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1
Introduction
THE CURRENT OPPORTUNITY
Biomedical research and the practice of medicine, separately and together,
are reaching an inflection point: the capacity for description and for collecting
data, is expanding dramatically, but the efficiency of compiling, organizing,
manipulating these data—and extracting true understanding of fundamental
biological processes, and insights into human health and disease, from them—
has not kept pace. There are isolated examples of progress: research in certain
diseases using genomics, proteomics, metabolomics, systems analyses, and other
modern tools has begun to yield tangible medical advances, while some insight-
ful clinical observations have spurred new hypotheses and laboratory efforts.
In general, however, there is a growing shortfall: without better integration of
information both within and between research and medicine, an increasing
wealth of information is left unused.
As illustration, consider the following clinical scenarios;1 in the first exam-
ple, molecular understanding of disease has already begun to play an important
role in informing treatment decisions, while in the second, it has not.
Patient 1 is consulting with her medical oncologist following breast cancer
surgery. Twenty-five years ago, the patient’s mother had breast cancer, when
therapeutic options were few: hormonal suppression or broad-spectrum che -
motherapy with significant side effects. Today, Patient 1’s physician can suggest
a precise regimen of therapeutic options tailored to the molecular character-
istics of her cancer, drawn from among multiple therapies that together focus
on her particular tumor markers. Moreover, the patient’s relatives can undergo
1 These scenarios are illustrative examples describing typical patients. They are not based on
individual patients, but reflect current medical practice.
9
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10 TOWARD PRECISION MEDICINE
testing to assess their individual breast cancer predisposition (Siemens Health -
care Diagnostics Inc. 2008).
In contrast, Patient 2 has been diagnosed at age 40 with type 2 diabetes, an
imprecise category that serves primarily to distinguish his disease from diabetes
that typically occurs at younger ages (type 1) or during pregnancy (gestational).
The diagnosis gives little insight into the specific molecular pathophysiology
of the disease and its complications; similarly there is little basis for tailor-
ing treatment to a patient’s pathophysiology. The patient’s internist will likely
prescribe metformin, a drug used for over 50 years and still the most common
treatment for type II diabetes in the United States. No concrete molecular
information is available to customize Patient 2’s therapy to reduce his risk for
kidney failure, blindness or other diabetes-related complications. No tests are
available to measure risk of diabetes for his siblings and children. Patient 2 and
his family are not yet benefitting from today’s explosion of information on the
pathophysiology of disease (A.D.A.M. Medical Encyclopedia 2011; Gordon
2011; Kellett 2011).
What elements of our research and medical enterprise contribute to mak-
ing the Patient 1 scenario exceptional, and Patient 2 typical? Could it be that
something as fundamental as our current system for classifying diseases is actu -
ally inhibiting progress? Today’s classification system is based largely on mea-
surable “signs and symptoms,” such as a breast mass or elevated blood sugar,
together with descriptions of tissues or cells, and often fail to specify molecular
pathways that drive disease or represent targets of treatment.2 Consider a world
where a diagnosis itself routinely provides insight into a specific pathogenic
pathway. Consider a world where clinical information, including molecular
features, becomes part of a vast “Knowledge Network of Disease” that would
support precise diagnosis and individualized treatment. What if the potential of
molecular features shared by seemingly disparate diseases to suggest radically
new treatment regimens were fully realized? In such a world, a new, more ac -
curate and precise “taxonomy of disease” could enable each patient to benefit
from and contribute to what is known.
THE CHARGE TO THE COMMITTEE
In consideration of such possibilities, and at the request of the Director
of the National Institutes of Health, an ad hoc Committee of the National Re -
search Council was convened to explore the feasibility and need, and to develop
a potential framework, for creating “a New Taxonomy of human diseases based
on molecular biology” (Box 1-1). The Committee hosted a two- day workshop
2 To clarify, the committee is not suggesting that all diseases would have an equally precise
taxonomy, rather each disease should be classified, and treatment provided, using the best available
molecular information about the mechanism of disease.
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INTRODUCTION
Box 1-1
Statement of Task
At the request of the Director’s Office of NIH, an ad hoc Committee of the
National Research Council will explore the feasibility and need, and develop a
potential framework, for creating a “New Taxonomy” of human diseases based
on molecular biology. As part of its deliberations, the Committee will host a large
two-day workshop that convenes diverse experts in both basic and clinical disease
biology to address the feasibility, need, scope, impact, and consequences of defin-
ing this New Taxonomy. The workshop participants will also consider the essential
elements of the framework by addressing topics that include, but are not limited to:
• ompiling the huge diversity of extant data from molecular studies of
C
human disease to assess what is known, identify gaps, and recommend
priorities to fill these gaps.
• eveloping effective and acceptable mechanisms and policies for selection,
D
collection, storage, and management of data, as well as means to provide
access to and interpret these data.
• efining the roles and interfaces among the stakeholder communities—
D
public and private funders, data contributors, clinicians, patients, industry,
and others.
• onsidering how to address the many ethical concerns that are likely to
C
arise in the wake of such a program.
The Committee will also consider recommending a small number of case
studies that might be used as an initial test for the framework.
The ad hoc Committee will use the workshop results in its deliberations as
it develops recommendations for a framework in a consensus report. The report
may form a basis for government and other research funding organizations re-
garding molecular studies of human disease. The report will not, however, include
recommendations related to funding, government organization, or policy issues.
(see Appendix C) that convened diverse experts in both basic biology and clini-
cal medicine to address the feasibility, need, scope, impact, and consequences
of creating a “New Taxonomy of human diseases based on molecular biology”.
The information and opinions conveyed at the workshop informed and influ -
enced an intensive series of Committee deliberations (in person and by telecon-
ference) over a six-month period. The Committee emphasized that molecular
biology was one important base of information for the “New Taxonomy”, but
not a limitation or constraint. Moreover, the Committee did not view its charge
as prescribing a specific new disease nomenclature. Rather, the Committee
saw its challenge as crafting a framework for integrating the rapidly expand -
ing range and detail of biological, behavioral, and experiential information to
facilitate basic discovery, and to drive the development of a more accurate and
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precise classification of disease (i.e., a “New Taxonomy”), which in turn will
enable better medicine.
The vision for a New Taxonomy informed by the proposed “Knowledge
Network” shares some similarities with the widely discussed concept of “Per-
sonalized Medicine,” recently defined by the President’s Council on Advisors
on Science and Technology (PCAST) as “the tailoring of medical treatment
to the individual characteristics of each patient . . . to classify individuals into
subpopulations that differ in their susceptibility to a particular disease or their
response to a specific treatment. Preventative or therapeutic interventions can
then be concentrated on those who will benefit, sparing expense and side ef -
fects for those who will not” (PCAST 2008, p. 1). Others have used the related
term “Precision Medicine” to refer to a very similar concept (see Glossary).
Those who favor the latter term do so in part because it is less likely to be mis-
interpreted as meaning that each patient will be treated differently from every
other patient. However, to be clear, the use of either term in this report refers
to the PCAST definition.
A BRIEF HISTORY OF DISEASE TAXONOMIES
One of the first attempts to establish a scientific classification of disease was
undertaken by Carolus Linnaeus, who developed the taxonomic system that is
still used to classify living organisms. His 1763 publication Genera Morborum
(Linné 1763) classified diseases into such categories as exanthematic (feverish
with skin eruptions), phlogistic (feverish with heavy pulse and topical pain),
and dolorous (painful). The effort was largely a failure because of the lack of an
adequate understanding of the biological basis of disease. For example, without
a germ theory of disease, rabies was characterized as a psychiatric disorder
because of the brain dysfunction that occurs in advanced cases. This illustrates
how a classification system for disease that is divorced from the biological basis
of disease can mislead and impede efforts to develop better treatments.
Even 100 years ago, the Manual of the International List of Causes of Death,
second revision, (Wilbur 1911), which over time would become the Interna -
tional Statistical Classification of Diseases and Related Health Problems (ICD),
lumped lung cancer and brain cancer into the category of “cancer of other
organs or not specified.” No distinction was made between type 1 and type
2 diabetes, endocrine diseases were categorized under General Diseases, and
categories existed for “nervous fever,” “inanition,” and “found dead.”
Today, the ICD, which is currently in its tenth revision, remains the most
commonly used categorization of disease (WHO 2007). Published by the World
Health Organization (WHO), ICD-10 is used for statistical analyses, reimburse-
ment, and decision support, making it an integral part of health-care systems
throughout the world. As will be discussed, the ICD is currently undergoing a
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INTRODUCTION
major revision, which will result in the publication of ICD-11 in approximately
2015.
THE TAXONOMIC NEEDS OF THE BIOMEDICAL
RESEARCH AND MEDICAL PRACTICE COMMUNITIES
Taxonomies underpin many health-related systems, such as the organization
of the curriculum of medical education, the published biomedical literature, 3
textbooks, and disease coding systems such as the ICD. Although grounded
in a scientific understanding of disease, taxonomies such as the ICD must ad -
dress the needs of the ever-expanding public health and health-care delivery
communities across the globe. Organizations such as WHO must have access
to accurate and timely measures of disease incidence and prevalence in multiple
continents to make recommendations. Similarly, the health-care industry in the
United States depends on an accurate disease classification system to track the
delivery of medical care and to determine reimbursement rates. Both of these
communities rely on highly robust data collection practices to make decisions
that can impact millions of individuals. In this context, a formalized nomen -
clature is essential for clear communication and understanding. The current
practice of updating the ICD nomenclature periodically attempts to balance:
(1) the need for a consistent terminology to permit clear communication about
diseases that are defined by agreed upon criteria, with (2) the need to ensure
that the classification system (i.e., the taxonomy) properly reflects advances
in our understanding of molecular pathways and environmental factors that
contribute to disease origin and pathology.
However, in part because it must serve the administrative needs of the
public health and health-care delivery communities, the current ICD taxonomy
is disconnected from much of the biomedical research community (see Figure
1-1). Indeed, few basic researchers know of the existence of ICD, and even fewer
use this classification in any way. Thus, two extensive stakeholder groups, repre -
sented on one hand by biomedical researchers, and biotechnology and pharma-
ceutical industries, and on the other by clinicians, health agencies and payers,
are widely perceived to be largely unrelated, and to have distinct interests and
goals, and therefore taxonomic needs. This is unfortunate because new insights
into human disease emerging from basic research and the explosion of infor-
mation both in basic biology and medicine have the potential to revolutionize
disease taxonomy, diagnosis, therapeutic development, and clinical decisions.
However, more integration of the informational resources available to these
diverse communities will be required before this potential can be fully realized
3For example, an information resource used extensively by both clinicians and researchers,
PubMed/MEDLINE, is built on the MeSH terminology hierarchy of diseases.
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FIGURE 1-1 Integration would benefit all stakeholder communities.
(A) Different stakeholder communities are perceived to have distinct taxonomic and
informational needs. (B) Integration ofFigure 1-1and a consolidation of needs could
information
better serve all stakeholders.
Bitmapped
SOURCE: Committee on A Framework for Developing a New Taxonomy of Disease.
with the attendant benefits of more individualized treatments and improved
outcomes for patients.
MISSED OPPORTUNITIES OF CURRENT TAXONOMIES
Currently used disease classifications have properties that limit their infor-
mation content and usability. Most importantly, current disease taxonomies,
including ICD-10, are primarily based on symptoms, on microscopic examina -
tion of diseased tissues and cells, and on other forms of laboratory and imaging
studies and are not designed optimally to incorporate or exploit rapidly emerg -
ing molecular data, incidental patient characteristics, or socio-environmental
influences on disease. The ability of current taxonomic systems to incorporate
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INTRODUCTION
Box 1-2
A Flexnerian Moment?
In 1910 educator Abraham Flexner released a report that revolutionized
American medical education by advocating a commitment to professionaliza-
tion, high academic standards, and close integration with basic science (Flexner
1910). The subsequent rise of academic medical centers with a strong emphasis
on research—coupled, after World War II, with greatly expanded merit-based
funding of research through the National Institutes of Health and other public and
private entities—allowed the United States to capture global leadership in medical
research, launch the biotechnology industry, and pioneer countless science-based
innovations in health care.
The vast expansion of molecular knowledge currently underway could have
benefits comparable to those that accompanied the professionalization of medi-
cine and biomedical research in the early part of the 20th century. Indeed, during
his talk at the Committee’s workshop, Dr. Christopher G. Chute, a Mayo Clinic
professor and leader in the development of ICD-11, said that the potential of the
genomic transformation of medicine “far exceeds the introduction of antibiotics and
aseptic surgery.” However, achieving the full potential of the molecular revolution
will require—and to an important extent enable—re-thinking both biomedical re-
search and health care on a Flexnerian scale. Creation of a Knowledge Network
of Disease that consolidates and integrates basic, clinical, social, and behavioral
information, and that helps to inform a New Taxonomy that enables the delivery
of improved, more individualized health care, will be a crucial element of this
revolutionary change (Chute 2011).
fundamental knowledge is also limited by their basic structure. Taxonomies
historically have relied on a hierarchical structure in which individual diseases
are successively subdivided into types and subtypes. This rigid organizational
structure precludes description of the complex interrelationships that link dis -
eases to each other, and to the vast array of causative factors. It also can lead to
the artificial separation of diseases based on distinct symptoms that have related
underlying molecular mechanisms. For example, mutations in the LMNA gene
give rise to a remarkably diverse set of diseases, including Emery-Dreyfus mus -
cular dystrophy, Charcot-Marie-Tooth axonal neuropathy, lipodystrophy, and
premature aging disorders. However, despite their remarkable genetic, molecu-
lar, and cellular similarities, these diseases are currently classified as distantly
related. While this approach may have been adequate in an era when treatments
were largely directed toward symptoms rather than underlying causes, there
is a clear risk that continued reliance on hierarchical taxonomies will inhibit
efforts—already successful in the case of some diseases—to exploit rapidly
expanding mechanistic insights therapeutically.
A further limitation of taxonomic systems is the intrinsically static nature
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of their information content. The ICD system is designed to be updated every
ten years with minor updates every three years. But many organizations are still
working with ICD-9, which was released in 1977, even though ICD-10 was
released in 1992. Because of the time it takes to implement ICD revisions in
administrative systems, the current taxonomic system is perpetually outdated.
Moreover, the static structure of current taxonomies does not lend itself to the
continuous integration of new disease parameters as they become available.
This is particularly troublesome given that new data regarding the molecular
nature of disease are becoming available at an ever-increasing rate.
Current efforts to revise the ICD classification attempt to address these
limitations. ICD-11 will be based on a foundational layer from which “lineariza-
tions” will be derived (Tu et al. 2010). While the linearizations will be relatively
static and hierarchical, the foundational layer is being designed to support
multi-parent hierarchies and connections, and to be updated continuously. 4
Importantly, the new classification will combine phenomenological characteriza-
tion of phenotype with genomic factors that might explain or at least distinguish
phenotypes.5 Different lung cancers, for example, could be explicitly differenti-
ated by genomic characterization. This is important because knowledge about
the specific molecular pathways contributing to the biology of particular types of
lung cancer can be used to guide selection of the most appropriate treatment for
such patients.
Although the release of ICD-11 will mark an important step forward, the
Committee thinks that the amount of information available for this effort can
be vastly increased by a two-stage process leading to a Knowledge Network of
Disease. As discussed in detail in following sections of this report, the first stage
in developing this Knowledge Network would involve creating an Informa-
tion Commons containing a combination of molecular data, medical histories
(including information about social and physical environments), and health
outcomes for large numbers of individual patients. The Committee envisions
this stage occurring in conjunction with the ongoing delivery of clinical care to
these patients, rather than in specialized settings specifically crafted for research
purposes. The second stage, the construction of the Knowledge Network itself,
4 The ICD-11 revision process is closely coordinated with SNOMED—the Systematized Nomen-
clature of Medicine developed by the International Health Terminology Standards Development
Organization (IHTSDO). SNOMED is a large, clinically focused ontology that uses high-level nodes to
aggregate more granular data. The WHO and IHTSDO have signed a memorandum of understanding
so that the two systems will be complementary rather than competing. The intent is to harmonize the
two systems so that the aggregation layer of SNOMED corresponds to ICD-11 and the extensions of
ICD-11 become elements of SNOMED.
5 It should be noted that the International Classification of Diseases for Oncology (ICD-O) already
attempts to capture genomic data relevant to disease definitions. The third series of the WHO
Monographs on the Pathology and Genetics of Tumours sought to integrate genomic data, where
available, into disease definitions and indeed today many tumor types are molecularly defined
(Vardiman et al. 2009;. Campo et al. 2011; Travis et al. 2011).
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INTRODUCTION
would involve data mining of the Information Commons and integration of
these data with the scientific literature—specifically with evolving knowledge
of the fundamental biological mechanisms underlying disease.
Such a Knowledge Network of Disease would enable development of a
more molecularly-based taxonomy. This “New Taxonomy” could, for example,
lead to more specific diagnosis and targeted therapies for muscular dystrophy
patients based on the specific mutations in their genes. In other cases, it could
suggest targeted therapies for patients with the same genetic mechanism of
disease despite very different clinical presentations.
AN INFORMATION COMMONS, A KNOWLEDGE NETWORK,
AND A NEW TAXONOMY THAT WOULD INTEGRATE MANY
TYPES OF INFORMATION AND SERVE ALL STAKEHOLDERS
As will be described later in the report, the Committee envisions that the
Information Commons, which would underlie the Knowledge Network of
Disease and the New Taxonomy, would have some analogies with geographical
information systems (GISs), which are designed to capture, store, manipulate,
and analyze all types of geographically referenced data and make them widely
accessible in applications (ESRI 1990) such as Google Maps (Figure 1-2). Most
FIGURE 1-2 An Information Commons might use a GIS-type structure.
The proposed, individual-centric Information Commons (right panel) is somewhat
analogous to a layered GIS (left panel). In both cases, the bottom layer defines the
organization of all the overlays. However, in a GIS, any vertical line through the layers
Figure 1-2
connects related snippets of information since all the layers are organized by geographi -
cal position. In contrast, data in each Bitmapped
of the higher layers of the Information Commons
will overlay on the patient layer in complex ways (e.g., patients with similar microbiomes
and symptoms may have very different genome sequences).
SOURCE: FPA 2011 (left panel).
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FIGURE 1-3 A knowledge network of disease would enable a new taxonomy.
An individual-centric Information Commons, in combination with all extant biologi -
cal knowledge, will inform a Knowledge Network of Disease, which will capture the
exceedingly complex causal influencesigurepathogenic mechanisms that determine
F and S-1, 1-3
Bitmapped
an individual’s health. The Knowledge Network of Disease would allow researchers
to hypothesize new intralayer cluster and interlayer connections. Validated findings
that emerge from the Knowledge Network, such as those which define new diseases or
subtypes of diseases that are clinically relevant (e.g., which have implications for patient
prognosis or therapy) would be incorporated into the New Taxonomy to improve di -
agnosis and treatment.
SOURCE: Committee on A Framework for Developing a New Taxonomy of Disease.
users would interact with these resources at the higher-value-added levels,
the Knowledge Network and the New Taxonomy, rather than at the level of
the underlying Information Commons (Figure 1-3). Investigators using the
Knowledge Network of Disease could propose hypotheses about the impor-
tance of various inter- and intra-layer connections that contribute to disease
origin, severity, or progression, or that support the subclassification of particu -
lar diseases into those with different molecular mechanisms, prognoses, and/
or treatments, and these ideas then could be tested in an attempt to establish
their validity, reproducibility, and robustness. Validated findings that emerge
from the Knowledge Network of Disease and are shown to be useful for defin -
ing new diseases or subtypes of diseases that are clinically relevant (e.g., which
have implications for patient prognosis or therapy) could be incorporated into
the New Taxonomy to improve diagnosis and treatment. Whether the “New
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INTRODUCTION
Taxonomy” that is informed and refined by the Knowledge Network of Disease
would best be realized as a modification of the ICD taxonomy, or should rep-
resent an entirely distinct taxonomy that exists in parallel with ICD and other
taxonomies, will depend on a number of factors. However, in either case, the
goal of basing the New Taxonomy on the Knowledge Network of Disease will
be to improve markedly the quantity and quality of information that can be
used in biomedicine for the basic discovery of disease mechanisms, improved
disease classification, and better medical care.
RATIONALE AND ORGANIZATION OF THE REPORT
Today, historic forces are transforming biomedical research and health
care. Information technology, clinical medicine, and the public attitudes that
govern the ways that science, medicine, and society interact are all in flux.
A Knowledge Network of Disease could embrace and inform rapidly ex-
panding efforts by the biomedical research community to define at the mo -
lecular level the disease predispositions and pathogenic processes occurring
in individuals. This network has the potential to play a critical role across the
globe for the public-health and health-care-delivery communities by enabling
development of a more accurate, molecularly-informed taxonomy of disease.
This report lays out the case for developing such a Knowledge Network
of Disease and associated New Taxonomy. Chapter 2 asks “Why now?” It
examines basic trends in research, information technology, clinical medicine,
and public attitudes that have created an unprecedented opportunity to influ -
ence biomedical research and health-care delivery in ways that will benefit all
stakeholders.
Chapter 3 asks “What would a Knowledge Network of Disease and New
Taxonomy look like?” It describes why the system needs to be dynamic, con -
tinuously evolving, integrative, and flexible, and why it needs to enable inter-
rogation by a wide range of users, from basic scientists to clinicians, health-care
workers, and the general public.
Chapter 4 asks “How do we get there?” It describes the need for a series of
pilot studies to evaluate the feasibility of creating an individual-centric Informa-
tion Commons and deriving a Knowledge Network and New Taxonomy from
it, and to begin to explore the utility of these resources for improving individual
health outcomes. This chapter also addresses the impediments that need to be
overcome and changes in medical education that will be required before the
Knowledge Network of Disease and resulting New Taxonomy can be expected
to achieve their full potential for improving human health.
In Chapter 5, the report closes with an epilogue that summarizes the Com -
mittee’s rationale for its recommendations and describes how the new resources
described in this report could serve the needs of basic scientists, translational
researchers, policy makers, insurers, medical trainees, clinicians, and patients.
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