2. 326 J. Xiong et al.
dependency or joint dependency between genes and disease phenotype, is the key
“battle map” for rational drug combinations and design of personalized therapy.
We also tentatively outline several aspects of the process which might help drive
innovation in network construction and shape the future development of network
pharmacology applications.
Keywords Network pharmacology • Rational drug design • Personalized medicine
• Synthetic lethality • Combination drug discovery
Abbreviations
EGFR Epidermal growth factor receptor
PARP Poly (ADP-ribose) phosphatase
ALL Acute lymphoblastic lymphoma
SOD Synergistic outcome determination
RNAi RNA interference
STAT3 Signal transducer and activator of transcription 3
RNA Ribonucleic acid
DNA Deoxyribonucleic acid
BRCA Breast cancer type 1 susceptibility protein
PLK1 Polo-like kinase 1
CDC16 Cell division cycle protein 16
STK33 Serine/threonine kinase 33
1 Introduction
Network Pharmacology attempts to model the effects of drug action by simultaneously
modulating multiple components in a gene network (Hopkins 2008; Yildirim et al.
2007). However, the theoretical basis for this new concept is still in its infancy and
the process by which we translate network modeling to clinical application remains
indirect (Hopkins 2008; Csermely et al. 2005; Kitano 2007). In this chapter, we try
to outline the principles of rational designs for drug combination and personalized
therapy based on network pharmacology by deciphering several milestone exam-
ples. We demonstrate that the network, which characterizes the dependency or joint
dependency between genes and disease phenotype, is the key “battle map” for rational
drug combinations and design of personalized therapy. We also tentatively outline
several aspects of the process which might help drive innovation in network con-
struction and shape the future development of network pharmacology applications.
A core task of a drug discovery study is to identify the dependency between the
genetic/molecular makeup of the human body and disease phenotype. Disease phe-
notype can be dependent on an individual causal gene, which means perturbations
3. 32714 The Principle of Rational Design of Drug Combination and Personalized…
acting on this gene might lead to a shift of the phenotype (from disease status to
normal status). In general, complex diseases are often dependent on many genes
rather than on a few genes, as has been demonstrated in the concept of “synthetic
lethal” (see below). Thus, it is also important to determine the co-dependency existing
between genes that drive the change in phenotype.
Synthetic lethal is one of most important concepts in current oncology drug
development and is a core research topic in network pharmacology studies (Hopkins
2008). Synthetic Lethality refers to a specific type of genetic interaction between
two genes, where mutation of one gene is viable but mutation of both leads to death
of cells (Kaelin 2005). The core of synthetic lethal concept is the joint dependency
or synergy between two genes in terms of cell fate. This concept can therefore, be
exploited to develop an effective therapeutic strategy. For example, by using an
inhibitor targeted to a Poly (ADP-Ribose) Polymerase (PARP) that is synthetically
lethal to a cancer-specific mutation (BRCA), researchers could target cancer cells to
achieve antitumor activity in tumors with the BRCA mutation (Fong et al. 2009).
According to a recent review, there are more than 21 compounds in clinical trials
that are based on a synthetic lethal approach, and there are at least 63 trials for
PARP inhibitors based on the synthetic lethal between PARP and BRCA (Shaheen
et al. 2011). Using PARP inhibitor for patients with BRCA gene mutation identified
via a genetic test of BRCA mutations is a typical use of a personalized therapy
strategy (Luo et al. 2009a). There are already several drug combinations, experi-
mentally validated, that clearly show sensitivity-improvement effects toward known
oncology drugs (Kim et al. 2011; Toledo et al. 2011; Whitehurst et al. 2007).
By extending this approach to the genome scale, a strategy based on screening syn-
thetic lethal relationships, then constructing a synthetic lethal gene network and
identifying multiple site perturbations, one can form a rational approach for drug
combination design.
2 Rational Drug Combination Design Based on Gene
Expression Pattern
One of the pioneering studies that used gene expression signatures to establish the
connections between small molecules, genes and disease was the “Connectivity
Map” project by Lamb et al. by which he illustrated the possibility of rational
drug combinations or personalized therapy design (Lamb et al. 2006). Taking
Dexamethasone for acute lymphoblastic leukemia (ALL) treatment as example,
they first generated the gene expression signatures associated with Dexamethasone
sensitivity/resistance, and then another drug, Sirolimus, which could revert dexam-
ethasone resistance (or “improve dexamethasone sensitivity”) was identified by
querying the perturbation to the gene expression pattern induced by small molecules.
The logical steps were as following:
Step 1: Having selected an initial drug D1 as a recognized treatment for the disease
of interest, the gene expression signature of drug sensitivity can be determined
4. 328 J. Xiong et al.
by comparing sensitive cell lines or patient cells against resistance cell lines (the
in vitro signature) or patient cells (the in vivo signature). An example of this is
illustrated in Fig. 14.1. In this figure, gene g1 is positively associated with drug
D1 sensitivity (Fig. 14.1a), whereas gene g2 negatively associated with drug D1
sensitivity (Fig. 14.1b).
Step 2: query the Connectivity Map with the D1 drug sensitivity signature, and
search for a candidate drug D2 which shows a positive correlation with drug
D1 sensitivity signature. As illustrated in Fig. 14.1c: if the treatment with drug D2
could up-regulate gene g1, and simultaneously down-regulate gene g2, then drug
D2 is a good candidate for improve drug D1 sensitivity (Fig. 14.1c). As a whole
treatment, the combined treatment with D1-D2 will show better sensitivity
than drug D1 alone (Fig. 14.1d). This method was actually “signature-based”
rather than “network-based”, because it used global gene expression profiling
as the space to search the optimal drug combination but did not explicitly model
the relationships between genes.
Drug D1
Drug D2
Drug D2Drug D1
Drug D1
Drug D1
Drug
Sensitivity
Drug
Sensitivity
Gene g1 Gene g2
a b
dc
Dexamethasone
+ Sirolimus
Dexamethasone
alone
dexamethasone concentation (μm)
0
0.001 0.01 0.1 1
20
40
60
80
100
120
viability(%ofcontrol)
Gene g1
Up
Regulation
Positive
Gene-Drug Sensitivity
Correlated Genes
Negative
Gene-Drug Sensitivity
Correlated Genes
Down
Regulation
Gene g2
Gene Expression Gene Expression
Positive
Gene-Drug Sensitivity
Correlation
Negative
Gene-Drug Sensitivity
Correlation
Drug
Sensitivity
Fig. 14.1 Rational drug combination design based on gene expression patterns. (a) Gene g1
is positively correlated with drug D1 sensitivity. (b) Gene g2 is negatively correlated with drug D1
sensitivity. (c) Query and search for a candidate drug D2 which show positive correlation with drug
D1 sensitivity signature (up-regulating g1, and down-regulating g2). (d) The sensitivity of drug D1
(Dexamethasone) and drug D2 (Sirolimus) combination (Adapted from Lamb et al. 2006)
5. 32914 The Principle of Rational Design of Drug Combination and Personalized…
3 Rational Drug Combination Design Based
on Synthetic Lethal
Synthetic lethal siRNA screens with chemical agents could facilitate to explore
the new determinants of sensitivity of known drugs, and identify new agents that
could selectively and synergistically enhance their therapeutic effects. Whitehurst
et al. combined a high-throughput cell-based genetic screening platform with a
genome-wide synthetic library of chemically synthesized small interfering RNAs
and established a paclitaxel-dependent synthetic lethal screen for identifying gene
targetsthatspecificallyreducedcellviabilityinthepresenceofpaclitaxel(Whitehurst
et al. 2007). The identified targets were enriched in proteasome subunit, microtu-
bule-related process and cell adhesion. Several of these targets sensitized lung cancer
cells to paclitaxel concentrations 1,000-fold lower than was otherwise required
for a significant response. Thus, this method demonstrates an effective approach to
design new drug combination: in this example, combining paclitaxel with the
identified small molecules interfered with the above biological processes which
were synthetic lethal to paclitaxel treatment. From these initial findings, a rational
drug treatment combination of proteasome inhibitor with paclitaxel could be designed.
Indeed, the collaboration of bortezomib, a proteasome inhibitor and paclitaxel has
already been clinically demonstrated (Davies et al. 2005).
Synthetic lethality could also be utilized to counteract drug resistance. Many tumors
fail to respond to therapy because of intrinsic or acquired resistance. To investigate
this possibility, Astsaturov et al. constructed an epidermal growth factor receptor
(EGFR)-centered signaling network by integrating multiple data sets, and then
conducted a targeted RNA interference screening (Astsaturov et al. 2010). In this
way, they identified subsets of genes that sensitize cells to EGFR inhibition. They
found that these sensitizing hits populate a protein network connected to EGFR,
which is in line with the concept that the gene sub-network closely linked to the
therapeutic target would be enriched for determinants of drug resistance. Erlotinib
is a reversible tyrosine kinase inhibitor, which acts on the EGFR. Chemical inhibi-
tion of proteins encoded by hit genes, e.g., the small-molecule inhibitor of STAT3
activation and dimerization, Stattic, could synergizes with erlotinib in reducing cell
viability and tumor growth (Astsaturov et al. 2010). In this way, synthetic lethality
screening provided a rational method to the design of combination cancer therapies
via counteracting drug resistance (Fig. 14.2).
4 Personalized Therapy Design Based on Synthetic Lethal
Recently, Luo et al. demonstrated a strategy to design personalized cancer therapy
based on synthetic lethal screening (Luo et al. 2009a). They first identified, via
a genome-wide RNAi screen, a group of genes which exhibited synthetic lethal
6. 330 J. Xiong et al.
interactions with the KRAS oncogene. The results highlighted a pathway involving
the mitotic kinase PLK1, the anaphase-promoting complex/cyclosome and the
proteasome that; when this pathway was inhibited, resulted in the death of Ras
mutant cells. Based on these findings and using the CDC16 gene as example, this
information could be used to design a personalized therapy as follows (see Fig. 14.3):
Step 1, analysis of the association of CDC16 gene expression with the prognosis
of cancer patients with the normal (wild type) Ras gene. As shown in Fig. 14.3a
(‘Ras signature-’), there are no significant differences in the survival curve of
CDC16 high expression patients (red line) and CDC16 low expression patients
(blue line), the log-rank test p-value is 0.67. It suggests that in Ras wild type
Fig. 14.2 The principle of design drug combination based on synthetic lethal
Fig. 14.3 The principle for design personalized therapy based on synthetic lethal (Adapted
from Figure 7 of Luo et al. 2009a)
7. 33114 The Principle of Rational Design of Drug Combination and Personalized…
patients, CDC16 gene expression is not associated with patient prognosis, and
it might be, therefore, ineffective to use this gene as therapy target in this group
of patients.
Step 2, analysis of the association of CDC16 gene expression with prognosis
of cancer patients with a Ras gene mutation. As shown in Fig. 14.3b (‘Ras signa-
ture+’), there are significant differences in the survival curve of CDC16 high
expression patients (red line) with CDC16 low expression patients (blue line),
here the log-rank test p-value is 0.02. This suggests that in Ras mutation patients,
CDC16 gene expression is significantly associated with patient prognosis, and the
therapy targeting to CDC16 has the potential to work in this group of patients.
Step 3, combining the above evidence, a hypothetic personalized therapeutic
strategy would be as follows: “if there is a therapy targeting CDC16, then it is
recommended that the patients will be tested for Ras gene mutation detection
before accepting this therapy. If the test result is positive (Ras mutation), then a
CDC16 targeted therapy is recommended. If the test result is negative, then the
patient is unlikely to benefit from this therapy”.
Similarly, Scholl et al. used high-throughput RNA interference (RNAi) to identify
synthetic lethal interactions in cancer cells harboring mutant KRAS, the most com-
monly mutated human oncogene. They identified the serine/threonine kinase STK33
as a target for treatment of mutant KRAS-driven cancers (Scholl et al. 2009).
However, there was a lack of structural abnormalities or deregulated expression of
STK33 in cancer cell lines and primary human cancer samples, which suggested
that STK33 does not act as a classical oncogene. Recent findings suggests that
cancers are not only dependent on mutated oncogenes, which drive the malignant
phenotype, but also dependent on some “normal” genes, which is termed “non-
oncogene addiction” (Solimini et al. 2007; Luo et al. 2009b). Thus a synthetic lethal
screen might be a practical approach to identify this type of association.
5 SOD – an In Vivo Genetic Interaction Similar
to Synthetic Lethality
Recently, our group proposed a novel in vivo genetic interaction which we call
‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic
Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer
patients’ outcome, whose correlation with outcome is due to cooperative, rather
than independent, contributions of genes (Xiong et al. 2010).
An illustration of this concept is in Fig. 14.4. Here the expression of two genes
(gene A, gene B) and their relationship between phenotype (patient prognosis) are
represented:
1. Gene A and gene B have two states: high expression or low expression levels.
2. Red triangles represent ‘bad outcome’ patients (shorter survival time or metastasis),
and green rectangles represent ‘good outcome’ patients (longer survival time
or non-metastasis).
8. 332 J. Xiong et al.
3. Individual gene expression is uncorrelated with patient outcome. For example,
given the gene A state is ‘low expression’, all patients with A (Low) are distrib-
uted in two clusters (50 % bad outcome and 50 % good outcome).
4. In combination, the expression states of two genes are sufficient to determine the
patient outcome. Given the combination of the states of A and B, i.e., A (Low) B
(high), 100 % patients are ‘good outcome’ (Fig. 14.4).
In this way, gene-gene pairs which synergistically determine patient outcome
could be identified by a “synergy” calculation based on information theory (Xiong
et al. 2010). Of interest, the concept of SOD has several unique features that differ
from those of the concept of Synthetic Lethality (Table 14.1):
1. In synthetic lethality, the phenotype is defined at the cell-level (i.e. cell death),
whereas SOD defines the phenotype at the physiological level (i.e. the survival
Gene A
Good Outcome
Bad Outcome
GeneB
LOW
LOW HIGH
HIGH
Fig. 14.4 The concept of ‘synergistic outcome determination’ (SOD) (Adapted from Xiong
et al. 2010)
Table 14.1 SOD vs synthetic lethality
Feature compared SOD Synthetic lethality
Phenotype Survival outcome of individual patient Cell death/growth
Systems level Tumor microenvironment (tissue level) Cell
Data accessible Human population (via computation) Yeast (SGA); Human cell lines
9. 33314 The Principle of Rational Design of Drug Combination and Personalized…
outcome of the individual). Thus, SOD provides a direct link between gene
level events and the clinical information.
2. Because of the ethical limitations, it is impossible to identify in vivo synthetic
lethal genes in human individuals, at present, high throughput synthetic lethality
screening is limited only to in vitro human cell lines (Whitehurst et al. 2007). But
in terms of SOD, it could be computationally inferred via combining the high-
throughput gene expression data with prognosis information from large human
populations.
3. Compared to using gene expression from in vitro cell lines in synthetic lethal
identification, we have used the gene expression information from a bulk of
tumor tissues when calculating SOD. Thus, it is possible to capture molecular
events at the tissue level rather than at the cellular level. This feature is important
to oncology studies, because the gene expression profiling data for a tumor tissue
is actually a representative of the information from a mixture of tissues which
include epithelial cells and other cells in the microenvironment. In this way, SOD
is useful for characterization of gene events in the tumor micro-environment.
6 Rational Drug Combination Design Based on SOD
Based on the SOD concept, a prognosis-guided synergistic gene-gene interaction
network could be constructed. Because this network characterizes the global joint
dependency between genes in a network manner, it is possible to design drug com-
binations based on the derived SOD network. As illustrated in our previous study, we
projected drug sensitivity-associated genes on to the cancer-specific SOD network, and
defined a perturbation index for each drug based upon its characteristic perturbation
pattern on network (Xiong et al. 2010). In this way, we demonstrated a strategy for
rational design of drug combinations. The steps and algorithms are as followings:
1. Given a cancer-specific SOD network, calculate the perturbation value of each
gene node by a specific drug. Here we can map the drug action to the gene network
by drug sensitivity-associated genes (Xiong et al. 2010). For example, the sensi-
tivity of primary drug (D1) is associated with four genes in Fig. 14.5a, thus we
label these genes as ‘1’ (Gene1, Gene 2, Gene3, Gene4) to represent the action
model of drug D1.
2. Calculate the perturbation value of each edge in the network for a particular drug.
If, and only if, both two nodes in an edge are labeled ‘1’, will the perturbation
value of this edge be labeled as ‘1’. For example, we can see drug D1 simultane-
ously perturbs Gene 1 and Gene 4 in Fig. 14.5a, thus the link between Gene 1 and
Gene 4 is labeled with ‘1’.
3. Calculate the perturbation index for each drugs according to:
=
=
=
∑
∑
1
1
PI
N
jj
M
ii
D
D
10. 334 J. Xiong et al.
Fig. 14.5 Rational drug combination design based on SOD
11. 33514 The Principle of Rational Design of Drug Combination and Personalized…
Here, the N is the number of edges, M is the number of genes in the network. Di
is
the perturbation value of gene node I and Dj
is the perturbation value of edge j.
(a) In the example illustrated in Fig. 14.5a, primary drug D1 perturbed 1 edge (link
from Gene 1 to Gene 4), and 4 nodes (Gene1, Gene 2, Gene3 and Gene4). Thus,
the perturbation index of primary drug D1 is 1/4=0.25;
(b) The action model of primary drug (D1) + candidate drug (D2) is illustrated in
Fig. 14.5b. Here the action of D2 added a perturbation to Gene 5, thus, this
changes the perturbation value of three edges into ‘1’ (the link from Gene 1 to
Gene 5, the link from Gene 2 to Gene 5, the link from Gene 3 to Gene 5) and
results in a perturbation index of D1+D2 is 4/5=0.8;
(c) For another candidate drug D3, the action model of primary drug (D1) + candidate
drug (D3) is illustrated in Fig. 14.5c. Here the action of D3 added a perturba-
tion to Gene 6, in this case this change the perturbation value of only one edge
into ‘1’ (the link from Gene 3 to Gene 6). Thus, the perturbation index of
D1+D3 is 2/4=0.5;
(d) Because the perturbation index of D1+D2 larger than that of D1+D3, the com-
bination D1-D2 is predicted to outperform the combination D1-D3.
In above case, the candidate drug D2 and D3 both perturbed one gene, but
resulted in significantly different perturbation indices. The reason for this is because
D2 perturbed gene 5, which exhibited more synergistic links with other genes (Gene
1, Gene 2 and Gene 3).
7 Conclusions and Perspective
From these examples, we have shown that the network characterizing the depen-
dency or joint dependency between genes and disease phenotype is the key “battle
map” for rational drug combination and personalized therapy design. In addition
to being able to determine synthetic lethal interactions via genetic screening,
computationally inferred in silico genetic interactions could also be utilized to
globally interrogate drug combination synergy. In theory, there are many potential
novel types of ‘genetic interaction’:
1. Genetic interactions could be defined by various types of phenotypes.
Traditionally, synthetic lethal is defined by the phenotype of cell viability
based on in vitro experiments. This type of information could be derived from model
organisms (e.g., yeast) or in vitro cultured human cell lines. A specific type of genetic
interaction is the interaction between drug and gene; for example, the sensitivity of
an oncology drug is dependent on individual genes that can be identified by chemical-
genetic screening (Muellner et al. 2011). Here, the phenotype is cell viability under
the two perturbed conditions (both drug treatment and RNAi interference for indi-
vidual genes) (Muellner et al. 2011).
2. Genetic interaction could be defined at different levels of “building blocks of life”.
12. 336 J. Xiong et al.
Since complex biology systems can be divided into various systems levels, so the
interactions between various systems levels (such gene level, gene module level,
protein complex, etc.) could also interrogated. For example, at the gene module
level, the combinatorial influence of deregulated gene modules on disease pheno-
type classification could be inferred by a synergy calculation (Park et al. 2010).
This interaction between gene modules could also computationally inferred and
applied to determination of drug combinations (Xiong et al. 2010). Beyond the
intra-cell events, the dependency between different cells could also contribute
to cancer phenotype and serve as potential targets for cancer treatment. It has
already been demonstrated that combinatorial therapy which targets inter-cell inter-
actions. i.e., interaction between cancer cells and stromal cells (Bronisz et al. 2011;
Aharinejad et al. 2009), as well as interaction between cancer stem cells and their
niche (Malanchi et al. 2012), could hold the potential to counteract the in vivo drug
resistance of cancer drugs.
Genetic interaction is a specific relationship within a triplet of gene-gene-phenotype
or gene-chemical-phenotype. Because it is possible to define a broad range of
phenotype at different levels within the human body, there are abundant opportuni-
ties to define new types of genetic interactions. Innovation in genetic interaction
definition and corresponding network construction holds great potential for applica-
tion to next generation oncology therapeutics.
Acknowledgements This work was partly supported by the grant from the Chinese Scientific and
Technological Major Special Project (2012ZX09301003-002-003), the National Natural Science
Foundation of China (91129708), the grant from State Key Lab of Space Medicine Fundamentals
and Application (SMFA) to J.X (SMFA09A07, SMFA10A03).
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