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Review 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

Pharmacogenomics of multiple sclerosis: in search for a personalized therapy 1. 2.

Introduction

Iván Martinez-Forero, Antonio Pelaez & Pablo Villoslada†

Interferon-β: potential mechanism of action involved in the treatment of multiple sclerosis

†Center

3.

How to define the response to interferon-β

4.

Type I interferon signaling pathway

5.

Cytokines and cytokine signaling

6.

T-cell signaling and co-stimulatory molecules

7.

Adhesion molecules

8.

Natalizumab

9.

Glatiramer acetate

10. Systems biology approach for the development of personalized medicine in multiple sclerosis 11. Conclusion 12. Expert opinion

for Applied Medical Research (CIMA), Neuroimmunology Lab 2.05, 31008 Pamplona, Spain

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system that affects young adults and provokes severe disability, imposing a high health and social burden. Current therapies for MS include interferon-β, glatiramer acetate, natalizumab and chemotherapy. These therapies decrease the number of relapses and partially prevent disability accumulation. However, their efficacy is only moderate, they have common adverse effects and impose a high cost to health systems. The identification of biomarkers will allow responders and non-responders to therapy to be identified, increasing the efficacy and adherence to therapy, and the pharmaco-economic profile of theses drugs. In this review we examine the pharmacogenetic studies that have evaluated the clinical response to interferon-β, and to a lesser extent, glatiramer acetate and natalizumab. Finally, we discuss how systems biology can be used to integrate biological and clinical data in order to develop personalized medicine for MS. Keywords: biomarker, glatiramer acetate, interferon-β, multiple sclerosis, natalizumab, pharmacogenetics, personalized medicine, systems biology Expert Opin. Pharmacother. (2008) 9(17):1-15

1.

Introduction

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS of unknown etiology that occurs more frequently in the northern areas of the world, and it is particularly frequent in Europe and North America [1]. No definitive cure is available for MS, and disease-modifying therapies such as interferon-β (IFN-β), glatiramer acetate and natalizumab are only partially effective and induce side effects that limit a patient’s quality of life (Table 1) [2,3]. Although two of these drugs were developed based on a well known therapeutic target (the T-cell receptor specific for myelin basic protein for glatiramer acetate, VLA4 integrin for natalizumab), the mechanisms of action of these drugs are only partially understood. The economical and social burden of the disease is considerable as it affects predominantly young adults (age 20 – 40). The number of persons affected in the EU-25 is estimated to be between 400,000 and 455,000 and similar numbers apply to the US. A Canadian study reported a substantial increase over the last 50 years in the female to male sex ratio, now exceeding > 3.2 females per male contracting the disease, likely due to the increasing incidence of the disease in women [4]. The average lifetime cost of the disease is estimated at more than 1.5 million per MS patient in the UK, which is likely to be representative of EU-25 countries [2]. Currently, MS therapies are indicated based on disease activity and not on the predicted response to a given drug. This is due to the lack of biomarkers for identifying responders and non-responders to therapy. For example, it has been estimated that up to 40% of patients do not respond to IFN-β [5,6], which implies that many patients are exposed to the side effects of a drug which is of no 10.1517/14656560802515553 © 2008 Informa UK Ltd ISSN 1465-6566 All rights reserved: reproduction in whole or in part not permitted

1

Pharmacogenomics of multiple sclerosis: in search for a personalized therapy

Table 1. Approved and pipeline drugs for MS treatment. Drug

Mechanism of action

Interferon-β

Immunomodulation

Glatiramer acetate

Immunomodulation

Natalizumab

Anti VLA-4 monoclonal antibody

Mitoxantrone

Immunosuppressant

Rituximab

Anti-CD20 monoclonal antibody

Fingolimod

Sphingosine-1-phosphate agonist

Laquinimod

Sphingosine-1-phosphate agonist

Daclizumab


Alemtuzumab


Statins (atorvastatin, simvastatin)

Immunomodulation

Pipeline drugs for MS are given in italic.

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

benefit to them and that health systems are spending money without providing a social benefit. There is therefore a pressing and timely need for new therapies with higher efficacy and good safety profiles and for improving the efficacy of existing treatments. The improvement of the efficacy and safety of existing drugs is a good strategy that would pay dividends both for society and for the pharmaceutical industry. The search for personalized medicine aims to identify the best therapy for a given patient, or more practically a subgroup of patients, with better efficacy and fewer side effects for a given drug (stratified medicine), which will have a big impact on chronic diseases [7]. Successful identification of treatment response – predictive genetic/biomarkers [8,9] would support early treatment of those patients most likely to respond to a particular treatment, while allowing treatment of non-responders with alternative medication, or at least preventing them from suffering side effects [10]. The success of specialty drugs, mainly for neurological diseases, is based on a good safety profile and enhancing efficacy by selecting good responders, more than on a new mechanism of action [11]. IFN-β is the most common treatment choice in patients affected by MS. IFN-β reduces the number of relapses by a third, and also delays the progression of the disease [12]. Currently, there are three commercial products available on the market: Betaferon® (IFN-β1b), Avonex® and Rebif® (both IFN-β1a). INF-β belongs to the Type I interferon cytokine family. Type I IFNs are cytokines that have antiviral, antiproliferative and immunomodulatory effects. There are many type I IFNs, including IFN-α (with 13 different subtypes), IFN-β, IFN-κ and IFN-ω. All type I IFNs bind to the same receptor. These molecules are widely used in the treatment of hepatitis C, MS and various types of cancer. The mechanism of action is not well understood, although considerable evidence suggests a combination of immunomodulatory and anti-inflammatory effects [13]. The efficacy of IFNs is limited, with up to 40% of MS patients 2

not responding to therapy [5,6]; similar rates of efficacy have been observed in hepatitis C patients [14]. The cause of this lack of efficacy is not known but it could be related to the pleiotropic activity of the drug and the generation of common adverse events that limit dosage. However, individual differences in the genes activated by IFN-β as well as differences in the pathogenic mechanisms at work in each individual might account for the lack of response to therapy. It will be of great clinical value to find markers of response to this drug in order to begin an early intervention in the responders and to avoid its use in non-responders. In this article we review biomarker identification for MS therapy using pharmacogenomics. Genetic variations in the form of single nucleotide polymorphisms (SNPs) have been implicated as a factor to predict clinical efficacy in other diseases [15]. Also gene expression and to a lesser extent protein studies have been used to identify biomarkers of the response to immunomodulatory therapy. We focus on IFN-β because it is one of the most prescribed drugs for MS, it exemplifies the complexity of identifying biomarkers in complex diseases treated with biological drugs, and because research in pharmacogenomics of IFN-β has pioneered the search for personalized medicine in this disease. We use a pathway description of possible MS treatment biomarkers and for each candidate molecule we display the genetic and genomic evidence available. We also comment on similar approaches with other current approved therapies for MS. Then, we examine the opportunities that a new approach, systems biology, is offering for integrating clinical and biological data, such as pharmacogenomics, for developing personalized medicine. 2. Interferon-b: potential mechanism of action involved in the treatment of multiple sclerosis

IFN-β is a regulator of the immune system and acts over a broad range of immune cells, including dendritic cells, T cells and B cells [13]. IFN-β also influences the activity of astrocytes, microglia and neurons [16]. The immunomodulatory effects of INF-β are diverse. IFN-β downregulates the expression of type II MHC molecules and inhibits T-cell proliferation. It also promotes the expression of helper T-cell (TH2) cytokines and the immunosuppressive cytokine interleukin (IL)-10 [13]. In the blood – brain barrier, IFN-β inhibits the traffic of T cells, improving the integrity of endothelial cells, which implies the activity of adhesion molecules is reduced [17] and the production of metalloproteases is decreased [18]. Interestingly, two different groups have reported the involvement of IFN-β in the control of inflammation inside the CNS, in addition to its better-known effects in peripheral immune system. Dendritic cells in the brain respond to IFN-β, diminishing IL-23 production and favoring IL-27 secretion [19]. IL-27 is actually known to inhibit TH17 cell differentiation. Also, microglia, in the presence of IFN-β,

Expert Opin. Pharmacother. (2008) 9(17)

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Martinez-Forero, Pelaez & Villoslada

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decrease the release of inflammatory chemokines, halting lymphocyte infiltration into the CNS [19-21]. In summary, IFN-β, ahead of its immunomodulatory activity, could protect the CNS during inflammation. 3.

How to define the response to interferon-b

The course of MS in individual patients is largely unpredictable and ranges from benign (roughly 10% of the patients) to a rare fulminant disease that leads to death within months after onset. Even if the majority of patients suffer with a relapsing – remitting disease, the frequency of relapses, its distribution with time and the presence of permanent disability after relapses are highly variable and unpredictable. Furthermore, in contrast to other diseases, there are no clear quantitative variables that allow disease course or response to therapy to be monitored – compared with, for example, glycemia in diabetes or viral load in hepatitis. Therefore, defining response to MS treatment is a challenging task of dramatic importance in pharmacogenomics. Validated methods to classify individual patients according to their response to therapy would facilitate rational therapeutic decisions, allowing new designs for the next generation of clinical trials, and encouraging interpretable studies correlating biomarkers with therapeutic response. Currently, we have no tools for distinguishing between the response to therapy and the natural history of MS for a given patient, making it difficult to perform therapeutic decisions. The difficulty of distinguishing between response to therapy and disease activity could not be resolved without the identification of biomarkers measuring the response to IFN-β. Several researchers proposed criteria for defining the response to IFN-β treatment in MS. We defined the response to IFN-β using clinical variables, namely the suppression of relapses during follow-up (exacerbation-free patients) and no increase in the Expanded Disability Status Scale (EDSS) score [22]. Follow-up studies included several combinations of clinical definitions of responders and non-responders, including a decrease in the relapse rate by 30%, being relapse free, and no increase in the EDSS [5,23,24]. Cunningham et al. [21] used a definition of non-responders as patients whose relapse rate remains the same or increases after the initial 9-month period, and responders as patients whose relapse rate reduces by one-third and who do not experience sustained disability progression [21]. Several other studies have used similar clinical criteria for defining the response to IFN-β [25-29]. Long-term prospective studies have shown that disability (i.e., EDSS) based criteria are better for identifying patients with poor outcome in the long term [30]. However, whether a poor outcome is related to a lack of response to IFN-β or to a more aggressive disease is unclear. When selecting a definition for stratifying populations for genetic studies, specificity is a key factor because including misclassified patients would have a strong negative impact on the statistical

analysis. For this reason, the use of the most well defined (extreme) phenotypes of responders and non-responders is critical, and EDSS-based definitions seem to be the most useful. Other studies have used MRI activity for defining the response to IFN-β [31,32] as it is very sensitive for identifying MS pathology. IFN-β clinical trials have indicated that IFN-β clearly reduces the number of gadoliniumenhancing lesions and, to a lesser extent, the number and volume of T1 and T2 lesions. However, MRI-based criteria are difficult to apply to big cohorts of patients, which are required for pharmacogenetic studies, due to their high cost and patient inconvenience. In addition, it is still not known whether the response observed using MRI is a good predictor of the biological response to IFN-β or whether MRI criteria also suffer from the previously discussed problem of the distinction between response to IFN-β and disease activity. 4.

Type I interferon signaling pathway

The type I IFN signaling pathway is composed of a receptor (IFN-AR) and three signaling pathways: Rac1/p38 mitogenactivated protein kinase (MAPK), JAK-STAT and phosphoinositide 3-kinase (PI3K) pathways (Figure 1) [33,34]. Components of the type 1 IFN signaling pathway are the ideal candidates to look for markers of response to IFN-β therapy. SNPs and differentially expressed genes have been widely investigated in this pathway. In this section, a survey of the most promising markers is undertaken. The first step in the IFN-β transduction pathway is the binding of IFN-β to the IFN type I receptor (IFN-AR). IFN-AR is composed of two subunits: IFN-AR1 and IFN-AR2 [35]. Preliminary results have identified several SNPs in the IFNAR1 and IFN-AR2 genes associated with the response to IFN-β therapy [21,23,36,37]. However, the evidence is incomplete and larger studies are needed to clarify the role of these genes in determining IFN-β response. IFN-β activates the JAK-STAT signaling pathway. In the case of IFN-β, JAK1 and TYK2 are the components of the JAK family of proteins that participate in IFN-β transduction [38]. In a seminal study, Baranzini et al. [39] used quantitative PCR and advanced data mining techniques to evaluate the gene expression profiles of 70 genes in a cohort of 52 MS patients over a 2-year period [39]. The genes analyzed included cytokine receptors, members of the IFN signaling and apoptosis pathways, and several transcription factors involved in immune regulation. This group identified a relationship between TYK2 or JAK2 gene expression and IFN-β responsiveness in MS. The complex formed by JAK1 and TYK2 phosphorylates on tyrosine residues the STAT1 and STAT2 proteins (Figure 1). STATs are transcription factors capable of traveling back and forth from the nucleus where they regulate the expression of hundreds of genes that mediate INF-β biological effects. For the appropriate control of transcription, the STAT1/STAT2


3

199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252


IFN-β

Cell membrane

IFN-AR JAK1

SOCS

STAT1 STAT2

TYK2

Rac1 p38

PI3K

MAPKK3, MAPKK6

AKT

p38 MAPKAP2, MAK1

ISRE PIAS

MX1

GAS CASP3

Interferon gene expression pattern FLIP

TRAIL

Figure 1. Interferon (IFN) signaling pathway. After binding to its receptor (IFN-AR), IFN-β activates a cascade that controls the gene expression of hundreds of genes. The signaling pathways involved in this process are JAK-STAT, phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK). Suppressors of cytokine signaling (SOCS) in the cytoplasm and protein inhibitors of activated STAT (PIAS) in the nucleus regulate the amplitude of the signal initiated by IFN-β. After activation of several transcription factors through the three signaling cascades, they migrate to the nucleus where they promote the expression of hundred of genes, including interferon-stimulated response element (ISRE) and IFN-γ-activated site (GAS) responding genes, which are responsible for the biological effects of IFN-β.

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complex binds to interferon regulatory factor (IRF)-9 protein [40]. Weinstock-Gutmann et al. [41] identified distinct gene expression profiles, including STAT1 protein, in time series analysis in a group of MS patients after the administration of IFN-β [41]. Another study observed a negative correlation between STAT1 phosphorylation and the expression of IFN-stimulated genes, IRF-1 and IRF-2 [42]. Moreover, another group studying hepatitis C reported that non-responders to IFN-α have a lesser degree of STAT1 activation compared with responders. In summary, the results from two different diseases suggest that STAT1 could be a marker for response to therapies using type I IFNs [14]. However, confirmation of the role of STAT1 as a biomarker of IFN-β therapy is still lacking. Although all subtypes of type I IFN interact with the same receptor and activate similar pathways, they produce different cellular responses. The origin of this behavior is perhaps a change in the regulatory dynamic of each of the signaling events according to the IFN that initiates the signal transduction process. The elaborate crosstalk between these signaling cascades guarantees IFN-β activity specificity. For instance, protein kinase C-δ, a component of the PI3K transduction pathway, phosphorylates STAT1 and STAT 2 in serine residues facilitating IFN gene expression control [43]. PI3K and p38 also promote mammalian target of rapamycin pathway activation, which regulates messenger RNA translation [44]. 4

Several mechanisms control the type I IFN signaling pathway. Suppressors of cytokine signaling proteins (SOCS) inhibit the type I IFN pathway, preventing the phosphorylation of STAT proteins (Figure 1). At the nuclear level, protein inhibitors of activated STAT (PIAS) block STAT1 binding to the DNA [45,46]. Although not yet studied, SOCS and PIAS genes are interesting candidate biomarkers. 4.1

Interferon-b-induced genes

IFN-β regulates the transcription of thousand of genes. In the study by Baranzini et al. described above [39], they found that a gene triplet composed of IFN-β induced genes CASP2, CASP10 and FLIP was able to predict the response to IFN-β therapy before starting the treatment with 86% accuracy. Some other genes that predicted response to therapy were CASP3, CASP7, IRF4, IL4Ra; MAP3K1IL-12Rb1, STAT4, IRF2 and IRF6 in different triplet combinations. Also, TNF-related apoptosis-inducing ligand (TRAIL) expression has been suggested as a marker of IFN-β clinical efficacy [25]. Interestingly, the genes reported by both studies are implicated in apoptosis regulation, a known IFN-β action that might help in the clearance of autoreactive lymphocytes in MS [47]. IAP, IAP2, XIAP and survivin are proteins capable of inhibiting caspase activation and apoptosis. A more recent study demonstrated that treatment with IFN-β reduced the gene expression of IAP-1, IAP-2 and XIAP in stimulated


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T lymphocytes from MS patients. Indeed, IAP and survivin expression declined in responders compared with nonresponders [48,49]. In a system biology study using network theory to analyze the results of microarray experiments, Fernald et al. [50] evaluated the expression profile induced after the intramuscular injection of IFN-β over a week and compared the results with untreated individuals. They identify differences in the topology of the network between treated and untreated individuals, evidencing a general decrease in the cellular activity of T lymphocytes resembling the endogenous antiviral response of IFNs in patients treated with IFN-β. These differences were more prominent in the pathways that control cell proliferation and death, including mitosis and protein transduction. The genetic analysis of the promoter of 100 genes containing interferon stimulated response elements (ISREs), which are signatures of IFN inducibility, identified four genes with SNPs differentially associated with response to IFN-β in a Northern Irish collection of MS samples (IFN-AR1, CTSS, LMP7 and MX1) [21]. LMP7 and CTSS (cathepsin S) are polypeptides involved in antigen processing and presentation and MX1 (or MXA) is an antiviral protein. This study has been extended to include further data on 60 SNPs in 30 candidate response genes (Prof. Koen Vandenbroeck, personal communication). MX1 is an IFN-β-induced protein with an antiviral effect. In-vitro MX1 production triggered by IFN-β has been used as an in-vitro assay for the detection of IFN-β neutralizing antibodies. Different studies have examined the expression of MX1 in patients treated with IFN-β. One study suggested a relationship between MX1 and responder status [25], although another study failed to confirm such an association [51]. Recently, Malucchi et al. reported in a cohort of 137 MS patients treated with IFN-β the association between MX1 measurement and lack of response to IFN-β. They also evaluated the presence of neutralizing antibodies and observed a prominent role of MXA as a treatment marker, highlighting the easier method to measure MXA in contrast to the one used to detect neutralizing antibodies [52]. MX1 has also been suggested as a marker of the response to IFN-β in primary progressive MS [53]. A genetic study discovered two SNPs in the MX1 promoter that display different frequencies in responders and non-responders [21]. In summary, MXA is one of the most studied candidate markers in IFN-β pharmacogenomics. PKR is another interferon-induced gene implicated in viral immunity. It is regulated by the JAK/STAT signaling pathway. PKR mRNA was found to be overexpressed in responders to IFN-β [32]. No genetic differences have been reported in the PKR gene to classify MS patients as responders to IFN-β therapy. In a cohort of 206 patients followed for 2 years, Byun et al. [24] conducted the first genome-wide pharmacogenetic analysis in the search for SNPs associated with IFN-β response. They found significant differences in the allelic

frequency between responders and non-responders in genes implicated in the control of signaling pathways and ion channels. The top scoring SNPs correspond to the proteins glypican 5, collagen type XXVa1, hyaluronan proteoglycan link protein, calpastatin and neuronal PAS domain protein 3 (Table 2). Some of these are responsible for neuronal damage and some others participate in neuronal repair, suggesting that IFN-β, ahead of being an immunomodulator, is also a neuroprotective agent [16]. The study by Byun et al. provides a wealth of data regarding the biology of IFN-β in MS. The functional implication of the SNP differences between responders and non-responders is worth exploring. 5.

Cytokines and cytokine signaling

Cytokines are extracellular proteins that have pleiotropic effects in the immune system. They participate in the control of T-cell growth, inflammation and tissue migration of activated leukocytes. The most successful therapies for autoimmune diseases are capable of blocking inflammatory cytokines. For example, anti-TNF-α (infliximab) and soluble TNF-α receptors (etanercept) are widely used in the treatment for rheumatoid arthritis, Crohn’s disease and ankylosing spondylitis [54]. Biomarkers of responsiveness to IFN-β have been screened within the cytokine family, including IL-2, IL-4, IL-8, IL-10, IL-12 and IL18 [55-60]. However, current data are still inconclusive as to whether they are suitable biomarkers for measuring the response to IFN-β. IL-10 was first described as a TH2 cytokine but nowadays it is considered as a cytokine that plays a central role in suppressing T-cell responses and in maintaining immunological tolerance [61]. IL-10 indirectly suppresses cytokine production and proliferation of antigen-specific CD4+ T effectors cells, modulates the stimulatory capacity of dendritic cells and other antigen-presenting cells (APCs). For this reason, IL-10 has been pursued as a therapeutic target for inflammatory diseases. Our group recently analyzed the IL-10 signaling pathway and found that STAT3 phosphorylation dynamics is altered in MS patients [62]. This downregulation of STAT3 phosphorylation could be dependent on the overexpression of SOCS3, an inhibitor of cytokine signaling. The IL-10 signaling impairment in MS may explain why a clinical trial with IL-10 failed to show a beneficial effect in patients with MS. When we looked at the dynamic behavior of IL-10 signaling in IFN-β-treated patients, we observed a trend towards normalization of the activity of the IL-10 pathway, suggesting that one of the mechanisms of action of IFN-β is by promoting IL-10 activity. However, the role of IL-10 and IL-10 signaling molecules as biomarkers of IFN-β responsiveness has not been fully explored. IL-8 is a cytokine implicated in the migration of leukocytes to sites of inflammation. Aberrant expression of IL-8 promotes the appearance of autoimmune diseases [63]. INF-β inhibits IL-8 function through the coordinated actions of NF-κB p65, C/EBPβ and c-Fos [64]. A microarray study reported


5

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Gene

Protein

Pathway

Ref.

CASP3

Caspase 3

Apoptosis

[36]

TRAIL

TRAIL

Apoptosis

[25,36]

FLIP

FLIP

Apoptosis

[36]

COL25

Collagen type XXV

Extracellular proteoglycans

[24]

GPC5

Glypican 5

Ion channel regulation

[24]

HAPLN1

Hyaluronan proteoglycan link protein

Extracellular proteoglycans

[24]

CAST

Calpastatin

Cell adhesion

[24]

MX1

Myxovirus resistance protein 1

Viral immunity

[52]

CD28-B7 co-stimulation, suppressing the immune response [73]. Abatacept has been approved for other autoimmune conditions, such as theumatoid arthritis. Polymorphisms of CTLA4 are one of the most studied markers for autoimmune disease susceptibility, with conflicting results in MS [74-78]. Moreover, recent genome-wide association studies failed to confirm the involvement of CTLA4 as a susceptibility gene for MS [79]. Regarding the role of CTLA4 as a biomarker of the response to therapy, SNPs -318C/T and 49A/G were described as markers of the responsiveness to IFN-α in hepatitis patients [80]. Moreover, CTLA4 SNP CT60, which is associated with other autoimmune diseases, influences the T-cell activation signaling pathway [81]. These results suggest a link between CTLA4 SNPs and functional alterations in the immune response. However, none of the SNPs in the CTLA4 gene predicted the response to IFN-β therapy [82].

STAT1

STAT1

JAK-STAT signaling pathway

[38]

7.

Table 2. Top candidate molecules as biomarkers for the response to interferon-b.

Several molecules of the interferon (IFN)-β pathway have been implicated in regulating the response to IFN-β based on pharmacogenetic and gene expression studies. To date, none of them have been validated as biomarkers of the response to IFN-β, yet several pathways have been implicated in the response to IFN-β. Top candidates were selected by authors based on the quality of the evidence available (i.e., genome-wide association studies, systems biology studies), as well as the involvement of specific cellular pathways, which seems to be critical in the mechanism of action of IFN-β.

415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440

that IL-8 gene expression is reduced in responders to IFN-β [32]. Further studies are required to validate these cytokines in defining the response to IFN-β therapy. 6.

T-cell signaling and co-stimulatory molecules

Inappropriate T-cell activation plays a central role in pathogenic immune responses, including MS [65]. For the proper initiation of T-cell function several co-stimulatory molecules must get involved after T-cell receptor binding to MHC in APCs. One group of such co-stimulatory molecules is the CD80 – CD86/CD28 – CTLA4 complex. CD80 (B7-1) and CD86 (B7-2) bind to CD28 [66]. INF-β treatment induces CD40, CD80 and CD86 protein upregulation on monocytes from MS patients [67-70]. IFN-β also promotes the expression of PD-L1 and PD-L2, which are inhibitory molecules. A recent study in a small cohort of MS patients treated with IFN-β indicated that the increased production of CD40, CD86 and PD-L2 is a marker of IFN-β efficacy [71]. CTLA4, a homolog of CD28, binds to B7-1/B7-2 and unleashes an inhibitory signal over T-cell response [72]. Because its key role is suppressing immune responses, CTLA4 has been proposed as a candidate gene in the susceptibility of MS as well as a therapeutic target for treating cancer and autoimmune diseases. Abatacept is a fusion protein between CTLA-4 and immunoglobulin that blocks 6

Adhesion molecules

Chronic inflammation is a hallmark of autoimmune diseases, therefore any intervention aiming to reduce the number and activity of inflammatory cells in disease sites will be beneficial. Lymphocytes enter tissues from the circulation by binding to specific adhesion molecules with endothelial receptors [83,84]; this process can be targeted for preventing lymphocyte access to the tissue. During the past few years the role of several adhesion molecules, mainly VLA-4 (integrin α4β1), has been clarified in the pathogenesis of MS [85]. VLA-4 is a lymphocyte surface antigen recognized by VCAM1 in endothelial cells and VLA-4 is the principal adhesion molecule for lymphocytes to access the CNS. Regarding the role of VLA-4 in the response to IFN-β therapy, this molecule is significantly downregulated in responders compared with non-responders [86]. However, other studies reported no difference of VLA-4 between responders and non-responders [51]. Similar conflicting results were found for VCAM1, the ligand for VLA-4 [60,87]. 8.

Natalizumab

Natalizumab is a humanized monoclonal antibody against α4β1 integrin (VLA-4) [88]. As discussed above, VLA-4 is an adhesion molecule expressed by TH1 lymphocytes that when bound to its ligand VCAM1, present in endothelial cells, facilitates the passage of immune cells to sites of inflammation. Inhibition of α4β1 integrin reduces lymphocyte migration to the brain and therefore diminishes inflammation, an essential hallmark of MS. Based on this mechanism of action, several trials have demonstrated that natalizumab reduces the number of clinical relapses and new brain lesions in patients with MS. Pharmacokinetic studies indicate that a single intravenous dose of natalizumab results in sustained serum levels 3 – 5 weeks after the dose, suggesting that natalizumab activity can be sustained over time [89].


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Recently Lindberg et al. [90] reported the transcriptional profile of peripheral blood mononuclear cells in a group of 11 MS patients treated with natalizumab for 2 years. Surprisingly they found that natalizumab has a role in the regulation of genes implicated in the activation of B lymphocytes and neutrophils. This study suggests a complementary mechanism of action of natalizumab ahead of the known action of this drug on T-cell trafficking. Natalizumab is now approved for use in MS [91], however it was rapidly withdrawn from the market due to reports of the development of three cases of fatal progressive multifocal leukoencephalothy (PML) [92]. PML is a fatal opportunistic infection of the brain caused by the polyomavirus JC. Further survey studies have indicated a good safety profile of natalizumab in monotherapy [93] and for this reason natalizumab can currently be used for MS treatment under strict inclusion criteria [94]. However, recent cases of PML in MS patients treated with natalizumab alone indicate that this rare but severe adverse event is associated with the use of natalizumab. To date there are no pharmacogenomic studies on the response to natalizumab therapy. It will be of great clinical interest to identify markers of response to this drug to avoid the use of a treatment that can induce severe adverse effects. In this case SNPs in the ITGA and ITGB integrin genes, the VCAM1 genes or in Fc receptors seem to be the ideal candidates, although genome-wide association studies appear to be the best approach. Also because of the risk of PML, biomarkers identifying individuals at risk for developing the condition will be of great importance. 9.

Glatiramer acetate

Glatiramer acetate is an immunomodulatory agent approved for use in MS [95]. Glatiramer acetate is a controlled mixture of polypeptides designed to compete for the binding of myelin (MBP)-specific T cells to suppress brain autoimmunity. Clinical trials in patients with MS have shown that glatiramer acetate is effective in reducing the frequency of new clinical relapses. The mechanism of action is not well understood. It is postulated that glatiramer acetate promotes TH2 cytokine production, inhibits TH1 cytokine secretion and abrogates APC activation. In a similar way, glatiramer acetate has the potential to alter chemokine receptor expression in CD4 T cells, interrupting the traffic of deleterious immune cells to the brain [96]. Few data are available on glatiramer acetate pharmacogenomics. A pilot study described the association between the presence of the HLA-DR2 haplotype and the response to glatiramer acetate [97]. Recently, the presence of a SNP in the T-cell receptor was described as a marker of response to glatiramer acetate in a group of MS patients [98]. Further studies are needed to clarify the functional role of this SNP in the mode of action of glatiramer acetate. Moreover, the identification of biomarkers of responsiveness to glatiramer

acetate will be highly beneficial because it may improve its 551 efficacy in a subgroup of patients, strengthening adherence 552 to this therapy. 553 554 555 10. Systems biology approach for the 556 development of personalized medicine 557 in multiple sclerosis 558 Systems biology is a growing area that aims to understand 559 biological systems at a holistic level [99,100]. Systems biology 560 has applications in various areas related to medicine, including 561 biomarker discovery, improvement of drug target identification 562 and prognosis assessment [101,102]. For example, using network 563 analysis, one of the most pursued approaches in systems 564 biology, we were able to identify Jagged-1/Notch as a candi- 565 date therapeutic target for MS [103]. Systems biology is able 566 to respond to the increasing need of personalized medicine 567 in complex diseases like MS, by integrating theoretical and 568 experimental research. From a theoretical point of view, 569 computational methods are required to study the structure 570 and dynamics of biological networks. From an experimental 571 point of view, it uses tools that permit the measurement of 572 genes, proteins and metabolites on a large scale – the omics 573 revolution. The end point is, by combining all pieces of 574 information in computational models, the generation of new 575 knowledge about how the system works (i.e., the immune 576 system) in health and disease and the best suitable approaches 577 for therapy. In particular, systems biology can address the 578 following questions in pharmacogenomics: 579 580 • What are the biological networks in which SNPs associated 581 with the response to therapy exert their influence? 582 • How do SNPs modify key biological processes such as cell 583 differentiation, apoptosis and cell communication? 584 • How do multiple SNPs modify the function of signaling 585 pathways implicated in complex disease pathogenesis? 586 • Which are the interventions in the signaling pathway that 587 are required to change from the non-responder to the 588 responder status? 589 Our point of interest, biomarkers of the response to therapy 590 in MS, systems biology can be helpful at different levels. 591 First, it can provide new candidate genes and proteins using 592 network and pathway analysis, to be assessed as biomarkers 593 of the response to disease-modifying drugs. Second, once a 594 set of biomarkers has been validated, we can create network 595 or dynamical models to assess their functional implications. 596 In the recent pharmacogenomic screening reported by 597 Byun et al. [24] a preliminary review of results suggests that 598 HAPLN1, COL25A1 and CAST have no relationship with 599 IFN signaling. However, in a preliminary study using net- 600 work analysis we were able to find a connection between 601 these genes and the IFN pathway (Figure 3). Moreover, 602 we could also identify relations between COL25A1 and 603 CAST. These results provide suggestions on how to assess 604 the functional role of SNPs in IFN-β response. 605


7


A.

Lymphocyte

Brain

Lymphocyte

Blood vessel Lymphocyte

Lymphocyte

Lymphocyte Natalizumab

Brain

Periphery

B.

CD4 T cell

CD8 T cell

GA APC

Brain derived growth factor (BDNF)

GA

CD4 T cell

Chemokine receptor expression

APC

TH2

T H1 Brain

Figure 2. Natalizumab and glatiramer acetate mechanism of action. A. Natalizumab blocks the adhesion molecule VLA-4 on immune cells, inhibiting its binding to vascular cell adhesion molecule-1 and preventing the adhesion of lymphocytes to the endothelia and their migration inside the brain. B. Glatiramer acetate (GA) induces GA-specific T cells with a type TH2/Treg profile [they secrete IL-10 and brain-derived neurotrophic factor (BDNF)], downregulates chemokine receptor expression and abolishes CD8 T-cell activation by CD4 T cells. This T-cell phenotype switch inhibits the TH1 response, which is the main driver of CNS autoimmunity in animal models of multiple sclerosis (MS), and exerts a beneficial effect on inflammation in MS patients. APC: Antigen-presenting cell; TH: Helper T cell.

8



A.

PTPRC IFNB1

PPPA1

PIK3R1 PTPN6

COL25A1 CAST

IFN-β1 IFNAR1

IFNAR1 APP

PSEN1

B.

IFN-β1

PIK3R1

CAST

FASLG

IFNAR1

APP

MMP7 PTPN6

COL25A1

CASP3 HAPLN1

Figure 3. Network analysis of single nucleotide polymorphims (SNPs) in interferon (IFN)-b treatment. A. Connection between COL25A1, HAPLN1 and CAST reported in Byun et al. [24] and the IFN signaling pathway. B. Possible relation between COL25A1 and CAST. Network analysis could be the starting point to elucidate the functional role of SNPs in IFN-β biology.

606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630

To realize the functional implications of SNPs in signaling pathways of interest, mathematical models of such pathways can provide important insights that cannot be predicted by intuition alone [104,105]. As an example, we model a mutually inhibitory feedback loop between two molecules y and z from a signaling pathway. The dynamic behavior of this network motif is described using a system of two differential equations (Figure 4A and Appendix). Positive feedback loops are the molecular equivalents of switches [106], and they allow one to translate information from signaling pathways to cellular functions (i.e., apoptosis or antiviral effect). Mathematical tools, like Phase plane analysis (Figure 3B), are able to show how changes in molecule concentrations can make the system commute between two different steady states [107]. This is of great interest because minimal variations in protein or mRNA levels can have profound implications in key biological processes, as experimentally demonstrated in a recent study of T-cell activation [108]. A similar approach has been used in cell cycle research, where cyclin-dependent kinases could serve as switches [105]. The model has two steady states, one with high z and low y concentrations (steady state 1) and the other with opposite characteristics (steady state 2). We consider steady state 1 as the basal state of the signaling pathway. In this example a slight modification of the y molecule concentration

(from 3.5 to 3.7) produces a trajectory that now turns to steady state 2 with high y and low z. A switch has occurred. In biological terms, if y and z are molecules controlling a cellular decision such as mitosis or apoptosis, this minimum alteration in y concentration allows the cell to change from one state (i.e., cell survival) to another (i.e., cell death). This new steady state 2 could be produced by a SNP that promotes y transcription. It could also be originated in differences in y gene expression. We would like to highlight that to show the consequences of such a slight change in the level of a molecule, which has this huge biological consequence, would be impossible without the use of mathematical simulation. This simple example, in addition to the network-based studies discussed in this review [39,50,103], highlight that systems biology is a promising approach to discerning the functional implications of SNPs or the differences in gene expression when used as molecular markers reported in pharmacogenomics studies. 11.

Conclusion

Current therapies for MS are partially effective and with common side effects, limiting their usefulness. This situation is due, in part, to the lack of biomarkers of response to therapy. Biomarkers of the response to therapy will allow the


9

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A.

Y

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B. 4 Solution 1 3.5 3

N

2.5 2 Solution 2 1.5 SS1

1

SS2 0.5

0

0.5

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1.5

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2.5

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Figure 4. Systems biology for the study of the response to therapy. A. A model of the interaction of two molecules implicated in the response to therapy. A single positive feedback links two molecules y and z in a signaling cascade. Because each molecule inhibits the other (denoted by ⊥), the outcome of the circuit is positive feedback. The differential equations and parameters used in the construction of the figure appear in the Appendix. B. Phase plane of the system. There are two steady states: SS1 and SS2. A slight increase in y produces two different trajectories: solution 1 and solution 2. Each of these solutions is associated with a specific cell state or cell response (i.e., resting state for solution 1 and apoptosis for solution 2).

656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671

best responders to be identified, increasing drug efficacy, and avoiding treating non-responders with these drugs, and therefore preventing adverse effects. This situation is going to be even more complicated with the appearance of new immunomodulatory drugs already in the final stages of Phase III clinical trials (i.e., fingolimod, laquinimod, rituximab, daclizumab, among others) and with the development of combination therapy. The discovery of biomarkers of the response to therapy can help to identify the best therapy for the most appropriate patient, with the ultimate goal of developing personalized medicine. Pharmacogenomics has been the most useful approach for discovering drug biomarkers. The power of genome-wide association studies in combination with high-throughput techniques such as DNA arrays, proteomics or metabolomics can enable the discovery of such biomarkers. However, due to the complexity of multifactorial 10

diseases such as MS and the pleiotropic activity of diseasemodifying drugs, we believe that systems biology will become a useful approach for integrating biological, clinical and imaging data and obtaining meaningful information for making decisions about patient’s care and therapy [109]. 12.

Expert opinion

Discovery of biomarkers for the response to disease-modifying therapy in MS is a high priority because it will have a profound impact on how we prescribe and monitor such drugs. Patients will benefit because by selecting the best responders, the efficacy is going to be enhanced, and by removing non-responders, we can prevent them from suffering inconvenient side effects and offer them other therapies. Overall, patients’ adherence to therapy will be increased and the economic cost of these


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therapies will be reimbursed to society by improving patients’ quality of life and reducing the costs due to permanent disability in the long term. However, this ideal scenario, the promise of personalized medicine, is not straightforward and research to date, as discussed in this review, reveals that the task is enormous, complex and still far from direct clinical application. To date, pharmacogenomics is the approach mainly pursued and has generated the most data, although information from other approaches will also be helpful. One of the greatest challenges in this area is how to get the maximum advantage from apparently unconnected scientific fields to benefit MS patients. Personalized medicine requires the integration of data and techniques from different disciplines, including population genetics, immunology, bioinformatics, clinical research, neuroimaging and systems biology. Current efforts to find predictors of efficacy for the most extensively studied therapy, IFN-β, have provided promising preliminary results that require further validation. However, these efforts have also revealed the complexity of the subject. No single SNP or gene expression marker accurately classifies INF-β-treated patients as responders or non-responders. This suggests that the combination of different types of information, such as SNPs, gene expression patterns, proteins and clinical variables, in addition to well-defined and powerful patient cohorts, are necessary to achieve clinical utility. Moreover, more validation studies with larger patient cohorts are urgently needed. Understanding SNPs and differences in gene expression functional implications is key to the elucidation of MS pathogenesis and the mechanism of action of disease-modifying drugs. In particular, it would be great to demonstrate with experimental evidence the biological relationship between the genotype (SNP, gene expression) and the phenotype (IFN-β response). Unfortunately this is not an easy task, as demonstrated by the lack of published works explaining the impact of a given polymorphism on known cellular functions, although new efforts are devoted to this subject in the field of biomarkers of responsiveness to therapy in MS. It is often the case that biomarkers identified in a genome-wide screening study are related to molecules of which we have little biological knowledge. The challenge is to characterize the mechanism by which the new biomarker is implicated in disease pathogenesis or in response to a specific treatment such as IFN-β for MS. Discerning the biological processes regulated by the biomarker requires an extensive effort both in time and

733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 Appendix 762 763 The equations and parameters used to obtain Figure 3B are: 764 765 y’ = (1–y)–b1*y*(v*z)g/k1+(v*z)g 766 g/k2 g z’ = (1–z)–b2*z*y +y 767 v=1 768 b1 = 200 769 b2 = 10 770 g1 = 4 771 k1 = 30 772 k2 = 1 773 774 Declaration of interest 775 The authors state no conflict of interest and have received 776 no payment in preparation of this manuscript. 777 resources [9]. We believe that systems biology is a key tool in facing this challenge. If we survey current therapeutic approaches for MS we realize that other current approved therapies such as glatiramer acetate and natalizumab also lack biomarkers and little research has been done in this direction. More worryingly, new therapies in the final stages of clinical trials, such as fingolimod, laquinimod, rituximab and daclizumab, have been developed without the corresponding biomarker of efficacy. As neurologists, we envision the future therapy of MS to be combination therapy to achieve greater control of the disease. This will require improved knowledge of the pharmacokinetics and pharmacodynamics of the drugs, but also the availability of biomarkers that identify the subgroup of patients who are better responders to a particular therapy or the most convenient combination therapy for a given patient. Since this task is going to be complex, it cannot be accomplished based only on clinical skills obtained with experience, because of the long observation periods required to assess the prognosis, the heterogeneity of the disease and the patient’s individual responses, the risk of a patient suffering permanent disability if not properly treated and the risk of severe adverse effects related to combinations of immunomodulatory drugs (i.e., leukemia, PML etc.). Thus, in future, therapy for MS and other complex diseases is going to require help from computational tools and systems biology that integrate the different types of information available to help patients make the right decisions for controlling their disease [109].


11


Bibliography Papers of special note have been highlighted as either of interest (•) or of considerable interest (••) to readers. 1. Hauser SL, Oksenberg JR. The neurobiology of multiple sclerosis: genes, inflammation, and neurodegeneration. Neuron 2006;52(1):61-76 •• Comprehensive review of the pathogenesis of MS, with special focus on the genomics of MS. 2.

Miller D. Multiple sclerosis: new insights and therapeutic progress. Lancet Neurol 2007;6(1):5-6

3.

Greenberg BM, Calabresi PA. Future research directions in multiple sclerosis therapies. Semin Neurol 2008;28(1):121-7

4.

Orton SM, Herrera BM, Yee IM, et al. Sex ratio of multiple sclerosis in Canada: a longitudinal study. Lancet Neurol 2006;5(11):932-6

5.

Villoslada P, Oksenberg JR, Rio J, Montalban X. Clinical characteristics of responders to interferon therapy for relapsing MS. Neurology 2004;62(9):1653 One of the first studies to define the response to IFN-β, reporting a percentage of non-responders of around 40%.

•

6.

•

7.

••

8.

• 9.

12

Waubant E, Vukusic S, Gignoux L, et al. Clinical characteristics of responders to interferon therapy for relapsing MS. Neurology 2003;61(2):184-9 One of the first studies to define the response to IFN-β, reporting a percentage of non-responders of around 40%.

10.

11.

12.

••

Miller DH. Biomarkers and surrogate outcomes in neurodegenerative disease: lessons from multiple sclerosis. NeuroRx 2004;1(2):284-94 Gudiksen M, Fleming E, Furstenthal L, Ma P. What drives success for specialty pharmaceuticals? Nat Rev Drug Discov 2008;7(7):563-7 Trojano M, Pellegrini F, Fuiani A, et al. New natural history of interferon-beta-treated relapsing multiple sclerosis. Ann Neurol 2007;61(4):300-6 The fist longitudinal and independent study proving that IFN-β can reduce MS progression and highlighting the need to find appropriate biomarkers to select the patients most likely to benefit from IFN-β treatment.

•

22.

•

23.

13.

Platanias LC. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat Rev Immunol 2005;5(5):375-86

•

14.

He XS, Ji X, Hale MB, et al. Global transcriptional response to interferon is a determinant of HCV treatment outcome and is modified by race. Hepatology 2006;44(2):352-9

24.

15.

16.

Goldstein DB, Tate SK, Sisodiya SM. Pharmacogenetics goes genomic. Nat Rev Genet 2003;4(12):937-47 Axtell RC, Steinman L. Type 1 interferons cool the inflamed brain. Immunity 2008;28(5):600-2

••

interferon-inducible genes. Clin Pharmacol Ther 2005;78(6):635-46 In this article the authors report the association between SNPs in IFNAR1, LMP7, CTSS and MXA genes to IFN-β response in a cohort of 162 MS patients. Villoslada P, Barcellos LF, Rio J, et al. The HLA locus and multiple sclerosis in Spain. Role in disease susceptibility, clinical course and response to interferon-beta. J Neuroimmunol 2002;130(1-2):194-201 First study analyzing the relationship between response to IFN-β therapy and a genetic marker such as HLA-DR2. Sriram U, Barcellos LF, Villoslada P, et al. Pharmacogenomic analysis of interferon receptor polymorphisms in multiple sclerosis. Genes Immun 2003;4(2):147-52 First study analyzing the relationship between response to IFN-β therapy and a genetic polymorphism in the IFN receptor. Byun E, Caillier SJ, Montalban X, et al. Genome-wide pharmacogenomic analysis of the response to interferon beta therapy in multiple sclerosis. Arch Neurol 2008;65(3):337-44 The first genome-wide scan pharmacogenomic study evaluating IFN-β response in MS patients.

25.

Wandinger KP, Lunemann JD, Wengert O, et al. TNF-related apoptosis inducing ligand (TRAIL) as a potential response marker for interferon-beta treatment in multiple sclerosis. Lancet 2003;361(9374):2036-43

17.

Benveniste EN, Qin H. Type I interferons as anti-inflammatory mediators. Sci STKE 2007;2007(416):e70

18.

Waubant E, Goodkin D, Bostrom A, et al. IFNbeta lowers MMP-9/TIMP-1 ratio, which predicts new enhancing lesions in patients with SPMS. Neurology 2003;60(1):52-7

26.

Petzold A, Brassat D, Mas P, et al. Treatment response in relation to inflammatory and axonal surrogate marker in multiple sclerosis. Mult Scler 2004;10(3):281-3

19.

Prinz M, Schmidt H, Mildner A, et al. Distinct and nonredundant in vivo functions of IFNAR on myeloid cells limit autoimmunity in the central nervous system. Immunity 2008;28(5):675-86

27.

Fernandez O, Fernandez V, Mayorga C, et al. HLA class II and response to interferon-beta in multiple sclerosis. Acta Neurol Scand 2005;112(6):391-4

28.

Bielekova B, Martin R. Development of biomarkers in multiple sclerosis. Brain 2004;127(Pt 7):1463-78 A comprehensive review of the characteristics that biomarkers must fulfill.

20.

Guo B, Chang EY, Cheng G. The type I IFN induction pathway constrains Th17-mediated autoimmune inflammation in mice. J Clin Invest 2008;118(5):1680-90

Leyva L, Fernandez O, Fedetz M, et al. IFNAR1 and IFNAR2 polymorphisms confer susceptibility to multiple sclerosis but not to interferon-beta treatment response. J Neuroimmunol 2005;163(1-2):165-71

Danna EA, Nolan GP. Transcending the biomarker mindset: deciphering disease mechanisms at the single cell level. Curr Opin Chem Biol 2006;10(1):20-7

21.

Cunningham S, Graham C, Hutchinson M, et al. Pharmacogenomics of responsiveness to interferon IFN-beta treatment in multiple sclerosis: a genetic screen of 100 type I

29.

Weinstock-Guttman B, Tamano-Blanco M, Bhasi K, et al. Pharmacogenetics of MXA SNPs in interferon-beta treated multiple sclerosis patients. J Neuroimmunol 2007;182(1-2):236-9

Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov 2007;6(4):287-93 Excellent review of the benefits of using biomarkers to select the best populations to be treated with a given therapy. The review is mainly written from the perspective of pharmaceutical companies.



30.

Rio J, Nos C, Tintore M, et al. Defining the response to interferon-beta in relapsing–remitting multiple sclerosis patients. Ann Neurol 2006;59(2):344-52

IFN-beta in multiple sclerosis patients. J Immunol 2003;171(5):2694-702 42.

Feng X, Petraglia AL, Chen M, et al. Low expression of interferon-stimulated genes in active multiple sclerosis is linked to subnormal phosphorylation of STAT1. J Neuroimmunol 2002;129(1-2):205-15

52.

Malucchi S, Gilli F, Caldano M, et al. Predictive markers for response to interferon therapy in patients with multiple sclerosis. Neurology 2008;70(13 Pt 2):1119-27

43.

Uddin S, Sassano A, Deb DK, et al. Protein kinase C-delta (PKC-delta ) is activated by type I interferons and mediates phosphorylation of Stat1 on serine 727. J Biol Chem 2002;277(17):14408-16

53.

Millonig A, Dressel A, Bahner D, et al. MxA protein–an interferon beta biomarker in primary progressive multiple sclerosis patients. Eur J Neurol 2008;15(8):822-6

54.

Moreland LW, Baumgartner SW, Schiff MH, et al. Treatment of rheumatoid arthritis with a recombinant human tumor necrosis factor receptor (p75)-Fc fusion protein. N Engl J Med 1997;337(3):141-7

55.

Koike F, Satoh J, Miyake S, et al. Microarray analysis identifies interferon beta-regulated genes in multiple sclerosis. J Neuroimmunol 2003;139(1-2):109-18

56.

Huang YM, Adikari S, Bave U, et al. Multiple sclerosis: interferon-beta induces CD123(+)BDCA2- dendritic cells that produce IL-6 and IL-10 and have no enhanced type I interferon production. J Neuroimmunol 2005;158(1-2):204-12

57.

Nicoletti F, Di Marco R, Patti F, et al. Short-term treatment of relapsing remitting multiple sclerosis patients with interferon (IFN)-beta1B transiently increases the blood levels of interleukin (IL)-6, IL-10 and IFN-gamma without significantly modifying those of IL-1beta, IL-2, IL-4 and tumour necrosis factor-alpha. Cytokine 2000;12(6):682-7

58.

Espejo C, Brieva L, Ruggiero G, et al. IFN-beta treatment modulates the CD28/CTLA-4-mediated pathway for IL-2 production in patients with relapsing–remitting multiple sclerosis. Mult Scler 2004;10(6):630-5

59.

Schreiner B, Mitsdoerffer M, Kieseier BC, et al. Interferon-beta enhances monocyte and dendritic cell expression of B7-H1 (PD-L1), a strong inhibitor of autologous T-cell activation: relevance for the immune modulatory effect in multiple sclerosis. J Neuroimmunol 2004;155(1-2):172-82

60.

Jensen J, Krakauer M, Sellebjerg F. Cytokines and adhesion molecules in multiple sclerosis patients treated with interferon-beta1b. Cytokine 2005;29(1):24-30

31.

Rudick RA, Lee JC, Simon J, et al. Defining interferon beta response status in multiple sclerosis patients. Ann Neurol 2004;56(4):548-55

32.

Sturzebecher S, Wandinger KP, Rosenwald A, et al. Expression profiling identifies responder and non-responder phenotypes to interferon-beta in multiple sclerosis. Brain 2003;126(Pt 6):1419-29

33.

Katsoulidis E, Li Y, Mears H, Platanias LC. The p38 mitogen-activated protein kinase pathway in interferon signal transduction. J Interferon Cytokine Res 2005;25(12):749-56

44.

Lekmine F, Uddin S, Sassano A, et al. Activation of the p70 S6 kinase and phosphorylation of the 4E-BP1 repressor of mRNA translation by type I interferons. J Biol Chem 2003;278(30):27772-80

34.

Kaur S, Uddin S, Platanias LC. The PI3′ kinase pathway in interferon signaling. J Interferon Cytokine Res 2005;25(12):780-7

45.

Fujimoto M, Naka T. Regulation of cytokine signaling by SOCS family molecules. Trends Immunol 2003;24(12):659-66

35.

Mogensen KE, Lewerenz M, Reboul J, et al. The type I interferon receptor: structure, function, and evolution of a family business. J Interferon Cytokine Res 1999;19(10):1069-98

46.

Liu B, Liao J, Rao X, et al. Inhibition of Stat1-mediated gene activation by PIAS1. Proc Natl Acad Sci USA 1998;95(18):10626-31

36.

Pfeffer LM, Basu L, Pfeffer SR, et al. The short form of the interferon alpha/beta receptor chain 2 acts as a dominant negative for type I interferon action. J Biol Chem 1997;272(17):11002-5

Darnell JE Jr, Kerr IM, Stark GR. Jak–STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science 1994;264(5164):1415-21

49.

Sharief MK, Semra YK. Down-regulation of survivin expression in T lymphocytes after interferon beta-1a treatment in patients with multiple sclerosis. Arch Neurol 2002;59(7):1115-21

Baranzini SE, Mousavi P, Rio J, et al. Transcription-based prediction of response to IFNbeta using supervised computational methods. PLoS Biol 2005;3(1):e2 A seminal paper identifying biological markers for the response to IFN-β. In particular, this article emphasizes the use of biomarker combinations to increase their predictive power.

50.

Fernald GH, Knott S, Pachner A, et al. Genome-wide network analysis reveals the global properties of IFN-beta immediate transcriptional effects in humans. J Immunol 2007;178(8):5076-85 The first system biology analysis of the effects of IFN-β in the immune system. The authors apply network analysis techniques for identifying the cellular processes most affected after IFN-β treatment.

38.

39.

40.

41.

Gniadek P, Aktas O, Wandinger KP, et al. Systemic IFN-beta treatment induces apoptosis of peripheral immune cells in MS patients. J Neuroimmunol 2003;137(1-2):187-96 Sharief MK, Noori MA, Zoukos Y. Reduced expression of the inhibitor of apoptosis proteins in T cells from patients with multiple sclerosis following interferon-beta therapy. J Neuroimmunol 2002;129(1-2):224-31

Domanski P, Witte M, Kellum M, et al. Cloning and expression of a long form of the beta subunit of the interferon alpha beta receptor that is required for signaling. J Biol Chem 1995;270(37):21606-11

••

47.

48.

37.

Reich NC, Liu L. Tracking STAT nuclear traffic. Nat Rev Immunol 2006;6(8):602-12 Weinstock-Guttman B, Badgett D, Patrick K, et al. Genomic effects of

in multiple sclerosis. J Neuroimmunol 2004;152(1-2):126-39

••

51.

Hong J, Zang YC, Hutton G, et al. Gene expression profiling of relevant biomarkers for treatment evaluation


13


61.

O’Garra A, Vieira P. T(H)1 cells control themselves by producing interleukin-10. Nat Rev Immunol 2007;7(6):425-8

72.

Rudd CE. The reverse stop-signal model for CTLA4 function. Nat Rev Immunol 2008;8(2):153-60

86.

Soilu-Hanninen M, Laaksonen M, Hanninen A, et al. Downregulation of VLA-4 on T cells as a marker of long term treatment response to interferon beta-1a in MS. J Neuroimmunol 2005;167(1-2):175-82

62.

Martinez-Forero I, Garcia-Munoz R, Martinez-Pasamar S, et al. IL-10 suppressor activity and ex vivo Tr1 cell function are impaired in multiple sclerosis. Eur J Immunol 2008;38(2):576-86

73.

Bruce SP, Boyce EG. Update on abatacept: a selective costimulation modulator for rheumatoid arthritis. Ann Pharmacother 2007;41(7):1153-62

74.

Suppiah V, Alloza I, Heggarty S, et al. The CTLA4+49 A/G*G-CT60*G haplotype is associated with susceptibility to multiple sclerosis in Flanders. J Neuroimmunol 2005;164(1-2):148-53

87.

Graber J, Zhan M, Ford D, et al. Interferon-beta-1a induces increases in vascular cell adhesion molecule: implications for its mode of action in multiple sclerosis. J Neuroimmunol 2005;161(1-2):169-76

75.

Ligers A, Xu C, Saarinen S, et al. The CTLA-4 gene is associated with multiple sclerosis. J Neuroimmunol 1999;97(1-2):182-90

88.

Rice GP, Hartung HP, Calabresi PA. Anti-alpha4 integrin therapy for multiple sclerosis: mechanisms and rationale. Neurology 2005;64(8):1336-42

76.

89.

Sospedra M, Martin R. Immunology of multiple sclerosis. Annu Rev Immunol 2005;23:683-747 Comprehensive review about the immunological dysfunctions in MS.

Harbo HF, Celius EG, Vartdal F, Spurkland A. CTLA4 promoter and exon 1 dimorphisms in multiple sclerosis. Tissue Antigens 1999;53(1):106-10

Sheremata WA, Vollmer TL, Stone LA, et al. A safety and pharmacokinetic study of intravenous natalizumab in patients with MS. Neurology 1999;52(5):1072-4

77.

90.

Bugeon L, Dallman MJ. Costimulation of T cells. Am J Respir Crit Care Med 2000;162(4 Pt 2):S164-8

Alizadeh M, Babron MC, Birebent B, et al. Genetic interaction of CTLA-4 with HLA-DR15 in multiple sclerosis patients. Ann Neurol 2003;54(1):119-22

78.

Malferrari G, Stella A, Monferini E, et al. Ctla4 and multiple sclerosis in the Italian population. Exp Mol Pathol 2005;78(1):55-7

Lindberg RL, Achtnichts L, Hoffmann F, et al. Natalizumab alters transcriptional expression profiles of blood cell subpopulations of multiple sclerosis patients. J Neuroimmunol 2008;194(1-2):153-64

91.

Ransohoff RM. Natalizumab for multiple sclerosis. N Engl J Med 2007;356(25):2622-9

92.

Kleinschmidt-DeMasters BK, Tyler KL. Progressive multifocal leukoen cephalopathy complicating treatment with natalizumab and interferon beta-1a for multiple sclerosis. N Engl J Med 2005;353(4):369-74

93.

Yousry TA, Major EO, Ryschkewitsch C, et al. Evaluation of patients treated with natalizumab for progressive multifocal leukoencephalopathy. N Engl J Med 2006;354(9):924-33

94.

Gold R, Jawad A, Miller DH, et al. Expert opinion: guidelines for the use of natalizumab in multiple sclerosis patients previously treated with immunomodulating therapies. J Neuroimmunol 2007;187(1-2):156-8

95.

Farina C, Weber MS, Meinl E, et al. Glatiramer acetate in multiple sclerosis: update on potential mechanisms of action. Lancet Neurol 2005;4(9):567-75

96.

Allie R, Hu L, Mullen KM, et al. Bystander modulation of chemokine receptor expression on peripheral blood T lymphocytes mediated by glatiramer therapy. Arch Neurol 2005;62(6):889-94

97.

Fusco C, Andreone V, Coppola G, et al. HLA-DRB1*1501 and response to

63.

Cho JH. The genetics and immunopathogenesis of inflammatory bowel disease. Nat Rev Immunol 2008;8(6):458-66

64.

Laver T, Nozell SE, Benveniste EN. IFN-beta-mediated inhibition of IL-8 expression requires the ISGF3 components Stat1, Stat2, and IRF-9. J Interferon Cytokine Res 2008;28(1):13-23

65.

•• 66.

67.

68.

69.

70.

71.

14

Liu Z, Pelfrey CM, Cotleur A, et al. Immunomodulatory effects of interferon beta-1a in multiple sclerosis. J Neuroimmunol 2001;112(1-2):153-62 Marckmann S, Wiesemann E, Hilse R, et al. Interferon-beta up-regulates the expression of co-stimulatory molecules CD80, CD86 and CD40 on monocytes: significance for treatment of multiple sclerosis. Clin Exp Immunol 2004;138(3):499-506 Shapiro S, Galboiz Y, Lahat N, et al. The ‘immunological-synapse’ at its APC side in relapsing and secondary-progressive multiple sclerosis: modulation by interferon-beta. J Neuroimmunol 2003;144(1-2):116-24 Iglesias AH, Camelo S, Hwang D, et al. Microarray detection of E2F pathway activation and other targets in multiple sclerosis peripheral blood mononuclear cells. J Neuroimmunol 2004;150(1-2):163-77 Wiesemann E, Deb M, Trebst C, et al. Effects of interferon-beta on co-signaling molecules: upregulation of CD40, CD86 and PD-L2 on monocytes in relation to clinical response to interferon-beta treatment in patients with multiple sclerosis. Mult Scler 2008;14(2):166-76

79.

Hafler DA, Compston A, Sawcer S, et al. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 2007;357(9):851-62

80.

Thio CL, Mosbruger TL, Kaslow RA, et al. Cytotoxic T-lymphocyte antigen 4 gene and recovery from hepatitis B virus infection. J Virol 2004;78(20):11258-62

81.

Maier LM, Anderson DE, De Jager PL, et al. Allelic variant in CTLA4 alters T cell phosphorylation patterns. Proc Natl Acad Sci USA 2007;104(47):18607-12

82.

Palacios R, Comas D, Villoslada P. Genomic regulation of CTLA4 and autoimmunity. J Neuroimmunol 2008; In press

83.

Bechmann I, Galea I, Perry VH. What is the blood–brain barrier (not)? Trends Immunol 2007;28(1):5-11

84.

Galea I, Bechmann I, Perry VH. What is immune privilege (not)? Trends Immunol 2007;28(1):12-18

85.

Yednock TA, Cannon C, Fritz LC, et al. Prevention of experimental autoimmune encephalomyelitis by antibodies against alpha 4 beta 1 integrin. Nature 1992;356(6364):63-6



• 98.

• 99.

copolymer-1 therapy in relapsing-remitting multiple sclerosis. Neurology 2001;57(11):1976-9 First attempt to identify a genetic marker of the response to glatiramer acetate. Grossman I, Avidan N, Singer C, et al. Pharmacogenetics of glatiramer acetate therapy for multiple sclerosis reveals drug-response markers. Pharmacogenet Genomics 2007;17(8):657-66 Pharmacogenetic analysis of glatiramer acetate base in candidate genes search. Kitano H. Systems biology: a brief overview. Science 2002;295(5560):1662-4

100. Kitano H. Computational systems biology. Nature 2002;420(6912):206-10 101. Butcher EC. Can cell systems biology rescue drug discovery? Nat Rev Drug Discov 2005;4(6):461-7 •• This review describes how the new discipline of systems biology can help to rescue the drug discovery field. 102. Goni J, Esteban FJ, Velez De Mendizabal N, et al. A computational analysis of the protein–protein interaction networks in neurodegenerative diseases. BMC Syst Biol 2008;2(1):52

103. Palacios R, Goni J, Martinez-Forero I, et al. A network analysis of the human T-cell activation gene network identifies JAGGED1 as a therapeutic target for autoimmune diseases. PLoS One 2007;2(11):e1222 •• First systems biology study to identify new therapeutic targets for MS. Using this approach, the authors were capable of validating Jagged-1/Notch pathway as a therapeutic target for MS. 104. Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet 2007;8(6):450-61 105. Angeli D, Ferrell JE Jr, Sontag ED. Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci USA 2004;101(7):1822-7 106. Craciun G, Tang Y, Feinberg M. Understanding biostability in complex enzyme-driven reaction networks. Proc Natl Acad Sci USA 2006;103(23):8697-702 107. Kholodenko BN. Cell-signalling dynamics in time and space. Nat Rev Mol Cell Biol 2006;7(3):165-76 108. Feinerman O, Veiga J, Dorfman JR, et al. Variability and robustness in T cell


activation from regulated heterogeneity in protein levels. Science 2008;321(5892):1081-4 109. Villoslada P, Oksenberg JR. Neuroinformatics in clinical practice: are computers going to help neurological patients and their physicians? Future Neurol 2006;1(2):1-12 • Comprehensive review of the opportunities offered by computational biology to the field of neurology. The vast amount of current information is going to need computational text mining methods to extract valuable information from biomedical databases.

Affiliation Iván Martinez-Forero1,2 MD, Antonio Pelaez2 PhD & Pablo Villoslada†1 MD †Author for correspondence 1University of Navarra, Center for Applied Medical Research (CIMA), Department of Neuroscience, Neuroimmunology Lab 2.05, 31008 Pamplona, Spain Tel: + 34 948 194 700; E-mail: [email protected] 2University of Navarra, Department of Physics and Applied Mathematics, Spain

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