**Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases**

Véronique Freund-Michel1,2, Bernard Muller1,2 and Nelly Frossard3,4 *1INSERM U1045 "Centre de Recherche Cardio-thoracique de Bordeaux", Bordeaux 2University Bordeaux Segalen, Bordeaux 3UMR 7200 CNRS "Laboratoire d'Innovation Thérapeutique", Strasbourg 4University of Strasbourg, Strasbourg France* 

#### **1. Introduction**

The nerve growth factor NGF belongs to the neurotrophin family and was described for the first time more than fifty years ago by Rita Levi-Montalcini and collaborators (Levi-Montalcini et al., 1995; Levi-Montalcini & Hamburger, 1951), who showed its major role in neuronal growth and survival. NGF effects are mediated by activation of two receptor types: the low-affinity p75 receptor for neurotrophins (p75NTR) and the high-affinity tropomyosinrelated kinase A (TrkA) receptor (Freund-Michel & Frossard, 2008a). The p75NTR receptor belongs to the death receptor family and its activation by NGF at nanomolar concentrations leads either to pro- or anti-apoptotic signalling pathways. The p75NTR receptor is not selective for NGF as it can also bind pro-neurotrophins and the other neurotrophins at the same nanomolar concentrations (Chao, 2003). Inversely, the TrkA receptor is selective for NGF and belongs to the tyrosine-kinase receptor family. Its activation by NGF at picomolar concentrations activates signalling pathways inducing cell proliferation, differentiation and survival in particular through activation of phosphatidylinositol-3 kinase (PI3K), small protein G Ras, phospholipase C (PLC) and mitogen-activated protein kinases (MAPK) (Freund-Michel & Frossard, 2008a).

The role of NGF in neuronal growth and survival has been widely studied and led to consider NGF as a promising therapeutic target in several pathologies of the nervous system, in particular neurodegenerative diseases (Prakash et al., 2010). In addition, many studies have suggested that NGF also plays the role of an inflammatory mediator, in particular in the lung (Freund-Michel & Frossard, 2008a). Indeed, numerous sources of NGF have been described in the lung, including infiltrated inflammatory cells, sensory nerves, and many lung structural cells such as fibroblasts, epithelial, endothelial, and

Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases 77

domain dedicated to NGF binding (Wiesmann & de Vos, 2001). This extracellular domain is highly glycosylated, which is essential for activation of TrkA signalling pathways (Friedman

Alternative splicing leads to several isoforms of the TrkA receptor. TrkA I and TrkA II splice variants differ only in the presence or absence of a 6 amino acid sequence. However, even if TrkA II expression is restricted to the nervous system, whereas TrkA I is more ubiquitously expressed (Clary & Reichardt, 1994), no differences in NGF binding or in TrkA function have been identified between these two isoforms (Barker et al., 1993). More recently, a novel hypoxia-regulated TrkA III splice variant has also been described: this isoform is expressed on internal membranes (Tacconelli et al., 2005) and exhibits oncogenic activity (Farina et al., 2009). Finally, a metalloproteinase-dependent cleavage of TrkA extracellular domain has been described, with release of a soluble fragment whose function remains unknown (Cabrera et al., 1996). In parallel, this cleavage induces activation of TrkA intracellular kinase domain, thus providing a TrkA NGF-independent activation, which may contribute to TrkA-dependent effects *in vivo* (Diaz-Rodriguez et al.,

As classically described for other tyrosine-kinase receptors, NGF binds to the extracellular domain of the TrkA receptor and induces its dimerization thereby activating its intracellular tyrosine kinase domain (Kaplan et al., 1991b). Each kinase domain induces phosphorylation of three tyrosine residues (Y670, Y674 and Y675) on the contralateral kinase domain (Mitra, 1991), thus leading to enhancement of kinase activity and further phosphorylation of three other tyrosine residues outside the kinase domain (Y490, Y751 and Y785) (Stephens et al., 1994). These newly phosphorylated tyrosine residues are then recognized by proteins through their SH2 (Src homology domain 2) domains. The adapter protein Shc (Src homology 2-containing protein) interacts with the phosphorylated Y490 residue, phosphatidyl-inositol 3-kinase (PI3K) interacts with the phosphorylated Y751 residue, and phospholipase C (PLC) interacts with the phosphorylated Y785 residue, thereby initiating three main signalling pathways that have been widely studied in particular in neuronal cells (Skaper, 2008). However, some recent studies also show activation of these TrkA signalling pathways in non neuronal cells, and in particular in the airways (for reviews: Freund-Michel & Frossard, 2008a;

Shc intracellular binding to the TrkA receptor leads to phosphorylation of its tyrosine residues and further recognition by the adapter protein Grb-2 (growth factor receptor bound protein-2) through SH3 (Src homology domain 3) domains. Grb-2 then binds to the factor sos (factor son of sevenless) to induce recruitment of the small G protein Ras to the cell membrane and its activation (Segal & Greenberg, 1996). This translocation to the cell membrane enables Ras-induced activation of the Raf kinase and therefore phosphorylation of Raf and activation of the MAPK (mitogen-activated protein kinase) ERK1/2 (extracellular-regulated protein kinase 1/2), leading to activation of survival mechanisms and proliferation (Freund-Michel et al., 2006). Concomitant activation of Rap1, another small

**2.3 Activation and signalling pathways of the TrkA receptor** 

& Greene, 1999).

1999).

Prakash et al., 2010).

**2.3.1 Ras/Raf pathway** 

airway or pulmonary vascular smooth muscle cells (Ricci et al., 2004b). These cells have been shown to release more NGF in inflammatory conditions, and may thus participate in increased NGF levels observed in pulmonary inflammatory diseases. In parallel, many studies have shown an active role of NGF in pulmonary inflammation, airway sensory nerve plasticity, airway and vascular hyperreactivity and remodelling (Freund-Michel & Frossard, 2008a). Most of these NGF effects occur through activation of the TrkA receptor, thus highlighting the pivotal role played by this receptor in pulmonary inflammatory diseases.

The aim of the present chapter is to describe the role of the TrkA receptor activated by NGF in pulmonary inflammatory diseases. We will first present the TrkA receptor by describing its discovery, its structure and activity as well as its major signalling pathways. We will then focus on the TrkA receptor in the lung, by describing its pulmonary expression and review its involvement in NGF-mediated effects in the lung. We will describe in particular how the TrkA receptor participates to NGF-induced inflammation, airway and vascular hyperreactivity and remodelling in the lung, focusing on two major pulmonary diseases: asthma and pulmonary hypertension.

### **2. Presentation of the TrkA receptor**

The TrkA receptor belongs to the Trk receptor family, together with TrkB and TrkC receptors. Each Trk receptor binds with a picomolar affinity to a preferred ligand: NGF for TrkA, BDNF (brain-derived neurotrophic factor) and NT-4/5 (neurotrophin-4/5) for TrkB, and NT-3 (neurotrophin-3) for TrkC (Chao, 2003). However, some crosstalks have been described, in particular for NT-3 being able to bind TrkA and TrkB receptors but at higher concentrations (Ryden & Ibanez, 1996).

#### **2.1 Discovery of the TrkA receptor**

A proto-oncogene was identified in 1986 by Martin-Zanca and co-workers in human colon carcinomas (Martin-Zanca et al., 1986). This proto-oncogene, resulting from fusion between genes encoding for a tyrosine-kinase domain and a non muscular tropomyosin, was named NTRK or trk for « tropomyosin-related kinase ». Three isoforms were identified and named NTRK1 (or TRKA), NTRK2 (or TRKB) and NTRK3 (or TRKC), with proteins encoded by these genes named Trk (TrkA, TrkB and TrkC) (Martin-Zanca et al., 1986). Expression of Trk proteins was later also detected in thyroid carcinomas and other cancers such as melanomas or breast cancers, as well as in non cancer tissues, in particular in the nervous system (Greco et al., 1997). In 1991, the TrkA protein was identified as the high affinity receptor for NGF (Kaplan et al., 1991a; Klein et al., 1991).

#### **2.2 Structure of the TrkA receptor**

The human TrkA receptor is encoded by a gene of 23kb located on chromosome 1q21-q22 (Weier et al., 1995). This gene contains 16 introns of 70bp to 3.3kb and 17 exons of 18 to 394bp (Indo et al., 1997), with the 9 first exons encoding for the extracellular part of the receptor (Metsis, 2001). The TrkA protein contains 790 amino acids with a molecular weight of 140 kDa (Meakin & Shooter, 1992), and is composed of an intracellular domain containing a tyrosine-kinase intrinsic activity, a unique transmembrane helix, and an extracellular domain dedicated to NGF binding (Wiesmann & de Vos, 2001). This extracellular domain is highly glycosylated, which is essential for activation of TrkA signalling pathways (Friedman & Greene, 1999).

Alternative splicing leads to several isoforms of the TrkA receptor. TrkA I and TrkA II splice variants differ only in the presence or absence of a 6 amino acid sequence. However, even if TrkA II expression is restricted to the nervous system, whereas TrkA I is more ubiquitously expressed (Clary & Reichardt, 1994), no differences in NGF binding or in TrkA function have been identified between these two isoforms (Barker et al., 1993). More recently, a novel hypoxia-regulated TrkA III splice variant has also been described: this isoform is expressed on internal membranes (Tacconelli et al., 2005) and exhibits oncogenic activity (Farina et al., 2009). Finally, a metalloproteinase-dependent cleavage of TrkA extracellular domain has been described, with release of a soluble fragment whose function remains unknown (Cabrera et al., 1996). In parallel, this cleavage induces activation of TrkA intracellular kinase domain, thus providing a TrkA NGF-independent activation, which may contribute to TrkA-dependent effects *in vivo* (Diaz-Rodriguez et al., 1999).

#### **2.3 Activation and signalling pathways of the TrkA receptor**

As classically described for other tyrosine-kinase receptors, NGF binds to the extracellular domain of the TrkA receptor and induces its dimerization thereby activating its intracellular tyrosine kinase domain (Kaplan et al., 1991b). Each kinase domain induces phosphorylation of three tyrosine residues (Y670, Y674 and Y675) on the contralateral kinase domain (Mitra, 1991), thus leading to enhancement of kinase activity and further phosphorylation of three other tyrosine residues outside the kinase domain (Y490, Y751 and Y785) (Stephens et al., 1994). These newly phosphorylated tyrosine residues are then recognized by proteins through their SH2 (Src homology domain 2) domains. The adapter protein Shc (Src homology 2-containing protein) interacts with the phosphorylated Y490 residue, phosphatidyl-inositol 3-kinase (PI3K) interacts with the phosphorylated Y751 residue, and phospholipase C (PLC) interacts with the phosphorylated Y785 residue, thereby initiating three main signalling pathways that have been widely studied in particular in neuronal cells (Skaper, 2008). However, some recent studies also show activation of these TrkA signalling pathways in non neuronal cells, and in particular in the airways (for reviews: Freund-Michel & Frossard, 2008a; Prakash et al., 2010).

#### **2.3.1 Ras/Raf pathway**

76 Inflammatory Diseases – A Modern Perspective

airway or pulmonary vascular smooth muscle cells (Ricci et al., 2004b). These cells have been shown to release more NGF in inflammatory conditions, and may thus participate in increased NGF levels observed in pulmonary inflammatory diseases. In parallel, many studies have shown an active role of NGF in pulmonary inflammation, airway sensory nerve plasticity, airway and vascular hyperreactivity and remodelling (Freund-Michel & Frossard, 2008a). Most of these NGF effects occur through activation of the TrkA receptor, thus highlighting the pivotal role played by this receptor in pulmonary inflammatory

The aim of the present chapter is to describe the role of the TrkA receptor activated by NGF in pulmonary inflammatory diseases. We will first present the TrkA receptor by describing its discovery, its structure and activity as well as its major signalling pathways. We will then focus on the TrkA receptor in the lung, by describing its pulmonary expression and review its involvement in NGF-mediated effects in the lung. We will describe in particular how the TrkA receptor participates to NGF-induced inflammation, airway and vascular hyperreactivity and remodelling in the lung, focusing on two major pulmonary diseases:

The TrkA receptor belongs to the Trk receptor family, together with TrkB and TrkC receptors. Each Trk receptor binds with a picomolar affinity to a preferred ligand: NGF for TrkA, BDNF (brain-derived neurotrophic factor) and NT-4/5 (neurotrophin-4/5) for TrkB, and NT-3 (neurotrophin-3) for TrkC (Chao, 2003). However, some crosstalks have been described, in particular for NT-3 being able to bind TrkA and TrkB receptors but at higher

A proto-oncogene was identified in 1986 by Martin-Zanca and co-workers in human colon carcinomas (Martin-Zanca et al., 1986). This proto-oncogene, resulting from fusion between genes encoding for a tyrosine-kinase domain and a non muscular tropomyosin, was named NTRK or trk for « tropomyosin-related kinase ». Three isoforms were identified and named NTRK1 (or TRKA), NTRK2 (or TRKB) and NTRK3 (or TRKC), with proteins encoded by these genes named Trk (TrkA, TrkB and TrkC) (Martin-Zanca et al., 1986). Expression of Trk proteins was later also detected in thyroid carcinomas and other cancers such as melanomas or breast cancers, as well as in non cancer tissues, in particular in the nervous system (Greco et al., 1997). In 1991, the TrkA protein was identified as the high affinity receptor for NGF

The human TrkA receptor is encoded by a gene of 23kb located on chromosome 1q21-q22 (Weier et al., 1995). This gene contains 16 introns of 70bp to 3.3kb and 17 exons of 18 to 394bp (Indo et al., 1997), with the 9 first exons encoding for the extracellular part of the receptor (Metsis, 2001). The TrkA protein contains 790 amino acids with a molecular weight of 140 kDa (Meakin & Shooter, 1992), and is composed of an intracellular domain containing a tyrosine-kinase intrinsic activity, a unique transmembrane helix, and an extracellular

diseases.

asthma and pulmonary hypertension.

**2. Presentation of the TrkA receptor** 

concentrations (Ryden & Ibanez, 1996).

**2.1 Discovery of the TrkA receptor** 

(Kaplan et al., 1991a; Klein et al., 1991).

**2.2 Structure of the TrkA receptor** 

Shc intracellular binding to the TrkA receptor leads to phosphorylation of its tyrosine residues and further recognition by the adapter protein Grb-2 (growth factor receptor bound protein-2) through SH3 (Src homology domain 3) domains. Grb-2 then binds to the factor sos (factor son of sevenless) to induce recruitment of the small G protein Ras to the cell membrane and its activation (Segal & Greenberg, 1996). This translocation to the cell membrane enables Ras-induced activation of the Raf kinase and therefore phosphorylation of Raf and activation of the MAPK (mitogen-activated protein kinase) ERK1/2 (extracellular-regulated protein kinase 1/2), leading to activation of survival mechanisms and proliferation (Freund-Michel et al., 2006). Concomitant activation of Rap1, another small

Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases 79

Segal, 2002). Once activated by NGF, the TrkA receptor is internalized, mainly through activation of three mechanisms: clathrine-dependent internalization, caveolae-dependent internalization, or macroendocytosis (Philippidou et al., 2011; Zweifel et al., 2005). All these mechanisms are involved in TrkA internalization and depend i) on the cell type studied, ii) on the concentration of NGF, and iii) on the amplitude of the signal generated by TrkA activation (Zweifel et al., 2005). Once internalized, only a few number of TrkA receptors are transported close to the nucleus, using early endosomes characterized by expression of the small G protein Rab5 and its effector EEA1 (Early endosome antigen 1) (Delcroix et al., 2003). TrkA retrograde transport is dependent upon activation of the PI3K-PKB pathway (Delcroix et al., 2003; Kuruvilla et al., 2000; York et al., 2000). Most of internalized TrkA receptors are either degraded through targeting to lysosomes (Jullien et al., 2002; Saxena et al., 2005) or to the proteasome after ubuquitination (Georgieva et al., 2011; Takahashi et al.,

Neurotrophin expression was first described in the central and peripheral nervous systems, participating to nerve growth and survival through activation of Trk and p75NTR receptors. However, neurotrophins and their receptors were later also described in a variety of non-

NGF expression, which was first reported in T lymphocytes (Ehrhard et al., 1993a), was later also described in a variety of inflammatory cells including B lymphocytes (Torcia et al., 1996), mast cells (Leon et al., 1994), eosinophils (Solomon et al., 1998) and macrophages (Ricci et al., 2000b). Expression of the TrkA receptor was shown on mast cells (Tam et al., 1997), Th2 lymphocytes (Ehrhard et al., 1993a; Lambiase et al., 1997), B lymphocytes (Torcia et al., 1996), eosinophils (Hahn et al., 2006; Nassenstein et al., 2003; Noga et al., 2002), monocytes and macrophages (Ehrhard et al., 1993b; Otten et al., 1994), and basophils (Burgi

Many airway structural cells such as fibroblasts (Antonelli et al., 2005; Olgart & Frossard, 2001), epithelial cells (Fox et al., 2001; Pons et al., 2001), airway smooth muscle cells (Freund et al., 2002), pulmonary endothelial and vascular smooth muscle cells (Freund-Michel et al., 2009) are sources of NGF (**Fig. 1**). Investigation of TrkA expression on these cells showed TrkA expression in particular on pulmonary fibroblasts (Micera et al., 2001), airway smooth muscle cells (Dagnell et al., 2007; Freund-Michel et al., 2006; Freund-Michel & Frossard, 2008b), airway epithelial cells (Othumpangat et al., 2009), and pulmonary endothelial and

Expression of TrkA mRNA was initially evidenced in rat and human lung homogenates (Barbacid et al., 1991; Lomen-Hoerth & Shooter, 1995). Expression of TrkA protein was then

vascular smooth muscle cells (Freund-Michel et al., 2010) (**Fig. 1**).

neuronal tissues, and in particular in the lung (Lomen-Hoerth & Shooter, 1995).

2011), or recycled at the cell membrane (Chen et al., 2005).

**3. TrkA expression in the lung** 

**3.1** *In vitro* **studies 3.1.1 Inflammatory cells** 

et al., 1996).

**3.1.2 Airway structural cells** 

**3.2** *In vivo* **studies** 

G protein, can potentiate Ras activation and enhance activation of the ERK1/2 pathway (York et al., 2000).

#### **2.3.2 PI3K pathway**

PI3K intracellular binding to the TrkA receptor leads to its phosphorylation and activation. PI3K then induces synthesis of phosphatidyl-inositol 3,4-bisphosphate that recruits PDK-1 (phosphoinositide-dependent kinase-1) to the cell membrane and induces activation of PKB (protein-kinase B, also called Akt) (Ashcroft et al., 1999). PKB then leads to activation of gene transcription, either through activation of the small G protein Rac and the MAPK pathway (Kita et al., 1998; Yamaguchi et al., 2001), or through activation of the atypical PKC zeta in a MAPK-independent manner (Wooten et al., 1994). In addition, PKB can lead to activation of proteins belonging to the IAP (inhibitors of apoptosis) family that are involved in cell survival (Wiese et al., 1999). Finally, a Ras-dependent activation of PI3K has also been described, through direct interaction between Ras and PI3K in a complex also containing the adapter protein Gab-1 (Grb2-associated binder-1) after activation of Shc and Grb-2 (Holgado-Madruga et al., 1997; Korhonen et al., 1999).

#### **2.3.3 PLC/PKC pathway**

PLC is activated by its interaction with the TrkA receptor and its phosphorylation by TrkA intrinsic kinase domains. PLC then induces cleavage of phosphatidyl inositol 4,5 bisphosphate into inositol trisphosphate (IP3) and diacylglycerol (DAG). DAG activates protein-kinase C (PKC) to activate the MAPK pathway, with in particular activation of JNK (c-jun N-terminal kinase) and p38 (Patapoutian & Reichardt, 2001). IP3 binds to its receptor localized on the endoplasmic reticulum and induces calcium release into the cell cytoplasm, thus contributing to PKC activation (Obermeier et al., 1993).

#### **2.4 Transactivation of the TrkA receptor by G protein-coupled receptors**

Neurotrophin-independent activation of Trk receptors, and in particular of the TrkA receptor, has been evidenced in rat neuronal cells after adenosine treatment (Lee & Chao, 2001). Activation of the adenosine A2A receptor, a G protein-coupled receptor (GPCR), induces activation of a kinase belonging to the Src family that is then able to phosphorylate the TrkA receptor and activate the PI3K/PKB pathway (Lee & Chao, 2001; Lee et al., 2002a). This effect has also been evidenced with another GPCR agonist, the pituitary adenylate cyclase-activating peptide (PACAP), being able to induce TrkA transactivation and specific activation of the PI3K/PKB pathway in absence of NGF (Lee et al., 2002b). Since neuroprotective effects of adenosine and PACAP had been previously demonstrated, it has been suggested that this TrkA transactivation mechanism may contribute to these neuroprotective effects through activation of PI3K/PKB (Lee et al., 2002b). However more recent studies suggested that this TrkA transactivation mechanism occurred on newly synthesized TrkA receptors that were not already expressed at the cell membrane (Rajagopal et al., 2004).

#### **2.5 Trafficking of the TrkA receptor**

NGF activation of the TrkA receptor expressed on neurons can activate signalling pathways close to the nucleus through a specific mechanism called retrograde transport (Heerssen & Segal, 2002). Once activated by NGF, the TrkA receptor is internalized, mainly through activation of three mechanisms: clathrine-dependent internalization, caveolae-dependent internalization, or macroendocytosis (Philippidou et al., 2011; Zweifel et al., 2005). All these mechanisms are involved in TrkA internalization and depend i) on the cell type studied, ii) on the concentration of NGF, and iii) on the amplitude of the signal generated by TrkA activation (Zweifel et al., 2005). Once internalized, only a few number of TrkA receptors are transported close to the nucleus, using early endosomes characterized by expression of the small G protein Rab5 and its effector EEA1 (Early endosome antigen 1) (Delcroix et al., 2003). TrkA retrograde transport is dependent upon activation of the PI3K-PKB pathway (Delcroix et al., 2003; Kuruvilla et al., 2000; York et al., 2000). Most of internalized TrkA receptors are either degraded through targeting to lysosomes (Jullien et al., 2002; Saxena et al., 2005) or to the proteasome after ubuquitination (Georgieva et al., 2011; Takahashi et al., 2011), or recycled at the cell membrane (Chen et al., 2005).

### **3. TrkA expression in the lung**

Neurotrophin expression was first described in the central and peripheral nervous systems, participating to nerve growth and survival through activation of Trk and p75NTR receptors. However, neurotrophins and their receptors were later also described in a variety of nonneuronal tissues, and in particular in the lung (Lomen-Hoerth & Shooter, 1995).

### **3.1** *In vitro* **studies**

78 Inflammatory Diseases – A Modern Perspective

G protein, can potentiate Ras activation and enhance activation of the ERK1/2 pathway

PI3K intracellular binding to the TrkA receptor leads to its phosphorylation and activation. PI3K then induces synthesis of phosphatidyl-inositol 3,4-bisphosphate that recruits PDK-1 (phosphoinositide-dependent kinase-1) to the cell membrane and induces activation of PKB (protein-kinase B, also called Akt) (Ashcroft et al., 1999). PKB then leads to activation of gene transcription, either through activation of the small G protein Rac and the MAPK pathway (Kita et al., 1998; Yamaguchi et al., 2001), or through activation of the atypical PKC zeta in a MAPK-independent manner (Wooten et al., 1994). In addition, PKB can lead to activation of proteins belonging to the IAP (inhibitors of apoptosis) family that are involved in cell survival (Wiese et al., 1999). Finally, a Ras-dependent activation of PI3K has also been described, through direct interaction between Ras and PI3K in a complex also containing the adapter protein Gab-1 (Grb2-associated binder-1) after activation of Shc and Grb-2

PLC is activated by its interaction with the TrkA receptor and its phosphorylation by TrkA intrinsic kinase domains. PLC then induces cleavage of phosphatidyl inositol 4,5 bisphosphate into inositol trisphosphate (IP3) and diacylglycerol (DAG). DAG activates protein-kinase C (PKC) to activate the MAPK pathway, with in particular activation of JNK (c-jun N-terminal kinase) and p38 (Patapoutian & Reichardt, 2001). IP3 binds to its receptor localized on the endoplasmic reticulum and induces calcium release into the cell cytoplasm,

Neurotrophin-independent activation of Trk receptors, and in particular of the TrkA receptor, has been evidenced in rat neuronal cells after adenosine treatment (Lee & Chao, 2001). Activation of the adenosine A2A receptor, a G protein-coupled receptor (GPCR), induces activation of a kinase belonging to the Src family that is then able to phosphorylate the TrkA receptor and activate the PI3K/PKB pathway (Lee & Chao, 2001; Lee et al., 2002a). This effect has also been evidenced with another GPCR agonist, the pituitary adenylate cyclase-activating peptide (PACAP), being able to induce TrkA transactivation and specific activation of the PI3K/PKB pathway in absence of NGF (Lee et al., 2002b). Since neuroprotective effects of adenosine and PACAP had been previously demonstrated, it has been suggested that this TrkA transactivation mechanism may contribute to these neuroprotective effects through activation of PI3K/PKB (Lee et al., 2002b). However more recent studies suggested that this TrkA transactivation mechanism occurred on newly synthesized TrkA receptors that were not already expressed at the cell membrane

NGF activation of the TrkA receptor expressed on neurons can activate signalling pathways close to the nucleus through a specific mechanism called retrograde transport (Heerssen &

(York et al., 2000).

**2.3.2 PI3K pathway** 

**2.3.3 PLC/PKC pathway** 

(Rajagopal et al., 2004).

**2.5 Trafficking of the TrkA receptor** 

(Holgado-Madruga et al., 1997; Korhonen et al., 1999).

thus contributing to PKC activation (Obermeier et al., 1993).

**2.4 Transactivation of the TrkA receptor by G protein-coupled receptors** 

#### **3.1.1 Inflammatory cells**

NGF expression, which was first reported in T lymphocytes (Ehrhard et al., 1993a), was later also described in a variety of inflammatory cells including B lymphocytes (Torcia et al., 1996), mast cells (Leon et al., 1994), eosinophils (Solomon et al., 1998) and macrophages (Ricci et al., 2000b). Expression of the TrkA receptor was shown on mast cells (Tam et al., 1997), Th2 lymphocytes (Ehrhard et al., 1993a; Lambiase et al., 1997), B lymphocytes (Torcia et al., 1996), eosinophils (Hahn et al., 2006; Nassenstein et al., 2003; Noga et al., 2002), monocytes and macrophages (Ehrhard et al., 1993b; Otten et al., 1994), and basophils (Burgi et al., 1996).

#### **3.1.2 Airway structural cells**

Many airway structural cells such as fibroblasts (Antonelli et al., 2005; Olgart & Frossard, 2001), epithelial cells (Fox et al., 2001; Pons et al., 2001), airway smooth muscle cells (Freund et al., 2002), pulmonary endothelial and vascular smooth muscle cells (Freund-Michel et al., 2009) are sources of NGF (**Fig. 1**). Investigation of TrkA expression on these cells showed TrkA expression in particular on pulmonary fibroblasts (Micera et al., 2001), airway smooth muscle cells (Dagnell et al., 2007; Freund-Michel et al., 2006; Freund-Michel & Frossard, 2008b), airway epithelial cells (Othumpangat et al., 2009), and pulmonary endothelial and vascular smooth muscle cells (Freund-Michel et al., 2010) (**Fig. 1**).

#### **3.2** *In vivo* **studies**

Expression of TrkA mRNA was initially evidenced in rat and human lung homogenates (Barbacid et al., 1991; Lomen-Hoerth & Shooter, 1995). Expression of TrkA protein was then

Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases 81

**INFLAMMATORY CELLS**

**NGF**

**Mast cell Eosinophil Monocyte /**

Activation Survival

**Neuron Fibroblast**

Differentiation Migration

Activation Survival

inflammatory diseases.

Activation Survival

> **Airway smooth muscle**

**STRUCTURAL CELLS**

**NGF**

**TrkA**

Proliferation

NGF-induced activation of the TrkA receptor participates to attraction and activation of inflammatory cells in the lung and may therefore contribute to lung inflammation. The TrkA receptor is also expressed on lung structural cells and participates to NGF-induced effects that may contribute to altered reactivity and remodelling processes existing in pulmonary

Fig. 2. NGF effects in the lung mediated via activation of the TrkA receptor

Differentiation Chemotaxis Activation Survival

**TrkA**

**macrophage Basophil Neutrophil T cell / B cell**

Activation Chemotaxis

Survival

**Vascular smooth muscle** Proliferation Migration

Differentiation Proliferation Activation Survival IgE production

**Endothelium**

Proliferation Migration

shown by immunohistochemistry in isolated human alveolar macrophages (Ricci et al., 2000b), in isolated extrapulmonary arteries (Ricci et al., 2000a), and was later also evidenced on human airway and vascular smooth muscles, on alveolar cells, on airway sensory nerves, as well as on infiltrated inflammatory cells, in particular macrophages, mast cells and lymphocytes (Kassel et al., 2001; Olgart Hoglund et al., 2002; Ricci et al., 2004b). Similar TrkA expression was shown in the mouse lung (Hikawa et al., 2002; Nassenstein et al., 2006).

Fig. 1. Expression of NGF and its receptors in human pulmonary vascular cells

A) NGF protein levels (pg/ml) secreted after 24h by human pulmonary arterial smooth muscle cells (hPASMC) or human pulmonary arterial endothelial cells (hPAEC) in primary culture were assessed by ELISA in the culture cell supernatant. Data are means ± S.E.M. of n=3 experiments performed in triplicates with cells from two different donors. B) TrkA and p75NTR proteins were detected by Western blotting in cultured hPASMC or hPAEC from two different donors (D1 and D2), with rabbit polyclonal anti-human TrkA or p75NTR antibodies as specific protein bands of 140 and 75 kDa respectively. β-Actin probed in the same blots was used to control for protein loading.

#### **4. NGF effects in the lung mediated by activation of the TrkA receptor**

NGF is able to stimulate inflammatory cells infiltrated in the bronchial mucosa, promoting in particular their activation and survival in the airways (Freund-Michel & Frossard, 2008a). NGF also displays its role of growth factor on airway nerves, in particular on sensory airway nerves (Hoyle et al., 1998), and is able to stimulate other airway structural cells such as pulmonary fibroblasts or airway smooth muscle cells (Freund-Michel & Frossard, 2008a). Some of these effects involve activation of the TrkA receptor expressed on these cells (**Fig. 2**).

shown by immunohistochemistry in isolated human alveolar macrophages (Ricci et al., 2000b), in isolated extrapulmonary arteries (Ricci et al., 2000a), and was later also evidenced on human airway and vascular smooth muscles, on alveolar cells, on airway sensory nerves, as well as on infiltrated inflammatory cells, in particular macrophages, mast cells and lymphocytes (Kassel et al., 2001; Olgart Hoglund et al., 2002; Ricci et al., 2004b). Similar TrkA

**TrkA**

**hPASMC**

**hPAEC**

**D1 D2**

**p75NTR**

**-Actin**

expression was shown in the mouse lung (Hikawa et al., 2002; Nassenstein et al., 2006).

<sup>0</sup> **D1 D2**

Fig. 1. Expression of NGF and its receptors in human pulmonary vascular cells

**4. NGF effects in the lung mediated by activation of the TrkA receptor** 

NGF is able to stimulate inflammatory cells infiltrated in the bronchial mucosa, promoting in particular their activation and survival in the airways (Freund-Michel & Frossard, 2008a). NGF also displays its role of growth factor on airway nerves, in particular on sensory airway nerves (Hoyle et al., 1998), and is able to stimulate other airway structural cells such as pulmonary fibroblasts or airway smooth muscle cells (Freund-Michel & Frossard, 2008a). Some of these effects involve activation of the TrkA receptor expressed on these cells

A) NGF protein levels (pg/ml) secreted after 24h by human pulmonary arterial smooth muscle cells (hPASMC) or human pulmonary arterial endothelial cells (hPAEC) in primary culture were assessed by ELISA in the culture cell supernatant. Data are means ± S.E.M. of n=3 experiments performed in triplicates with cells from two different donors. B) TrkA and p75NTR proteins were detected by Western blotting in cultured hPASMC or hPAEC from two different donors (D1 and D2), with rabbit polyclonal anti-human TrkA or p75NTR antibodies as specific protein bands of 140 and 75 kDa respectively. β-Actin probed in the

A) B)

**hPASMC hPAEC**

same blots was used to control for protein loading.

5

10

**[NGF] (pg/ml)**

(**Fig. 2**).

15

20

Fig. 2. NGF effects in the lung mediated via activation of the TrkA receptor

NGF-induced activation of the TrkA receptor participates to attraction and activation of inflammatory cells in the lung and may therefore contribute to lung inflammation. The TrkA receptor is also expressed on lung structural cells and participates to NGF-induced effects that may contribute to altered reactivity and remodelling processes existing in pulmonary inflammatory diseases.

Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases 83

*In vitro*, eosinophil degranulation is promoted by NGF-induced activation of the TrkA receptor, inducing release of inflammatory mediators such as interleukin-4 (Noga et al., 2002). *In vitro* NGF treatment of eosinophils from patients with allergic bronchial asthma increases viability of these cells, and this effect is correlated to increased expression of the TrkA receptor on eosinophils (Nassenstein et al., 2003). In addition, coculture of lung eosinophils with airway epithelial cells resulted in enhanced epithelial neurotrophin production, as well as in prolonged survival of eosinophils (Hahn et al., 2006). Complete inhibition of eosinophil survival in the presence of the TrkA kinase inhibitor K252a confirmed the important role of the TrkA receptor in eosinophil survival (Hahn et al., 2006).

NGF induces TrkA activation in monocytes *in vitro* to trigger a respiratory burst, the major component of monocyte cytotoxic activity (Ehrhard et al., 1993b). Activation of the TrkA receptor by NGF also promotes monocytes survival (la Sala et al., 2000). TrkA transactivation mechanisms with GPCR ligands, recently evidenced in monocytes, contribute to proinflammatory activities such as for example synthesis of reactive oxygen species (El Zein et al., 2007, 2010). Expression of the TrkA receptor was shown to decrease during *in vitro* differentiation of monocytes to macrophages, suggesting a maturation-dependent regulation

NGF was reported to activate macrophages *in vitro* in the process of inflammatory and immune actions, inducing phagocytosis, parasite killing, and production of inflammatory cytokines in a TrkA dependent-manner (Barouch et al., 2001; Susaki et al., 1996). *In vivo*, TrkA expression was reported on human alveolar macrophages (Ricci et al., 2004b; Ricci et al., 2000b), and the TrkA receptor and its binding protein SH2-B participate to activation of

In a murine model of rhinitis induced by toluene diisocyanate exposure, a massive increased number of neutrophils in the nasal mucosa correlates to increased levels of NGF (Wilfong & Dey, 2004 & 2005). Neutrophil infiltration was inhibited after *in vivo* pre-treatment with the TrkA kinase inhibitor K252a, thus showing the important role of the TrkA receptor on

A role for the TrkA receptor has been evidenced in NGF-induced effects on airway sensory nerves. In particular, NGF induces release of neuropeptides such as substance P by airway neurons, and this effect is TrkA-dependent (de Vries et al., 2006; Dinh et al., 2004). A similar effect has been reported in nasal sensory neurons (Wilfong & Dey, 2004). NGF induces proliferation of airway smooth muscle cells through activation of the TrkA receptor (Freund-Michel et al., 2006). We also showed that NGF multiple stimulation of these cells induce internalization and degradation of the TrkA receptor followed by upregulated resynthesis of functional TrkA receptors and increased proliferative effect (Freund-Michel & Frossard, 2008b). In ongoing studies, we have recently found that NGF induces proliferation and migration of human pulmonary endothelial and vascular smooth muscle cells *in vitro*,

alveolar macrophages *in vivo* in a guinea pig model of asthma (Li et al., 2009).

neutrophil attraction in the nasal mucosa (Wilfong & Dey, 2004).

**4.1.4 Eosinophils** 

**4.1.6 Neutrophils** 

**4.2 TrkA and airway structural cells** 

**4.1.5 Monocytes / macrophages** 

of TrkA expression in these cells (Ehrhard et al., 1993b).

#### **4.1 TrkA and inflammatory cells**

#### **4.1.1 Mast cells**

*In vitro*, activation of the TrkA receptor by NGF induces granule formation in immature mast cells and therefore contributes to their differentiation (Kim et al., 2008). In addition, NGF is a chemotactic factor for mast cells through both MAPK and PI3K signalling pathways following TrkA activation (Sawada et al., 2000). TrkA activation is also involved in NGF-induced degranulation of mast cells and mediators release such as for example chemokines (Ahamed et al., 2004), or serotonin (Kawamoto et al., 2002). Finally, NGF acts as a key factor to promote mast cell survival through TrkA-induced suppression of apoptosis (Kawamoto et al., 1995). *In vivo*, a correlation between NGF levels in bronchoalveolar lavage (BAL) fluids and the number of mast cells infiltrated in the bronchial mucosa has been evidenced in asthmatic patients after allergenic challenge (Kassel et al., 2001). Expression of TrkA receptors on these mast cells therefore suggests a role for this receptor in NGF-induced attraction and survival of these cells in the lung *in vivo* (Kassel et al., 2001).

#### **4.1.2 Basophils**

*In vitro*, NGF potentiates mediator release from human basophils as well as primes the cells to produce leukotriene C4, and these effects are TrkA-dependent (Burgi et al., 1996). NGF can also modulate IgE-mediated responses in human basophils, and these effects are enhanced on cells from allergic subjects (Sin et al., 2001). However, flow cytometry studies revealed no significant differences in TrkA receptor expression on basophils in this study (Sin et al., 2001).

#### **4.1.3 T and B cells**

Although various effects of NGF have been described on T lymphocytes, few studies have investigated the role of the TrkA receptor in these effects. Only one study by Ehrhard and co-workers clearly demonstrates involvement of the TrkA receptor in NGF-induced activation of T lymphocytes *in vitro* (Ehrhard et al., 1994). *In vivo*, NGF effects on T lymphocytes remain controversial, since two studies conducted in a mouse model of asthma failed to show NGF-related effects on T cells (Braun et al., 1998; Path et al., 2002). However, in a transgenic mouse tissue-specifically overexpressing NGF in the lung, increased numbers of T lymphocytes have been shown in the lung after allergenic challenge (Quarcoo et al., 2004). The role of NGF and its TrkA receptor on T lymphocytes in pulmonary inflammatory diseases needs therefore to be further clarified *in vivo*.

NGF has been shown to induce proliferation of B lymphocytes *in vitro*, and this effect occurs through activation of the TrkA receptor and its signalling pathways involving PLC, PI3K and MAPK (Melamed et al., 1996). NGF-induced activation of the TrkA receptor also participates to B cell survival through PI3K-dependent activation of PKC zeta (Kronfeld et al., 2002). *In vivo*, in mice lacking TrkA in non-neuronal tissues, all major immune system cell populations were present in normal numbers and distributions, excepted for B lymphocytes, demonstrating that endogenous NGF modulates B cell development through activation of the TrkA receptor (Coppola et al., 2004). Moreover, during allergic airway inflammation in the mouse *in vivo*, NGF contributes to B cell differentiation into plasma cells and activates the TrkA receptor to enhance plasma cell survival and production of immunoglobulins E (Abram et al., 2009).

#### **4.1.4 Eosinophils**

82 Inflammatory Diseases – A Modern Perspective

*In vitro*, activation of the TrkA receptor by NGF induces granule formation in immature mast cells and therefore contributes to their differentiation (Kim et al., 2008). In addition, NGF is a chemotactic factor for mast cells through both MAPK and PI3K signalling pathways following TrkA activation (Sawada et al., 2000). TrkA activation is also involved in NGF-induced degranulation of mast cells and mediators release such as for example chemokines (Ahamed et al., 2004), or serotonin (Kawamoto et al., 2002). Finally, NGF acts as a key factor to promote mast cell survival through TrkA-induced suppression of apoptosis (Kawamoto et al., 1995). *In vivo*, a correlation between NGF levels in bronchoalveolar lavage (BAL) fluids and the number of mast cells infiltrated in the bronchial mucosa has been evidenced in asthmatic patients after allergenic challenge (Kassel et al., 2001). Expression of TrkA receptors on these mast cells therefore suggests a role for this receptor in NGF-induced attraction and survival of these cells in the lung *in* 

*In vitro*, NGF potentiates mediator release from human basophils as well as primes the cells to produce leukotriene C4, and these effects are TrkA-dependent (Burgi et al., 1996). NGF can also modulate IgE-mediated responses in human basophils, and these effects are enhanced on cells from allergic subjects (Sin et al., 2001). However, flow cytometry studies revealed no significant differences in TrkA receptor expression on basophils in this study

Although various effects of NGF have been described on T lymphocytes, few studies have investigated the role of the TrkA receptor in these effects. Only one study by Ehrhard and co-workers clearly demonstrates involvement of the TrkA receptor in NGF-induced activation of T lymphocytes *in vitro* (Ehrhard et al., 1994). *In vivo*, NGF effects on T lymphocytes remain controversial, since two studies conducted in a mouse model of asthma failed to show NGF-related effects on T cells (Braun et al., 1998; Path et al., 2002). However, in a transgenic mouse tissue-specifically overexpressing NGF in the lung, increased numbers of T lymphocytes have been shown in the lung after allergenic challenge (Quarcoo et al., 2004). The role of NGF and its TrkA receptor on T lymphocytes in pulmonary

NGF has been shown to induce proliferation of B lymphocytes *in vitro*, and this effect occurs through activation of the TrkA receptor and its signalling pathways involving PLC, PI3K and MAPK (Melamed et al., 1996). NGF-induced activation of the TrkA receptor also participates to B cell survival through PI3K-dependent activation of PKC zeta (Kronfeld et al., 2002). *In vivo*, in mice lacking TrkA in non-neuronal tissues, all major immune system cell populations were present in normal numbers and distributions, excepted for B lymphocytes, demonstrating that endogenous NGF modulates B cell development through activation of the TrkA receptor (Coppola et al., 2004). Moreover, during allergic airway inflammation in the mouse *in vivo*, NGF contributes to B cell differentiation into plasma cells and activates the TrkA receptor to enhance plasma cell survival and production of

inflammatory diseases needs therefore to be further clarified *in vivo*.

immunoglobulins E (Abram et al., 2009).

**4.1 TrkA and inflammatory cells** 

**4.1.1 Mast cells** 

*vivo* (Kassel et al., 2001).

**4.1.2 Basophils** 

(Sin et al., 2001).

**4.1.3 T and B cells** 

*In vitro*, eosinophil degranulation is promoted by NGF-induced activation of the TrkA receptor, inducing release of inflammatory mediators such as interleukin-4 (Noga et al., 2002). *In vitro* NGF treatment of eosinophils from patients with allergic bronchial asthma increases viability of these cells, and this effect is correlated to increased expression of the TrkA receptor on eosinophils (Nassenstein et al., 2003). In addition, coculture of lung eosinophils with airway epithelial cells resulted in enhanced epithelial neurotrophin production, as well as in prolonged survival of eosinophils (Hahn et al., 2006). Complete inhibition of eosinophil survival in the presence of the TrkA kinase inhibitor K252a confirmed the important role of the TrkA receptor in eosinophil survival (Hahn et al., 2006).

#### **4.1.5 Monocytes / macrophages**

NGF induces TrkA activation in monocytes *in vitro* to trigger a respiratory burst, the major component of monocyte cytotoxic activity (Ehrhard et al., 1993b). Activation of the TrkA receptor by NGF also promotes monocytes survival (la Sala et al., 2000). TrkA transactivation mechanisms with GPCR ligands, recently evidenced in monocytes, contribute to proinflammatory activities such as for example synthesis of reactive oxygen species (El Zein et al., 2007, 2010). Expression of the TrkA receptor was shown to decrease during *in vitro* differentiation of monocytes to macrophages, suggesting a maturation-dependent regulation of TrkA expression in these cells (Ehrhard et al., 1993b).

NGF was reported to activate macrophages *in vitro* in the process of inflammatory and immune actions, inducing phagocytosis, parasite killing, and production of inflammatory cytokines in a TrkA dependent-manner (Barouch et al., 2001; Susaki et al., 1996). *In vivo*, TrkA expression was reported on human alveolar macrophages (Ricci et al., 2004b; Ricci et al., 2000b), and the TrkA receptor and its binding protein SH2-B participate to activation of alveolar macrophages *in vivo* in a guinea pig model of asthma (Li et al., 2009).

#### **4.1.6 Neutrophils**

In a murine model of rhinitis induced by toluene diisocyanate exposure, a massive increased number of neutrophils in the nasal mucosa correlates to increased levels of NGF (Wilfong & Dey, 2004 & 2005). Neutrophil infiltration was inhibited after *in vivo* pre-treatment with the TrkA kinase inhibitor K252a, thus showing the important role of the TrkA receptor on neutrophil attraction in the nasal mucosa (Wilfong & Dey, 2004).

#### **4.2 TrkA and airway structural cells**

A role for the TrkA receptor has been evidenced in NGF-induced effects on airway sensory nerves. In particular, NGF induces release of neuropeptides such as substance P by airway neurons, and this effect is TrkA-dependent (de Vries et al., 2006; Dinh et al., 2004). A similar effect has been reported in nasal sensory neurons (Wilfong & Dey, 2004). NGF induces proliferation of airway smooth muscle cells through activation of the TrkA receptor (Freund-Michel et al., 2006). We also showed that NGF multiple stimulation of these cells induce internalization and degradation of the TrkA receptor followed by upregulated resynthesis of functional TrkA receptors and increased proliferative effect (Freund-Michel & Frossard, 2008b). In ongoing studies, we have recently found that NGF induces proliferation and migration of human pulmonary endothelial and vascular smooth muscle cells *in vitro*,

Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases 85

Circulating NGF levels are increased in human allergic and inflammatory diseases (Bonini et al., 1996). A local increase in NGF secretion has also been evidenced in BAL fluid from asthmatic patients (Kassel et al., 2001; Olgart Hoglund et al., 2002). In addition, our ongoing studies show that pulmonary arteries from patients suffering from pulmonary hypertension secondary to chronic obstructive pulmonary diseases (COPD) secrete more NGF than pulmonary arteries from control donors (Freund-Michel et al., 2010). Asthma and pulmonary hypertension share in common three major features occurring either in airways or in pulmonary arteries: inflammation, tissue hyperreactivity and remodelling (Barnes, 2010; Broide et al., 2011; Hassoun et al., 2009; Humbert, 2010). Several *in vitro* and *in vivo* studies suggest that NGF may play a role in these three physiopathological mechanisms, in

**TrkA**

**Pulmonary**

hypertension

**5. Role of the TrkA receptor in pulmonary inflammatory diseases** 

**NGF**

**INFLAMMATION**

**HYPERREACTIVITY**

**arteries Airways**

**REMODELLING**

Asthma Pulmonary

*In vitro* and *in vivo* studies suggest that activation of the TrkA receptor by NGF contributes to inflammation as well as tissue remodelling and altered reactivity, three features occurring in particular in airways and in pulmonary arteries and playing a major role in the

In a mouse model of asthma, NGF inhibition induced by blocking antibodies administered *in vivo* decreases airway inflammation (Braun et al., 1998; Path et al., 2002). On the contrary, allergen sensitization and challenge in a transgenic mouse tissue specifically overexpressing NGF in the lung displays greater airway inflammation (Path et al., 2002; Quarcoo et al., 2004). *In vivo* administration of a pan-Trk receptor decoy in a mouse model of asthma

Fig. 4. Potential role of the TrkA receptor in asthma and pulmonary hypertension

physiopathology of asthma and pulmonary hypertension.

**5.1 NGF, TrkA and inflammation** 

**5.1.1 Asthma** 

particular through activation of the TrkA receptor (**Fig. 4**).

and that these effects are inhibited by pre-treatment with the TrkA kinase inhibitor K252a, thus suggesting a role for the TrkA receptor in these NGF-mediated effects (Freund-Michel et al., 2009) (**Fig. 3**).

Fig. 3. Involvement of the TrkA receptor in NGF-induced effects on human pulmonary vascular cells.

Effect of NGF (0.1, 10 or 100 ng/ml) after 24h on A) human pulmonary arterial smooth muscle cells (hPASMC) or B) human pulmonary arterial endothelial cells (hPAEC) in primary culture. Cell proliferation was assessed by the BrdU technique and cell migration was evaluated by the Transwell assay. Data are presented as the maximal percentage of increased proliferation or migration compared to untreated control cells. NGF effect was evaluated in the presence or absence of the TrkA kinase inhibitor K252a (100nM, 30min pre-treatment followed by 24h concomitant treatment with NGF). \*\*\*: P<0.001 versus NGF alone with n=5 independent experiments performed in triplicates with cells from two different donors.

and that these effects are inhibited by pre-treatment with the TrkA kinase inhibitor K252a, thus suggesting a role for the TrkA receptor in these NGF-mediated effects (Freund-Michel

et al., 2009) (**Fig. 3**).

**% Increase**

**compared to controls**

**% Increased effect**

B)

vascular cells.

**NGF 100ng/ml**

experiments performed in triplicates with cells from two different donors.

**\*\*\***

**% Increase**

**compared to controls**

**% Increased effect**

**NGF 10ng/ml**

A) **hPASMC**

**\*\*\***

**+K252a 100nM**

**hPAEC**

**MIGRATION PROLIFERATION**

**+K252a 100nM**

Fig. 3. Involvement of the TrkA receptor in NGF-induced effects on human pulmonary

Effect of NGF (0.1, 10 or 100 ng/ml) after 24h on A) human pulmonary arterial smooth muscle cells (hPASMC) or B) human pulmonary arterial endothelial cells (hPAEC) in primary culture. Cell proliferation was assessed by the BrdU technique and cell migration was evaluated by the Transwell assay. Data are presented as the maximal percentage of increased proliferation or migration compared to untreated control cells. NGF effect was evaluated in the presence or absence of the TrkA kinase inhibitor K252a (100nM, 30min pre-treatment followed by 24h concomitant treatment with NGF). \*\*\*: P<0.001 versus NGF alone with n=5 independent

**NGF 0.1ng/ml**

**\*\*\***

**NGF 100ng/ml**

**\*\*\***

**MIGRATION PROLIFERATION**

**+K252a 100nM**

**+K252a 100nM**

#### **5. Role of the TrkA receptor in pulmonary inflammatory diseases**

Circulating NGF levels are increased in human allergic and inflammatory diseases (Bonini et al., 1996). A local increase in NGF secretion has also been evidenced in BAL fluid from asthmatic patients (Kassel et al., 2001; Olgart Hoglund et al., 2002). In addition, our ongoing studies show that pulmonary arteries from patients suffering from pulmonary hypertension secondary to chronic obstructive pulmonary diseases (COPD) secrete more NGF than pulmonary arteries from control donors (Freund-Michel et al., 2010). Asthma and pulmonary hypertension share in common three major features occurring either in airways or in pulmonary arteries: inflammation, tissue hyperreactivity and remodelling (Barnes, 2010; Broide et al., 2011; Hassoun et al., 2009; Humbert, 2010). Several *in vitro* and *in vivo* studies suggest that NGF may play a role in these three physiopathological mechanisms, in particular through activation of the TrkA receptor (**Fig. 4**).

Fig. 4. Potential role of the TrkA receptor in asthma and pulmonary hypertension

*In vitro* and *in vivo* studies suggest that activation of the TrkA receptor by NGF contributes to inflammation as well as tissue remodelling and altered reactivity, three features occurring in particular in airways and in pulmonary arteries and playing a major role in the physiopathology of asthma and pulmonary hypertension.

#### **5.1 NGF, TrkA and inflammation 5.1.1 Asthma**

In a mouse model of asthma, NGF inhibition induced by blocking antibodies administered *in vivo* decreases airway inflammation (Braun et al., 1998; Path et al., 2002). On the contrary, allergen sensitization and challenge in a transgenic mouse tissue specifically overexpressing NGF in the lung displays greater airway inflammation (Path et al., 2002; Quarcoo et al., 2004). *In vivo* administration of a pan-Trk receptor decoy in a mouse model of asthma

Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases 87

phenylephrine or prostaglandin F2 (Freund-Michel et al., 2010). Our preliminary data suggest that this effect may be due in part to activation of the TrkA receptors and increased intracellular calcium concentrations. These mechanisms are in accordance with the preliminary data recently described for BDNF and NT-3 by Prakash and co-workers (Prakash et al., 2010). Altogether, these results therefore suggest that neurotrophins, through activation of both Trk and p75NTR receptors, participate in both endothelial dysfunction and

Airway remodelling in asthma is characterized by a sub-epithelial fibrosis with an increased proliferation of fibroblasts and a thickening of the basement membrane, hypervascularisation, sensory hyperinnervation, oedema, and hypertrophy and hyperplasia of the smooth muscle layer (Bara et al., 2010). *In vitro*, NGF activates the TrkA receptor to induce migration of pulmonary fibroblasts (Kohyama et al., 2002) and regulation of extracellular matrix synthesis (Khan et al., 2002; Takahashi et al., 2000). These results therefore suggest a role for the TrkA receptor in NGF-induced airway sub-epithelial fibrosis *in vivo* (Hoyle et al., 1998). We also reported that NGF induces proliferation of the airway smooth muscle through activation of the TrkA receptor and may therefore participate to hyperplasia of the smooth muscle layer *in vivo* (Freund-Michel et al., 2006). Activation of the TrkA receptor by NGF also stimulates vascular cells from other origins than the lung to induce migration and proliferation of endothelial cells (Cantarella et al., 2002; Dolle et al., 2005; Lecht et al.,2010 ; Rahbek et al., 2005) as well as migration of vascular smooth muscle cells (Donovan et al., 1995; Kraemer et al., 1999). In addition, NGF stimulates angiogenesis *in vivo* through activation of the TrkA receptor (Cantarella et al., 2002; Caporali & Emanueli, 2009). NGF is also able to stimulate synthesis of angiogenic factors such as vascular endothelial growth factor (VEGF) from various cells through activation of its TrkA receptor (Nakamura et al., 2011). Altogether, these results suggest that activation of the TrkA receptor participates to NGF-mediated hypervascularisation in the lung (Hoyle et al., 1998).

Vascular remodelling in pulmonary hypertension is characterized by increased proliferation, decreased apoptosis and increased migration of pulmonary vascular cells (Humbert et al., 2004). NGF-induced activation of the TrkA receptor contributes to migration and proliferation of vascular cells from other origins than the lung and stimulates angiogenesis (see paragraph above). Our recent results show that NGF induces proliferation and migration of pulmonary vascular cells through activation of the TrkA receptor (see paragraph 4.2 and Fig. 3) (Freund-Michel et al., 2010). Therefore, our findings support a role

In regard of the different results presented in this review, NGF seems to play a major role in altered inflammatory, remodelling and reactivity processes occurring in pulmonary inflammatory diseases such as asthma or pulmonary hypertension. The TrkA receptor is involved in many NGF effects in the lung and targeting NGF or its TrkA receptor may be a

for NGF and its TrkA receptor in pulmonary vascular remodelling in this disease.

smooth muscle hyperreactivity observed in pulmonary hypertension.

**5.3 NGF, TrkA and tissue remodelling** 

**5.3.2 Pulmonary hypertension** 

**6. Therapeutic perspectives and conclusion** 

new therapeutic perspective in these diseases.

**5.3.1 Asthma** 

reduces interleukin-(IL-)4 and IL-5 cytokine levels (Nassenstein et al., 2006). Substance P is one of the neuropeptides released by airway sensory nerves that participates to neurogenic inflammation in asthma (Quarcoo et al., 2004). *In vivo* treatment with the TrkA kinase inhibitor K252a prevents the increase in substance P observed in a guinea pig model of asthma (de Vries et al., 2006). *In vivo* pre-treatment with TrkA blocking antibodies decreases IL-1 and IL-4 levels in the BAL fluid after allergen sensitization and challenge in the guinea pig (Li et al., 2009). Similar results are observed with TrkA blocking antibodies in a mouse model of asthma (Ni et al., 2010). Altogether, these results show a major role of NGF in airway inflammation through activation of its TrkA receptor.

#### **5.1.2 Pulmonary hypertension**

We recently showed that NGF stimulates secretion of inflammatory cytokines such as IL-1 and tumor necrosis factor- from rat and human pulmonary arteries (Freund-Michel et al., 2010). Moreover, *in vivo* treatment with anti-NGF blocking antibodies in animal models of pulmonary hypertension prevents the increased secretion of these inflammatory cytokines from diseased pulmonary arteries (Freund-Michel et al., unpublished data). Contribution of the TrkA receptor in these mechanisms remains to be determined, but our preliminary data support a role for NGF in the inflammatory mechanisms associated to pulmonary hypertension.

#### **5.2 NGF, TrkA and tissue hyperresponsiveness 5.2.1 Asthma**

A role for NGF was reported in airway hyperresponsiveness (AHR) associated to asthma, since pre-treatment with anti-NGF blocking antibodies reduces AHR in various animal models of asthma (Braun et al., 1998; de Vries et al., 2006; Glaab et al., 2003). In addition, AHR is observed after *in vitro* NGF pre-treatment of guinea pig (de Vries et al., 2001), ferret (Wu & Dey, 2006) or human bronchi (Frossard et al., 2005). AHR is reduced *in vivo* after administration of a pan-Trk receptor decoy in a mouse model of asthma (Nassenstein et al., 2006), or of the TrkA kinase inhibitor K252a in a guinea pig model of asthma (de Vries et al., 2006), thus showing involvement of the TrkA receptor in NGF-induced AHR.

#### **5.2.2 Pulmonary hypertension**

In the systemic circulation, neurotrophins play a role in the control of vascular tone (Caporali & Emanueli, 2009), and a role for NGF has been suggested in systemic arterial hypertension (Sherer et al., 1998). Neurotrophins and their receptors are expressed on pulmonary arteries (Ricci et al., 2000a), and their expression is increased in the lung of spontaneously hypertensive rats (Ricci et al., 2004a). A role for neurotrophins in the control of the pulmonary arterial tone was recently proposed, through activation of the p75NTR receptor (Xu et al., 2008). BDNF and NT-3 induce relaxation of porcine pulmonary arterial rings, through activation of the endothelial nitric oxide synthase (Meuchel et al., 2011). Suppression of TrkB or TrkC expression via siRNA as well as functional blockade of p75NTR suggest a role of both Trk and p75NTR receptors in these effects (Meuchel et al., 2011). In our ongoing studies in rat or human pulmonary arteries, we show that NGF does not induce rat or human pulmonary arterial contraction or relaxation by itself. However, NGF pretreatment induces pulmonary arterial hyperresponsiveness to contractile agents such as phenylephrine or prostaglandin F2 (Freund-Michel et al., 2010). Our preliminary data suggest that this effect may be due in part to activation of the TrkA receptors and increased intracellular calcium concentrations. These mechanisms are in accordance with the preliminary data recently described for BDNF and NT-3 by Prakash and co-workers (Prakash et al., 2010). Altogether, these results therefore suggest that neurotrophins, through activation of both Trk and p75NTR receptors, participate in both endothelial dysfunction and smooth muscle hyperreactivity observed in pulmonary hypertension.

#### **5.3 NGF, TrkA and tissue remodelling 5.3.1 Asthma**

86 Inflammatory Diseases – A Modern Perspective

reduces interleukin-(IL-)4 and IL-5 cytokine levels (Nassenstein et al., 2006). Substance P is one of the neuropeptides released by airway sensory nerves that participates to neurogenic inflammation in asthma (Quarcoo et al., 2004). *In vivo* treatment with the TrkA kinase inhibitor K252a prevents the increase in substance P observed in a guinea pig model of asthma (de Vries et al., 2006). *In vivo* pre-treatment with TrkA blocking antibodies decreases IL-1 and IL-4 levels in the BAL fluid after allergen sensitization and challenge in the guinea pig (Li et al., 2009). Similar results are observed with TrkA blocking antibodies in a mouse model of asthma (Ni et al., 2010). Altogether, these results show a major role of NGF in

We recently showed that NGF stimulates secretion of inflammatory cytokines such as IL-1 and tumor necrosis factor- from rat and human pulmonary arteries (Freund-Michel et al., 2010). Moreover, *in vivo* treatment with anti-NGF blocking antibodies in animal models of pulmonary hypertension prevents the increased secretion of these inflammatory cytokines from diseased pulmonary arteries (Freund-Michel et al., unpublished data). Contribution of the TrkA receptor in these mechanisms remains to be determined, but our preliminary data support a role for NGF in the inflammatory mechanisms associated to pulmonary

A role for NGF was reported in airway hyperresponsiveness (AHR) associated to asthma, since pre-treatment with anti-NGF blocking antibodies reduces AHR in various animal models of asthma (Braun et al., 1998; de Vries et al., 2006; Glaab et al., 2003). In addition, AHR is observed after *in vitro* NGF pre-treatment of guinea pig (de Vries et al., 2001), ferret (Wu & Dey, 2006) or human bronchi (Frossard et al., 2005). AHR is reduced *in vivo* after administration of a pan-Trk receptor decoy in a mouse model of asthma (Nassenstein et al., 2006), or of the TrkA kinase inhibitor K252a in a guinea pig model of asthma (de Vries et al.,

In the systemic circulation, neurotrophins play a role in the control of vascular tone (Caporali & Emanueli, 2009), and a role for NGF has been suggested in systemic arterial hypertension (Sherer et al., 1998). Neurotrophins and their receptors are expressed on pulmonary arteries (Ricci et al., 2000a), and their expression is increased in the lung of spontaneously hypertensive rats (Ricci et al., 2004a). A role for neurotrophins in the control of the pulmonary arterial tone was recently proposed, through activation of the p75NTR receptor (Xu et al., 2008). BDNF and NT-3 induce relaxation of porcine pulmonary arterial rings, through activation of the endothelial nitric oxide synthase (Meuchel et al., 2011). Suppression of TrkB or TrkC expression via siRNA as well as functional blockade of p75NTR suggest a role of both Trk and p75NTR receptors in these effects (Meuchel et al., 2011). In our ongoing studies in rat or human pulmonary arteries, we show that NGF does not induce rat or human pulmonary arterial contraction or relaxation by itself. However, NGF pretreatment induces pulmonary arterial hyperresponsiveness to contractile agents such as

2006), thus showing involvement of the TrkA receptor in NGF-induced AHR.

airway inflammation through activation of its TrkA receptor.

**5.2 NGF, TrkA and tissue hyperresponsiveness** 

**5.1.2 Pulmonary hypertension** 

**5.2.2 Pulmonary hypertension** 

hypertension.

**5.2.1 Asthma** 

Airway remodelling in asthma is characterized by a sub-epithelial fibrosis with an increased proliferation of fibroblasts and a thickening of the basement membrane, hypervascularisation, sensory hyperinnervation, oedema, and hypertrophy and hyperplasia of the smooth muscle layer (Bara et al., 2010). *In vitro*, NGF activates the TrkA receptor to induce migration of pulmonary fibroblasts (Kohyama et al., 2002) and regulation of extracellular matrix synthesis (Khan et al., 2002; Takahashi et al., 2000). These results therefore suggest a role for the TrkA receptor in NGF-induced airway sub-epithelial fibrosis *in vivo* (Hoyle et al., 1998). We also reported that NGF induces proliferation of the airway smooth muscle through activation of the TrkA receptor and may therefore participate to hyperplasia of the smooth muscle layer *in vivo* (Freund-Michel et al., 2006). Activation of the TrkA receptor by NGF also stimulates vascular cells from other origins than the lung to induce migration and proliferation of endothelial cells (Cantarella et al., 2002; Dolle et al., 2005; Lecht et al.,2010 ; Rahbek et al., 2005) as well as migration of vascular smooth muscle cells (Donovan et al., 1995; Kraemer et al., 1999). In addition, NGF stimulates angiogenesis *in vivo* through activation of the TrkA receptor (Cantarella et al., 2002; Caporali & Emanueli, 2009). NGF is also able to stimulate synthesis of angiogenic factors such as vascular endothelial growth factor (VEGF) from various cells through activation of its TrkA receptor (Nakamura et al., 2011). Altogether, these results suggest that activation of the TrkA receptor participates to NGF-mediated hypervascularisation in the lung (Hoyle et al., 1998).

#### **5.3.2 Pulmonary hypertension**

Vascular remodelling in pulmonary hypertension is characterized by increased proliferation, decreased apoptosis and increased migration of pulmonary vascular cells (Humbert et al., 2004). NGF-induced activation of the TrkA receptor contributes to migration and proliferation of vascular cells from other origins than the lung and stimulates angiogenesis (see paragraph above). Our recent results show that NGF induces proliferation and migration of pulmonary vascular cells through activation of the TrkA receptor (see paragraph 4.2 and Fig. 3) (Freund-Michel et al., 2010). Therefore, our findings support a role for NGF and its TrkA receptor in pulmonary vascular remodelling in this disease.

### **6. Therapeutic perspectives and conclusion**

In regard of the different results presented in this review, NGF seems to play a major role in altered inflammatory, remodelling and reactivity processes occurring in pulmonary inflammatory diseases such as asthma or pulmonary hypertension. The TrkA receptor is involved in many NGF effects in the lung and targeting NGF or its TrkA receptor may be a new therapeutic perspective in these diseases.

Expression and Role of the TrkA Receptor in Pulmonary Inflammatory Diseases 89

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Outside the lung, blockade of NGF is of therapeutic interest in other areas, in particular in pain therapy (Hefti et al., 2006). Humanized monoclonal antibodies against NGF or against TrkA, as well as small molecules acting as TrkA antagonists or as TrkA kinase inhibitors have been developed and are currently under investigation (Ma et al., 2010; Martin et al., 2011; McNamee et al., 2010; Ueda et al., 2010; Watson et al., 2008) **(Fig. 5**). In particular, tanezumab, a recombinant humanized monoclonal antibody against NGF, has been recently tested in clinical trials in osteoarthritic pain and chronic lower back pain and demonstrated good efficacy (Cattaneo, 2010; Lane et al., 2010). Such strategies may be applied in the near future to target NGF or its receptors in pulmonary inflammatory diseases such as asthma or pulmonary hypertension in which NGF and its TrkA receptor play an important role.

Fig. 5. Potential therapeutic strategies to target the TrkA receptor in pulmonary inflammatory diseases.

To trap circulating NGF and prevent its binding to the TrkA receptor, tools such as anti-NGF blocking antibodies or soluble chimeric TrkA receptors have been developed. Other tools have been developed to target the TrkA receptor itself, either by blocking NGF binding to its extracellular part with antagonists or anti-TrkA antibodies, or by blocking TrkA kinase activity with kinase inhibitors.

#### **7. References**

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Outside the lung, blockade of NGF is of therapeutic interest in other areas, in particular in pain therapy (Hefti et al., 2006). Humanized monoclonal antibodies against NGF or against TrkA, as well as small molecules acting as TrkA antagonists or as TrkA kinase inhibitors have been developed and are currently under investigation (Ma et al., 2010; Martin et al., 2011; McNamee et al., 2010; Ueda et al., 2010; Watson et al., 2008) **(Fig. 5**). In particular, tanezumab, a recombinant humanized monoclonal antibody against NGF, has been recently tested in clinical trials in osteoarthritic pain and chronic lower back pain and demonstrated good efficacy (Cattaneo, 2010; Lane et al., 2010). Such strategies may be applied in the near future to target NGF or its receptors in pulmonary inflammatory diseases such as asthma or pulmonary hypertension in which NGF and its TrkA receptor play an important role.

> **Anti-NGF blocking antibodies TrkAd5** *Watson et al., 2008; McNamee et al., 2010*

> > *Watson et al., 2008*

*Ma et al., 2010*

 **Anti-TrkA blocking antibodies TrkA antagonists**

 **Kinase inhibitors Inhibitory peptides** *Martin et al., 2011; Ueda et al., 2010;* 

**TrkA**

Fig. 5. Potential therapeutic strategies to target the TrkA receptor in pulmonary

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Abram, M., Wegmann, M., Fokuhl, V., Sonar, S., Luger, E.O., Kerzel, S., Radbruch, A., Renz,

H. & Zemlin, M. (2009). Nerve growth factor and neurotrophin-3 mediate survival of pulmonary plasma cells during the allergic airway inflammation. *Journal of* 

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peptide mimetics of neurotrophins and their receptors. *CNS and Neurological* 


**Part 6** 

**The Value of the Cytokinome Profile** 


**Part 6** 

**The Value of the Cytokinome Profile** 

100 Inflammatory Diseases – A Modern Perspective

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**6** 

 *Italy* 

**The Value of the Cytokinome Profile** 

Many scientific articles describe the pathogenesis of diseases that afflict the modern man (cancer, diabetes, obesity, degenerative diseases, etc.) as a slow common inflammatory process that is the basis of all these diseases. Therefore, we commonly speak of chronic inflammatory diseases (Allavena et al., 2008). The basis of this statement are the numerous experimental observations which show that these diseases are driven, from the earliest moments, by exchange between cells of tissues and organs of molecules that operate as messengers. These molecules, carrying biological messages of great importance, inform and lead a complex system of different cell types on what happens and towards which physiological and metabolic changes they are being carried. The chemical nature of these signaling molecules is diverse, but a group of them, the cytokines, is among the most important and studied inter-cellular messengers (Germano et al., 2008). We know the biological meaning of the signal of many of them, thus we can generally divide these molecules into two major classes: pro-inflammatory cytokines and anti-inflammatory cytokines. They are small proteins, quite numerous, more than about 100, expressed in very low amounts (pico and nano molar) and often short-lived, to cover specific information

The cells recognize these signals through appropriate receptors placed on their external membranes. However their study had some limitations due to the fact that (i) only those more abundant were studied, even if with very sensitive assays based on use of antibodies and fluorescence (ELISA); (ii) the receptors show pleiotropy, i.e. they have good affinity for various cytokines and hence the message can be brought by different cytokines; (iii) the biological significance of the message is known only for some of them, for example, it is not known which is the biological meaning carried out by the under-represented cytokines (the less concentrated ones at phenotypic level) and if the different messages are recognized by the receptor as only redundant or with diverse biological content (Colvin et al., 2004;

**1. Introduction** 

needs (Macarthur et al., 2004).

Costantini et al., 2009; Trotta et al., 2009).

Susan Costantini1, Ankush Sharma2

*Cancer Research Center, Mercogliano (AV) 2Doctorate in Computational Biology - CRISCEB* 

*(Second University of Naples), Naples 3Department of Biochemistry and Biophysics (Second University of Naples), Naples* 

*1"Pascale Foundation" National Cancer Institute -* 

and Giovanni Colonna2,3

### **The Value of the Cytokinome Profile**

Susan Costantini1, Ankush Sharma2 and Giovanni Colonna2,3 *1"Pascale Foundation" National Cancer Institute - Cancer Research Center, Mercogliano (AV) 2Doctorate in Computational Biology - CRISCEB (Second University of Naples), Naples 3Department of Biochemistry and Biophysics (Second University of Naples), Naples Italy* 

#### **1. Introduction**

Many scientific articles describe the pathogenesis of diseases that afflict the modern man (cancer, diabetes, obesity, degenerative diseases, etc.) as a slow common inflammatory process that is the basis of all these diseases. Therefore, we commonly speak of chronic inflammatory diseases (Allavena et al., 2008). The basis of this statement are the numerous experimental observations which show that these diseases are driven, from the earliest moments, by exchange between cells of tissues and organs of molecules that operate as messengers. These molecules, carrying biological messages of great importance, inform and lead a complex system of different cell types on what happens and towards which physiological and metabolic changes they are being carried. The chemical nature of these signaling molecules is diverse, but a group of them, the cytokines, is among the most important and studied inter-cellular messengers (Germano et al., 2008). We know the biological meaning of the signal of many of them, thus we can generally divide these molecules into two major classes: pro-inflammatory cytokines and anti-inflammatory cytokines. They are small proteins, quite numerous, more than about 100, expressed in very low amounts (pico and nano molar) and often short-lived, to cover specific information needs (Macarthur et al., 2004).

The cells recognize these signals through appropriate receptors placed on their external membranes. However their study had some limitations due to the fact that (i) only those more abundant were studied, even if with very sensitive assays based on use of antibodies and fluorescence (ELISA); (ii) the receptors show pleiotropy, i.e. they have good affinity for various cytokines and hence the message can be brought by different cytokines; (iii) the biological significance of the message is known only for some of them, for example, it is not known which is the biological meaning carried out by the under-represented cytokines (the less concentrated ones at phenotypic level) and if the different messages are recognized by the receptor as only redundant or with diverse biological content (Colvin et al., 2004; Costantini et al., 2009; Trotta et al., 2009).

The Value of the Cytokinome Profile 105

types of leukocytes, lymphocytes, and other inflammatory cells are activated and attracted to the inflamed site by a signaling network involving a great number of growth factors, cytokines, and chemokines (Coussens & Werb, 2002). All cells recruited to the inflammatory site contribute to tissue breakdown and are beneficial by strengthening and maintaining the defense against infection (Coussens & Werb, 2002). The resolution of inflammation also requires a rapid programmed clearance of inflammatory cells: neighboring macrophages, dendritic cells, and backup phagocytes do this job by inducing apoptosis and conducting

However, inflammation may become chronic either because an inflammatory stimulus persists or because of dysregulation in the control mechanisms that normally turn the process off. Recently, it has been suggested that inflammation associated with cancer is similar to that seen with chronic inflammation, which includes the production of growth and angiogenic factors that stimulate tissue repair, factors that can also promote cancer-cell survival, implantation, and growth (Philip et al., 2004; Macarthur et al., 2004; Balkwill and Mantovani, 2001). Interestingly, inflammation functions at all three stages of tumor

Since many cancers arise from sites of infection, chronic irritation, and inflammation, it is now clear that the tumor microenvironment, which is largely orchestrated by inflammatory cells and cytokines (Fig. 1), is an indispensable participant in the neoplastic process altering not only the metabolic needs of the tissue, but also fostering DNA and protein damage, proliferation, survival, mutagenesis, migration and metastasis of malignant cells (Allavena et al., 2008). Indeed all tumors in the presence of stromal and infiltrating inflammatory cells are facilitated and helped to maintain these metastatic processes. Leukocytes, lymphocytes and other inflammatory cells are activated in this process and attracted to the inflamed site. Inflammation contributes to initiation by inducing the release of a variety of pro-inflammatory cytokines and chemokines and inflammatory enzymes as cyclo-oxygenases that alert the vasculature to release inflammatory cells and factors into the tissue milieu, thereby causing oxidative damage, DNA mutations, and other changes in the microenvironment, making it more conducive to cell transformation, increased survival and proliferation (Germano et al., 2008). We must not forget that many cytokines and chemokines are inducible by hypoxia which is a major physiological difference (Mancino et al., 2008). An important aspect of the tumor microenvironment is the cytokine mediated communication between the tumor and cells. Cytokines and chemokines have many activities that permit cell–cell communication locally at the tissue, with the outcome determined by cytokine concentration milieu and cell type (Germano et al., 2008). Current thinking is that activated immune cells provide both anti- and protumorigenic signals, thus representing targets to be harnessed or attacked for therapeutic advantage depending upon environmental and/or cellular context. Because the control of cytokine production is highly complex and multifactorial, the effects of cytokines are mediated through multiple regulatory networks. The intricate complexity of both cytokine networks clearly conceals the role that a single cytokine may play in the pathogenesis of the disease. It is therefore informative to investigate the immunopathogenesis of a disease process by analyzing multiple cytokines. In this way it is possible to provide a better understanding of the role of cellular, humoral and chemotactic immunity at a critical time in some cancer diseases and also in the treatment

phagocytosis (Savill et al., 2002).

development: initiation, progression and metastasis.

course of a correlated infection (Costantini et al., 2009).

Recently, specific protein chips of considerable and improved sensitivity are being developed. They allow the simultaneous determination of different cytokines based on a fluorescence/laser/antibodies technology which uses microparticle beads (multiplex technology) that allows the analysis of tiny samples (few dozens of microliter) of serum, plasma, or cell cultures supernatant. Each bead set is coated with capture antibody specific for one analyte. The result is the most accurate, sensitive, and reproducible cytokine assay available. An important point of this technology is the ability to appreciate quantitatively also the presence of the under-represented cytokines (Capone et al., 2010; Costantini et al., 2010a). The pattern of these cytokines, being part of the new global or holistic logic, which is used today in the "omics" approach to the study of biological phenomena, can be indicated as "cytokinome" (Costantini et al., 2010b).

The fact is that the cytokines form an informative network, for some ways very similar to the Internet, that capillary connects, as knots to the network, cellular systems also different. The study of this network is important for understanding the evolution of the pathogenesis of many chronic inflammatory diseases. However, there are many questions that must still find the answer. In the case of chronic inflammatory diseases, which development in the time the whole pattern of cytokines shows? Their evolution in time begins in the same way and is common for all the diseases or is pathology correlated and addressed by different types or classes of cytokines? Which one is the cytokinome development during the disease? Answers to these and other questions are essential not only to be able to describe the cytokinome dynamics during the progression of chronic inflammatory diseases but, above all, to try to predict in large advance the prognosis of the disease. If this will be possible, we will be able to intervene with great advance in the early stages of the disease with much more chance of healing or of extending the duration and the expectations of life. Therefore, the review will focus on:


#### **2. Role of the cytokines in chronic inflammatory diseases and cancers**

Inflammation is a physiologic process in response to acute tissue damage resulting from physical injury, ischemic injury, infection, exposure to toxins, chemical irritation, and/or wounding or other types of trauma (Lu et al., 2006; Philip et al., 2004); it is a protective attempt by the organism to remove the injurious stimuli as well as initiate the healing process for the tissue. At the very early stage of inflammation, the phagocytic cells are mainly involved: neutrophils are the first cells to migrate to the inflammatory sites under the regulation of molecules produced by rapidly responding macrophages and mast cells prestationed in tissues (Coussens & Werb, 2002). As the inflammation progresses, various

Recently, specific protein chips of considerable and improved sensitivity are being developed. They allow the simultaneous determination of different cytokines based on a fluorescence/laser/antibodies technology which uses microparticle beads (multiplex technology) that allows the analysis of tiny samples (few dozens of microliter) of serum, plasma, or cell cultures supernatant. Each bead set is coated with capture antibody specific for one analyte. The result is the most accurate, sensitive, and reproducible cytokine assay available. An important point of this technology is the ability to appreciate quantitatively also the presence of the under-represented cytokines (Capone et al., 2010; Costantini et al., 2010a). The pattern of these cytokines, being part of the new global or holistic logic, which is used today in the "omics" approach to the study of biological phenomena, can be indicated

The fact is that the cytokines form an informative network, for some ways very similar to the Internet, that capillary connects, as knots to the network, cellular systems also different. The study of this network is important for understanding the evolution of the pathogenesis of many chronic inflammatory diseases. However, there are many questions that must still find the answer. In the case of chronic inflammatory diseases, which development in the time the whole pattern of cytokines shows? Their evolution in time begins in the same way and is common for all the diseases or is pathology correlated and addressed by different types or classes of cytokines? Which one is the cytokinome development during the disease? Answers to these and other questions are essential not only to be able to describe the cytokinome dynamics during the progression of chronic inflammatory diseases but, above all, to try to predict in large advance the prognosis of the disease. If this will be possible, we will be able to intervene with great advance in the early stages of the disease with much

more chance of healing or of extending the duration and the expectations of life.

 Evaluation of cytokines in patients with chronic HCV or with HCV-related cirrhosis The need of cytokinome data mining system for a predictive medicine for chronic

CXCL10 and CXCL11 chemokines and their membrane receptor CXCR3

**2. Role of the cytokines in chronic inflammatory diseases and cancers** 

The need for structural studies of cytokine/receptor complex: the example of CXCL9,

Inflammation is a physiologic process in response to acute tissue damage resulting from physical injury, ischemic injury, infection, exposure to toxins, chemical irritation, and/or wounding or other types of trauma (Lu et al., 2006; Philip et al., 2004); it is a protective attempt by the organism to remove the injurious stimuli as well as initiate the healing process for the tissue. At the very early stage of inflammation, the phagocytic cells are mainly involved: neutrophils are the first cells to migrate to the inflammatory sites under the regulation of molecules produced by rapidly responding macrophages and mast cells prestationed in tissues (Coussens & Werb, 2002). As the inflammation progresses, various

Role of the cytokines in chronic inflammatory diseases and cancers

 Hepatocarcinoma as an example of chronic inflammatory disease Metabolic pathway analysis of significant genes in hepatoma cells Evaluation of cytokines in HCC patients with HCV-related cirrhosis

Challenge and significance of the cytokinome profile

as "cytokinome" (Costantini et al., 2010b).

Therefore, the review will focus on:

inflammatory diseases

types of leukocytes, lymphocytes, and other inflammatory cells are activated and attracted to the inflamed site by a signaling network involving a great number of growth factors, cytokines, and chemokines (Coussens & Werb, 2002). All cells recruited to the inflammatory site contribute to tissue breakdown and are beneficial by strengthening and maintaining the defense against infection (Coussens & Werb, 2002). The resolution of inflammation also requires a rapid programmed clearance of inflammatory cells: neighboring macrophages, dendritic cells, and backup phagocytes do this job by inducing apoptosis and conducting phagocytosis (Savill et al., 2002).

However, inflammation may become chronic either because an inflammatory stimulus persists or because of dysregulation in the control mechanisms that normally turn the process off. Recently, it has been suggested that inflammation associated with cancer is similar to that seen with chronic inflammation, which includes the production of growth and angiogenic factors that stimulate tissue repair, factors that can also promote cancer-cell survival, implantation, and growth (Philip et al., 2004; Macarthur et al., 2004; Balkwill and Mantovani, 2001). Interestingly, inflammation functions at all three stages of tumor development: initiation, progression and metastasis.

Since many cancers arise from sites of infection, chronic irritation, and inflammation, it is now clear that the tumor microenvironment, which is largely orchestrated by inflammatory cells and cytokines (Fig. 1), is an indispensable participant in the neoplastic process altering not only the metabolic needs of the tissue, but also fostering DNA and protein damage, proliferation, survival, mutagenesis, migration and metastasis of malignant cells (Allavena et al., 2008). Indeed all tumors in the presence of stromal and infiltrating inflammatory cells are facilitated and helped to maintain these metastatic processes. Leukocytes, lymphocytes and other inflammatory cells are activated in this process and attracted to the inflamed site. Inflammation contributes to initiation by inducing the release of a variety of pro-inflammatory cytokines and chemokines and inflammatory enzymes as cyclo-oxygenases that alert the vasculature to release inflammatory cells and factors into the tissue milieu, thereby causing oxidative damage, DNA mutations, and other changes in the microenvironment, making it more conducive to cell transformation, increased survival and proliferation (Germano et al., 2008). We must not forget that many cytokines and chemokines are inducible by hypoxia which is a major physiological difference (Mancino et al., 2008). An important aspect of the tumor microenvironment is the cytokine mediated communication between the tumor and cells. Cytokines and chemokines have many activities that permit cell–cell communication locally at the tissue, with the outcome determined by cytokine concentration milieu and cell type (Germano et al., 2008). Current thinking is that activated immune cells provide both anti- and protumorigenic signals, thus representing targets to be harnessed or attacked for therapeutic advantage depending upon environmental and/or cellular context. Because the control of cytokine production is highly complex and multifactorial, the effects of cytokines are mediated through multiple regulatory networks. The intricate complexity of both cytokine networks clearly conceals the role that a single cytokine may play in the pathogenesis of the disease. It is therefore informative to investigate the immunopathogenesis of a disease process by analyzing multiple cytokines. In this way it is possible to provide a better understanding of the role of cellular, humoral and chemotactic immunity at a critical time in some cancer diseases and also in the treatment course of a correlated infection (Costantini et al., 2009).

The Value of the Cytokinome Profile 107

In this complex interactions network, Systems Biology and/or Biologically Integrated Approaches are powerful tools to analyze as a whole, the enormous amount of data coming from the so-called "omics" disciplines (genomics, transcriptomics, proteomics) by computational methods and algorithms, in order to create an information body that allows us to have a comprehensive and integrated vision of the biological phenomenon under investigation. In fact, until the last century, the approach of biological science was to break down the object of study in its elementary parts and to study all the singular units in order to explain the life processes. This was a typical analytical and reductionist procedure, which allowed the understanding of almost all properties of molecular parts of living organisms, such as genes, proteins, metabolites, and was focused on the study of each single component of the system under consideration but was not able to predict the behavior of the systems as a whole. A system can be defined as a number of interacting elements existing within a boundary that is surrounded by an environment. Therefore, a complex system is able to create new properties from the interactions between its components, and also to interact and to respond with the external inputs. When the interactions between the parties are determined by the dynamical processes inducing the emerging properties like adaptability, self-organization and the ability to respond under disturbance, the system becomes complex. In this way these non linear interactions allow a number of possible several states and new emergent behaviors are not predictable from the simple sum of the component parts. These principles were applied to study the living organisms, the stock markets, the ecosystems and the flock of birds. In biology it's necessary to study the living organism as a whole, and the laws of regarding the organizational forces of systems, which yet are not well known, but are essential to solve and to understand the collective phenomena and the

framework for the functionality of the systems (Costantini et al., 2008).

prognostic and diagnostic tool for clinicians.

treatments are inadeguate (Altekruse et al., 2009).

Therefore, all the data related to the cytokine evaluations can be analyzed and modeled computationally by using graphs or networks connecting the various data groups (related to gene and protein expression obtained by microarrays and by multiplex biometric ELISAbased immunoassay) in terms of dynamic probabilistic maps of metabolic and/or physiological activities and/or pathogenetic pathways. Hence, the definition and evaluation of a human cytokinome is an important future tool to analyze the interaction network of cytokines both in healthy individuals and in patients affected from a cancer. Using these computational models it will be easier and immediate to understand and investigate how the regression of a chronic inflammation process, by acting on the cellular populations of cytokines, can block the progression of the cancer and how this knowledge can be an useful

**4. Hepatocellular carcinoma as an example of chronic inflammatory disease**  Hepatocellular carcinoma (HCC) accounts for >5% of all human cancers and for 80% - 90% of primary liver cancer. It is a major health problem worldwide being the fifth most common malignancy in men and the eighth in women; the third most common cause of cancer-related death in the world. Moreover early diagnosis is uncommom and medical

Yearly 550,000 people worldwide die for HCC, with a 2:1 ratio for men versus women. Its incidence is increasing dramatically, with marked variations among geographic areas (Jemal et al., 2007), racial and ethnic groups, environmental risk factors. The estimated annual number of HCC cases exceeds 700,000, with a mean annual incidence of 3-4% (Jemal et al.,

Fig. 1. Relationship between inflammation, cytokines and cancer

#### **3. Challenge and significance of the cytokinome profile**

In order to understand the whole universe of human cytokines, the socalled cytokinome, according the "omics" system of definition, it needs to evaluate these proteins and analyse their complex network of interactions by which they regulate their own synthesis or that of their receptors, and antagonize or synergize with each other in many and often redundant ways (Costantini et al., 2010b).

A major effort is the achievement of an efficient database that can collect together correct ontologies, algorithms and tools of analyses, structural and "omics" data of cytokines and their receptors, metabolic pathways, and the whole interactome. Another intriguing problem related to the cytokine family and their receptors is the pleiotropy existing in the cytokine system, where one cytokine is able to activate various receptors and many different cytokines activate the same receptor. When the frame of the whole cytokine network will be known, we will have the possibility to create best and more efficient drugs against the cancer, most probably able to interact with the receptors rather than directly with the cytokine molecules because of their pleiotropic effect. Another element of complexity in the cytokine network is introduced also by the fact that some genes encoding cytokines can give rise to variant forms of cytokines (isoforms) by means of alternative splicing, yielding molecules with slight structural differences but biologically significant changes of activities. This explains why it is always useful to analyze the gene expression profile correlated to the cytokines. In fact, previous studies have identified important mutations in some cancers, but they were primarily focused on a limited set of genes and, thus, provided a constrained view of the mutational spectrum.

However, a correct and comprehensive understanding of cytokine functions can be obtained from simultaneous and coherent measurements of the serum concentrations of cytokines. This point raises the inherent difficulty of a simultaneous measurement of the cytokine concentrations to obtain correct internal ratios among the various molecules present in the same biological fluid due to the often large difference in concentrations spanning several magnitude orders. At present, it is possible to effectively characterize the serum levels of cytokines using a broad-spectrum bead based multiplex immunoassay.

In order to understand the whole universe of human cytokines, the socalled cytokinome, according the "omics" system of definition, it needs to evaluate these proteins and analyse their complex network of interactions by which they regulate their own synthesis or that of their receptors, and antagonize or synergize with each other in many and often redundant

A major effort is the achievement of an efficient database that can collect together correct ontologies, algorithms and tools of analyses, structural and "omics" data of cytokines and their receptors, metabolic pathways, and the whole interactome. Another intriguing problem related to the cytokine family and their receptors is the pleiotropy existing in the cytokine system, where one cytokine is able to activate various receptors and many different cytokines activate the same receptor. When the frame of the whole cytokine network will be known, we will have the possibility to create best and more efficient drugs against the cancer, most probably able to interact with the receptors rather than directly with the cytokine molecules because of their pleiotropic effect. Another element of complexity in the cytokine network is introduced also by the fact that some genes encoding cytokines can give rise to variant forms of cytokines (isoforms) by means of alternative splicing, yielding molecules with slight structural differences but biologically significant changes of activities. This explains why it is always useful to analyze the gene expression profile correlated to the cytokines. In fact, previous studies have identified important mutations in some cancers, but they were primarily focused on a limited set of genes and, thus, provided a constrained

However, a correct and comprehensive understanding of cytokine functions can be obtained from simultaneous and coherent measurements of the serum concentrations of cytokines. This point raises the inherent difficulty of a simultaneous measurement of the cytokine concentrations to obtain correct internal ratios among the various molecules present in the same biological fluid due to the often large difference in concentrations spanning several magnitude orders. At present, it is possible to effectively characterize the serum levels of

cytokines using a broad-spectrum bead based multiplex immunoassay.

Fig. 1. Relationship between inflammation, cytokines and cancer

**3. Challenge and significance of the cytokinome profile** 

ways (Costantini et al., 2010b).

view of the mutational spectrum.

In this complex interactions network, Systems Biology and/or Biologically Integrated Approaches are powerful tools to analyze as a whole, the enormous amount of data coming from the so-called "omics" disciplines (genomics, transcriptomics, proteomics) by computational methods and algorithms, in order to create an information body that allows us to have a comprehensive and integrated vision of the biological phenomenon under investigation. In fact, until the last century, the approach of biological science was to break down the object of study in its elementary parts and to study all the singular units in order to explain the life processes. This was a typical analytical and reductionist procedure, which allowed the understanding of almost all properties of molecular parts of living organisms, such as genes, proteins, metabolites, and was focused on the study of each single component of the system under consideration but was not able to predict the behavior of the systems as a whole. A system can be defined as a number of interacting elements existing within a boundary that is surrounded by an environment. Therefore, a complex system is able to create new properties from the interactions between its components, and also to interact and to respond with the external inputs. When the interactions between the parties are determined by the dynamical processes inducing the emerging properties like adaptability, self-organization and the ability to respond under disturbance, the system becomes complex. In this way these non linear interactions allow a number of possible several states and new emergent behaviors are not predictable from the simple sum of the component parts. These principles were applied to study the living organisms, the stock markets, the ecosystems and the flock of birds. In biology it's necessary to study the living organism as a whole, and the laws of regarding the organizational forces of systems, which yet are not well known, but are essential to solve and to understand the collective phenomena and the framework for the functionality of the systems (Costantini et al., 2008).

Therefore, all the data related to the cytokine evaluations can be analyzed and modeled computationally by using graphs or networks connecting the various data groups (related to gene and protein expression obtained by microarrays and by multiplex biometric ELISAbased immunoassay) in terms of dynamic probabilistic maps of metabolic and/or physiological activities and/or pathogenetic pathways. Hence, the definition and evaluation of a human cytokinome is an important future tool to analyze the interaction network of cytokines both in healthy individuals and in patients affected from a cancer. Using these computational models it will be easier and immediate to understand and investigate how the regression of a chronic inflammation process, by acting on the cellular populations of cytokines, can block the progression of the cancer and how this knowledge can be an useful prognostic and diagnostic tool for clinicians.

#### **4. Hepatocellular carcinoma as an example of chronic inflammatory disease**

Hepatocellular carcinoma (HCC) accounts for >5% of all human cancers and for 80% - 90% of primary liver cancer. It is a major health problem worldwide being the fifth most common malignancy in men and the eighth in women; the third most common cause of cancer-related death in the world. Moreover early diagnosis is uncommom and medical treatments are inadeguate (Altekruse et al., 2009).

Yearly 550,000 people worldwide die for HCC, with a 2:1 ratio for men versus women. Its incidence is increasing dramatically, with marked variations among geographic areas (Jemal et al., 2007), racial and ethnic groups, environmental risk factors. The estimated annual number of HCC cases exceeds 700,000, with a mean annual incidence of 3-4% (Jemal et al.,

The Value of the Cytokinome Profile 109

et al., 2010b). Increased Treg cells were found in peripheral blood of HCV-infected patients (Boettler et al., 2005) as well as in the tumor microenvironment of HCC patients (Ormandi et al., 2005). The frequency of naturally arising CD4+CD25high+ Tregs in the periphery of HCVinfected patients was reported to be higher than that in patients who resolved the infection or uninfected controls (Cabrera et al., 2004). TH1 cytokines are generally up-regulated in patients with HCC, resulting in higher levels of pro-inflammatory cytokines, as IL-1, IL-15, IL-18, TNF-, TNF-Rs, TNF-RI, TNF-RII, and IL-6 in comparison with healthy individuals (Huang et al., 1999). However, the intra/peri-tumoral cytokines levels are often different from the serum levels (Budhu & Wang, 2006). Higher serum IL-6 level was an independent risk factor for HCC development in female but not male chronic hepatitis C patients (Nakagawa et al., 2009). IL-10 was highly expressed in HCC tumors and serum, correlating with disease progression (Budhu & Wang, 2006). Budhu and Wang reviewed the association between cytokine abnormalities and HCC patients and found that a dominant TH2-like cytokine profile (IL-4, IL-8, IL-10, and IL-5) and a decrease in the TH1-like cytokines (IL-1, IL-1, IL-2, IL-12p35, IL-12p40, IL-15, TNF-, and IFN- was associated with the metastatic phenotype of disease (Budhu & Wang, 2006). Thus, it has been hypothesized that TH1 cytokines are involved in tumor development, whereas TH2 cytokines in tumor progression. Recently the cytokine concentrations have been evaluated

in patients with HCC patients with HCV-related cirrhosis (Capone et al., 2010).

**5. Metabolic pathway analysis of significant genes in hepatoma cells** 

The cytokinome study is an important step to our understanding of chronic inflammatory diseases because a global and dynamic knowledge of cellular signaling, at moment only static and mechanistic, will improve our ability to adequately read and understand the

Fig. 2. Evolution from HCV infection to HCC.

2007). Most HCC cases (>80%) occur in either sub-Saharan Africa or in Eastern Asia (China alone accounts for more than 50% of the world's cases) (Jemal et al., 2007). In the United States (US) HCC incidence is lower than other countries (0.3/100000) even if there has been a significant and alarming increase in the incidence of HCC in the US, from 1.3 in the late 70s' to 3 in the late 90s', due to HCV infection. In 2008, 21370 new cases of HCC and intrahepatic bile duct cancer were estimated with 18410 deaths (Jemal et al., 2007). In Europe, Oceania and America, chronic hepatitis C and alcoholic cirrhosis are the main risk factors for HCC. The main risk factor for HCC development in patients with hepatitis C is the presence of cirrhosis. Among patients with hepatitis C and cirrhosis, the annual incidence rate of HCC ranges between 1-8%, being higher in Japan (4-8%) intermediate in Italy (2-4%) and lower in USA (1.4%) (Fassio, 2010). Analysis of mortality from HCC in Europe confirmed large variability, with high rates in France (6.79/100000) and Italy (6.72/100000) due to hepatitis C virus (HCV) during the 1960s and 1970s (Bosetti et al., 2008). Southern Italy has the highest rates of HCC in Europe (Fusco et al., 2008).

HCC is unique among cancers occurring mostly in patients with a known risk factor: ninety percent of HCCs develop in the context of chronic liver inflammation and cirrhosis (Altekruse et al., 2009). Hepatitis B (HBV) and C (HCV) viruses are the major cause of liver disease worldwide. Fortunately, the hepatitis B virus vaccine has resulted in a substantial decline in the number of new cases of acute hepatitis B among children, adolescents, and adults in western countries since the mid-1980s. This success is not duplicable for HCV where active or passive vaccination is not available yet. Therefore, the present and next future HCC history will be mainly related to HCV infection. The incidence of HCV infection is hard to quantify since it is often asymptomatic. The World Health Organization estimates that 3% of the world's population - more than 170 million people - are chronically infected (3-4 million new infections every year). Therefore, a tremendous number of people are currently at elevated risk for HCC and its early diagnosis (when surgical intervention is possible) may significantly affect the patients prognosis (Ryder, 2003).

However it is possible also a direct carcinogenesis by hepatitis viruses, without a cirrhotic step (Nash et al., 2010). In particular, it was reported that patients without cirrhosis were younger, survived longer than patients with cirrhosis (P < 0.0001) and had a better 5-year survival experience (Chiesa et al., 2000).

In contrast to HBV, HCV does not integrate into the host genome and does not contain a reverse transcriptase. In particular, in the infected subjects both viruses trigger an immunemediated inflammatory response (hepatitis) that either clears the infection or slowly destroys the liver (Bowen & Walker, 2005).

Effective HCV immunity is limited by the high variability of virion genome; HCV virions turn over rapidly (with a half-life of about 3 h), and up to about 1012 complete viruses are produced per day in an infected person (Ueno et al., 2009). About 80% of newly infected patients develop chronic infection; an estimated 10% to 20% will develop cirrhosis and 1-5% proceeds to end-stage liver cancer over a period of 20 to 30 years (Fig. 2). In the case of HCV, HCC is invariably observed as a complication of cirrhosis, whereas in the case of HBV HCC is often found in non-cirrhotic liver. Therefore, the hepatic fibrosis dramatically increase the incidence of HCC (Castello et al., 2010a).

Many studies were conducted in the last years in regard to anti-HCV immune response. In fact, much attention has recently focused on regulatory T cells (Tregs) being able to secrete inhibitory cytokines such as IL-10 or TGF-, even if their contribution is yet unclear (Castello

2007). Most HCC cases (>80%) occur in either sub-Saharan Africa or in Eastern Asia (China alone accounts for more than 50% of the world's cases) (Jemal et al., 2007). In the United States (US) HCC incidence is lower than other countries (0.3/100000) even if there has been a significant and alarming increase in the incidence of HCC in the US, from 1.3 in the late 70s' to 3 in the late 90s', due to HCV infection. In 2008, 21370 new cases of HCC and intrahepatic bile duct cancer were estimated with 18410 deaths (Jemal et al., 2007). In Europe, Oceania and America, chronic hepatitis C and alcoholic cirrhosis are the main risk factors for HCC. The main risk factor for HCC development in patients with hepatitis C is the presence of cirrhosis. Among patients with hepatitis C and cirrhosis, the annual incidence rate of HCC ranges between 1-8%, being higher in Japan (4-8%) intermediate in Italy (2-4%) and lower in USA (1.4%) (Fassio, 2010). Analysis of mortality from HCC in Europe confirmed large variability, with high rates in France (6.79/100000) and Italy (6.72/100000) due to hepatitis C virus (HCV) during the 1960s and 1970s (Bosetti et al.,

2008). Southern Italy has the highest rates of HCC in Europe (Fusco et al., 2008).

possible) may significantly affect the patients prognosis (Ryder, 2003).

survival experience (Chiesa et al., 2000).

destroys the liver (Bowen & Walker, 2005).

incidence of HCC (Castello et al., 2010a).

HCC is unique among cancers occurring mostly in patients with a known risk factor: ninety percent of HCCs develop in the context of chronic liver inflammation and cirrhosis (Altekruse et al., 2009). Hepatitis B (HBV) and C (HCV) viruses are the major cause of liver disease worldwide. Fortunately, the hepatitis B virus vaccine has resulted in a substantial decline in the number of new cases of acute hepatitis B among children, adolescents, and adults in western countries since the mid-1980s. This success is not duplicable for HCV where active or passive vaccination is not available yet. Therefore, the present and next future HCC history will be mainly related to HCV infection. The incidence of HCV infection is hard to quantify since it is often asymptomatic. The World Health Organization estimates that 3% of the world's population - more than 170 million people - are chronically infected (3-4 million new infections every year). Therefore, a tremendous number of people are currently at elevated risk for HCC and its early diagnosis (when surgical intervention is

However it is possible also a direct carcinogenesis by hepatitis viruses, without a cirrhotic step (Nash et al., 2010). In particular, it was reported that patients without cirrhosis were younger, survived longer than patients with cirrhosis (P < 0.0001) and had a better 5-year

In contrast to HBV, HCV does not integrate into the host genome and does not contain a reverse transcriptase. In particular, in the infected subjects both viruses trigger an immunemediated inflammatory response (hepatitis) that either clears the infection or slowly

Effective HCV immunity is limited by the high variability of virion genome; HCV virions turn over rapidly (with a half-life of about 3 h), and up to about 1012 complete viruses are produced per day in an infected person (Ueno et al., 2009). About 80% of newly infected patients develop chronic infection; an estimated 10% to 20% will develop cirrhosis and 1-5% proceeds to end-stage liver cancer over a period of 20 to 30 years (Fig. 2). In the case of HCV, HCC is invariably observed as a complication of cirrhosis, whereas in the case of HBV HCC is often found in non-cirrhotic liver. Therefore, the hepatic fibrosis dramatically increase the

Many studies were conducted in the last years in regard to anti-HCV immune response. In fact, much attention has recently focused on regulatory T cells (Tregs) being able to secrete inhibitory cytokines such as IL-10 or TGF-, even if their contribution is yet unclear (Castello et al., 2010b). Increased Treg cells were found in peripheral blood of HCV-infected patients (Boettler et al., 2005) as well as in the tumor microenvironment of HCC patients (Ormandi et al., 2005). The frequency of naturally arising CD4+CD25high+ Tregs in the periphery of HCVinfected patients was reported to be higher than that in patients who resolved the infection or uninfected controls (Cabrera et al., 2004). TH1 cytokines are generally up-regulated in patients with HCC, resulting in higher levels of pro-inflammatory cytokines, as IL-1, IL-15, IL-18, TNF-, TNF-Rs, TNF-RI, TNF-RII, and IL-6 in comparison with healthy individuals (Huang et al., 1999). However, the intra/peri-tumoral cytokines levels are often different from the serum levels (Budhu & Wang, 2006). Higher serum IL-6 level was an independent risk factor for HCC development in female but not male chronic hepatitis C patients (Nakagawa et al., 2009). IL-10 was highly expressed in HCC tumors and serum, correlating with disease progression (Budhu & Wang, 2006). Budhu and Wang reviewed the association between cytokine abnormalities and HCC patients and found that a dominant TH2-like cytokine profile (IL-4, IL-8, IL-10, and IL-5) and a decrease in the TH1-like cytokines (IL-1, IL-1, IL-2, IL-12p35, IL-12p40, IL-15, TNF-, and IFN- was associated with the metastatic phenotype of disease (Budhu & Wang, 2006). Thus, it has been hypothesized that TH1 cytokines are involved in tumor development, whereas TH2 cytokines in tumor progression. Recently the cytokine concentrations have been evaluated in patients with HCC patients with HCV-related cirrhosis (Capone et al., 2010).

Fig. 2. Evolution from HCV infection to HCC.

#### **5. Metabolic pathway analysis of significant genes in hepatoma cells**

The cytokinome study is an important step to our understanding of chronic inflammatory diseases because a global and dynamic knowledge of cellular signaling, at moment only static and mechanistic, will improve our ability to adequately read and understand the

The Value of the Cytokinome Profile 111

(Kyoto encyclopedia of genes) (Ogata et al 1999). In particular, CCL20 interacts with VCAN gene (Fig. 3) which encodes for the Versican protein that plays a role in angiogenesis, inflammation. This protein prevents the growth of cancerous tumors and regulates the activity of several growth factors, which control a diverse range of processes important for cell growth. Moreover elevated levels of Versican have been reported in most malignancies, including brain tumors, melanomas, lymphomas and breast cancers, prostate, colon, lung, pancreas, endometrium, and ovary (Miranda et al., 2011; Kusumoto et al., 2010). CCR6 and CCL20 are known to interact with Versican in the HCC network. This protein was found to be upregulated in HCC patients correlated to Hepatitis B Virus and was indicated as a possible candidate for mediating tumor progression and proliferation in liver and more importantly visual impairment (Paraneoplastic syndrome) associated with HCC patients

Fig. 3. VCAN Network where the genes are shown by circle and proteins by stars. Different colors: red, up-regulated one; light green, down-regulated one; blue, bidirection-regulated one; grey, not high confident HCC-related one; dark green, genes with selected function

The CXCR4/CXCL12 axis is up-regulated in HCC and participates in HCC cell proliferation. Upon interaction with SDC4 (Syndecan 4), being a heparan sulfate proteoglycans, these proteins function as key regulators of cell signaling via their interactions with multiple growth and angiogenic factors, and promote an aggressive tumor phenotype (Sanderson et al, 2010). CXCL12/SDC4 makes a complex with O Phospho L Tyrosine and upon complex formation it can be hypothesized that it provides stability to p53 protein avoiding cancerous sitations as shown in various cancer cell lines like cervical cancer and lung cancer Renal and

info; purple, genes with selected pathway info.

correlated to HBV.

meaning in the early time of the information that cells exchange each other. The present technology supports this view from an experimental point of view by DNA and Protein microarrays. These techniques taken together can support a better knowledge of the relationships existing between genome behavior and related phenotypes (cytokines in this case) during the progression of a disease. More difficult is to correlate genomic and phenotypic levels by a logical analysis able to correlate the genes action with their products to extract useful biological information on the disease progression, such as changes in metabolic pathways or activation of new metabolic paths. In general, the knowledge of early metabolic changes during the first stages of an illness is an important moment to develop new more specific drugs by pharmacogenomics as well as to operate a metabolic repair by nutrigenomics. Probably this holistic view of the medicine is perhaps more expensive for the community but it is necessary to efficiently fight the numerous diseases of our time unfortunately founded on chronic inflammation.

The global gene expression has been evaluated in HepG2 cells in comparison to normal human hepatocytes using Illumina microarray. In particular, cRNAs were hybridized to the HumanWG-6 Bead-Chip array which allows to assess the presence of more than 48,000 transcripts (Whole Genome). Our metabolic pathway analysis aims at discovering modifications in and/or activation of new metabolic pathways involved into a perturbation of the hepatoma cell homeostasis. We have used a cluster analysis or "clustering" that is the assignment of a set of data or observations into subsets (called clusters) so that observations in the same cluster are similar. This is a method of analysis used also in bioinformatics to evaluate high-throughput genotyping platforms to build groups of genes with related expression patterns (also known as coexpressed genes). The algorithms we have used are hierarchical algorithms that find successive clusters using previously established clusters and create a hierarchy of clusters which may be represented by a hierarchical clustering dendrogram. The method builds the hierarchy from the individual elements by progressively merging clusters. In particular, hierarchical cluster analysis of genes showed the differential expression of genes in HepG2 cells respect to human hepatocytes used as healthy controls. 2646 genes were significantly down-regulated in HepG2 cells respect to the hepatocytes whereas a further 3586 genes were significantly up-regulated. Moreover, information on the biological functions of the genes that were significantly regulated was obtained by a pathway analysis. Pathways related to these genes were extracted from KEGG (Kyoto Encyclopedia of Genes and Genomes) (Ogata et al 1999), Pathway Interaction Database, and network for CXCL12 in Hepatocellular carcinoma is derived from the HCCnet (Hepatocellular carcinoma network database) (Bing et al, 2010), which contains around 37811 protein-protein interactions from 13 individual datasets having 894 HCC samples containing 30.5%, 44.7 % and 18.7% of HCV infected, HBV infected and unknown factors respectively. Indeed a network of transcription factors that are extracted from microarray data and interact with EGFR gene is constructed by Cytoscape.

Amongst the significantly up-regulated and down-regulated genes several chemokines and some transcription factors (CCL20, CCR6, CX3CL1, Grb2, p53, VCAN, C-MYC, CXCL12, SDC4 and Cyclin D1) were found. CCR6, being the receptor for the chemokine CCL20, is expressed on inactivated memory T-cells, on some dendritic cells and also on Th17 cells. Some studies suggest the involvement of the CCL20/CCR6 system in the carcinogenesis and progression of human HCC (Rubie et al., 2006). CCR6 is implicated in the Chemokine signaling pathway and cytokine–cytokine receptor interaction as obtained from Kegg

meaning in the early time of the information that cells exchange each other. The present technology supports this view from an experimental point of view by DNA and Protein microarrays. These techniques taken together can support a better knowledge of the relationships existing between genome behavior and related phenotypes (cytokines in this case) during the progression of a disease. More difficult is to correlate genomic and phenotypic levels by a logical analysis able to correlate the genes action with their products to extract useful biological information on the disease progression, such as changes in metabolic pathways or activation of new metabolic paths. In general, the knowledge of early metabolic changes during the first stages of an illness is an important moment to develop new more specific drugs by pharmacogenomics as well as to operate a metabolic repair by nutrigenomics. Probably this holistic view of the medicine is perhaps more expensive for the community but it is necessary to efficiently fight the numerous diseases of our time

The global gene expression has been evaluated in HepG2 cells in comparison to normal human hepatocytes using Illumina microarray. In particular, cRNAs were hybridized to the HumanWG-6 Bead-Chip array which allows to assess the presence of more than 48,000 transcripts (Whole Genome). Our metabolic pathway analysis aims at discovering modifications in and/or activation of new metabolic pathways involved into a perturbation of the hepatoma cell homeostasis. We have used a cluster analysis or "clustering" that is the assignment of a set of data or observations into subsets (called clusters) so that observations in the same cluster are similar. This is a method of analysis used also in bioinformatics to evaluate high-throughput genotyping platforms to build groups of genes with related expression patterns (also known as coexpressed genes). The algorithms we have used are hierarchical algorithms that find successive clusters using previously established clusters and create a hierarchy of clusters which may be represented by a hierarchical clustering dendrogram. The method builds the hierarchy from the individual elements by progressively merging clusters. In particular, hierarchical cluster analysis of genes showed the differential expression of genes in HepG2 cells respect to human hepatocytes used as healthy controls. 2646 genes were significantly down-regulated in HepG2 cells respect to the hepatocytes whereas a further 3586 genes were significantly up-regulated. Moreover, information on the biological functions of the genes that were significantly regulated was obtained by a pathway analysis. Pathways related to these genes were extracted from KEGG (Kyoto Encyclopedia of Genes and Genomes) (Ogata et al 1999), Pathway Interaction Database, and network for CXCL12 in Hepatocellular carcinoma is derived from the HCCnet (Hepatocellular carcinoma network database) (Bing et al, 2010), which contains around 37811 protein-protein interactions from 13 individual datasets having 894 HCC samples containing 30.5%, 44.7 % and 18.7% of HCV infected, HBV infected and unknown factors respectively. Indeed a network of transcription factors that are extracted from

microarray data and interact with EGFR gene is constructed by Cytoscape.

Amongst the significantly up-regulated and down-regulated genes several chemokines and some transcription factors (CCL20, CCR6, CX3CL1, Grb2, p53, VCAN, C-MYC, CXCL12, SDC4 and Cyclin D1) were found. CCR6, being the receptor for the chemokine CCL20, is expressed on inactivated memory T-cells, on some dendritic cells and also on Th17 cells. Some studies suggest the involvement of the CCL20/CCR6 system in the carcinogenesis and progression of human HCC (Rubie et al., 2006). CCR6 is implicated in the Chemokine signaling pathway and cytokine–cytokine receptor interaction as obtained from Kegg

unfortunately founded on chronic inflammation.

(Kyoto encyclopedia of genes) (Ogata et al 1999). In particular, CCL20 interacts with VCAN gene (Fig. 3) which encodes for the Versican protein that plays a role in angiogenesis, inflammation. This protein prevents the growth of cancerous tumors and regulates the activity of several growth factors, which control a diverse range of processes important for cell growth. Moreover elevated levels of Versican have been reported in most malignancies, including brain tumors, melanomas, lymphomas and breast cancers, prostate, colon, lung, pancreas, endometrium, and ovary (Miranda et al., 2011; Kusumoto et al., 2010). CCR6 and CCL20 are known to interact with Versican in the HCC network. This protein was found to be upregulated in HCC patients correlated to Hepatitis B Virus and was indicated as a possible candidate for mediating tumor progression and proliferation in liver and more importantly visual impairment (Paraneoplastic syndrome) associated with HCC patients correlated to HBV.

Fig. 3. VCAN Network where the genes are shown by circle and proteins by stars. Different colors: red, up-regulated one; light green, down-regulated one; blue, bidirection-regulated one; grey, not high confident HCC-related one; dark green, genes with selected function info; purple, genes with selected pathway info.

The CXCR4/CXCL12 axis is up-regulated in HCC and participates in HCC cell proliferation. Upon interaction with SDC4 (Syndecan 4), being a heparan sulfate proteoglycans, these proteins function as key regulators of cell signaling via their interactions with multiple growth and angiogenic factors, and promote an aggressive tumor phenotype (Sanderson et al, 2010). CXCL12/SDC4 makes a complex with O Phospho L Tyrosine and upon complex formation it can be hypothesized that it provides stability to p53 protein avoiding cancerous sitations as shown in various cancer cell lines like cervical cancer and lung cancer Renal and

The Value of the Cytokinome Profile 113

bearing cells to tumor site either providing antitumor responses or either pathogenic angiogenesis (Deleterious effects) (Turner et al, 2010). When CX3CL1 is expressed in the tumor, it appears to recruit cytotoxic T cells and NK cells to the tumor site and its expression level is found to correlate with the density of Tumor Infiltrating Lymphocytes

In addition to these chemokines, Grb2 is also one of the most important upregulated proteins in HCC that was found to be functioning in number of pathways involved in cancer. In particular, Grb2 recruits SOS (exchange protein) for the activating RAS that operates as a molecular switch between MEK and ERK (MAPK) which in nucleus acts on numerous important transcriptions factors like STAT 3 and the expression of STAT3 regulates genes including BCL-x1, CYCLIN D1 and c-MYC which involve in cell apoptosis and cell cycle progression (Sun et al.,2008). In IL6 mediated signaling events, GRB2 interacts with some proteins like FOS and JUN, that are both down regulated in HCC and importantly transcriptional activity of JUN is attenuated and sometimes antagonized by JUNB. This activation takes place in chemically induced murine liver tumours and HCCs of humans, suggesting oncogenic function for this gene in liver tumors of mammals with HSP70 that exhibits regulatory functions of c-JUN, ERK and the JNK pathway, thus inhibiting cell apoptosis (Lee et al., 2005). Moreover, Grb2 plays a specific role in EGFstimulated EGFR internalization (e.g. receptor sorting, vesicle budding/pinching or vesicle

From KEGG, other two pathways are found to be deregulated in HCC metastasis, i.e. P53 and MAPK pathways. TP53 plays an important role on regulation of apoptosis and cell cycle arrest and external environment factors or agents are implicated in the development of HCC in correlation with P53, including nutrition, diabetes, oral infection, oral contraceptive, alcohol consumption and some trace elements such as Selenium (Irmak et al., 2003; Wei et al., 2001). A second pathway, which is deregulated in metastatis, is MAPK pathway that is considered to control the most of the activities related to HCC condition by activating around 90 transcription factors although tyrosine kinase inhibitor Sorafenib is used as potential inhibitor of MAPK pathway by inhibiting RAF in HCC and Renal carcinoma

**6. Evaluation of cytokines in HCC patients with HCV-related cirrhosis** 

The serum levels of 50 different cytokines, chemokines and growth factors were evaluated in patients affected by HCC with chronic HCV-related hepatitis and liver cirrhosis using multiplex biometric ELISA-based immunoassay (Capone et al., 2010). The HCC patients showed a different secretion profile of these proteins compared to healthy controls. Greater amounts of IL-1α, IL-3, IL-12p40, IL-6, IL-8, IL-10, CCL27, CXCL10, CXCL1, IFN-α2, M-CSF, GM-CSF, CXCL9, β-NGF, SCF, SCGF-β, CXCL12, TNF-β were secreted by the HCC patients. No correlation was observed between serum levels and patients age/gender or between patients with a solitary tumour and those with multiple tumours. In particular, the attention was focused only on the proinflammatory molecules (IL-1α, IL-6, IL-8, IL-12p40, GM-CSF, CCL27, CXCL1, CXCL9, CXCL10, CXCL12, β-NGF) that were found to be significantly increased in HCC patients compared to healthy controls. The significantly increased serum levels of IL-6 and IL-8 found in HCC patients agreed with data reported in other studies (Ataseven et al., 2006; Burger et al., 2006). In particular, IL-8 levels measured in HCC patients were found to be increased, and correlated significantly with large tumor size (> 5

(TILs) in some cancers (Ohta et al ,2005).

transport (Yamazaki et al., 2002).

(Cabrera et al., 2011).

Lung cancer (Sanderson et al, 2010). CXCL12 is found to trans-activating Epidermal growth factor receptor (EGFR) (Porcile,C et al ,2005), that is considered as an important signaling hub where different proliferative and survival signals converge. It is highly evident that EGFR has most important roles to play for controlling signaling cascades from extracellular regions. In particular, the interaction network of EGFR with transcription factors can provide much needed insights to multi factor governing HCC. There were found numerous up- and down- regulated transcription factors interacting with EGFR from microarray data (Fig. 4) and they can produce actions in invasion and metastatis state of the cells and induce simulateounously many biological processes to prevent metastatis, invasion and damage to liver cells.

Fig. 4. Interactions of EGFR with de-regulated genes of our microarray data. The downexpressed genes are reported in red, those up-regulated in green and in yellow those no expressed.

Moreover, between the important factors that are governing HCC there is also CX3CL1 (Fractalkine), being a chemokine with both chemoattractant and cell-adhesive functions. Under specific inflammatory conditions, it could regulate the attractions of CX3CR1

Lung cancer (Sanderson et al, 2010). CXCL12 is found to trans-activating Epidermal growth factor receptor (EGFR) (Porcile,C et al ,2005), that is considered as an important signaling hub where different proliferative and survival signals converge. It is highly evident that EGFR has most important roles to play for controlling signaling cascades from extracellular regions. In particular, the interaction network of EGFR with transcription factors can provide much needed insights to multi factor governing HCC. There were found numerous up- and down- regulated transcription factors interacting with EGFR from microarray data (Fig. 4) and they can produce actions in invasion and metastatis state of the cells and induce simulateounously many biological processes to prevent metastatis, invasion and damage to

Fig. 4. Interactions of EGFR with de-regulated genes of our microarray data. The downexpressed genes are reported in red, those up-regulated in green and in yellow those no

Moreover, between the important factors that are governing HCC there is also CX3CL1 (Fractalkine), being a chemokine with both chemoattractant and cell-adhesive functions. Under specific inflammatory conditions, it could regulate the attractions of CX3CR1

liver cells.

expressed.

bearing cells to tumor site either providing antitumor responses or either pathogenic angiogenesis (Deleterious effects) (Turner et al, 2010). When CX3CL1 is expressed in the tumor, it appears to recruit cytotoxic T cells and NK cells to the tumor site and its expression level is found to correlate with the density of Tumor Infiltrating Lymphocytes (TILs) in some cancers (Ohta et al ,2005).

In addition to these chemokines, Grb2 is also one of the most important upregulated proteins in HCC that was found to be functioning in number of pathways involved in cancer. In particular, Grb2 recruits SOS (exchange protein) for the activating RAS that operates as a molecular switch between MEK and ERK (MAPK) which in nucleus acts on numerous important transcriptions factors like STAT 3 and the expression of STAT3 regulates genes including BCL-x1, CYCLIN D1 and c-MYC which involve in cell apoptosis and cell cycle progression (Sun et al.,2008). In IL6 mediated signaling events, GRB2 interacts with some proteins like FOS and JUN, that are both down regulated in HCC and importantly transcriptional activity of JUN is attenuated and sometimes antagonized by JUNB. This activation takes place in chemically induced murine liver tumours and HCCs of humans, suggesting oncogenic function for this gene in liver tumors of mammals with HSP70 that exhibits regulatory functions of c-JUN, ERK and the JNK pathway, thus inhibiting cell apoptosis (Lee et al., 2005). Moreover, Grb2 plays a specific role in EGFstimulated EGFR internalization (e.g. receptor sorting, vesicle budding/pinching or vesicle transport (Yamazaki et al., 2002).

From KEGG, other two pathways are found to be deregulated in HCC metastasis, i.e. P53 and MAPK pathways. TP53 plays an important role on regulation of apoptosis and cell cycle arrest and external environment factors or agents are implicated in the development of HCC in correlation with P53, including nutrition, diabetes, oral infection, oral contraceptive, alcohol consumption and some trace elements such as Selenium (Irmak et al., 2003; Wei et al., 2001). A second pathway, which is deregulated in metastatis, is MAPK pathway that is considered to control the most of the activities related to HCC condition by activating around 90 transcription factors although tyrosine kinase inhibitor Sorafenib is used as potential inhibitor of MAPK pathway by inhibiting RAF in HCC and Renal carcinoma (Cabrera et al., 2011).

#### **6. Evaluation of cytokines in HCC patients with HCV-related cirrhosis**

The serum levels of 50 different cytokines, chemokines and growth factors were evaluated in patients affected by HCC with chronic HCV-related hepatitis and liver cirrhosis using multiplex biometric ELISA-based immunoassay (Capone et al., 2010). The HCC patients showed a different secretion profile of these proteins compared to healthy controls. Greater amounts of IL-1α, IL-3, IL-12p40, IL-6, IL-8, IL-10, CCL27, CXCL10, CXCL1, IFN-α2, M-CSF, GM-CSF, CXCL9, β-NGF, SCF, SCGF-β, CXCL12, TNF-β were secreted by the HCC patients. No correlation was observed between serum levels and patients age/gender or between patients with a solitary tumour and those with multiple tumours. In particular, the attention was focused only on the proinflammatory molecules (IL-1α, IL-6, IL-8, IL-12p40, GM-CSF, CCL27, CXCL1, CXCL9, CXCL10, CXCL12, β-NGF) that were found to be significantly increased in HCC patients compared to healthy controls. The significantly increased serum levels of IL-6 and IL-8 found in HCC patients agreed with data reported in other studies (Ataseven et al., 2006; Burger et al., 2006). In particular, IL-8 levels measured in HCC patients were found to be increased, and correlated significantly with large tumor size (> 5

The Value of the Cytokinome Profile 115

1α, IL-12p40, CCL27, CXCL1 and CXCL12. This was confirmed by the related computational clustering analysis which shows that the molecules cluster in two groups, as demonstrated by branches joining them. In particular, β-NGF was grouped with the proteins indicated above (Fig. 5). Therefore, it is possible to suggest that a panel composed of β-NGF and these

In conclusion, this approach showed that some pro-inflammatory molecules were significantly up-regulated in these patients, and highlighted the complexity of the cytokine network in this disease. Moreover, this suggests the need to monitor these proteins in order to define a profile that could characterize patients with HCC or to help identify useful markers. In fact, this could lead to better definition of the disease state, and to an increased understanding of the relationships between chronic inflammation and

**7. Evaluation of cytokines in patients with chronic HCV or with HCV-related** 

The serum concentrations of a panel of 30 cytokines, chemokines and growth factors were evaluated in patients with chronic inflammation (HC) and liver cirrhosis (LC), and in healthy donors by multiplex biometric ELISA-based assays (Costantini et al., 2010a). The molecules that showed different serum levels in patients respect to healthy controls are

> **HC vs controls LC vs controls IL-1a** 0.0196\* 0.0077\*\* **IL-1b** <0.0001\*\*\* <0.0001\*\*\* **IL-2R** 0.0355\* 0.0053\*\* **IL-6** 0.0032\*\* 0.0024\*\* **IL-8** 0.0004\*\*\* 0.0001\*\*\* **CXCL1** 0.0076\*\* 0.0034\*\* **CXCL9** 0.0004\*\*\* 0.0002\*\*\* **CXCL10** 0.0015\*\* 0.0003\*\*\* **CXCL12** 0.0364\* 0.0443\* **MIF** 0.04\* 0.0033\*\* **b-NGF** 0.0008\*\*\* 0.0002\*\*\* **HGF** 0.0028\*\*

Table 1. P values obtained for all significant molecules in HC and LC patients respect to

Greater amounts of IL-1α, IL-1, IL-2R, IL-6, IL-8, CXCL1, CXCL9, CXCL10, CXCL12, MIF,

In particular, in the chronic inflammation and liver cirrhosis patients the same proteins were increased and the only difference was related to HGF being resulted significant and upregulated only in the patients with liver cirrhosis and not in those with chronic

controls using the nonparametric Mann-Whitney U test.

and -NGF were secreted by both HC and LC patients.

five proteins may be useful for diagnostic/prognostic purposes.

cancer (Capone et al., 2010).

**cirrhosis** 

reported in Table 1.

cm) suggesting that IL-8 may be involved in disease progression and might prove to be both a useful marker of tumor invasiveness and an independent prognostic factor for HCC patients (Burger et al., 2006, Capone et al., 2010).

Fig. 5. Protein hierarchy assessed by a computational clustering analysis. More specifically, the length of branches indicates, in inverse proportion, the similarity of protein concentrations, and the scale of protein intensity is indicated by the different colors: overexpressed cytokines, chemokines and growth factors in red, lower values in grey, and values equal to zero in black.

Since IL-8 and IL-6 concentrations correlated significantly with large tumor size (p-value < 0.05 and R > 0.83), this confirmed the clinical significance of IL-6 as a prognostic factor of cancer and, in particular, its association with the development of HCC (Łukaszewicz et al., 2007; Wong et al., 2009; Nakagawa et al., 2009). Indeed, CXCL10 levels correlated both with any tumour size and with transaminase levels suggesting that it could be used as marker of liver inflammation status and cancer progression. CXCL12 is known to play a role both in pathogenesis by promoting tumor growth and malignancy, and in the HCC metastatic network by recruiting endothelial cell tumor progenitors (Liu et al., 2008; Burns et al., 2006; Kryczek et al., 2007). Recently, some papers have suggested that β-NGF was involved in cancer growth and metastasis and was detected in diseased liver tissues; in fact this protein has been suggested to be involved in chronic inflammation leading to cancer (Rasi et al., 2007). The correlation evaluation between the concentrations of over-expressed proinflammatory molecules measured in HCC patients showed that β-NGF correlated with IL-

cm) suggesting that IL-8 may be involved in disease progression and might prove to be both a useful marker of tumor invasiveness and an independent prognostic factor for HCC

Fig. 5. Protein hierarchy assessed by a computational clustering analysis. More specifically,

concentrations, and the scale of protein intensity is indicated by the different colors: overexpressed cytokines, chemokines and growth factors in red, lower values in grey, and

Since IL-8 and IL-6 concentrations correlated significantly with large tumor size (p-value < 0.05 and R > 0.83), this confirmed the clinical significance of IL-6 as a prognostic factor of cancer and, in particular, its association with the development of HCC (Łukaszewicz et al., 2007; Wong et al., 2009; Nakagawa et al., 2009). Indeed, CXCL10 levels correlated both with any tumour size and with transaminase levels suggesting that it could be used as marker of liver inflammation status and cancer progression. CXCL12 is known to play a role both in pathogenesis by promoting tumor growth and malignancy, and in the HCC metastatic network by recruiting endothelial cell tumor progenitors (Liu et al., 2008; Burns et al., 2006; Kryczek et al., 2007). Recently, some papers have suggested that β-NGF was involved in cancer growth and metastasis and was detected in diseased liver tissues; in fact this protein has been suggested to be involved in chronic inflammation leading to cancer (Rasi et al., 2007). The correlation evaluation between the concentrations of over-expressed proinflammatory molecules measured in HCC patients showed that β-NGF correlated with IL-

the length of branches indicates, in inverse proportion, the similarity of protein

values equal to zero in black.

patients (Burger et al., 2006, Capone et al., 2010).

1α, IL-12p40, CCL27, CXCL1 and CXCL12. This was confirmed by the related computational clustering analysis which shows that the molecules cluster in two groups, as demonstrated by branches joining them. In particular, β-NGF was grouped with the proteins indicated above (Fig. 5). Therefore, it is possible to suggest that a panel composed of β-NGF and these five proteins may be useful for diagnostic/prognostic purposes.

In conclusion, this approach showed that some pro-inflammatory molecules were significantly up-regulated in these patients, and highlighted the complexity of the cytokine network in this disease. Moreover, this suggests the need to monitor these proteins in order to define a profile that could characterize patients with HCC or to help identify useful markers. In fact, this could lead to better definition of the disease state, and to an increased understanding of the relationships between chronic inflammation and cancer (Capone et al., 2010).

#### **7. Evaluation of cytokines in patients with chronic HCV or with HCV-related cirrhosis**

The serum concentrations of a panel of 30 cytokines, chemokines and growth factors were evaluated in patients with chronic inflammation (HC) and liver cirrhosis (LC), and in healthy donors by multiplex biometric ELISA-based assays (Costantini et al., 2010a). The molecules that showed different serum levels in patients respect to healthy controls are reported in Table 1.


Table 1. P values obtained for all significant molecules in HC and LC patients respect to controls using the nonparametric Mann-Whitney U test.

Greater amounts of IL-1α, IL-1, IL-2R, IL-6, IL-8, CXCL1, CXCL9, CXCL10, CXCL12, MIF, and -NGF were secreted by both HC and LC patients.

In particular, in the chronic inflammation and liver cirrhosis patients the same proteins were increased and the only difference was related to HGF being resulted significant and upregulated only in the patients with liver cirrhosis and not in those with chronic

The Value of the Cytokinome Profile 117

Fig. 6. Mean concentrations of significant cytokines, chemokines and growth factors in healthy control subjects (light grey) and in chronic inflammation (yellow) and liver cirrhosis

After the classification of their fibrosis stage of chronic inflammation by F Ishak index (Costantini et al., 2010a), the patients were divided in three different subgroups corresponding to F2, F3 and F4 stages and their mean concentrations of significant molecules in three groups compared by t-test. No relevant difference was found between chronic inflammation patients with F3 and F4 fibrosis grade because they corresponded to two already advanced fibrosis stages. Comparing F2 and F4 patients the concentrations of IL-2R, IL-6, IL-8, CXCL9, CXCL10, CXCL12 and MIF were found statistically higher (with p<0.05) in chronic inflammation patients with F4 fibrosis grade in respect to F2 fibrosis grade (Costantini et al., 2010a). These data agreed with a recent paper reporting that CXCL9 and CXCL10 were significantly elevated in patients with advanced fibrosis

In conclusion these results suggested that i) IL-2R, IL-6, IL-8, CXCL9, CXCL10, CXCL12 and MIF could be markers of the progression of chronic hepatitis C leading to liver cirrhosis by increasing fibrosis and ii) HGF, being over-expressed only in liver cirrhosis patients, could

However this work indicated the need of cytokinome data mining system for a predictive medicine, and suggested the utility to integrate all the cytokine data in a network and to make drug design studies on the chemokines resulted significant in the progression from

**7.2 Chronic inflammation patients with different fibrosis stages** 

be index of fibrosis progression versus liver cirrhosis.

(pink) patients.

(Zeremski et al. 2009).

chronic inflammation to HCC.

inflammation. In particular, HGF is a multifunctional growth factor that regulates growth and cell motility, exerts mitogenic effects on hepatocytes and epithelial cells and plays diverse roles in organ development, tissue regeneration, and tumor progression (Gentile et al., 2008). Moreover, it is implicated with IL-6, IL-8 and IL-1 in the hepatic stellate cell activation pathway.

However, numerous reports have examined the relationship between HGF and either the facilitation or suppression of HCC occurrence and have suggested that this growth factor could be used as index of cellular growth and of HCC development in liver cirrhosis patients (Yagamamim et al., 2002). In fact, it is interesting that the amount of this molecule was significantly different in liver cirrhosis patients in respect to both healthy controls and chronic inflammation patients and that its concentration in HCC patients was higher than in liver cirrhosis patients. This means that HGF increased in the progression of chronic inflammation leading to liver cirrhosis and cancer and can be used for predicting the occurrence of HCC in chronic HCV-related liver diseases (Costantini et al., 2010a).

#### **7.1 Chronic inflammation versus liver cirrhosis patients**

Since IL-1α, IL-1, IL-2R, IL-6, IL-8, CXCL1, CXCL9, CXCL10, CXCL12, MIF, and -NGF were increased in both HC and LC patients in respect to healthy control subjects, their mean concentrations were compared by t-test. Fig. 6 shows that the concentrations of all the proteins and, in particular, IL-8, CXCL9 and -NGF were higher (with p<0.05) in patients with liver cirrhosis than in those with chronic inflammation. Afterwards, comparing the serum levels of all cytokines, chemokines and growth factors in HC and LC patients respect to those in HCC patients tested in our recent paper (Capone et al.,2010) is resulted that the mean concentrations of all molecules resulted higher in HCC patients than in those with liver cirrhosis. This indicates that the expression of these proinflammatory molecules tends to increase in the chronic inflammation progression leading to liver cirrhosis and HCC and, thus, their evaluation could be used for prognostic studies. The serum levels of statistically significant cytokines, chemokines and growth factors in the HC and LC patients were correlated with clinical data by using the Pearson correlation coefficient. In chronic inflammation patients IL-1, IL-2R, MIF and - NGF showed a significant correlation with a positive correlation coefficient between them and with the transaminase values, that were higher in these patients than in healthy controls. Therefore these proteins can be considered as index of immune activation. In particular, these results agreed with literature data reporting that IL-1 and IL-2R participate in the progression from liver injury to fibrosis (Zekri et al., 2010) and that β-NGF is involved in liver cancer growth and metastasis and can be used as an index of chronic infection leading to LC and HCC (Gieling et al., 2009). Indeed, this work suggested for the first time a role of MIF in HCV-related chronic inflammation patients because an increased serum MIF was reported only in HBV patients (Kimura et al., 2006). Moreover, CXCL1, CXCL9, CXCL10 and HFG in liver cirrhosis patients showed a significant correlation and, in details, a positive correlation coefficient between them and a negative correlation coefficient with the albumin values, that were lower in these patients respect to controls. Concerning that HGF resulted the only molecule that was statistically different between HC and LC patients, these data suggested that the four proteins could be useful for diagnostic/prognostic purposes.

inflammation. In particular, HGF is a multifunctional growth factor that regulates growth and cell motility, exerts mitogenic effects on hepatocytes and epithelial cells and plays diverse roles in organ development, tissue regeneration, and tumor progression (Gentile et al., 2008). Moreover, it is implicated with IL-6, IL-8 and IL-1 in the hepatic stellate cell

However, numerous reports have examined the relationship between HGF and either the facilitation or suppression of HCC occurrence and have suggested that this growth factor could be used as index of cellular growth and of HCC development in liver cirrhosis patients (Yagamamim et al., 2002). In fact, it is interesting that the amount of this molecule was significantly different in liver cirrhosis patients in respect to both healthy controls and chronic inflammation patients and that its concentration in HCC patients was higher than in liver cirrhosis patients. This means that HGF increased in the progression of chronic inflammation leading to liver cirrhosis and cancer and can be used for predicting the occurrence of HCC in chronic HCV-related liver diseases

Since IL-1α, IL-1, IL-2R, IL-6, IL-8, CXCL1, CXCL9, CXCL10, CXCL12, MIF, and -NGF were increased in both HC and LC patients in respect to healthy control subjects, their mean concentrations were compared by t-test. Fig. 6 shows that the concentrations of all the proteins and, in particular, IL-8, CXCL9 and -NGF were higher (with p<0.05) in patients with liver cirrhosis than in those with chronic inflammation. Afterwards, comparing the serum levels of all cytokines, chemokines and growth factors in HC and LC patients respect to those in HCC patients tested in our recent paper (Capone et al.,2010) is resulted that the mean concentrations of all molecules resulted higher in HCC patients than in those with liver cirrhosis. This indicates that the expression of these proinflammatory molecules tends to increase in the chronic inflammation progression leading to liver cirrhosis and HCC and, thus, their evaluation could be used for prognostic studies. The serum levels of statistically significant cytokines, chemokines and growth factors in the HC and LC patients were correlated with clinical data by using the Pearson correlation coefficient. In chronic inflammation patients IL-1, IL-2R, MIF and - NGF showed a significant correlation with a positive correlation coefficient between them and with the transaminase values, that were higher in these patients than in healthy controls. Therefore these proteins can be considered as index of immune activation. In particular, these results agreed with literature data reporting that IL-1 and IL-2R participate in the progression from liver injury to fibrosis (Zekri et al., 2010) and that β-NGF is involved in liver cancer growth and metastasis and can be used as an index of chronic infection leading to LC and HCC (Gieling et al., 2009). Indeed, this work suggested for the first time a role of MIF in HCV-related chronic inflammation patients because an increased serum MIF was reported only in HBV patients (Kimura et al., 2006). Moreover, CXCL1, CXCL9, CXCL10 and HFG in liver cirrhosis patients showed a significant correlation and, in details, a positive correlation coefficient between them and a negative correlation coefficient with the albumin values, that were lower in these patients respect to controls. Concerning that HGF resulted the only molecule that was statistically different between HC and LC patients, these data suggested that the four

activation pathway.

(Costantini et al., 2010a).

**7.1 Chronic inflammation versus liver cirrhosis patients** 

proteins could be useful for diagnostic/prognostic purposes.

Fig. 6. Mean concentrations of significant cytokines, chemokines and growth factors in healthy control subjects (light grey) and in chronic inflammation (yellow) and liver cirrhosis (pink) patients.

#### **7.2 Chronic inflammation patients with different fibrosis stages**

After the classification of their fibrosis stage of chronic inflammation by F Ishak index (Costantini et al., 2010a), the patients were divided in three different subgroups corresponding to F2, F3 and F4 stages and their mean concentrations of significant molecules in three groups compared by t-test. No relevant difference was found between chronic inflammation patients with F3 and F4 fibrosis grade because they corresponded to two already advanced fibrosis stages. Comparing F2 and F4 patients the concentrations of IL-2R, IL-6, IL-8, CXCL9, CXCL10, CXCL12 and MIF were found statistically higher (with p<0.05) in chronic inflammation patients with F4 fibrosis grade in respect to F2 fibrosis grade (Costantini et al., 2010a). These data agreed with a recent paper reporting that CXCL9 and CXCL10 were significantly elevated in patients with advanced fibrosis (Zeremski et al. 2009).

In conclusion these results suggested that i) IL-2R, IL-6, IL-8, CXCL9, CXCL10, CXCL12 and MIF could be markers of the progression of chronic hepatitis C leading to liver cirrhosis by increasing fibrosis and ii) HGF, being over-expressed only in liver cirrhosis patients, could be index of fibrosis progression versus liver cirrhosis.

However this work indicated the need of cytokinome data mining system for a predictive medicine, and suggested the utility to integrate all the cytokine data in a network and to make drug design studies on the chemokines resulted significant in the progression from chronic inflammation to HCC.

The Value of the Cytokinome Profile 119

and evaluate results at different stages of the disease. In the statistical analysis section, the user can select the disease, filter the patients on the basis of gender, age and experiment date and select the most appropriate tool to perform the statistical analysis. In particular, we have implemented: (i) median, mean, variance, standard deviation, min and max values for the selected protein; (ii) t-test value related to the comparison between cytokine concentrations in control group and patients; (iii) Pearson correlation between different cytokines with related graph; (iv) Pearson correlation between each cytokine and some clinical data (i.e. tumor size) with related graph. CDMS represents the first 'user-friendly' tool that can be used by researchers as well as physicians and clinicians to significatively correlate clinical data and cytokine profiles and to identify what cytokines can be significant for the examined disease at a given time. Using its available statistical tools, it has been possible to identify the cyto-chemokines pattern involved in the chronic inflammation processes versus HCC and to verify that IL-8 correlated significantly with large tumor size (>5 cm), and it can be used both as a useful marker of tumor invasiveness and as an independent prognostic factor for HCC patients (Capone et al., 2010; Evangelista et al. 2010). Therefore, this tool can be a useful support to develop a reliable predictive medicine and to

**9. The need for structural studies of cytokine/receptor complex: The example of CXCL9, CXCL10 and CXCL11 chemokines and their membrane receptor** 

The data obtained on sera of patients with chronic inflammation (HC), liver cirrhosis (LC) and HCC suggested the utility to make drug design studies on three CXCL9, CXCL10 and CXCL11 chemokines for obtaining molecules able to block the progression of fibrotic damage in chronic inflammation patients leading to liver cirrhosis and, then, to HCC

CXCL9, CXCL10 and CXCL11 are members of a family of small (8–10 kDa) proteins, the chemokines (or chemoattractant cytokines). They play a key role in immune and inflammatory responses by promoting recruitment and activation of different subpopulations of leukocytes, hence they have important proinflammatory and immune modulatory functions (Booth et al., 2002). CXCL9 as well as do CXCL10 and CXCL11 binds and activates the same receptor CXCR3 (chemokine (C-X-C motif) receptor 3)

CXCR3 is mainly expressed on activated T and Natural Killer (NK) cells (Zeremski et al., 2007). While CXCL11, CXCL10, and CXCL9 are agonists for CXCR3, they can also act as antagonists for CCR3 (Loetscher et al. 2001). Tumor cells aberrantly express chemokines and/or chemokine receptors, and the interaction of chemokine ligand-receptor pairs is increasingly implicated as a mediator of tumor growth and metastasis. In particular, CXCR3 has now been identified in a variety of malignant cells, including melanoma, breast and prostate carcinomas, neuroblastoma, and a subset of B cell lymphomas (Colvin et al., 2004). CXCL9 and CXCL10 may promote the recruitment of lymphocytes to HCC and released from the HCC cells may induce lymphocyte infiltration. Ruehlmann et al. (2001) suggested that the expression of CXCL9 and CXCL10 might lead to lymphocytic infiltration into HCC, and gene therapy with these CXC chemokines may be effective for patients with HCC. Hrnce, during the past few years, several studies have demonstrated a pathogenenetic role of CXCR3 and its ligands in human inflammatory diseases suggesting the involvement of

improve or discover new predictive relationships among data groups.

**CXCR3** 

(Costantini et al., 2010a).

(Booth et al., 2004).

#### **8. The need of cytokinome data mining system for a predictive medicine**

The progressive increase in electronically stored clinical data is opening the possibility of carrying out large-scale studies aimed to discover correlations between new research data and related diseases. For these reasons, many relational databases have implemented data mining techniques (Harrison, 2008) that have been described as the 'extraction of implicit, previously unknown and potentially useful information', such as associations and correlations between data elements from large repositories of data (Lee & Siau, 2001). However, the scientific community needs clinical laboratory databases to collect medical data related to diseases progression and therapy response. In the last years, particular attention has been focused on the protein class comprising cytokines, chemokines and growth factors, because they play a crucial role in promoting angiogenesis, metastasis and subversion of adaptive immunity. Since the control of cytokine production is highly complex and multifactorial, their effects are mediated through multiple regulatory networks. The intricate complexity of these networks clearly conceals the role that a single cytokine may play in the pathogenesis of the disease. Therefore, it is more informative to investigate the immunopathogenesis of a disease process by analyzing a multiple panel of cytokines (Costantini et al., 2009). Utilizing a bead-based broad-spectrum multiplex immunoassay, it is possible not only to evaluate the serum levels of those cytokines ensemble that effectively correlate with the progression of the disease activity but also to define the immunomodulatory effects of a therapy even after months of treatment (Sato et al., 2009; Ozturk et al., 2009; Capone et al., 2010). This indicates that the definition and evaluation of a human cytokinome represents an important future tool to analyze the interaction network of cytokines both in healthy individuals and in patients affected by different diseases. In fact, it will permit one to understand and investigate how the regression of a chronic inflammation process, by acting on the cellular populations of cytokines, can block the progression of a cancer and, therefore, it can be a useful prognostic and diagnostic tool for clinicians.

For these reasons, a portal with user-friendly interfaces, which can be used both by physicians and researchers not only to collect and to correlate clinical data and serum levels of cytokines but also to know quickly what cytokines, chemokines or growth factors are significant in the progression state of a given disease, represents an important and useful tool for clinical prognosis and therapy studies.

Recently it has been developed a software named CDMS (Clinical Data Mining Software) and accessible at the URL: http://www.cro-m.eu/CDMS/ to collect clinical data and serum levels of many cytokines, chemokines and growth factors evaluated on healthy subjects and patients affected by different diseases (i.e. chronic hepatitis C and HCC) using multiplex immunoassays (Evangelista et al., 2010). Moreover, some statistical tools were implemented to correlate significatively clinical and experimental data and to quickly compare standardized cytokinome profile of a patient against a whole data bank that collects cytokinome data from some different diseases. CDMS allows certified users to access some of its services on the basis of their privileges. In detail, physicians and researchers can access the patient administration and statistical analysis sections, and all other authorized figures can access only statistical analysis section. In the patient administration section, there are case histories of patients with information related to their diagnosis, biological analyses as well as clinical data, and evaluations of 50 cytokine concentrations. Moreover, for the same patients, it is possible to insert the cytokine profiles evaluated at different times to compare

The progressive increase in electronically stored clinical data is opening the possibility of carrying out large-scale studies aimed to discover correlations between new research data and related diseases. For these reasons, many relational databases have implemented data mining techniques (Harrison, 2008) that have been described as the 'extraction of implicit, previously unknown and potentially useful information', such as associations and correlations between data elements from large repositories of data (Lee & Siau, 2001). However, the scientific community needs clinical laboratory databases to collect medical data related to diseases progression and therapy response. In the last years, particular attention has been focused on the protein class comprising cytokines, chemokines and growth factors, because they play a crucial role in promoting angiogenesis, metastasis and subversion of adaptive immunity. Since the control of cytokine production is highly complex and multifactorial, their effects are mediated through multiple regulatory networks. The intricate complexity of these networks clearly conceals the role that a single cytokine may play in the pathogenesis of the disease. Therefore, it is more informative to investigate the immunopathogenesis of a disease process by analyzing a multiple panel of cytokines (Costantini et al., 2009). Utilizing a bead-based broad-spectrum multiplex immunoassay, it is possible not only to evaluate the serum levels of those cytokines ensemble that effectively correlate with the progression of the disease activity but also to define the immunomodulatory effects of a therapy even after months of treatment (Sato et al., 2009; Ozturk et al., 2009; Capone et al., 2010). This indicates that the definition and evaluation of a human cytokinome represents an important future tool to analyze the interaction network of cytokines both in healthy individuals and in patients affected by different diseases. In fact, it will permit one to understand and investigate how the regression of a chronic inflammation process, by acting on the cellular populations of cytokines, can block the progression of a cancer and, therefore, it can be a useful prognostic

For these reasons, a portal with user-friendly interfaces, which can be used both by physicians and researchers not only to collect and to correlate clinical data and serum levels of cytokines but also to know quickly what cytokines, chemokines or growth factors are significant in the progression state of a given disease, represents an important and useful

Recently it has been developed a software named CDMS (Clinical Data Mining Software) and accessible at the URL: http://www.cro-m.eu/CDMS/ to collect clinical data and serum levels of many cytokines, chemokines and growth factors evaluated on healthy subjects and patients affected by different diseases (i.e. chronic hepatitis C and HCC) using multiplex immunoassays (Evangelista et al., 2010). Moreover, some statistical tools were implemented to correlate significatively clinical and experimental data and to quickly compare standardized cytokinome profile of a patient against a whole data bank that collects cytokinome data from some different diseases. CDMS allows certified users to access some of its services on the basis of their privileges. In detail, physicians and researchers can access the patient administration and statistical analysis sections, and all other authorized figures can access only statistical analysis section. In the patient administration section, there are case histories of patients with information related to their diagnosis, biological analyses as well as clinical data, and evaluations of 50 cytokine concentrations. Moreover, for the same patients, it is possible to insert the cytokine profiles evaluated at different times to compare

**8. The need of cytokinome data mining system for a predictive medicine** 

and diagnostic tool for clinicians.

tool for clinical prognosis and therapy studies.

and evaluate results at different stages of the disease. In the statistical analysis section, the user can select the disease, filter the patients on the basis of gender, age and experiment date and select the most appropriate tool to perform the statistical analysis. In particular, we have implemented: (i) median, mean, variance, standard deviation, min and max values for the selected protein; (ii) t-test value related to the comparison between cytokine concentrations in control group and patients; (iii) Pearson correlation between different cytokines with related graph; (iv) Pearson correlation between each cytokine and some clinical data (i.e. tumor size) with related graph. CDMS represents the first 'user-friendly' tool that can be used by researchers as well as physicians and clinicians to significatively correlate clinical data and cytokine profiles and to identify what cytokines can be significant for the examined disease at a given time. Using its available statistical tools, it has been possible to identify the cyto-chemokines pattern involved in the chronic inflammation processes versus HCC and to verify that IL-8 correlated significantly with large tumor size (>5 cm), and it can be used both as a useful marker of tumor invasiveness and as an independent prognostic factor for HCC patients (Capone et al., 2010; Evangelista et al. 2010). Therefore, this tool can be a useful support to develop a reliable predictive medicine and to improve or discover new predictive relationships among data groups.

#### **9. The need for structural studies of cytokine/receptor complex: The example of CXCL9, CXCL10 and CXCL11 chemokines and their membrane receptor CXCR3**

The data obtained on sera of patients with chronic inflammation (HC), liver cirrhosis (LC) and HCC suggested the utility to make drug design studies on three CXCL9, CXCL10 and CXCL11 chemokines for obtaining molecules able to block the progression of fibrotic damage in chronic inflammation patients leading to liver cirrhosis and, then, to HCC (Costantini et al., 2010a).

CXCL9, CXCL10 and CXCL11 are members of a family of small (8–10 kDa) proteins, the chemokines (or chemoattractant cytokines). They play a key role in immune and inflammatory responses by promoting recruitment and activation of different subpopulations of leukocytes, hence they have important proinflammatory and immune modulatory functions (Booth et al., 2002). CXCL9 as well as do CXCL10 and CXCL11 binds and activates the same receptor CXCR3 (chemokine (C-X-C motif) receptor 3) (Booth et al., 2004).

CXCR3 is mainly expressed on activated T and Natural Killer (NK) cells (Zeremski et al., 2007). While CXCL11, CXCL10, and CXCL9 are agonists for CXCR3, they can also act as antagonists for CCR3 (Loetscher et al. 2001). Tumor cells aberrantly express chemokines and/or chemokine receptors, and the interaction of chemokine ligand-receptor pairs is increasingly implicated as a mediator of tumor growth and metastasis. In particular, CXCR3 has now been identified in a variety of malignant cells, including melanoma, breast and prostate carcinomas, neuroblastoma, and a subset of B cell lymphomas (Colvin et al., 2004). CXCL9 and CXCL10 may promote the recruitment of lymphocytes to HCC and released from the HCC cells may induce lymphocyte infiltration. Ruehlmann et al. (2001) suggested that the expression of CXCL9 and CXCL10 might lead to lymphocytic infiltration into HCC, and gene therapy with these CXC chemokines may be effective for patients with HCC. Hrnce, during the past few years, several studies have demonstrated a pathogenenetic role of CXCR3 and its ligands in human inflammatory diseases suggesting the involvement of

The Value of the Cytokinome Profile 121

positively charged residues located in N-terminal region of three chemokines (i.e. three in CXCL9 and CXCL11 and two in CXCL10). Moreover, the presence of aromatic residues stabilized mainly the interaction between CXCR3 and CXCL11, having two Phe residues in N-terminal and might play an important role to favour the stacking interactions with

Therefore the study of the structural basis of the CXCR3 receptor-ligand system through the modeling of three complexes CXCL9/CXCR3, CXCL10/CXCR3, and CXCL11/CXCR3 has evidenced the interaction regions between three chemokines and CXCR3 (Trotta et al., 2009). The related analysis of the physico-chemical properties of residues in these regions suggested that the predominant interaction between CXCR3 and its ligands was on electrostatic basis and favored by the presence of positively charged residues located in the N-terminal region of the three chemokines. The comparison of the three complexes showed that CXCR3 had the highest affinity for CXCL11 in terms of binding energy and higher number of H-bonds, of salt bridges and of interaction residues (Trotta et al., 2009). Since the in silico modelling provided a time- and cost-effective tool for the screening of molecules as well as for designing of novel molecules of desired activity, it was possible to focus the attention on CXCL11. Therefore, in order to develop putative antagonists to CXCR3, a peptide, derived from the N-terminal region of CXCL11, has been synthesized. Preliminary results of this study, taken as a whole, indicated that this peptide may be regarded as a small molecule that, opportunely modified, could represent a good model for an antagonist to CXCR3. Hence, further studies are currently underway to design analogs of this peptide to optimize its physico-chemical properties and to improve the electrostatic and stacking

Over the past several years, there has been a renaissance of research into connection between inflammation and cancer. The inflammation can play a role in tumor suppression by stimulating an antitumor immune response, but more often, under certain conditions, it appears to stimulate tumor development (Mantovani et al., 2008). The intensity and nature of the inflammation could explain this apparent contradiction. In fact, the inflammation may become chronic when the inflammatory stimulus persists. However, it has been suggested that inflammation associated with cancer is similar to that seen with chronic inflammation, which includes the production of growth and angiogenic factors that stimulate tissue repair, factors that can also promote cancer-cell survival, implantation, and growth (Allavena et al., 2008). Thus immune response can promote anticancer effects or carcinogenesis and tumor growth (Mantovani et al., 2008). Cytokines are among molecules that play an important role in the evolution of these processes. In fact, they are proteins that are expressed before and during the inflammatory process and play a key role at the various disease levels so that they can be considered as specific markers of cancer and of its specific evolutive steps

The studying model chosen in this chapter is the hepatocellular carcinoma (HCC) that represents a major health problem worldwide being the fifth most common malignancy in men and the eighth in women and the third most common cause of cancer-related death in the world. Indeed its incidence is increasing dramatically, with marked variations among geographic regions, racial and ethnic groups, relatively to the exposure documented environmental risk factors (Castello et al., 2010a, 2010b). In particular, Southern Italy has the

putative drugs and organic compounds.

interactions with CXCR3 for novel therapeutic approaches.

(Capone et al., 2010; Costantini et al., 2010a).

**10. Conclusion** 

various segments of their sequences. Therefore, the blockade of CXCR3 interactions with its ligands in vivo has been suggested as a possible therapeutic goal for the treatment of these disorders (Xanthou et al. 2003). Recently the three-dimensional structure of CXCL9 and CXCR3 has been simulated (Trotta et al., 2009). Successively, also the CXCL9/CXCR3 complex (Fig. 7) has been modelled in comparison to CXCL10/CXCR3 and CXCL11/CXCR3 complexes in order to evaluate in details the interaction residues involved in the formation of the complexes and their properties as important structural features to be used for drug design (Trotta et al., 2009).

Fig. 7. 3D model of CXCL9/CXCR3 complex where CXCR3 is reported with green ribbon but CXCL9 with cyan ribbon. In details, the loops of the interaction regions are evidenced (i.e. N-terminal, loop1, loop 2 and loop3 of the receptor are shown in red, yellow, blue and magenta, respectively, and N-terminal and N-loop of the chemokine in orange and grey, respectively).

Three chemokines resulted always to interact with their receptor by N-terminal region and N-loop but the receptor by N-terminal region and three extracellular loops according to precedent studies (Xanthou et al. 2003). Moreover the analysis of three complexes showed that the N-loop of all three chemokines was essential for binding the N-terminal region of CXCR3 in agreement to Clark-Lewis et al. (2003) whereas the loop 1 of CXCR3 was essential to bind only CXCL11 and CXCL10 as well as indicated by Xanthou et al. 2003. The analysis of the physical-chemical properties of residues present in these regions in CXCR3 highlighted that: i) N-terminal, loop1 and loop 2 contained some aromatic residues (Phe, Tyr and Trp); ii) N-terminal presented three negatively charged residues (3 Glu), loop2 one (Asp) but loop 3 three (2 Asp and 1 Glu) and iii) both loop 2 and loop 3 had two positively charged residues (2 Arg).These data suggested that the predominant interaction between CXCR3 and its ligands was on electrostatic basis and was favored also from the presence of

various segments of their sequences. Therefore, the blockade of CXCR3 interactions with its ligands in vivo has been suggested as a possible therapeutic goal for the treatment of these disorders (Xanthou et al. 2003). Recently the three-dimensional structure of CXCL9 and CXCR3 has been simulated (Trotta et al., 2009). Successively, also the CXCL9/CXCR3 complex (Fig. 7) has been modelled in comparison to CXCL10/CXCR3 and CXCL11/CXCR3 complexes in order to evaluate in details the interaction residues involved in the formation of the complexes and their properties as important structural features to be used for drug

Fig. 7. 3D model of CXCL9/CXCR3 complex where CXCR3 is reported with green ribbon but CXCL9 with cyan ribbon. In details, the loops of the interaction regions are evidenced (i.e. N-terminal, loop1, loop 2 and loop3 of the receptor are shown in red, yellow, blue and magenta, respectively, and N-terminal and N-loop of the chemokine in orange and grey,

Three chemokines resulted always to interact with their receptor by N-terminal region and N-loop but the receptor by N-terminal region and three extracellular loops according to precedent studies (Xanthou et al. 2003). Moreover the analysis of three complexes showed that the N-loop of all three chemokines was essential for binding the N-terminal region of CXCR3 in agreement to Clark-Lewis et al. (2003) whereas the loop 1 of CXCR3 was essential to bind only CXCL11 and CXCL10 as well as indicated by Xanthou et al. 2003. The analysis of the physical-chemical properties of residues present in these regions in CXCR3 highlighted that: i) N-terminal, loop1 and loop 2 contained some aromatic residues (Phe, Tyr and Trp); ii) N-terminal presented three negatively charged residues (3 Glu), loop2 one (Asp) but loop 3 three (2 Asp and 1 Glu) and iii) both loop 2 and loop 3 had two positively charged residues (2 Arg).These data suggested that the predominant interaction between CXCR3 and its ligands was on electrostatic basis and was favored also from the presence of

design (Trotta et al., 2009).

respectively).

positively charged residues located in N-terminal region of three chemokines (i.e. three in CXCL9 and CXCL11 and two in CXCL10). Moreover, the presence of aromatic residues stabilized mainly the interaction between CXCR3 and CXCL11, having two Phe residues in N-terminal and might play an important role to favour the stacking interactions with putative drugs and organic compounds.

Therefore the study of the structural basis of the CXCR3 receptor-ligand system through the modeling of three complexes CXCL9/CXCR3, CXCL10/CXCR3, and CXCL11/CXCR3 has evidenced the interaction regions between three chemokines and CXCR3 (Trotta et al., 2009). The related analysis of the physico-chemical properties of residues in these regions suggested that the predominant interaction between CXCR3 and its ligands was on electrostatic basis and favored by the presence of positively charged residues located in the N-terminal region of the three chemokines. The comparison of the three complexes showed that CXCR3 had the highest affinity for CXCL11 in terms of binding energy and higher number of H-bonds, of salt bridges and of interaction residues (Trotta et al., 2009). Since the in silico modelling provided a time- and cost-effective tool for the screening of molecules as well as for designing of novel molecules of desired activity, it was possible to focus the attention on CXCL11. Therefore, in order to develop putative antagonists to CXCR3, a peptide, derived from the N-terminal region of CXCL11, has been synthesized. Preliminary results of this study, taken as a whole, indicated that this peptide may be regarded as a small molecule that, opportunely modified, could represent a good model for an antagonist to CXCR3. Hence, further studies are currently underway to design analogs of this peptide to optimize its physico-chemical properties and to improve the electrostatic and stacking interactions with CXCR3 for novel therapeutic approaches.

#### **10. Conclusion**

Over the past several years, there has been a renaissance of research into connection between inflammation and cancer. The inflammation can play a role in tumor suppression by stimulating an antitumor immune response, but more often, under certain conditions, it appears to stimulate tumor development (Mantovani et al., 2008). The intensity and nature of the inflammation could explain this apparent contradiction. In fact, the inflammation may become chronic when the inflammatory stimulus persists. However, it has been suggested that inflammation associated with cancer is similar to that seen with chronic inflammation, which includes the production of growth and angiogenic factors that stimulate tissue repair, factors that can also promote cancer-cell survival, implantation, and growth (Allavena et al., 2008). Thus immune response can promote anticancer effects or carcinogenesis and tumor growth (Mantovani et al., 2008). Cytokines are among molecules that play an important role in the evolution of these processes. In fact, they are proteins that are expressed before and during the inflammatory process and play a key role at the various disease levels so that they can be considered as specific markers of cancer and of its specific evolutive steps (Capone et al., 2010; Costantini et al., 2010a).

The studying model chosen in this chapter is the hepatocellular carcinoma (HCC) that represents a major health problem worldwide being the fifth most common malignancy in men and the eighth in women and the third most common cause of cancer-related death in the world. Indeed its incidence is increasing dramatically, with marked variations among geographic regions, racial and ethnic groups, relatively to the exposure documented environmental risk factors (Castello et al., 2010a, 2010b). In particular, Southern Italy has the

The Value of the Cytokinome Profile 123

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Recently the serum levels of many cytokines have been evaluated by a broad spectrum bead-based multiplex immunoassay both in patients with chronic HCV or with HCV-related cirrhosis and in patients with HCC patients with HCV-related cirrhosis. These studies have evidenced that some interleukins and chemokines (Fig. 5 and 6) are putative markers of the progression of chronic hepatitis C leading to liver cirrhosis by increasing fibrosis and can be used as templates for designing new drugs able to block the progression of the inflammatory processes (Capone et al., 2010; Costantini et al., 2010a).

However, all the data related to the cytokine evaluations should be modeled computationally by using graphs or networks connecting the various data groups in terms of dynamic probabilistic maps of metabolic and/or physiological activities and/or pathogenetic pathways. In fact only in this way it is possible to define the human cytokinome that can be an useful tool to analyze the interaction network of cytokines both in healthy individuals and in patients affected from HCC (Costantini et al., 2010b). Therefore, CDMS represents the first 'user-friendly' tool that can be used by researchers as well as physicians and clinicians to significatively correlate clinical data and cytokine profiles and to identify what cytokines can be significant for the examined disease at a given time (Evangelista et al., 2010). However further studies will regard the opening of the data sets to other diseases and the implementation of other statistical tools and classification methods to improve or to discover new predictive relationships among data groups.

#### **11. References**


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Recently the serum levels of many cytokines have been evaluated by a broad spectrum bead-based multiplex immunoassay both in patients with chronic HCV or with HCV-related cirrhosis and in patients with HCC patients with HCV-related cirrhosis. These studies have evidenced that some interleukins and chemokines (Fig. 5 and 6) are putative markers of the progression of chronic hepatitis C leading to liver cirrhosis by increasing fibrosis and can be used as templates for designing new drugs able to block the progression of the

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**Part 7** 

**Pancreatic Cancer** 

