the principle of rational design of drug combination and personalized therapy based on network...

325A.S. Azmi (ed.), Systems Biology in Cancer Research and Drug Discovery, DOI 10.1007/978-94-007-4819-4_14, © Springer Science+Business Media Dordrecht 2012

Contents

1 Introduction ........................................................................................................................ 326 2 Rational Drug Combination Design Based on Gene Expression Pattern ........................... 327 3 Rational Drug Combination Design Based on Synthetic Lethal ........................................ 329 4 Personalized Therapy Design Based on Synthetic Lethal .................................................. 329 5 SOD – an In Vivo Genetic Interaction Similar to Synthetic Lethality................................ 331 6 Rational Drug Combination Design Based on SOD .......................................................... 333 7 Conclusions and Perspective .............................................................................................. 335 References ................................................................................................................................ 336

J. Xiong (*) • F. Liang • Y. Li State Key Lab of Space Medicine Fundamentals and Application , China Astronaut Research and Training Center , Beijing , People’s Republic China e-mail: [email protected]

S. Rayner Bioinformatics Group, State Key Laboratory of Virology, Wuhan Institute of Virology , Chinese Academy of Sciences , Wuhan , People’s Republic China

Chapter 14 The Principle of Rational Design of Drug Combination and Personalized Therapy Based on Network Pharmacology

Jianghui Xiong , Simon Rayner , Fengji Liang , and Yinghui Li

Abstract Network Pharmacology attempts to model the effects of drug action by simultaneously modulating multiple components in a gene network. 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. 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 examples. We demonstrate that the network, which characterizes the

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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

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 speci fi c 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-speci fi c 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 identi fi ed 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 fi rst 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 identi fi ed 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

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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 fi gure, 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 pro fi ling 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

DrugSensitivity

DrugSensitivity

Gene g1 Gene g2

a b

dc

Dexamethasone+ Sirolimus

Dexamethasonealone

dexamethasone concentation (μm)

00.001 0.01 0.1 1

20

40

60

80

100

120

viab

ility

(%

of co

ntro

l)Gene g1Up

RegulationPositiveGene-Drug SensitivityCorrelated Genes

NegativeGene-Drug SensitivityCorrelated Genes

DownRegulation

Gene g2

Gene Expression Gene Expression

PositiveGene-Drug Sensitivity

Correlation

NegativeGene-Drug Sensitivity

Correlation

DrugSensitivity

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 )


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 targets that speci fi cally reduced cell viability in the presence of paclitaxel (Whitehurst et al. 2007 ) . The identi fi ed 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 signi fi cant response. Thus, this method demonstrates an effective approach to design new drug combination: in this example, combining paclitaxel with the identi fi ed small molecules interfered with the above biological processes which were synthetic lethal to paclitaxel treatment. From these initial fi ndings, 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 identi fi ed 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 fi rst identi fi ed, via a genome-wide RNAi screen, a group of genes which exhibited synthetic lethal

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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 fi ndings 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 signi fi cant 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 )


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 signi fi cant 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 signi fi cantly 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 bene fi t 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 identi fi ed 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 fi ndings 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 de fi ned 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).

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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 suf fi cient 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 identi fi ed 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 de fi ned at the cell-level (i.e. cell death), whereas SOD de fi nes the phenotype at the physiological level (i.e. the survival

Gene A

Good OutcomeBad Outcome

Gen

e B

LOW

LOW HIGH

HIG

H

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


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 identi fi cation, 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 pro fi ling 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-speci fi c SOD network, and de fi ned 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-speci fi c SOD network, calculate the perturbation value of each gene node by a speci fi c 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

334 J. Xiong et al.

Fig. 14.5 Rational drug combination design based on SOD


Here, the N is the number of edges, M is the number of genes in the network. D i is

the perturbation value of gene node I and D j 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 signi fi cantly 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 de fi ned by various types of phenotypes.

Traditionally, synthetic lethal is de fi ned 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 speci fi c 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 identi fi ed 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 de fi ned at different levels of “building blocks of life”.

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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 in fl uence of deregulated gene modules on disease pheno-type classi fi cation 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 speci fi c relationship within a triplet of gene-gene-phenotype or gene-chemical-phenotype. Because it is possible to de fi ne a broad range of phenotype at different levels within the human body, there are abundant opportuni-ties to de fi ne new types of genetic interactions. Innovation in genetic interaction de fi nition 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 Scienti fi c 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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