked for permission, to have a clear
explanation of how the data will be used, and to be treated as true partners in
the research process (Damschroder et al. 2007; Trinidad et al. 2010; Haga and
O’Daniel 2011). Although privacy concerns remain, there is little evidence that
the public has the extreme sensitivity toward genetic data that many researchers
anticipated 25 years ago.
THE PROPOSED KNOWLEDGE NETWORK OF DISEASE
COULD CATALYZE CHANGES IN BIOLOGY, INFORMATION
TECHNOLOGY, MEDICINE, AND SOCIETY
The powerful forces affecting basic biological research, information tech -
nology, clinical medicine, and public attitudes toward the privacy of health
records and personal genetic information create an unprecedented opportunity
to change how biomedical research is conducted and to improve health out -
comes. The development of the proposed Knowledge Network of Disease and
its associated New Taxonomy could take advantage of these forces to inspire
revolutionary change. This Committee regards commitment to the development
of these resources as a powerful unifying idea that could harness—and, to an
appropriate degree, redirect—the creative energies of the key constituencies to
achieve the full potential of biology to improve health outcomes.
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