Genome Medicine 2009, 1 1: :11
S Sy ys st te em ms s p ph ha ar rm ma ac co ol lo og gy y a an nd d g ge en no om me e m me ed di ic ci in ne e: : a a f fu ut tu ur re e p pe er rs sp pe ec ct ti iv ve e
Aislyn D Wist, Seth I Berger and Ravi Iyengar
Address: Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, One Gustave Levy Place, New York,
NY 10029, USA.
Correspondence: Ravi Iyengar. Email: email@example.com
A Ab bs st tr ra ac ct t
Genome medicine uses genomic information in the diagnosis of disease and in prescribing
treatment. This transdisciplinary field brings together knowledge on the relationships between
genetics, pathophysiology and pharmacology. Systems pharmacology aims to understand the
actions and adverse effects of drugs by considering targets in the context of the biological
networks in which they exist. Genome medicine forms the base on which systems pharmacology
can develop. Experimental and computational approaches enable systems pharmacology to obtain
holistic, mechanistic information on disease networks and drug responses, and to identify new
drug targets and specific drug combinations. Network analyses of interactions involved in
pathophysiology and drug response across various scales of organization, from molecular to
organismal, will allow the integration of the systems-level understanding of drug action with
genome medicine. The interface of the two fields will enable drug discovery for personalized
medicine. Here we provide a perspective on the questions and approaches that drive the
development of these new interrelated fields.
Published: 22 January 2009
Genome Medicine 2009, 1 1: :11 (doi:10.1186/gm11)
The electronic version of this article is the complete one and can be
found online at http://genomemedicine.com/content/1/1/11
© 2009 BioMed Central Ltd
I In nt tr ro od du uc ct ti io on n
Our knowledge of the mechanisms by which drugs act
physiologically advanced radically during the twentieth
century. With the advent of biochemistry and molecular
biology, the targets of drugs became increasingly well
characterized. The development of receptor theory by Clark 
and Black [2,3], followed by analyses that distinguished
between competitive and non-competitive inhibition, began to
shed light on the mechanisms by which drugs worked at the
molecular level . The influence and relevance of receptor
theory in modern pharmacology is derived from the large
number of drugs that target membrane receptors, the majority
of which are G protein-coupled receptors (GPCRs). The theory
of enzyme kinetics led to substrate-based inhibitor design of
drugs. These theoretical underpinnings, the size of the market
for specific classes of drugs and the ease of drug design for a
proven target have resulted in many similar drugs that can
target a single protein. ACE inhibitors that are used to treat
hypertension are good examples of this approach. The drug
pipeline has evolved, with the appearance of targeted
therapies and biological therapeutics, such as monoclonal
antibody therapies. Many diseases, such as hypertension,
ulcers and several types of cancer, that could not be treated
two generations ago, can now successfully be managed, if not
cured. Yet the ‘drugome’ (the proteins and genes that are
targeted by drugs approved by national regulators such as the
US Food and Drug Administration, FDA) covers only a small
fraction of the proteome or the ‘diseaseome’ (genes that have
been linked with disease), and many drugs are focused in just
a few areas (Figure 1) [5,6]. This disparity reflects the current
relationship between basic biological science and its use for
therapeutic purposes. There are substantial opportunities to
use the accumulated knowledge of biological processes for
drug discovery and clinical applications. If we are to take
advantage of such opportunities, genome medicine and
systems pharmacology need to be well integrated.
As the systems-level understanding of biological processes
expands, it is becoming a crucial driver of pharmacology that
is anchored in the human genome and personalized
medicine. The path from laboratory research to clinical
application is becoming short as translational research
grows, facilitating collaborations between basic researchers
and clinicians. Genomic and proteomic technologies drive
discovery of biomarker sets for the classification of diseases
and the stages of their progression, as exemplified by
microarray-based marker sets that have been developed to
identify stages of cancer progression [7,8]. Although more of
these approaches need to be discovered and then standard-
ized before they are routinely used in clinical practice, the
importance of using systems-type methodologies to charac-
terize therapeutic interventions, to delineate the pathways
(or more often networks) involved in disease, and to identify
the mechanisms of action and off-target effects of current
drugs is becoming clearer. A multi-faceted understanding of
therapeutic intervention is necessary, given the complexity
of human physiology and the increasing availability of
numerous clinical parameters and analyses.
Here, we explain the reasoning underlying the assertion that
systems-level knowledge of pharmacology and patho-
physiology, rooted in genomic information, will increase the
efficacy of existing drugs by aiding in the development of
personalized medicine and will facilitate the rational
discovery of new drugs using a much wider target base. For
pharmacology to be understood at a systems level, it is
necessary to use a genome-based approach to systems-level
studies of physiology and pathophysiology.
G Ge en no om me e m me ed di ic ci in ne e
An operational definition of genome medicine is the way in
which genomic information from a patient helps in the
diagnosis or treatment of disease. This includes areas such
as the genetics-based diagnosis of the origin of diseases,
their progression and the response to drugs. In the area of
drug response, genome medicine overlaps with pharmaco-
genomics, a field that studies how genome variation affects
drug response. It is well established that some diseases arise
from mutations in single genes and can be treated using the
wild-type product from that particular gene. Fabry’s disease
is an example of such a monogenic disease [9,10]. However,
it is now known that many complex diseases arise from
interactions and changes in multiple genes. The two-hit
model for cancer  was an early example of this recog-
nition. Mapping of genetic variations, such as single-nucleo-
tide polymorphisms (SNPs), in the human genome has given
rise to the idea that combinations of variant genes can alter
susceptibility to various diseases. Genome-wide association
studies have become popular over the past few years ,
although their ability to predict disease susceptibility is only
beginning to be determined. Nevertheless, it is clear there is
sufficient genomic variation between individuals to affect
the origin and progression of diseases, as well as drug
response. For example, genetic testing for cancer drugs, such
as imatinib [13,14], trastuzumab , gefitinib  and
bucindolol , provides information about whether the
form of the protein target that is expressed in each
individual will be responsive to the drug. Testing for the
gene BRCA1 is used to determine the feasibility of preventa-
tive measures for breast cancer . Other types of genetic
testing are more pharmacogenomic in nature, such as those
for warfarin [19,20] or tamoxifen . These tests screen for
polymorphisms in distinct cytochrome P450 isoforms, which
are responsible for differences in how the drug is metabo-
lized and thus affect the therapeutic index, the ratio between
the toxicity and effectiveness of the drug. Pharmaco-
genomics can thus be considered to be a very important part
of genome medicine. Recent views on pharmacogenomics
describe in greater depth the relationship between genomics
and drug response [22,23].
Our understanding of a patient’s genome will increasingly
drive medical practice in the 21st century. The development
of technologies for rapid sequencing of whole genomes is an
important factor in this changing approach to medical
practice. The growing number of treatment options for
pathophysiology, and the accumulation of knowledge about
the risk-to-benefit ratio of prescribing one therapy over
another when anchored in individual genomic information,
should allow the individualized tailoring of therapies. Such
tailoring would be based on an individual definition of the
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F Fi ig gu ur re e 1 1
Relationships between the genome, proteome, diseaseome and drugome.
The number of distinct protein species (about 400,000) comprising the
proteome (green circle, scaled down by 25% relative to the other circles),
is estimated by taking the approximately 25,000 currently annotated
genes (yellow circle) and assuming about four splice variants per gene and
about four post-translationally modified proteins per splice variant. The
genome, diseaseome and drugome form a Venn diagram. The red circle
represents the approximately 1,800 genes known to be involved in
various diseases (the diseaseome). Of these, a small fraction (the
drugome) is targeted by FDA-approved drugs. Not all drug targets have
been characterized as disease genes. In total, proteins encoded by
approximately 400 genes (0.1% of the proteome) are targeted by about
1,200 FDA-approved drugs. There are more drugs than protein targets
because more than one drug can target the same protein.
~4 splice variants per gene
~4 post-translational states
therapeutic index of a drug for a specific individual.
Currently, many clinical practices are based on empirical
trial-and-error approaches, and drug usage typically follows
a ‘one size fits all’ approach.
Even for diseases for which therapies have been very
successful, we generally do not understand why one type of
therapy works for one individual and not for another. For
example, there are four general types of therapy to treat
hypertension (thiazide-based diuretics, angiotension convert-
ing enzyme inhibitors or angiotension II receptor blockers,
calcium channel blockers and β-adrenergic receptor blockers
or beta-blockers) . There is relatively little predictability
as to which hypertension treatment will be more effective for
any given patient, and there is a large patient-to-patient
variability in response to each therapy and required dosage.
Current data suggest that 50% of patients who do not
respond to one type of therapy will respond to another and
that 70-80% of patients will respond if switched for a second
time to yet another type of antihypertensive drug . The
characterization of all of the hypertension-related genes in
the patient’s genome may facilitate the construction and
systems-level analyses of the regulatory networks, using
those hypertension-related genes as seed nodes. From the
physiological functions of these networks, we may be able to
identify the pathways involved in disease of an individual to
predict the action of a drug. Such personalized disease
networks for patients would allow clinical practice to move
away from a trial-and-error approach to prescribing drugs
and to advance to a more genome-informed disease manage-
ment. This type of clinical practice could easily be called
Although the state of the genome will be a major determi-
nant of how diseases originate and progress, it is unlikely to
be the only cause in many cases. Environmental factors also
have an important role. In many cases these two factors are
related, whereby a particular genotype changes the risk of an
environmentally induced disease. This relationship can be
understood by considering the biochemistry and physiology
of the system. Genes are most often transcribed and
translated into components of biochemical networks that
underlie cellular processes. Often there is more than one
type of change in a particular gene; some of these types of
change lead to a change in the function of a key cellular
component and ultimately to a disease state, whereas others
are inconsequential. Sometimes, even if a mutant gene
product behaves aberrantly at a biochemical level, this
behavior can be compensated for and the physiology remains
normal. Alternatively, the aberrant behavior of diseased cells
might not be only genetic in origin but also a result of
changes in normal cell signaling processes that regulate
transcription, translation and effector protein function. The
hypothesis that inflammation underlies the origins of several
diseases, for example, is based on the assumption that the
normal process has gone awry because of environmental
signals, resulting in sustained activation of intracellular
signaling networks that results in pathophysiologies. There
is accumulating evidence to support this hypothesis [26,27].
The effects of post-transcriptional and translational regula-
tion of signaling can increase the difficulty of developing
methods for individualized mechanism-based therapeutic
interventions. A systems understanding of disease and drug
action at the level of cellular biochemical reactions and
physiological function would be useful in framing the effect
of genomic variations in the context of environmental cues.
This is where systems pharmacology comes in.
S Sy ys st te em ms s p ph ha ar rm ma ac co ol lo og gy y
Systems pharmacology seeks to develop a global under-
standing of the interactions between pathophysiology and
drug action. To develop such an understanding it is
necessary to analyze interactions across and between various
scales of organization. The representations of different
scales are illustrated in Figure 2. The biological insights
gained from multi-scale analyses of physiological processes
have been noted previously [28,29]. Analysis of such multi-
scale systems requires one to ‘zoom’ in and out depending on
the type of analysis being conducted. During the process of
multi-scale analysis (zooming), it is essential that we develop
a mechanistic understanding of the relationships across the
various levels. Simply correlating structural information or
molecular interactions with clinical phenotypes is a good
starting point, but it will not yield the ability to predict
disease progression or drug treatment outcomes. The type of
‘system’ to be analyzed can vary depending on the zoom level
(Figure 2) of information desired. The system can be con-
sidered at the organismal, organ, tissue, cellular or
molecular levels. The effects of a drug on pathophysiology
that are seen at the organismal level, that is, symptoms or
clinical measurements, are zoomed-out observations. These
observations usually consist of clinical data, ranging from
blood chemistry to measurements reflecting organismal
function, such as blood pressure and stress tests, all of
which are documented in the electronic medical records of
the patient that will aid future computational analyses
The physiological responses to a drug or disease are
manifestations of events that can be studied at zoomed-in
perspectives. Emerging imaging technologies are comple-
menting pathology to observe disease progression and
treatment outcome. The entire network of events that are
involved in therapeutic intervention can be analyzed in
proteomic or genomic studies, or, at a more detailed level,
specific protein-protein interactions or enzymatic reactions
can be examined. Much of the zoomed-in systems pharma-
cology understanding comes from basic laboratory research.
The methodologies available to address the questions across
zoom levels include biochemical experiments, microarray
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and other high-throughput methodologies, animal models
and computational modeling and analysis of molecular and
cellular functions. Generally, a systems approach that inte-
grates knowledge from analyses across multiple zoom levels
is likely to uncover a new target in a pathway of interest, or
the basis for an observed adverse effect, that would probably
not have been discovered using traditional techniques. In
order for a discovery made at the molecular or cellular level
to be applicable clinically, the effect must be demonstrated
at the organ and organismal levels and established to be
applicable to a population. Thus, systems pharmacology uses
a range of approaches that spans multiple scales of
organization, as is shown in Figure 2. The different areas of
study in systems pharmacology and their relationship to
genome medicine are briefly discussed below.
Research in systems pharmacology integrated into genome
medicine tends to follow two directions: the ‘physiology to
therapeutic intervention’ direction (bottom-up) comprises
approaches that facilitate new drug or drug-target discovery;
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F Fi ig gu ur re e 2 2
Multi-scale analyses in systems pharmacology. The top half of the figure is a schematic representation of different scales of organization involved in human
pathophysiology and systems pharmacology. Clinical indicators and analyses (left) indicate measurements of various types of blood concentrations, blood
pressure, stress and so on; these parameters are available in the electronic medical records of patients. From left to right, the scale becomes smaller, or
‘zoomed in’. The human body (or organism) can be analyzed at the levels of organs, tissues, cells (represented here together with tissues) or molecules.
Drugs are prescribed and taken at the organismal level but exert their effects by interacting with their target at the molecular level (red arrow). The
gradient from white to blue corresponds to the various levels of interaction systems: white represents a clinical setting; blue represents a laboratory
setting. Studies in systems pharmacology fully span all levels shown here.
cardiology, nephrology etc.
Drug action on targets
Levels of interacting systems
and the ‘therapeutic intervention to physiology’ direction
(top-down) focuses on the characterization of current drugs
across scales of interactions in terms of their mechanism of
action, off-target effects or similarity to other drugs. Studies
in the bottom-up direction include network analysis, which
provides a foundation for systems pathophysiology. Network
analysis can be used to identify new targets for therapeutic
intervention and to understand adverse events and drug
resistance from genomic information. Bottom-up analyses
are based on the integration between systems pharmacology
and genome medicine (Figure 3). The bottom-up approach
can be complemented by top-down studies and include
analyses of networks at various levels to study the effect of a
drug on a system, commonalities among drugs (global
analyses of the ‘drugome’), drug resistance and effects of
T Th he e r ro ol le e o of f s sy ys st te em ms s p pa at th ho op ph hy ys si io ol lo og gy y i in n b bo ot tt to om m- -u up p
i in nt te er rv ve en nt ti io on n
Many efforts to systematically understand the cellular
processes involved in disease lead to sets of proteins that are
mutated to become under- or overactive, deleted, over-
expressed or aberrantly post-translationally modified in the
disease state. Genomic and proteomic studies can be
designed to globally screen for cellular components that
stand out in the disease model. Subsequent small-scale
studies often focus on verifying one or several of the
predictions from the large-scale screen. In order to obtain a
multi-faceted analysis, techniques such as RNA interference
(RNAi) screens, gene-expression profiling, chromatin
immunoprecipitation (ChIP) analyses or protein micro-
arrays are combined. Analyses on patient-derived samples
allow data from humans to constrain and validate animal or
cell models of disease. Approaches such as these have been
applied to cancer [30-32], malaria , heart failure 
and HIV [35,36].
These global analyses to identify genes, proteins and other
cellular components related to the origin or progression of a
specific disease yield lists of ‘seed nodes’ that can be used to
computationally construct disease networks by integrating
knowledge about protein interactions reported in biomedical
literature. With additional experiments or analyses, it
becomes possible to place seemingly unrelated proteins in
the context of a pathophysiology and thus to explore whether
they are potential therapeutic targets or biomarkers. The use
of a network of known pathways surrounding genes with
annotated cardiovascular involvement, for example, enabled
the identification of a series of mass spectrometry bio-
markers for major adverse cardiac events . Similarly, a
network-based approach was able to identify sets of
interactions between nuclear receptors and insulin signaling-
pathway proteins related to type 2 diabetes . Identifi-
cation of previously unrelated relationships between disease
pathways and cellular functions is a first step in identifying
The accumulation of published systems-level datasets
related to drug action calls for new ways to compile the data
into a computable format. An example of this type of effort is
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F Fi ig gu ur re e 3 3
The relationship between genome medicine and systems pharmacology.
The diagram summarizes various aspects of genome medicine (in blue)
and systems pharmacology (in yellow). Overlapping aspects of analyses
and practice are in green (intersection of circles). The positioning of the
circles indicates the operational classification of ‘genome medicine to
systems pharmacology’ as top-down and ‘systems pharmacology to
genome medicine’ as bottom-up. The key analyses and practices are in the
circle for the field that uses them. Approaches and practices that are used
in both fields are in the overlapping region. Genome medicine starts with
genetic and genomic testing. Experimental data are computationally
processed using statistical genetics tools to yield information that is used
in personalized medicine for therapeutic-index targeting (such as dosage
of warfarin) and combination therapy. Network analysis is a common
approach that integrates genome medicine and systems pharmacology.
Systems pharmacology starts from cataloguing the characteristics of
individual drugs and targets from biochemistry and cell-physiology
experiments. Computational methods and genomic and proteomic data
together enable the use of this catalog of information to make predictions
regarding drug discovery, drug action and adverse events. Such
predictions can be experimentally and clinically tested. Approaches
common to both genome medicine and systems pharmacology are based
on network analyses that underlie systems pathophysiology, whereby the
origins of disease are understood in the context of multi-scale systems.
Such understanding enables network-based drug screening and whole
genome-based predictions of adverse events and drug resistance. Thus,
ultimately, therapeutics intervention will be guided by integrating genome
medicine and systems pharmacology.
Predicting new targets, drug action and adverse events
Experimental and clinical
Genetic and genomic testing
Computational and statistical genetics
Drug and target characterization
Genome - adverse events and
drug resistance relationships
the Connectivity Map , which finds correlations between
gene-expression signatures and the sets of proteins involved
with the action of a class of drugs or with a particular disease.
Although the correlations found through the Connectivity
Map approach are an excellent starting point to gain insights
into how the gene-expression signatures indicate aberrant
cellular or physiological behavior, more detailed mechanistic
studies are necessary to develop predictive capabilities.
Even at the level of a single protein, a systems-level under-
standing becomes useful. A mutant protein implicated in
disease can have multiple changes in its behavior in the
network. These changes must be put into the context of the
disease ‘system’ in order to determine which property has
the highest impact. Different mutations in the small G
protein Ras, which are found in many cancers, increase Ras
activity by several different mechanisms . To under-
stand which mutation has a more important role in
oncogenesis, the effect of two mechanistically distinct Ras
mutations on the entire downstream signaling network was
analyzed computationally and the results were confirmed
experimentally . The computational model predicted
that a drug that specifically binds GTP-bound Ras will have a
more specific effect on the mutant than it will on the wild-
type system, and thus also on cancerous than on normal
tissue. Such a prediction could not have been made by
simply correlating mutations with disease phenotype. Thus,
a systems understanding of a single protein function in a
disease signaling network can predict new targets for
therapeutic intervention in a mechanism-based manner.
T To op p d do ow wn n a ap pp pr ro oa ac ch he es s: : g gl lo ob ba al l d dr ru ug g a an na al ly ys se es s
Direct relationships between diseases, drugs and proteins
enable statistical inferences about less obvious relationships
between them. Global analysis of FDA-approved drugs has
found that, unlike ubiquitously expressed essential genes,
drug targets tend to be expressed in specific tissues yet tend
to interact with many other proteins in the cellular network
while being independently regulated [5,6]. A bipartite graph
of 1,052 FDA-approved drugs interacting with 485 targets
contained 179 ‘islands’ (sets of nodes that are not connected
to any other node in the graph). Most of these islands are
made up of 10-30 interacting cellular components. A single
large island of 481 components consisted of drugs that target
GPCRs . This large island exists in part because of the
many physiological processes, as varied as cardiac contractility,
acid secretion and airway constriction, that are regulated by
GPCRs, and in part because the extracellular ligand-binding
domains of these receptors make ideal drug targets.
Many drugs target GPCRs and, despite the physiological
diversity of these proteins, all GPCR-mediated signaling is
coupled through heterotrimeric G proteins. Commonalities
of signaling mechanism among existing drug targets, such as
this, create a basis for identifying what network properties
make a protein a potentially good drug target. Statistical
metrics from network analyses, such as a centrality measure,
which quantifies the relative importance of a protein in
communicating between different modules within a network,
have been suggested for identifying nodes (proteins in a
network) that have attractive properties as potential drug
targets . Recent work has generated a network
connecting drugs on the basis of their structural similarity
and similarity of side-effect profiles . This method has
been proven effective at identifying groups of drugs that
share common targets. When drugs with predicted shared
targets did not in fact have known targets in common, the
predictions provided a framework for testing these drugs for
binding against each other’s targets. Hence, this approach
led to the identification of new targets for existing drugs.
I In nt te eg gr ra at ti in ng g t th he e b bo ot tt to om m- -u up p a an nd d t to op p- -d do ow wn n a ap pp pr ro oa ac ch he es s: : n ne et tw wo or rk k
a an na al ly ys si is s
Studies on cellular signaling pathways and networks drive
systems pharmacology because they can lead to discoveries
that enable new therapeutic interventions. These networks
are often based on genomic information. Network analysis
integrates genome medicine and systems pharmacology, as
is shown by its central location in the overlapping part of the
Venn diagram in Figure 3. Proteomic and genomic studies
have increased dramatically in the past five years, and
datasets describing global behavior, such as genomic or
proteomic analyses of many cellular pathways and processes,
are now available . These data expand our knowledge of
signaling pathways by implicating many more signaling
molecules, which may serve as new drug targets or have
implications for drug resistance or off-target effects.
Certain proteins or protein families are targeted more
frequently in therapeutic interventions than others, because
of their involvement in disease and the straightforwardness
of designing drugs to target the proteins . Systems
pharmacology studies on these systems are usually more
common. For example, there are at least eight different
approved or pipeline drugs that target one or several
members of the ErbB family of receptor tyrosine kinases,
which are modified in many kinds of cancer and other
diseases . There are also many studies that computation-
ally model [47-52] or experimentally analyze [51,53-56] the
dynamics of the ErbB signaling network. For example, the
ErbB network has been analyzed by examining the
interactions between the known phosphotyrosine interaction
domains (PTB and SH2 domains) and phosphopeptides
representing the phosphorylated forms of the four ErbB
family members using microarrays . This study 
uncovered behavior of the ErbB network that is pertinent to
its role in oncogenesis and the implications of thera-
peutically modulating this family of receptors.
Network analysis has facilitated the development of
computational prediction algorithms that predict all possible
molecules affected by specified perturbations of upstream
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targets by hypothetical drugs . These algorithms can also
solve the ‘minimum knockout’ problem by yielding predic-
tions of the smallest number of drug targets needed to fully
block a cellular process . A similar computational frame-
work for identifying targeted disruption of signaling
networks allows identification of all sets of potential drug
targets to block a process, while not affecting other processes
. These types of network analysis suggest that compu-
tational analyses of cell signaling networks are likely to be an
important aspect of systems pharmacology and future drug
A Ad dv ve er rs se e e ev ve en nt ts s a an nd d d dr ru ug g r re es si is st ta an nc ce e: : r re el la at ti io on ns sh hi ip p t to o
g ge en no om me e c ch ha ar ra ac ct te er ri is st ti ic cs s a an nd d s sy ys st te em ms s p pa at th ho op ph hy ys si io ol lo og gy y
Most studies that focus on understanding resistance to
therapies are in oncology. Many cancer therapies, such as
microtubule stabilizers , tamoxifen or endocrine therapy
 and drugs targeted at epithelial growth factor receptors
(EGFRs) , are effective for a limited period and/or in a
limited population and then resistance develops. Screens
using RNAi or with genomics and proteomics identify
proteins that are up- or down-regulated or are necessary for
drug action in resistant cell populations. The receptor
tyrosine kinase c-MET has recently been implicated in
resistance to EGFR-targeted therapies and, on the basis of
these discoveries, therapies directed against c-MET are in
development . In order to understand better the
convergence of signals from these two receptors, the tyrosine
signaling networks of severalcancer cell lines that
overexpress c-MET or EGFR or express a mutant form of
EGFR were examined to identify a core network of 50
proteins mediating drug response . This type of network
analysis can form the basis for the selection of drugs that
target the proteins common to both pathways and thus
overcome drug-induced resistance.
Genome medicine and systems pharmacology need to be
integrated for defining the genes and proteins involved in
drug treatment or drug resistance. Such integrated analyses
can lead to identification of targets that are likely to
synergize with or add to the effect of the drug or therapy. For
example, studies focused on defining the genes involved in
radiation therapy aim to discover new targets that would
increase the effectiveness of radiation therapy [66,67]. In
fact, many drugs are prescribed in combination with other
drugs, because the effectiveness of both drugs is increased
when they are combined. Predicting which combinations
will show this effect, and the doses of such combinations, is
not entirely intuitive or simple. Sometimes, serendipitous
results from genomic or proteomic screens identify targets
that might have synergistic effects in conjunction with
commonly used therapies, such as ceramide transport
protein in taxane-based therapy . In proactive approaches,
network analysis  or computational modeling  can
provide information on the effect of intracellular signaling
on inhibition of the two nodes, predicting how dosing with
pairs or groups of drugs that target different proteins would
work. Such a model was used to predict combination
therapies in the EGFR pathway . These types of study
can also be used to look for ways to lower dosages while
sustaining effectiveness, avoiding unnecessary drug toxicity.
Adverse events from drugs are a major concern in the
development and prescription of pharmaceuticals. The
susceptibility and severity of adverse events can vary
tremendously between patients as a result of, among other
confounders, genomic factors. At the simplest level, adverse
events can be due to dosage effects of the drug. Pharmaco-
genomic factors affecting drug metabolism can lead to
increased levels of active drug in the body. For drugs such as
warfarin, which acts as an anticoagulant, too high a blood
level owing to reduced metabolism (resulting from variant
CYPs) can lead to uncontrolled bleeding and cerebral
hemorrhage . For patients with CYPs that have lower
warfarin metabolizing capabilities, the dosage of warfarin
needs to be reduced so as to obtain its therapeutic benefits
without increasing the risk of adverse events.
Genetic factors can affect how patients respond to
therapeutic doses of a drug. Patients with a deficiency in
glucose 6-phosphatase dehydrogenase (G6PD) cannot respond
sufficiently to multiple forms of oxidative stress and, thus,
many drugs, including many anti-malarials, analgesics and
antibiotics, can cause these patients to develop hemolytic
anemia . Adverse events can also be modulated by the
immune system, whereby the patient’s immune system
responds to the drug, leading to damage of otherwise healthy
cells. For example, this can lead to drug-induced neutro-
penia . Several of this class of adverse event have been
associated genetically with specific HLA alleles, the genes
that control presentation of immunogenic epitopes .
Drug-induced cardiac arrhythmias comprise another impor-
tant class of side-effects. One example, the long QT
syndrome, has been extensively studied because it can in
many cases lead to fatal arrhythmias. Increased under-
standing of a congenital form of the syndrome, the frequency
of the side-effect and the variability in its severity has led
researchers to suggest that individual variability in cardiac
repolarization reserve is among the major risk modifiers
. Cardiac repolarization reserve is the capability of cells
to compensate for changes in ion channel function that
underlie the normal myocyte action potential. Thus,
understanding how drugs regulate specific ion channels to
modulate electrical interactions between channels to produce
a physiological event is critical. This type of adverse event,
which reflects an interaction between a drug and a complex
biological system, is where systems pharmacology will prove
most effective. Systems pharmacology projects have begun
to identify targets that are important for causing the side-
effect by grouping drugs according to their side-effect profiles
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Genome Medicine 2009, 1 1: :11
. In addition, large-scale studies have begun to identify
cellular modules that are important for causing the side-
effects. For example, a large-scale study of mitochondrial
function identified mitochondrial expression profiles affected
by drugs that can cause drug-induced myopathies .
C Co on nc cl lu us si io on ns s
Both genome medicine and systems pharmacology, as
interdisciplinary research fields, are in their infancy. The
convergent goals of both fields are to treat disease in each
patient on the basis of the patient’s genome and their unique
environmental interactions. Thus, they can be considered to
be the twin pillars supporting the gateway to personalized
medicine. In both fields, there are considerable oppor-
tunities for new conceptual and technological developments.
Success in personalized medicine will require advances in
technology and concepts in both fields to be well integrated.
The most influential driver for this integration is a mechanism-
based understanding of disease and therapy across scales of
biological organization. As a multi-scale understanding of
the human body develops, it is likely to influence not only
the treatment and prevention of disease, but also the
economics and social aspects of health care.
C Co om mp pe et ti in ng g i in nt te er re es st ts s
The authors declare that they have no competing interests.
A Ab bb br re ev vi ia at ti io on ns s
ACE, angiotensin converting enzyme; BRCA1, breast cancer
1, early onset (mutations in this gene greatly increase
predisposition to breast cancer); ChIP, chromatin
immunoprecipitation; CYP, cytochrome P450 (drug
metabolizing enzymes); EGFR, epithelial growth factor
receptor; FDA, US Food and Drug Administration; G6PD,
glucose 6-phosphatase dehydrogenase; GPCR, G-protein
coupled receptor; RNAi, RNA interference; SNP, single
A Ac ck kn no ow wl le ed dg ge em me en nt ts s
This work was supported by the NIH grants GM54508 and New York
Systems Biology Center grant P50-GM071558. SB is supported by pre-
doctoral training grant in Pharmacological Sciences GM-062754. We thank
Avi Ma’ayan and Emmanuel Landau for comments.
R Re ef fe er re en nc ce es s
1. Kenakin T: P Pr ri in nc ci ip pl le es s: : r re ec ce ep pt to or r t th he eo or ry y i in n p ph ha ar rm ma ac co ol lo og gy y. . Trends
Pharmacol Sci 2004, 2 25 5: :186-192.
2.Black J, Leff P: O Op pe er ra at ti io on na al l m mo od de el ls s o of f p ph ha ar rm ma ac co ol lo og gi ic ca al l a ag go on ni is st t. . Proc
R Soc Lond B Biol Sci 1983, 2 22 20 0: :141-162.
3.Maehle AH, Prull CR, Halliwell RF: T Th he e e em me er rg ge en nc ce e o of f t th he e d dr ru ug g
r re ec ce ep pt to or r t th he eo or ry y. . Nat Rev Drug Discov 2002, 1 1: :637-641.
4.Colquhoun D: T Th he e q qu ua an nt ti it ta at ti iv ve e a an na al ly ys si is s o of f d dr ru ug g- -r re ec ce ep pt to or r i in nt te er ra ac c- -
t ti io on ns s: : a a s sh ho or rt t h hi is st to or ry y. . Trends Pharmacol Sci 2006, 2 27 7: :149-157.
5.Ma’ayan A, Jenkins SL, Goldfarb J, Iyengar R: N Ne et tw wo or rk k a an na al ly ys si is s o of f F FD DA A
a ap pp pr ro ov ve ed d d dr ru ug gs s a an nd d t th he ei ir r t ta ar rg ge et ts s. . Mt Sinai J Med 2007, 7 74 4: :27-32.
6.Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M: D Dr ru ug g- -t ta ar rg ge et t
n ne et tw wo or rk k. . Nat Biotechnol 2007, 2 25 5: :1119-1126.
Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A, Glas
AM, Bogaerts J, Cardoso F, Piccart-Gebhart MJ, Rutgers ET, Van’t
Veer LJ; on behalf of the TRANSBIG consortium: T Th he e 7 70 0- -g ge en ne e p pr ro og g- -
n no os si is s- -s si ig gn na at tu ur re e p pr re ed di ic ct ts s d di is se ea as se e o ou ut tc co om me e i in n b br re ea as st t c ca an nc ce er r p pa at ti ie en nt ts s
w wi it th h 1 1- -3 3 p po os si it ti iv ve e l ly ym mp ph h n no od de es s i in n a an n i in nd de ep pe en nd de en nt t v va al li id da at ti io on n s st tu ud dy y. .
Breast Cancer Res Treat 2008, doi: 10.1007/s10549-008-0130-2.
Kroon BK, Leijte JA, van Boven H, Wessels LF, Velds A, Horenblas S,
van’t Veer LJ: M Mi ic cr ro oa ar rr ra ay y g ge en ne e- -e ex xp pr re es ss si io on n p pr ro of fi il li in ng g t to o p pr re ed di ic ct t l ly ym mp ph h
n no od de e m me et ta as st ta as si is s i in n p pe en ni il le e c ca ar rc ci in no om ma a. . BJU Int 2008, 1 10 02 2: :510-515.
Desnick RJ: E En nz zy ym me e r re ep pl la ac ce em me en nt t t th he er ra ap py y f fo or r F Fa ab br ry y d di is se ea as se e: : l le es ss so on ns s
f fr ro om m t tw wo o a al lp ph ha a- -g ga al la ac ct to os si id da as se e A A o or rp ph ha an n p pr ro od du uc ct ts s a an nd d o on ne e F FD DA A
a ap pp pr ro ov va al l. . Expert Opin Biol Ther 2004, 4 4: :1167-1176.
Desnick RJ, Schuchman EH: E En nz zy ym me e r re ep pl la ac ce em me en nt t a an nd d e en nh ha an nc ce em me en nt t
t th he er ra ap pi ie es s: : l le es ss so on ns s f fr ro om m l ly ys so os so om ma al l d di is so or rd de er rs s. . Nat Rev Genet 2002,
3 3: :954-966.
Land H, Parada LF, Weinberg RA: T Tu um mo or ri ig ge en ni ic c c co on nv ve er rs si io on n o of f
p pr ri im ma ar ry y e em mb br ry yo o f fi ib br ro ob bl la as st ts s r re eq qu ui ir re es s a at t l le ea as st t t tw wo o c co oo op pe er ra at ti in ng g o on nc co o- -
g ge en ne es s. . Nature 1983, 3 30 04 4: :596-602.
Manolio TA, Brooks LD, Collins FS: A A H Ha ap pM Ma ap p h ha ar rv ve es st t o of f i in ns si ig gh ht ts s
i in nt to o t th he e g ge en ne et ti ic cs s o of f c co om mm mo on n d di is se ea as se e. . J Clin Invest 2008, 1 11 18 8: :1590-
DeMatteo RP: T Th he e G GI IS ST T o of f t ta ar rg ge et te ed d c ca an nc ce er r t th he er ra ap py y: : a a t tu um mo or r ( (g ga as s- -
t tr ro oi in nt te es st ti in na al l s st tr ro om ma al l t tu um mo or r) ), , a a m mu ut ta at te ed d g ge en ne e ( (c c- -k ki it t) ), , a an nd d a a m mo ol le ec cu ul la ar r
i in nh hi ib bi it to or r ( (S ST TI I5 57 71 1) ). . Ann Surg Oncol 2002, 9 9: :831-839.
Schiffer CA, Hehlmann R, Larson R: P Pe er rs sp pe ec ct ti iv ve es s o on n t th he e t tr re ea at tm me en nt t
o of f c ch hr ro on ni ic c p ph ha as se e a an nd d a ad dv va an nc ce ed d p ph ha as se e C CM ML L a an nd d P Ph hi il la ad de el lp ph hi ia a c ch hr ro om mo o- -
s so om me e p po os si it ti iv ve e A AL LL L. . Leukemia 2003, 1 17 7: :691-699.
Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, Somer-
field MR, Hayes DF, Bast RC Jr: A Am me er ri ic ca an n S So oc ci ie et ty y o of f C Cl li in ni ic ca al l O On nc co ol l- -
o og gy y 2 20 00 07 7 u up pd da at te e o of f r re ec co om mm me en nd da at ti io on ns s f fo or r t th he e u us se e o of f t tu um mo or r m ma ar rk ke er rs s
i in n b br re ea as st t c ca an nc ce er r. . J Clin Oncol 2007, 2 25 5: :5287-5312.
Sequist LV, , Martins RG, , Spigel D, , Grunberg SM, , Spira A, , Jänne PA, ,
Joshi VA, , McCollum D, , Evans TL, , Muzikansky A, , Kuhlmann GL, , Han
M, , Goldberg JS, , Settleman J, , Iafrate AJ, , Engelman JA, , Haber DA, ,
Johnson BE, , Lynch TJ: F Fi ir rs st t- -l li in ne e g ge ef fi it ti in ni ib b i in n p pa at ti ie en nt ts s w wi it th h a ad dv va an nc ce ed d
n no on n- -s sm ma al ll l- -c ce el ll l l lu un ng g c ca an nc ce er r h ha ar rb bo or ri in ng g s so om ma at ti ic c e eg gf fr r m mu ut ta at ti io on ns s. . J Clin
Oncol 2008, 2 26 6: :2442-2449.
Liggett SB, , Mialet-Perez J, , Thaneemit-Chen S, , Weber SA, , Greene
SM, , Hodne D, , Nelson B, , Morrison J, , Domanski MJ, , Wagoner LE, ,
Abraham WT, , Anderson JL, , Carlquist JF, , Krause-Steinrauf HJ, , Lazze-
roni LC, , Port JD, , Lavori PW, , Bristow MR: A A p po ol ly ym mo or rp ph hi is sm m w wi it th hi in n a a
c co on ns se er rv ve ed d β β1 1 - -a ad dr re en ne er rg gi ic c r re ec ce ep pt to or r m mo ot ti if f a al lt te er rs s c ca ar rd di ia ac c f fu un nc ct ti io on n a an nd d
β β- -b bl lo oc ck ke er r r re es sp po on ns se e i in n h hu um ma an n h he ea ar rt t f fa ai il lu ur re e. . Proc Natl Acad Sci USA
2006, 1 10 03 3: :11288-11293.
Nusbaum R, Isaacs C: M Ma an na ag ge em me en nt t u up pd da at te es s f fo or r w wo om me en n w wi it th h a a
B BR RC CA A1 1 o or r B BR RC CA A2 2 m mu ut ta at ti io on n. . Mol Diagn Ther 2007, 1 11 1: :133-144.
Schwarz UI, Ritchie MD, Bradford Y, Li C, Dudek SM, Frye-Anderson
A, Kim RB, Roden DM, Stein CM: G Ge en ne et ti ic c d de et te er rm mi in na an nt ts s o of f r re es sp po on ns se e
t to o w wa ar rf fa ar ri in n d du ur ri in ng g i in ni it ti ia al l a an nt ti ic co oa ag gu ul la at ti io on n. . N Engl J Med 2008, 3 35 58 8: :999-
Au N, Rettie AE: P Ph ha ar rm ma ac co og ge en no om mi ic cs s o of f 4 4- -h hy yd dr ro ox xy yc co ou um ma ar ri in n a an nt ti ic co o- -
a ag gu ul la an nt ts s. . Drug Metab Rev 2008, 4 40 0: :355-375.
Goetz MP, Kamal A, Ames MM: T Ta am mo ox xi if fe en n p ph ha ar rm ma ac co og ge en no om mi ic cs s: : t th he e
r ro ol le e o of f C CY YP P2 2D D6 6 a as s a a p pr re ed di ic ct to or r o of f d dr ru ug g r re es sp po on ns se e. . Clin Pharmacol
Ther 2008, 8 83 3: :160-166.
Evans WE, Relling MV: M Mo ov vi in ng g t to ow wa ar rd ds s i in nd di iv vi id du ua al li iz ze ed d m me ed di ic ci in ne e w wi it th h
p ph ha ar rm ma ac co og ge en no om mi ic cs s. . Nature 2004, 4 42 29 9: :464-468.
Court MH: A A p ph ha ar rm ma ac co og ge en no om mi ic cs s p pr ri im me er r. . J Clin Pharmacol 2007, 4 47 7: :
Frank J: M Ma an na ag gi in ng g h hy yp pe er rt te en ns si io on n u us si in ng g c co om mb bi in na at ti io on n t th he er ra ap py y. . Am Fam
Physician 2008, 7 77 7: :1279-1286.
Materson BJ, Reda DJ, Preston RA, Cushman WC, Massie BM, Freis
ED, Kochar MS, Hamburger RJ, Fye C, Lakshman R, et al.: R Re es sp po on ns se e
t to o a a s se ec co on nd d s si in ng gl le e a an nt ti ih hy yp pe er rt te en ns si iv ve e a ag ge en nt t u us se ed d a as s m mo on no ot th he er ra ap py y f fo or r
h hy yp pe er rt te en ns si io on n a af ft te er r f fa ai il lu ur re e o of f t th he e i in ni it ti ia al l d dr ru ug g. . D De ep pa ar rt tm me en nt t o of f V Ve et te er ra an ns s
A Af ff fa ai ir rs s C Co oo op pe er ra at ti iv ve e S St tu ud dy y G Gr ro ou up p o on n A An nt ti ih hy yp pe er rt te en ns si iv ve e A Ag ge en nt ts s. . Arch
Intern Med 1995, 1 15 55 5: :1757-1762.
Libby P: I In nf fl la am mm ma at to or ry y m me ec ch ha an ni is sm ms s: : t th he e m mo ol le ec cu ul la ar r b ba as si is s o of f i in nf fl la am mm ma a- -
t ti io on n a an nd d d di is se ea as se e. . Nutr Rev 2007, 6 65 5: :S140-146.
Lucas SM, Rothwell NJ, Gibson RM: T Th he e r ro ol le e o of f i in nf fl la am mm ma at ti io on n i in n C CN NS S
i in nj ju ur ry y a an nd d d di is se ea as se e. . Br J Pharmacol 2006, 1 14 47 7( (S Su up pp pl l 1 1) ): :S232-S240.
Hunter PJ, Borg TK: I In nt te eg gr ra at ti io on n f fr ro om m p pr ro ot te ei in ns s t to o o or rg ga an ns s: : t th he e P Ph hy ys s- -
i io om me e P Pr ro oj je ec ct t. . Nat Rev Mol Cell Biol 2003, 4 4: :237-243.
Hunter PJ, Crampin EJ, Nielsen PM: B Bi io oi in nf fo or rm ma at ti ic cs s, , m mu ul lt ti is sc ca al le e m mo od de el l- -
i in ng g a an nd d t th he e I IU UP PS S P Ph hy ys si io om me e P Pr ro oj je ec ct t. . Brief Bioinform 2008, 9 9: :333-343.
http://genomemedicine.com/content/1/1/11 Genome Medicine 2009,Volume 1, Issue 1, Article 11Wist et al. 11.8
Genome Medicine 2009, 1 1: :11
30.Shaffer AL, Emre NC, Lamy L, Ngo VN, Wright G, Xiao W, Powell J,
Dave S, Yu X, Zhao H, Zeng Y, , Chen B, , Epstein J, , Staudt LM: I IR RF F4 4
a ad dd di ic ct ti io on n i in n m mu ul lt ti ip pl le e m my ye el lo om ma a. . Nature 2008, 4 45 54 4: :226-231.
Wang Z, Shen D, Parsons DW, Bardelli A, Sager J, Szabo S, Ptak J,
Silliman N, Peters BA, van der Heijden MS, , Parmigiani G, , Yan H, ,
Wang TL, , Riggins G, , Powell SM, , Willson JK, , Markowitz S, , Kinzler
KW, , Vogelstein B, , Velculescu VE: M Mu ut ta at ti io on na al l a an na al ly ys si is s o of f t th he e t ty yr ro os si in ne e
p ph ho os sp ph ha at to om me e i in n c co ol lo or re ec ct ta al l c ca an nc ce er rs s. . Science 2004, 3 30 04 4: :1164-1166.
Ruiz-Vela A, Aggarwal M, de la Cueva P, Treda C, Herreros B,
Martin-Perez D, Dominguez O, Piris MA: L Le en nt ti iv vi ir ra al l ( (H HI IV V) )- -b ba as se ed d
R RN NA A i in nt te er rf fe er re en nc ce e s sc cr re ee en n i in n h hu um ma an n B B- -c ce el ll l r re ec ce ep pt to or r r re eg gu ul la at to or ry y n ne et t- -
w wo or rk ks s r re ev ve ea al ls s M MC CL L1 1- -i in nd du uc ce ed d o on nc co og ge en ni ic c p pa at th hw wa ay ys s. . Blood 2008,
1 11 11 1: :1665-1676.
Tarun AS, Peng X, Dumpit RF, Ogata Y, Silva-Rivera H, Camargo N,
Daly TM, Bergman LW, Kappe SHI: A A c co om mb bi in ne ed d t tr ra an ns sc cr ri ip pt to om me e a an nd d
p pr ro ot te eo om me e s su ur rv ve ey y o of f m ma al la ar ri ia a p pa ar ra as si it te e l li iv ve er r s st ta ag ge es s. . Proc Natl Acad Sci
USA 2008, 1 10 05 5: :305-310.
Frost RJ, Engelhardt S: A A s se ec cr re et ti io on n t tr ra ap p s sc cr re ee en n i in n y ye ea as st t i id de en nt ti if fi ie es s p pr ro o- -
t te ea as se e i in nh hi ib bi it to or r 1 16 6 a as s a a n no ov ve el l a an nt ti ih hy yp pe er rt tr ro op ph hi ic c p pr ro ot te ei in n s se ec cr re et te ed d f fr ro om m
t th he e h he ea ar rt t. . Circulation 2007, 1 11 16 6: :1768-1775.
Brass AL, Dykxhoorn DM, Benita Y, Yan N, Engelman A, Xavier RJ,
Lieberman J, Elledge SJ: I Id de en nt ti if fi ic ca at ti io on n o of f h ho os st t p pr ro ot te ei in ns s r re eq qu ui ir re ed d f fo or r
H HI IV V i in nf fe ec ct ti io on n t th hr ro ou ug gh h a a f fu un nc ct ti io on na al l g ge en no om mi ic c s sc cr re ee en n. . Science 2008,
3 31 19 9: :921-926.
Lu TC, Wang Z, Feng X, Chuang P, Fang W, Chen Y, Neves S,
Maayan A, Xiong H, Liu Y, Iyengar R, , Klotman PE, , He JC: R Re et ti in no oi ic c
a ac ci id d u ut ti il li iz ze es s C CR RE EB B a an nd d U US SF F1 1 i in n a a t tr ra an ns sc cr ri ip pt ti io on na al l f fe ee ed d- -f fo or rw wa ar rd d l lo oo op p i in n
o or rd de er r t to o s st ti im mu ul la at te e M MK KP P1 1 e ex xp pr re es ss si io on n i in n h hu um ma an n i im mm mu un no od de ef fi ic ci ie en nc cy y
v vi ir ru us s- -i in nf fe ec ct te ed d p po od do oc cy yt te es s. . Mol Cell Biol 2008, 2 28 8: :5785-5794.
Jin G, Zhou X, Wang H, Zhao H, Cui K, Zhang XS, Chen L, Hazen
SL, Li K, Wong ST: T Th he e k kn no ow wl le ed dg ge e- -i in nt te eg gr ra at te ed d n ne et tw wo or rk k b bi io om ma ar rk ke er rs s
d di is sc co ov ve er ry y f fo or r m ma aj jo or r a ad dv ve er rs se e c ca ar rd di ia ac c e ev ve en nt ts s. . J Proteome Res 2008,
7 7: :4013-4021.
Liu M, Liberzon A, Kong SW, Lai WR, Park PJ, Kohane IS, Kasif S:
N Ne et tw wo or rk k- -b ba as se ed d a an na al ly ys si is s o of f a af ff fe ec ct te ed d b bi io ol lo og gi ic ca al l p pr ro oc ce es ss se es s i in n t ty yp pe e 2 2 d di ia a- -
b be et te es s m mo od de el ls s. . PLoS Genet 2007, 3 3: :e96.
Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ,
Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, , Hieronymus
H, , Wei G, , Armstrong SA, , Haggarty SJ, , Clemons PA, , Wei R, , Carr SA, ,
Lander ES, , Golub TR: T Th he e C Co on nn ne ec ct ti iv vi it ty y M Ma ap p: : u us si in ng g g ge en ne e- -e ex xp pr re es ss si io on n
s si ig gn na at tu ur re es s t to o c co on nn ne ec ct t s sm ma al ll l m mo ol le ec cu ul le es s, , g ge en ne es s, , a an nd d d di is se ea as se e. . Science
2006, 3 31 13 3: :1929-1935.
Repasky GA, Chenette EJ, Der CJ: R Re en ne ew wi in ng g t th he e c co on ns sp pi ir ra ac cy y t th he eo or ry y
d de eb ba at te e: : d do oe es s R Ra af f f fu un nc ct ti io on n a al lo on ne e t to o m me ed di ia at te e R Ra as s o on nc co og ge en ne es si is s? ? Trends
Cell Biol 2004, 1 14 4: :639-647.
Stites EC, Trampont PC, Ma Z, Ravichandran KS: N Ne et tw wo or rk k a an na al ly ys si is s
o of f o on nc co og ge en ni ic c R Ra as s a ac ct ti iv va at ti io on n i in n c ca an nc ce er r. . Science 2007, 3 31 18 8: :463-467.
Hwang WC, Zhang A, Ramanathan M: I Id de en nt ti if fi ic ca at ti io on n o of f i in nf fo or rm ma at ti io on n
f fl lo ow w- -m mo od du ul la at ti in ng g d dr ru ug g t ta ar rg ge et ts s: : a a n no ov ve el l b br ri id dg gi in ng g p pa ar ra ad di ig gm m f fo or r d dr ru ug g d di is s- -
c co ov ve er ry y. . Clin Pharmacol Ther 2008, doi: 10.1038/clpt.2008.129.
Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P: D Dr ru ug g t ta ar rg ge et t
i id de en nt ti if fi ic ca at ti io on n u us si in ng g s si id de e- -e ef ff fe ec ct t s si im mi il la ar ri it ty y. . Science 2008, 3 32 21 1: :263-266.
Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C,
Kim IF, Soboleva A, Tomashevsky M, Edgar R: N NC CB BI I G GE EO O: : m mi in ni in ng g
t te en ns s o of f m mi il ll li io on ns s o of f e ex xp pr re es ss si io on n p pr ro of fi il le es s—
Nucleic Acids Res 2007, 3 35 5( (D Da at ta ab ba as se e i is ss su ue e) ): :D760-D765.
Lauss M, Kriegner A, Vierlinger K, Noehammer C: C Ch ha ar ra ac ct te er ri iz za at ti io on n
o of f t th he e d dr ru ug gg ge ed d h hu um ma an n g ge en no om me e. . Pharmacogenomics 2007, 8 8: :1063-
Johnston JB, Navaratnam S, Pitz MW, Maniate JM, Wiechec E, Baust
H, Gingerich J, Skliris GP, Murphy LC, Los M: T Ta ar rg ge et ti in ng g t th he e E EG GF FR R
p pa at th hw wa ay y f fo or r c ca an nc ce er r t th he er ra ap py y. . Curr Med Chem 2006, 1 13 3: :3483-3492.
Hendriks BS, Orr G, Wells A, Wiley HS, Lauffenburger DA: P Pa ar rs si in ng g
E ER RK K a ac ct ti iv va at ti io on n r re ev ve ea al ls s q qu ua an nt ti it ta at ti iv ve el ly y e eq qu ui iv va al le en nt t c co on nt tr ri ib bu ut ti io on ns s f fr ro om m
e ep pi id de er rm ma al l g gr ro ow wt th h f fa ac ct to or r r re ec ce ep pt to or r a an nd d H HE ER R2 2 i in n h hu um ma an n m ma am mm ma ar ry y
e ep pi it th he el li ia al l c ce el ll ls s. . J Biol Chem 2005, 2 28 80 0: :6157-6169.
Kholodenko BN, Demin OV, Moehren G, Hoek JB: Q Qu ua an nt ti if fi ic ca at ti io on n o of f
s sh ho or rt t t te er rm m s si ig gn na al li in ng g b by y t th he e e ep pi id de er rm ma al l g gr ro ow wt th h f fa ac ct to or r r re ec ce ep pt to or r. . J Biol
Chem 1999, 2 27 74 4: :30169-30181.
Nakakuki T, Yumoto N, Naka T, Shirouzu M, Yokoyama S,
Hatakeyama M: T To op po ol lo og gi ic ca al l a an na al ly ys si is s o of f M MA AP PK K c ca as sc ca ad de e f fo or r k ki in ne et ti ic c
E Er rb bB B s si ig gn na al li in ng g. . PLoS ONE 2008, 3 3: :e1782.
Schoeberl B, Eichler-Jonsson C, Gilles ED, Muller G: C Co om mp pu ut ta at ti io on na al l
m mo od de el li in ng g o of f t th he e d dy yn na am mi ic cs s o of f t th he e M MA AP P k ki in na as se e c ca as sc ca ad de e a ac ct ti iv va at te ed d b by y
s su ur rf fa ac ce e a an nd d i in nt te er rn na al li iz ze ed d E EG GF F r re ec ce ep pt to or rs s. . Nat Biotechnol 2002,
2 20 0: :370-375.
Citri A, Yarden Y: E EG GF F- -E ER RB BB B s si ig gn na al ll li in ng g: : t to ow wa ar rd ds s t th he e s sy ys st te em ms s l le ev ve el l. .
Nat Rev Mol Cell Biol 2006, 7 7: :505-516.
—d da at ta ab ba as se e a an nd d t to oo ol ls s u up pd da at te e. .
52.Wiley HS, Shvartsman SY, Lauffenburger DA: C Co om mp pu ut ta at ti io on na al l m mo od de el l- -
i in ng g o of f t th he e E EG GF F- -r re ec ce ep pt to or r s sy ys st te em m: : a a p pa ar ra ad di ig gm m f fo or r s sy ys st te em ms s b bi io ol lo og gy y. .
Trends Cell Biol 2003, 1 13 3: :43-50.
Bose R, Molina H, Patterson AS, Bitok JK, Periaswamy B, Bader JS,
Pandey A, Cole PA: P Ph ho os sp ph ho op pr ro ot te eo om mi ic c a an na al ly ys si is s o of f H He er r2 2/ /n ne eu u s si ig gn na al li in ng g
a an nd d i in nh hi ib bi it ti io on n. . Proc Natl Acad Sci USA 2006, 1 10 03 3: :9773-9778.
Sevecka M, MacBeath G: S St ta at te e- -b ba as se ed d d di is sc co ov ve er ry y: : a a m mu ul lt ti id di im me en ns si io on na al l
s sc cr re ee en n f fo or r s sm ma al ll l- -m mo ol le ec cu ul le e m mo od du ul la at to or rs s o of f E EG GF F s si ig gn na al li in ng g. . Nat Methods
2006, 3 3: :825-831.
Wolf-Yadlin A, Kumar N, Zhang Y, Hautaniemi S, Zaman M, Kim
HD, Grantcharova V, Lauffenburger DA, White FM: E Ef ff fe ec ct ts s o of f H HE ER R2 2
o ov ve er re ex xp pr re es ss si io on n o on n c ce el ll l s si ig gn na al li in ng g n ne et tw wo or rk ks s g go ov ve er rn ni in ng g p pr ro ol li if fe er ra at ti io on n
a an nd d m mi ig gr ra at ti io on n. . Mol Syst Biol 2006, 2 2: :54.
Hatakeyama M: S Sy ys st te em m p pr ro op pe er rt ti ie es s o of f E Er rb bB B r re ec ce ep pt to or r s si ig gn na al li in ng g f fo or r t th he e
u un nd de er rs st ta an nd di in ng g o of f c ca an nc ce er r p pr ro og gr re es ss si io on n. . Mol Biosyst 2007, 3 3: :111-116.
Jones RB, Gordus A, Krall JA, MacBeath G: A A q qu ua an nt ti it ta at ti iv ve e p pr ro ot te ei in n
i in nt te er ra ac ct ti io on n n ne et tw wo or rk k f fo or r t th he e E Er rb bB B r re ec ce ep pt to or rs s u us si in ng g p pr ro ot te ei in n m mi ic cr ro o- -
a ar rr ra ay ys s. . Nature 2006, 4 43 39 9: :168-174.
Ruths D, Muller M, Tseng JT, Nakhleh L, Ram PT: T Th he e s si ig gn na al li in ng g p pe et tr ri i
n ne et t- -b ba as se ed d s si im mu ul la at to or r: : a a n no on n- -p pa ar ra am me et tr ri ic c s st tr ra at te eg gy y f fo or r c ch ha ar ra ac ct te er ri iz zi in ng g t th he e
d dy yn na am mi ic cs s o of f c ce el ll l- -s sp pe ec ci if fi ic c s si ig gn na al li in ng g n ne et tw wo or rk ks s. . PLoS Comput Biol 2008,
4 4: :e1000005.
Dasika MS, Burgard A, Maranas CD: A A c co om mp pu ut ta at ti io on na al l f fr ra am me ew wo or rk k f fo or r
t th he e t to op po ol lo og gi ic ca al l a an na al ly ys si is s a an nd d t ta ar rg ge et te ed d d di is sr ru up pt ti io on n o of f s si ig gn na al l t tr ra an ns sd du uc ct ti io on n
n ne et tw wo or rk ks s. . Biophys J 2006, 9 91 1: :382-398.
Kumar N, Hendriks BS, Janes KA, de Graaf D, Lauffenburger DA:
A Ap pp pl ly yi in ng g c co om mp pu ut ta at ti io on na al l m mo od de el li in ng g t to o d dr ru ug g d di is sc co ov ve er ry y a an nd d d de ev ve el lo op p- -
m me en nt t. . Drug Discov Today 2006, 1 11 1: :806-811.
Fojo T, Menefee M: M Me ec ch ha an ni is sm ms s o of f m mu ul lt ti id dr ru ug g r re es si is st ta an nc ce e: : t th he e p po ot te en n- -
t ti ia al l r ro ol le e o of f m mi ic cr ro ot tu ub bu ul le e- -s st ta ab bi il li iz zi in ng g a ag ge en nt ts s. . Ann Oncol 2007, 1 18 8( (S Su up pp pl l
5 5) ): :v3-8.
Lord CJ, Iorns E, Ashworth A: D Di is ss se ec ct ti in ng g r re es si is st ta an nc ce e t to o e en nd do oc cr ri in ne e
t th he er ra ap py y i in n b br re ea as st t c ca an nc ce er r. . Cell Cycle 2008, 7 7: :1895-1898.
Ciardiello F, Tortora G: E EG GF FR R a an nt ta ag go on ni is st ts s i in n c ca an nc ce er r t tr re ea at tm me en nt t. .
N Engl J Med 2008, 3 35 58 8: :1160-1174.
Jin H, Yang R, Zheng Z, Romero M, Ross J, Bou-Reslan H, Carano
RA, Kasman I, Mai E, Young J, Zha J, , Zhang Z, , Ross S, , Schwall R, ,
Colbern G, , Merchant M: M Me et tM MA Ab b, , t th he e o on ne e- -a ar rm me ed d 5 5D D5 5 a an nt ti i- -c c- -M Me et t
a an nt ti ib bo od dy y, , i in nh hi ib bi it ts s o or rt th ho ot to op pi ic c p pa an nc cr re ea at ti ic c t tu um mo or r g gr ro ow wt th h a an nd d i im mp pr ro ov ve es s
s su ur rv vi iv va al l. . Cancer Res 2008, 6 68 8: :4360-4368.
Guo A, Villen J, Kornhauser J, Lee KA, Stokes MP, Rikova K, Posse-
mato A, Nardone J, Innocenti G, Wetzel R, Wang Y, , MacNeill J, ,
Mitchell J, , Gygi SP, , Rush J, , Polakiewicz RD, , Comb MJ: S Si ig gn na al li in ng g n ne et t- -
w wo or rk ks s a as ss se em mb bl le ed d b by y o on nc co og ge en ni ic c E EG GF FR R a an nd d c c- -M Me et t. . Proc Natl Acad Sci
USA 2008, 1 10 05 5: :692-697.
Sudo H, Tsuji AB, Sugyo A, Imai T, Saga T, Harada YN: A A l lo os ss s o of f
f fu un nc ct ti io on n s sc cr re ee en n i id de en nt ti if fi ie es s n ni in ne e n ne ew w r ra ad di ia at ti io on n s su us sc ce ep pt ti ib bi il li it ty y g ge en ne es s. .
Biochem Biophys Res Commun 2007, 3 36 64 4: :695-701.
Amundson SA, , Do KT, , Vinikoor LC, , Lee RA, , Koch-Paiz CA, , Ahn J, ,
Reimers M, , Chen Y, , Scudiero DA, , Weinstein JN, , Trent JM, , Bittner
ML, , Meltzer PS, , Fornace AJ Jr: I In nt te eg gr ra at ti in ng g g gl lo ob ba al l g ge en ne e e ex xp pr re es ss si io on n a an nd d
r ra ad di ia at ti io on n s su ur rv vi iv va al l p pa ar ra am me et te er rs s a ac cr ro os ss s t th he e 6 60 0 c ce el ll l l li in ne es s o of f t th he e N Na at ti io on na al l
C Ca an nc ce er r I In ns st ti it tu ut te e A An nt ti ic ca an nc ce er r D Dr ru ug g S Sc cr re ee en n. . Cancer Res 2008, 6 68 8: :415-
Swanton C, Marani M, Pardo O, Warne PH, Kelly G, Sahai E, Elus-
tondo F, Chang J, Temple J, Ahmed AA, , Brenton JD, , Downward J, ,
Nicke B: R Re eg gu ul la at to or rs s o of f m mi it to ot ti ic c a ar rr re es st t a an nd d c ce er ra am mi id de e m me et ta ab bo ol li is sm m a ar re e
d de et te er rm mi in na an nt ts s o of f s se en ns si it ti iv vi it ty y t to o p pa ac cl li it ta ax xe el l a an nd d o ot th he er r c ch he em mo ot th he er ra ap pe eu ut ti ic c
d dr ru ug gs s. . Cancer Cell 2007, 1 11 1: :498-512.
Fitzgerald JB, Schoeberl B, Nielsen UB, Sorger PK: S Sy ys st te em ms s b bi io ol lo og gy y
a an nd d c co om mb bi in na at ti io on n t th he er ra ap py y i in n t th he e q qu ue es st t f fo or r c cl li in ni ic ca al l e ef ff fi ic ca ac cy y. . Nat Chem
Biol 2006, 2 2: :458-466.
Araujo RP, Petricoin EF, Liotta LA: A A m ma at th he em ma at ti ic ca al l m mo od de el l o of f c co om mb bi i- -
n na at ti io on n t th he er ra ap py y u us si in ng g t th he e E EG GF FR R s si ig gn na al li in ng g n ne et tw wo or rk k. . Biosystems 2005,
8 80 0: :57-69.
Cappellini MD, Fiorelli G: G Gl lu uc co os se e- -6 6- -p ph ho os sp ph ha at te e d de eh hy yd dr ro og ge en na as se e d de ef fi i- -
c ci ie en nc cy y. . Lancet 2008, 3 37 71 1: :64-74.
Bux J: M Mo ol le ec cu ul la ar r n na at tu ur re e o of f a an nt ti ig ge en ns s i im mp pl li ic ca at te ed d i in n i im mm mu un ne e n ne eu ut tr ro op pe e- -
n ni ia as s. . Int J Hematol 2002, 7 76 6( (S Su up pp pl l 1 1) ): :399-403.
Opgen-Rhein C, Dettling M: C Cl lo oz za ap pi in ne e- -i in nd du uc ce ed d a ag gr ra an nu ul lo oc cy yt to os si is s a an nd d
i it ts s g ge en ne et ti ic c d de et te er rm mi in na an nt ts s. . Pharmacogenomics 2008, 9 9: :1101-1111.
Roden DM: C Ce el ll lu ul la ar r b ba as si is s o of f d dr ru ug g- -i in nd du uc ce ed d t to or rs sa ad de es s d de e p po oi in nt te es s. . Br J
Pharmacol 2008, 1 15 54 4: :1502-1507.
Wagner BK, Kitami T, Gilbert TJ, Peck D, Ramanathan A, Schreiber
SL, Golub TR, Mootha VK: L La ar rg ge e- -s sc ca al le e c ch he em mi ic ca al l d di is ss se ec ct ti io on n o of f m mi it to o- -
c ch ho on nd dr ri ia al l f fu un nc ct ti io on n. . Nat Biotechnol 2008, 2 26 6: :343-351.
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