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3
Informatics and
Personalized Medicine
POINTS HIGHLIGHTED IN THE
KEYNOTE ADDRESS BY LEROY HOOD
ˇ he digital information of the genome and the environmental
T
information that modifies the digital genomic information con-
nect to produce the phenotype through biological networks.
ˇ A systems view of disease postulates that disease is the result
of perturbation of one or more biological networks, leading to
altered expression of information.
ˇ User-, domain-, and data-driven analytic informatics tools will
allow researchers to decipher the billions of data points col-
lected for an individual and use them to define and understand
individual wellness and disease.
ˇ Together, systems biology and advanced informatics tools
applied to disease and wellness provide the foundation for a
personalized approach to medicine, allowing health care to be
more predictive, preventive, personalized, and participatory.
Presenting the keynote address of the workshop, Leroy Hood, president
and co-founder of the Institute for Systems Biology, shared his perspective
on how the tremendous volumes of data currently becoming available can
be utilized to advance medicine. An essential problem in biology in the 21st
31
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32 INFORMATICS NEEDS AND CHALLENGES IN CANCER RESEARCH
century is complexity, Hood said. He described his vision for the conver-
gence of systems biology and the "digital revolution" to transform medicine
into an enterprise that he has termed "proactive P4 medicine," in which P4
refers to predictive, preventive, personalized, and participatory.
Hood said that the digital revolution has provided three key elements
that will play a central role in medicine going forward: (1) big datasets,
(2) social and business networks that evolve from the knowledge gained
from big datasets, and (3) digital personalized devices that will lead to the
generation of a "quantized self."
Hood predicted that in 10 years, each individual will be surrounded
by a virtual data cloud of billions of data points from many different
types of networks (e.g., genome, proteome, transcriptome, epigenome,
phenome, single cells, transactional, telehealth, social media). Big datasets
have a lot of noise, and we must be able to analyze the individual types of
data and put them into higher meta-level structures that will increase the
signal and reduce the noise. Then we will be able to integrate these data
to make predictive and actionable models to guide and inform health and
medicine, he said.
AN INTEGRATIVE SYSTEMS APPROACH TO
BIOLOGY, MEDICINE, AND COMPLEXITY
Biological complexity comes from the random and chaotic process of
Darwinian evolution. Evolution arises from random mutations, is driven by
environmental challenges, and selects solutions building on past successes,
Hood said. To understand how a complex system achieves its end goal, one
has to define all of the elements present, their interconnectivity, and their
dynamics. This is the essence of systems biology.
Hood described his personal involvement in four paradigm changes
that led him to the conceptualization of P4 medicine. First, bringing
engineering to biology catalyzed high-throughput biology (e.g., instru-
ments for automated sequencing and synthesizing genes and proteins).
High- throughput biology, he noted, was the beginning of large datasets
in biomedical research. Second, automated sequencing led to the human
genome project, which democratized genes (i.e., made them available to
all biologists) and created a complete gene "parts list" that was essential for
systems biology. Making automated sequencing a reality required a chem-
ist, an engineer, a computer scientist, and a biologist, and Hood realized
that the futures of biology and technology were intertwined. For the third
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INFORMATICS AND PERSONALIZED MEDICINE 33
paradigm change, Hood founded the first cross-disciplinary university
biology department to couple technology and analytic tool development to
leading-edge biology research. Then, in 2000, he launched the first institute
for the study of systems biology.
Simply stated, systems biology is a holistic and integrative approach to
studying biological complexity, where frontier biology drives new technolo-
gies, which in turn catalyze novel domain-driven and data-driven computa-
tional tools; systems medicine is the application of the strategies, technologies,
and computational tools of systems biology to disease and wellness; and P4
medicine is the clinical application of systems medicine to patients.
An integrated systems approach to disease is essential for dealing
with complexity, Hood said, and he elaborated on the five pillars of that
philosophy:
1. Viewing biology and medicine as informational sciences is one key to
deciphering complexity.
2. Systems biology infrastructure involves a cross-disciplinary culture,
democratization of data-generation and data-analysis tools, and the
power of model organisms to decipher complexity.
3. Holistic systems experimental approaches enable deep insights into
disease mechanisms and new approaches to diagnosis and therapy.
4. Emerging technologies enable large-scale data acquisition and permit
exploration of new dimensions of patient data space.
5. Transforming analytic tools allow researchers to decipher the billions
of data points for the individual, detailing wellness and disease.
Biology and Medicine as Informational Sciences
Human phenotypes are specified by two types of biological infor-
mation: the digital information of the genome and the environmental
information that modifies this digital information. These interact to gen-
erate the phenotype through biological networks that capture, transmit,
process, and pass the information on to simple and complex molecular
machines that execute the biological functions of life. Both the digital infor-
mation and the environmental information are needed to understand how a
system works. Hood pointed out that all levels of the biological information
hierarchy--from DNA to RNA, proteins, interactions and networks, cells,
organs, individuals, populations, and ecologies--are modified by environ-
mental signals. If one wanted to understand the system at the level of the
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34 INFORMATICS NEEDS AND CHALLENGES IN CANCER RESEARCH
cell, for example, all of the preceding levels of information in the hierarchy
would have to be captured and integrated in a way that can elucidate the
contributions of the environmental signals in each of those steps.
Each technology will require a domain-driven software pipeline for
acquisition, validation, storage, mining, integration, visualization, and
modeling of data. Validation1 is key, Hood stressed, and there currently are
very few examples of good validation. In addition, different laboratories
may not generate data that are interoperable, even if they use the same
systems.
Systems Biology Infrastructure
Leading-edge biology research drives the development of technology
and computation, facilitating the exploration of data in new dimensions,
as well as a cycle of biological information (Figure 3-1). What is required
for this cycle to be successful, Hood said, is a cross-disciplinary research
environment, including biology, chemistry, computer science, engineering,
mathematics, physics, and perhaps other disciplines, working together and
communicating effectively in teams. A systems biology approach also facili-
tates democratization of the tools for data generation and analysis, so that
any individual scientist can use them, regardless of the size of the project.
This does not mean that every scientist has to learn how to do everything,
Hood said, but there should be an environment that creates opportunities.
Holistic Systems Experimental Approaches
The systems approach to biology is holistic, seeking to understand how
the individual components assemble and collectively create a functional
whole. Hood used the analogy of a radio as a collection of individual tran-
sistors assembled onto circuit boards that together convert radio waves into
sound waves. Human beings are essentially a collection of biological circuits
and networks that process information into health or disease outcomes.
In an experimental systems biology approach, one creates a model from
1Validation refers to processes to ensure that the data are accurate and reproducible.
Validation may also entail assessing an assay's measurement performance characteristics to
determine the range of conditions under which the assay will give reproducible and accurate
data (analytical validation), as well as assessing a test's ability to accurately and reliably predict
the clinically defined disorder or phenotype of interest (clinical/biological validation).
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INFORMATICS AND PERSONALIZED MEDICINE 35
Biological Information
BIOLOGY ˇ Biology
ˇ Chemistry
Cross-Disciplinary ˇ Computer Science
ˇ Engineering
Culture,
ˇ Mathematics
Team Science ˇ Physics
COMPUTATION TECHNOLOGY
FIGURE 3-1 Systems biology infrastructure.
SOURCE: Hood presentation (February 27, 2012); Hood and Flores, 2012. Reprinted
with permission from New Biotechnology.
extant data, formulates Figure 3-1 new
a hypothesis, and iteratively tests the model through
experimental perturbations of the system. This can be both hypothesis
driven and hypothesis generating and can produce large datasets. Experi-
mental systems biology involves global analysis of all components (e.g.,
DNA, RNA, protein), evaluation of the dynamics of systems (both tempo-
ral and spatial), and integration of the different datasets from the system.
Hood noted that large datasets are subject to two types of noise: technical
noise resulting from how the data are acquired, managed, and analyzed and
biological noise that arises as a natural consequence. Subtractive analysis can
be used to help minimize biological noise. Ultimately, the goal is to convert
data into knowledge.
A Systems View of Disease
A systems view of disease postulates that disease arises when one or
more biological networks become perturbed, thereby altering the infor-
mation they express. Systems medicine, Hood said, is really a network of
networks. There are genetic networks at the level of the DNA, molecular
networks at the level of proteins and other small molecules, cellular net-
works, organ networks, and social networks (Figure 3-2). We need to be
able, from the various -omics data, to generate networks at each of these
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36 INFORMATICS NEEDS AND CHALLENGES IN CANCER RESEARCH
Networks organize,
integrate, and model
data to enormously
increase the signal-to-
noise ratio
FIGURE 3-2 Systems medicine: A network of networks.
Figure
SOURCE: Hood presentation (February 3-2.eps
27, 2012); Hood and Flores, 2012. Reprinted
bitmap
with permission from New Biotechnology.
levels and to integrate those networks to begin to understand the system.
From a research perspective, networks are powerful ways of organizing,
integrating, and modeling data, significantly increasing the signal-to-noise
ratio. A systems approach to disease also allows the researcher to follow the
disease from inception to end.
As an example, Hood described a systems approach to studying neuro
degeneration in a mouse model. Briefly, inbred mice were injected with
infectious prion particles and followed for changes in the transcriptome
of their brains relative to the brains of normal littermates. Surprisingly,
Hood and colleagues observed that 7,400 genes, or one-third of the mouse
genome, were differentially expressed. The researchers then constructed
eight combinations of inbred mouse strains and prion strains, carefully
designed to exhibit different biologies that could be subtracted away. For
example, one construct was a double knockout for the prion gene. When
infectious prion particles were injected, the mice did not show signs of
disease, but their brains underwent changes. Those changes that were not
related to disease could be subtracted away. Subtractive transcriptome
analysis based on this and the seven other mouse-prion strain combinations
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INFORMATICS AND PERSONALIZED MEDICINE 37
resulted in 300 differentially expressed genes. Subsequent serial histopathol-
ogy identified four major disease-perturbed networks that played a major
role in this prion disease process, and two-thirds of the genes mapped into
these four networks. The remaining 100 genes defined six new networks
that were previously unknown participants in prion disease. This is the
power of global analyses, Hood said. There was also a sequential disease
perturbation of all 10 of these networks, and the combined dynamics of
these 10 networks can explain nearly all of the pathophysiology of prion
disease (Hwang et al., 2009).
This study provided many new insights into potential biomarkers
and diagnostics, Hood said. For example, he and his colleagues were able
to demonstrate presymptomatic diagnosis of prion disease. They also
established a fingerprint of the normal levels of 100 brain-specific proteins
in human blood. The levels of proteins whose cognate networks become
perturbed in disease will likely be altered in the blood, resulting in a unique
fingerprint for each disease. Hood predicted that organ-specific blood
proteins also will have utility for early disease detection, disease stratifica-
tion, disease progression, following the progress of therapy, and assessing
recurrences. Systems-driven blood-based diagnostics will be the key to P4
medicine, he said.
Emerging Technologies
Emerging technologies allow for large-scale data acquisition and analysis.
Hood described four technology-driven projects with potential commercial
applications on which the Institute for Systems Biology is currently working:
1.Complete genome sequencing of families--integrating genomics
and genetics to find disease genes
2.The Human Proteome Project--selected reaction monitoring
(SRM) mass spectrometry assays for all human proteins
3.Clinical assays for patients--allowing new dimensions of data
space to be explored
4.The Second Human Genome Project--mining all complete
human genomes and associated phenotypic or clinical data for the
predictive medicine of the future
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38 INFORMATICS NEEDS AND CHALLENGES IN CANCER RESEARCH
Family Genome Sequencing
The family genome sequencing project began with a family of two
unaffected parents and two children each with genetic disease (Roach
et al., 2010). By sequencing the genomes of a family of four and apply-
ing the principles of Mendelian genetics, one can identify 70 percent of
the sequencing errors in the family, identify rare variants, determine the
chromosomal haplotypes, determine the intergenerational mutation rate,
and identify candidate genes for simple Mendelian diseases.
Hood predicted that within a decade, the human genome will be a
part of every patient's medical record, and in 5 years, sequencing a human
genome could cost as little as $100. He also predicted that any societal and
scientific objections to routine genome sequencing will fade as actionable
gene variants are identified. Every year, for example, patients could be
checked for newly discovered actionable gene variants and provided with
information that might be relevant to optimizing their health.
Hood added that the Institute for Systems Biology has developed a
variety of software packages to manage the data from the family genomics
project.
The Human Proteome Project
The Institute for Systems Biology pioneered four major advances that
led to the consideration of a human proteome project, Hood explained.
The first advance, the Trans Proteomic Pipeline, is a suite of software pro-
grams that validate mass spectrometry data. The software was developed
using a bottom-up approach, driven by domain expertise and data. After
validation, data are entered into the second new tool, the Peptide Atlas. The
third new approach is targeted proteomics, which is the ability to use triple-
quadrupole mass spectrometry to analyze 100 to 200 proteins in one hour.
Finally, Hood and colleagues created targeted proteomic assays for most of
the known 20,333 human proteins, and the results are being cataloged in
the SRMAtlas database.
Moving forward, Hood suggested that the key clinical technology is not
likely to be mass spectrometry but microfluidic protein chips. The Institute
for Systems Biology, in collaboration with Caltech, currently has a proto-
type chip containing 50 ELISAs (enzyme-linked immunosorbent assays)
that can be completed in about 5 minutes using 300 nanoliters of plasma. In
the future, Hood would like to be able to assay 2,500 organ-specific blood
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INFORMATICS AND PERSONALIZED MEDICINE 39
proteins (50 from each of 50 organs) from millions of patients and follow
them longitudinally to monitor wellness (as opposed to disease assessment)
of each of those major organs.
Clinical Assays
Another focus of the Institute for Systems Biology is the development
of individual patient informationbased clinical assays. There are genomic
and proteomic assays in development as well as single-cell analysis and
induced pluripotent stem cells (iPS cells) for biology research and diagnos-
tics development.
Single-cell analysis will impact the way we think about cancer, Hood
said. For example, he observed quantitative transcriptome clustering of
single cells from the human glioblastoma cell line U87. This is in contrast
to a whole-tumor sequencing approach, where the signals of the individual
cells are averaged and noise is enhanced. The reasons for this cellular
heterogeneity are as yet unknown.
One ongoing project that Hood described involves differentiating iPS
cells from healthy and diseased individuals into neurons, exposing them to
environmental signals, and then using global and single-cell -omics analy-
sis to try to understand the relative contributions of the digital genome
and the environmental signals. Another example of the use of iPS cells is
the stratification of complex genetic diseases such as Alzheimer's disease.
The project involves creating iPS cells for each individual in the family of
an affected individual; differentiating the cells into neurons; conducting
single-cell analyses to identify and sort quantized cell states; exposing the
sorted neuron populations to environmental probes such as drugs, ligands,
or small interfering RNA (siRNA); and analyzing the transcriptome, select
proteomes, microRNAome, etc. The intent is to stratify different subtypes
of disease-perturbed networks and their response to environmental signals.
The substratified populations of Alzheimer's patient cells could be provided
to pharmaceutical companies to test the more than 100 drugs under inves-
tigation for Alzheimer's.
Domain-Driven, Transforming Analytic Tools
The last pillar of the integrative systems approach to disease that Hood
described is the development of bottom-up, domain-driven analytic tools
that will allow researchers to decipher the billions of data points collected
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40 INFORMATICS NEEDS AND CHALLENGES IN CANCER RESEARCH
for an individual and use them to define and understand individual well-
ness and disease. Hood mentioned The Cancer Genome Atlas (TCGA) as
one example of genomic data integration and analysis. There are also efforts
toward vertical integration, that is, integration of networks.
Hood advocated an open source platform for the development of soft-
ware, suggesting that the advantages outweigh the challenges. In addition,
he said software development should be driven by users, domain expertise,
and data. Because of the complexity of biology, development needs to be
bottom-up.
Hood highlighted several challenges to software development, includ-
ing integrating biological expertise with statistical and computational
expertise. Other challenges are integrating individual software packages or
modules into coherent platforms for comprehensive modeling and deter-
mining the level of granularity of biological information that is needed.
APPLICATIONS OF SYSTEMS MEDICINE: THE P4 APPROACH
Together, systems biology and advanced informatics tools applied to
disease and wellness provide the foundation for a personalized approach to
medicine, allowing health care to be more predictive, preventive, personal-
ized, and participatory. Hood outlined his perspectives and predictions
about how a P4 approach to medicine could look in the future (Box 3-1).
Information Technology for Health Care
Information technology infrastructure is key to the advancement of
P4 medicine, Hood said. There will be a need for sufficient infrastructure
development and maintenance cycles; storage solutions for the vast amount
of genomics, proteomics, and other data; and analytic tools that can access
stored data from desktop platforms. Hood reiterated that systems should
be open source, extensible, and interoperable, and the development of solu-
tions should be domain expertise driven and data driven (i.e., bottom-up).
Among the technology and informatics challenges Hood listed were
the lack of standards for electronic medical information; how to handle
conventional medical records and histories as well as molecular, cellular,
and phenotypic data; how to identify the actionable gene variants in indi-
vidual genome sequences; and how to handle the comparative and subtrac-
tive analyses of billions of genomes and associated phenotypic data. Such
challenges have also been noted by the President's Council of Advisors on
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INFORMATICS AND PERSONALIZED MEDICINE 41
BOX 3-1
P4 Medicine: Perspectives from Leroy Hood on
What the Future Could Hold
Predictive
ˇ n 10 years, most individuals will have had their genome
I
sequenced.
ˇ Genomic information will be used to design probabilistic health
history, a lifelong strategy that will optimize individual wellness
and manage the potential for disease.
ˇ Individuals will have regular, multiparameter blood measure-
ments done, assaying perhaps up to 2,500 organ-specific
blood proteins at once, to predict any transition from health into
disease and facilitate a timely response.
Preventive
ˇ aking a systems medicine approach, therapeutic and preven-
T
tive drugs and vaccines will be developed to modify disease-
perturbed networks so that they operate in a more normal
fashion.
ˇ Maintaining wellness (rather than treatment of disease) will
increasingly be the focus of medicine.
Personalized
ˇ ndividuals are genetically unique, differing by 6 million nucleo-
I
tides from one another.
ˇ Each patient will serve as his or her own control for analysis of
the vast, longitudinal datasets that will be generated.
Participatory
ˇ he patient will become the center of the P4 health care net-
T
work, and patient-driven social networks for disease and well-
ness will be a driving force for change.
ˇ S ociety should be able to access patient data after de-
identification and make them available to biologists for pioneer-
ing the predictive medicine approaches of the future.
ˇ Patients, physicians, and other members of the health care
community will have to be educated about P4 medicine so they
can participate fully.
SOURCE: Hood presentation (February 27, 2012).
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42 INFORMATICS NEEDS AND CHALLENGES IN CANCER RESEARCH
Science and Technology (PCAST) in its recent report on the use of health
information technology to improve health care (PCAST, 2010).
The digital revolution will generate enormous amounts of useful per-
sonal data including, for example, imaging data, longitudinal data, and
social network data, existing together in a dynamic "network of networks,"
Hood concluded. There will have to be ways to integrate all of these data
into predictive models and actionable opportunities, he said.
REFERENCES
Hood, L., and M. Flores. 2012. A personal view on systems medicine and the emergence
of proactive P4 medicine: Predictive, preventive, personalized and participatory. New
Biotechnology March 18. [Epub ahead of print].
Hwang, D., I. Y. Lee, H. Yoo, N. Gehlenborg, J. H. Cho, B. Petritis, D. Baxter, R. Pitstick,
R. Young, D. Spicer, N. D. Price, J. G. Hohmann, S. J. Dearmond, G. A. Carlson, and
L. E. Hood. 2009. A systems approach to prion disease. Molecular Systems Biology 5:252.
PCAST (President's Council of Advisors on Science and Technology). 2010. Report to the
President: Realizing the full potential of health information technology to improve healthcare
for all Americans: The path forward. http://www.whitehouse.gov/sites/default/files/
microsites/ostp/pcast-health-it-report.pdf (accessed July 10, 2012).
Roach, J. C., G. Glusman, A. F. Smit, C. D. Huff, R. Hubley, P. T. Shannon, L. Rowen, K. P.
Pant, N. Goodman, M. Bamshad, J. Shendure, R. Drmanac, L. B. Jorde, L. Hood, and
D. J. Galas. 2010. Analysis of genetic inheritance in a family quartet by whole-genome
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