Stromal cell types in tumor microenvironment: main markers and functions with potential therapeutic targets.
\r\n\tMain types of important health problems that occur in humans are: Wuchereria bancrofti, Brugia malayi, Brugia timori, Onchocerca volvulus, Loa loa, Mansonella ozzardi, Dipetolonema perstans, Dipetolonema streptocerca, Dirofilaria repens, Dirofilaria tenuis, Dirofilaria immitis, and Dracunculu smedinensis.
\r\n\tEpidemiologically Filarisis is estimated to be prevalent in more than 120 million people worldwide. Mostly it is prevalent in hot and humid subtropical regions. Countries where filariasis may be found in Asia: Amman, China, India, Japan, Korea, Vietnam, Indonesia, Ceylon, Malaysia and Thailand; in the Mediterranean region: Spain, Italy, Macedonia; in Africa: (between 150 North and 130 South parallels) Angola, Tanzania, Ghana, Morocco, Algeria, Tunis, Egypt; and in Central America: Mexico, Honduras, Venezuela, Caribbean, Guyana.
\r\n\tClinical manifestation may vary from painful inflammatory swellings of lymph nodes in acute infections to lymphedema due to blockage of lymphatic system in chronic cases. The diagnosis firstly depends on the “suggestive symptoms”. Blood tests such as Indirect Hemaglutination (IHA), Enzym-Linked Immunosorbent Assay (ELISA) are indirect diagnostic tests and PCR. Definitive diagnosis depends on direct identification of microfilariae in blood samples or involved-tissue biopsies. The treatment of choice in Filariasis is a combined regimen of diethylcarbamazine (DEC) 6 mg/kg, ivermectin 150 mg/kg and albendazole (ALB) 400 mg single-administration. Prevention: Treatment of patients with filariasis and vector control is possible
Cancer is usually viewed as a complex process of multiple disorders that are mostly driven by somatic mutation with the involvement of several hallmarks: genomic instability, sustaining proliferative signaling, resisting cell death, enabling replicative immortality, inflammation, evading the immune system, de novo angiogenesis, invasion, and metastasis. The outcome prediction in cancer is usually achieved by histopathological analysis of tissue samples obtained by biopsies or surgical specimens from primary tumor or metastatic localization. However, the heterogeneity in the histological appearance of different tumors (intertumor heterogeneity) as well as of different areas in the same tumor (intratumor heterogeneity) is of uncontested relevant and can explain the histopathological classification of tumors based on the morphological patterns. In the last decade, the advent of molecular pathology has allowed the definition of molecular subtyping for several cancers, which does not completely overlap with prevailing histopathological classifications [1].
In current practice, TNM classification appears as a sample method of tumor staging used worldwide, and based on tumor burden (T), lymph nodes status (N), and presence of metastases (M). However, the TNM classification provides limited prognostic information in cancer and does not predict response to therapy. Moreover, cancer outcome can differ significantly between patients whose cancers are at the same TNM stage.
Tumor appears as heterogeneous tissues that consist of tumor cells surrounded by a tumor microenvironment (TME). TME is a complex network composed of extracellular matrix (ECM), stromal cells (fibroblasts, adipocytes, neural and neuroendocrine (NE) cells, endothelial cells (ECs), and pericytes), immune and inflammatory cells that drive cancer cells fate from invasion to intravasation and metastasis. Cancer cells need cellular, biochemical, and biophysical stimuli originating from a more adapted microenvironment by recruiting and educating various types of normal cells into their neighborhood. The stromal-inflammatory interface represents a dynamic space characterized by reversible stromal and epithelial events. Within this dynamic space, exchange of numerous molecular information is associated with the transition into tumorigenic microenvironment and includes growth factors (GFs), cytokines, chemokines, enzymes, matrix proteins, and metabolic intermediates. Recruitment, activation, reprogramming, and persistence of stromal and immune/inflammatory cells in the extracellular space are the consequences of a reciprocal interaction between TME and cancer cells [2, 3].
Recent data suggest that cancer development is influenced by TME and controlled by the host’s immune system, underlying the importance of including TME components and immunological biomarkers in the determination of prognosis and response to therapy, a concept that has been termed as microenvironment score and immunoscore. Increasingly, data collected from cancer tissue samples demonstrate that immune classification has prognostic value and may be a useful supplement to the histopathological, molecular, and TNM classifications. Nevertheless, the complexity of quantitative immunohistochemistry and the variable assay protocols, stromal and immune cell types analyzed and tumor-sampling criteria underscore the need to harmonize the quantified methods. It is therefore important to incorporate TME and immune scoring in determinations of cancer prognosis and to make sure they become a routine part of the histopathological diagnostic and prognostic assessment of patients with cancer.
Non-immune/inflammatory stromal cells comprise fibroblasts, adipocytes, neural and neuroendocrine cells, endothelial cells, pericytes, and mesenchymal stem cells (MSCs) (Figure 1 and Table 1).
Tumor microenvironment is a complex network composed of extracellular matrix (ECM), stromal cells (fibroblasts, endothelial cells and pericytes) and immune and inflammatory cells (T cells, B cells, natural killer ‘NK’ cells, dendritic cells, macrophages and myeloid-derived suppressor cells). The stromal-inflammatory interface represents a dynamic space contains growth factors, cytokines and chemokines. Recruitment, activation, reprogramming and persistence of stromal and immune/inflammatory cells in the extracellular space are the consequences of reciprocal interactions between tumor microenvironment components and tumor cells.
Non-immune/inflammatory stromal cell | Main markers | Main functions | Potential therapeutic targets |
---|---|---|---|
Cancer-associated fibroblasts (CAFs) | Vimentin, fibronectin, FSP-1, αSMA, tenascin-C, PDGFR Endosialin (CD248), and FAP |
| Anti-CXCR-4 antibodies (CXCL12/SDF-1inhibition); anti-VEGF and anti-PDGF antibodies; MMP inhibitors; anti-IL6 antibodies; anti-HGF therapies; anti-FAP antibodies; Anti-TGFβ inhibitors; anti-IL-11 and anti-THSB1 therapies |
Cancer-associated adipocytes (CAAs) | FSP-1 expression |
| Antibodies anti-IL-6, anti-IL-8; anti-CCL2, COX2 and adiponectin inhibitors |
Mesenchymal stem cells (MSCs) | Vimentin, CD29 (β1integrin), CD44, CD73, CD90, CD105 and STRO-1 |
| Nano-engineered MSCs are used as targeted therapeutic carriers |
Endothelial cells | Tip cells: VEGFR1low, VEGFR2high, Dll4high, and CD34+ Stalk cells: VEGFR1high, VEGFR2low, Dll4low CD34−. |
| avβ1, avβ2, a5β1 integrin inhibitors; anti-VEGF and VEGFR agents |
Pericytes | αSMA, Desmin, NG2 (CSPG4), 3G5 antigen, PDGFR-β and Endosialin (CD248) |
| Anti-ANG2 antibody VEGFR and PDGFR-β antagonists; VEGFR, PDGFR-β, and Tie-2 agonists; anti-RSG5 and anti-PD/PD-L1 therapies |
Neural cells | PGP9.5. and NGF |
| Anti-NGF blocking antibodies, NT3 and NT4 targeted therapies; GDNF inhibitors; anti-NGF antibodies; anti-PTN antibodies and N-syndecan inhibitors; BDNF inhibitors |
Stromal cell types in tumor microenvironment: main markers and functions with potential therapeutic targets.
Abbreviations: ANG2: angiopoietin-2; Integrins αv: avβ1, avβ2; BDNF: brain-derived neurotrophic factor; CCL: chemokine ligand; COX: cyclooxygenase; CSF: colony stimulating factor; CXCL: C-X-C chemokine ligand; CXCR: C-X-C chemokine receptor; CSPG4: Chondroitin sulfate proteoglycan 4; FAP: fibroblast activation protein; FRb: folate receptor beta; GDNF: glial cell line-derived neurotrophic factor; HRG: histadine-rich glycoprotein; IL: interleukin; MMP: matrix metalloproteinase; NGF: nerve growth factor; NT: neurotrophin; PDGF: platelet-derived growth factor; PDGFR: platelet-derived growth receptor; PD1: programmed cell death protein; PD-L1: programmed cell death ligand; PTN: pleiotropin; RSG5: regulator of protein signaling 5; SDF: stromal-derived factor; TIMP: tissue inhibitor of metalloproteinase; TGF: transforming growth factor; TLR: toll-like receptor; VEGF: vascular endothelial growth factor; VEGFR: vascular endothelial growth factor and receptor.
Cancer-associated fibroblasts (CAFs) are a sub-population of activated fibroblasts with myofibroblastic phenotype that represent the predominant non-inflammatory stromal cell type in the TME. CAFs are heterogeneous cells of multiple origins, which are usually identified according to their different origins by expression of proteins such as mesenchymal biomarkers (vimentin and fibronectin), fibroblast-secreted protein-1 (FSP-1), α-smooth muscle actin (αSMA), tenascin-C, platelet-derived growth factor receptor (PDGFR), and fibroblast activation protein (FAP) [4, 5]. CAFs accumulation in the TME is often correlated with poor prognosis. They may promote tumor development and progression by promoting angiogenesis or by interacting with immune-inflammatory cells and neuroendocrine cells through different cell factors and cytokines [2]. CAFs may also hinder antitumor immune responses [4]. Indeed, cancer cells produce TGF-β that activates adjacent CAFs. In turn, CAFs promote tumor progression by releasing numerous interleukins (IL-1, IL-6, IL-8, and IL-22) and chemokines (CXC-chemokine ligand CXCL and CC-chemokine ligand CCL) [2]. CAFs can also secrete various chemotactic GFs (EGF, FGF, HGF PDGF, and VEGF), ECM proteins (collagens, fibronectins, tenascin C, and SPARC), enzymes such as matrix metalloproteinases (MMPs), lysyl oxidases (LOX) family, and cyclooxygenase 2 (COX2) [6].
Cancer-associated adipocytes (CAAs) possess important secretory properties that may enhance tumor aggressiveness. Compared to normal adipocytes, CAAs are characterized by the loss of adipocyte differentiation, a smaller size, and FSP-1 expression (with lack of αSMA expression). They produce adipokines (leptin, adiponectin, and apelin), angiogenic factors and GFs (VEGF and HGF), tumor necrosis factor-α (TNF-α), interleukins (IL-1β, IL-6, and IL-8), and chemokines (MCP1, CCL2, and CCL5) [7]. They also exhibit an increased secretion of fibronectin, collagen I/VI, and MMP-11/Stromelysin-3 [2, 8]. The activation of Wnt/β-catenin pathway in response to Wnt3a secreted by cancer cells is essential to adipocytes reprogramming. The reprogrammed CAAs located close to cancer cells can initiate protumoral heterotypic paracrine and endocrine interactions. Another type of CAAs is the adipose stem cells (ASCs). ASCs can influence the TME by worsening the tumorigenic behavior of c-Met-producing cancer cells, which in turn creates an inflammatory TME. ASCs can interact with TME through TGF-β1-signaling pathway or promote angiogenesis by migrating toward tumor-conditioned media through the PDGF-BB/PDGF-β-signaling pathway [5].
Blood vessels are composed of perivascular cells termed as pericytes, endothelial cells (ECs) which form the inner lining of the vessels wall and smooth muscle cells.
Pericytes differentiate from mesenchymal precursors and are recruited to tumors by PDGFβ. They possess characteristic cellular markers including 3G5 ganglioside and chondroitin sulfate proteoglycan 4 (CSPG4) also known as NG2. In tumor tissue, pericytes highly express αSMA, although it is often absent in quiescent pericytes in non-tumoral tissue. Recent experimental studies revealed that pericytes can actively modulate the magnitude of immune responses and may prevent lymphocyte extravasation and activation in tumor tissue [9].
ECs are subdivided into tip cells and stalk cells and function as active stromal regulators implicated in proliferation, invasion, secretion of inflammatory and growth mediators, and metastatic spread. Tip cell is highly migratory and polarized EC type that extends numerous filopodia and expresses low level of VEGF receptor 1 (VEGFR1low), with high levels of VEGFR2 and Delta-like ligand 4 (Dll4), and in vitro CD34. The tip cell is followed by stalk cell, a proliferative and less migratory type of EC, which expresses VEGFR1high, VEGFR2low, Dll4low and has undetectable levels of CD34 in vitro [10]. Importantly, neovascular tips are rich in active TGF-β1 and periostin, which promote tumor growth and regulate tumor dormancy [11].
Cancer cells can support the neoneurogenesis by secreting several neuronal growth factors and axon guidance molecules. The majority of factors known to induce neurogenesis, such as neurotrophins, insulin-like growth factor-II (IGF-II), and fibroblast growth factor (FGF), are usually secreted by tumors with bad prognosis. These factors exert autocrine or paracrine effects in cancer cells. Norepinephrine, another neurotransmitter, has a significant impact on T-cells. It can inhibit the generation of antitumor cytotoxic T-lymphocytes (CTLs) through the inhibition of TNF-α synthesis [11]. The neural-epithelial interaction and nerve growth factor (NGF) production by cancer cells favor tumor progression by promoting both the growth of cancer cells and neurites [12].
Neuroendocrine (NE) cells are part of the diffuse NE system and exhibit a combination of neuronal and endocrine features. NE system strongly influences the function of the immune system. It can regulate the migration and cytotoxicity in natural killer (NK) cells through neurotransmitters. Additionally, the neuroendocrine substance P (SP) blocks the β1-integrin-mediated adhesion of T lymphocytes and increases their migratory activity [13]. SP can also induce the production of various cytokines in leukocytes. SP and the subsequent activation of the neurokinin-1 receptor (NK1R) lead to mitogen-activated protein kinase (MAPK) activation. The involvement of NK1R activation in mitogenesis, angiogenesis, cell migration, and metastasis supports the hypothesis that SP and NK1R interactions influence the TME [14].
Mesenchymal stem cells (MSCs) are multipotent stem cells with the capacity to differentiate into fibroblasts, adipocytes, pericytes, osteocytes, and chondrocytes. MSCs express cell surface markers CD29, CD44, CD73, CD90, CD105, and STRO-1, and lack the expression of CD14, CD34, CD45, and human leukocyte antigen HLA-DR [15]. MSCs have immunomodulatory features and secrete cytokines, VEGF, and immune receptors which regulate the microenvironment in the host tissue. Based on their multilineage potentiate, immunoregulatory and tissue-protective properties, MSCs are being tested for the treatment and prevention of graft-versus-host disease, chronic diseases, and certain hematologic malignancies [16].
ECM is composed of proteins (collagens, laminins, and fibronectins), proteoglycans, and hyaluronans in a specific organization [17, 18]. CAFs are the major cell type responsible for the synthesis of ECM proteins. ECM contains all the cytokines, GFs, and hormones secreted by stromal and cancer cells. During tumor progression, ECM composition and structure change continuously. ECM heterogeneity is crucial for controlling collective cell-invasive behaviors and determining metastasis efficiency. ECM selects survival cancer cells to aid in tumor growth and invasion at the fastest rate. ECM can also affect tumor development and metastasis through extracellular secretion, or by altering the phenotype of stromal cells or cancer cells [3]. Moreover, ECM provides a hypoxic or acidic microenvironment in which cancer cells have greater survival advantages. The abundant ECM within the TME is correlated with increased tumor growth through various mechanisms, including activation of pro-survival phosphoinositide 3-kinase (PI3K)-signaling pathways and downstream of integrin receptors [2].
ECM interacts with lymphocytes and crucially influences immune cells motility and localization, which can help tumor cells to evade from immune surveillance. Increased stroma density reduces lymphocyte displacement, supporting the idea that ECM deposition can alter antitumor immune responses by limiting T-cell motility [4].
Tumor microenvironment contains numerous immune and inflammatory cells that originate from lymphoid precursors [CD8+ cytotoxic T-cells (CTLs), CD45+ memory T-cells, CD4+ T helper cells (Th1, Th2 and Th17), T regulatory cells (Tregs), T follicular helper cells (TFH), NKT cells, gamma delta T (γδ T) cells, B-cells, and plasmacytoid dendritic cells (pDCs)] and from myeloid precursors [tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), conventional DCs (cDCs), neutrophils, mast cells, and platelets]. The term tumor-infiltrating lymphocytes (TILs) are referred to a group of T-cells (CD3, CD4, CD8, and FoxP3) located around tumor cells [19]. In addition, the invasive margins of cancers may comprise tertiary lymphoid structures (TLSs) that exhibit strong similarities with lymph node organization.
These immune and inflammatory cells infiltrate TME via a network of inflammatory chemotactic cytokines and chemokines produced by cancer cells.
NK cells (CD56+/CD3−) belong to the innate immune system and play an important role in protecting the host from infections and cancer. NKT cells (CD56+/CD3+) share a variety of markers for both T lymphocytes and NK cells. The γδ T-cells are an independent population of circulating lymphocytes that can sense pathogens. γδ T-cells can also induce DC maturation, functional activation and migration, and antigen presentation. NK, NKT cells, and γδ T-cells are present in TME in various cancer, and express the natural killer group 2D (NKG2D) receptor. NKG2D recognizes proteins encoded by the MICA and MICB locus, which are located within the major histocompatibility complex (MHC) on chromosome 6 near the HLAB locus [20].
CD4+ and CD8+ are the two main lineages of T-cells. CD4+ T-cells are classified into CD4+ Th that mediate tumor immunity and CD4+ CD25+ FoxP3+ Tregs that suppress antitumor immunity and promote tumor growth [21, 22].
DCs are derived from myeloid precursors (cDCs) or lymphoid precursors (pDCs) and are considered as a crucial link between innate and adaptive immunity. DCs have three maturation stages: precursor DCs, immature DCs, and mature DCs. Immature DCs interact with antigens, migrate into secondary lymphoid organs, and become antigen-presenting cells (APCs). DCs are among the first cells migrating to the tumor site by means of GFs (VEGF and HGF), chemokines (CXCL12 and CXCL8), and antimicrobial peptide (β-defensin) secreted by cancer cells and stromal cells [23, 24, 25].
MDSCs have two distinct monocytic and granulocytic subsets and can differentiate into DCs or ECs. They coordinate tumor progression and angiogenesis through the release of MMP-9 and VEGF. MDSCs can also promote immune evasion by suppressing antitumor CTLs and NK cells [26].
TAMs are multifunctional cells characterized by the expression of CD68, plasticity, and secretion of numerous immune-modulatory cytokines. Macrophages differentiation and growth are regulated by several GFs, including CSF-1 and GMCSF. TAMs can release chemokines (CCL17, CCL18, and CCL22) and recruit non-CTLs, especially Tregs. Activated macrophages can be classified into M1 and M2 cells [27]. M1 cells are characterized by high capacity to present antigen and are involved in the response of Th1 cells to pathogens and cancer. M1 cells produce proinflammatory cytokines (TNFα and IFN-γ) and interleukins (IL-1 and IL-12) and generate reactive oxygen species (ROS) and nitric oxide (NO). By contrast, M2 cells have immunosuppressive phenotype, produce IL-10, and inhibit CTLs, which are crucial to initiate a Th2-type response. Within the TME, TAMs have generally a M2-skewed phenotype (CD163+, CD204+, and CD206+) that promote angiogenesis, ECM remodeling, and repair [28].
During tumor development, pre-invasive TME has antitumor property that includes predominantly M1 and Th1 with the production of IL-12, IFNγ, and inducible NO synthase (iNOS). Comparatively, the transition to invasive TME is marked by pro-tumoral properties with a shift from M1 to M2 and from Th1 to Th2 cells, a decrease of IFNγ, and an increase of IL-1, IL-6, VEGF, and indoleamine 2, 3-dioxygenase (IDO) [29].
Topographically, each type of immune and inflammatory cells has a preferred location within tumor site. CTLs and Th1 cells are located at the invasive margins and/or in the tumor core. Immature DCs are found in the tumor core, whereas mature DCs infiltrate T-cell zones in close contact with CD4+ and CD8+ T-cells. B-cells are found in TLS and at the invasive margins. TAMs and TFH are in contact or within B-cell zones, whereas NK cells are dispersed within the stroma and at the invasive margins [30].
Tumor-associated TLSs exhibit strong similarities with lymph node organization and comprise prominent B-cell follicles, T-cell marginal zones, and associated follicular DCs, very few Tregs, and high endothelial venules (HEVs). TLSs are usually located in the tumor-invasive margin and in the stroma of most cancers and their densities correlate with a favorable clinical outcome. HEVs express peripheral node addressins (PNAds) and specialized in the extravasation of circulating immune cells, and the secretion of chemokines that are crucial for lymphocyte recruitment and entry into the lymph node. Recently, a molecular signature of TLSs encoding 12 distinct chemokines (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) has been identified in various tumors [31].
TLSs are associated with the generation of an adaptive immune response and represent a formidable school for T-cell priming, B-cell activation, and differentiation into plasma cells and an exquisitely located factory for antibody production [32].
In the early stage of carcinogenesis, cancer cells are rejected by an innate immune mechanism also referred to as immunosurveillance. The innate immune system recognizes exogenous pathogen-associated molecular patterns (PAMPs) or endogenous danger-associated molecular patterns (DAMPs). These latter ones are recognized by the host organism through various pattern recognition receptors (PRRs) that activate DNA sensors and downstream adaptors to trigger stimulation of innate immune system and to induce adaptive T-cell responses. Multiple families of PRRs, including Toll-like receptors (TLRs), have been identified within plasma membrane, intracellular vesicles, and within the cytosol of APCs [33]. Binding of ligands to PRRs activates various adaptor molecules and downstream signaling pathways, orchestrating innate immune responses and maturation of APCs (DCs), leading to production of antimicrobial peptides, cytokines, chemokines, and type I interferon (IFN) including IFN-α and IFN-β. In cancer, PRRs can also recognize various endogenous DAMPs, such as cancer-associated antigens (CAAs). Among regulators of innate immune system, recent evidence has indicated that the major pathway involved in the induction of a spontaneous antitumor adaptive T-cell response is the stimulator of interferon genes (STING) signaling [34].
Experimental studies indicate that immune system plays a dual role in cancer, a theory known as cancer immunoediting. It can not only eliminate cancer cells or inhibit their growth but also promote tumor progression by modifying conditions within TME or by selecting more resistant cancer cells. Cancer immunoediting contains three phases: elimination, equilibrium, and escape. The immune system is directed against cancer cells through the “cancer immunity cycle” described by Chen and Mellman [35], which associates cancer antigen release by tumor cells, presentation by DCs and priming of T lymphocytes in lymph nodes, activation of peripheral immune cells, trafficking and infiltration of T-cells to the TME, cancer cells recognition, and immune-mediated cell death (T-cell-inflamed phenotype). In the elimination phase, T-cells attack tumor cells that express tumor-specific antigens in the form of complexes of tumor-derived peptides bound to MHC molecules on the cell. Naïve T-cells that differentiate in bone marrow express a unique T-cell receptor (TCR) and undergo positive and negative selection processes in thymus. T-cells become activated when tumor antigens are recognized. Then, T-cells proliferate and differentiate, leading to the T-cell’s ability to attack and destroy cells that express relevant antigens. The recognition of antigen-MHC complexes by the T-cell antigen receptor is not sufficient for the activation of naïve T-cells. However, the engagement of CD28 on T-cell surface and the expression of B7 molecules (CD80 and CD86) on APCs (DCs) provide additional costimulatory signals [36]. Cancer cells usually do not express B7 molecules (except for certain lymphomas) and hence are largely invisible to the immune system. This can be overcome by an inflammatory response, which permits APCs to take up antigen and present antigen-MHC along with B7 molecules initially in tumor-draining lymph nodes for effective activation of T-cells. After the costimulation process, tumor-specific T-cells acquire effector function, move to the tumor site, and infiltrate TME, which activates the antitumor immune response. However, the antitumor efficacy of T-cells within TME is determined by their ability to overcome barriers and counter-defenses they encounter from tumor and stromal cells, Tregs, MDSCs, and inhibitory cytokines that act to mitigate antitumor immune responses [37].
Activated T-cells express immune checkpoints such as cytotoxic T lymphocyte-associated protein 4 (CTLA-4 also known as CD152) and programmed death 1 (PD-1 also known as CD279) which act to abrogate T-cells responses. CTLA-4 competes with CD28 for binding to CD80/86, providing an inhibitory stimulus upon engagement [38].
PD-1 is a T-cell surface receptor that delivers inhibitory signals upon engagement with its ligands. PD-1 ligands (PD-L1 and PD-L2) are expressed via oncogenic expression on tumor cells or by stromal cells and may also be upregulated in the setting of high levels of IFN-γ, termed adaptive immune resistance [39].
During tumor development, a subpopulation of non-immunogenic cancer cells develops new mechanisms to evade immune surveillance and induce tumor tolerance. They include decreased expression of MHC-I and expression of immunosuppressive factors that contribute to escape from immune recognition. Consequently, tumors display a strong immune-suppressive TME and fail to elicit an appropriate adaptive immune response. This TME is associated with several molecular mechanisms in place to interfere with CTLs, resulting in poor infiltration of reactive tumor-rejecting T-cells [40].
After an efficient immune response, immune tolerance reduces ability for immune-mediated tumor eradication by associating upregulation of tumor and immune cells PD-L1, DCs and macrophages IDO expression in response to IFNγ signaling, expression of additional immunosuppressive checkpoints (LAG3), and enhanced regulatory T-cells and MDSCs activities [41].
An innate immune response leads to activation of the adaptive immune system (B- and T-cells), provided direct interactions with APCs and a proinflammatory environment. Primary adaptive responses are slower than the innate responses, as clonal expansion due to the recognition of foreign antigens is required.
The current understanding of the dichotomous nature of immune cells in tumors is that IFN-γ-producing CD4+ Th1 and CD8+ CTL along with mature DCs, NK cells, M1 macrophages and type 1 NKT cells can generate antitumor responses. Conversely, CD4+ Th2, CD4+ Tregs, MDSCs, immature DCs, M2 macrophages, and type 2 NKT cells promote tumor tolerance and support tumor growth and progression [40]. Furthermore, the knowledge on the crucial antitumor activity of the immune system has generated great interest in immunotherapy of cancer, including non-immunogenic tumors.
The production of autoantibodies (AAbs) reflects the immunologic reactivity in cancer patients and enhances immune surveillance for cancer cells. AAbs level is detectable in very early cancer stages and may persist for an extended period after cancer removal, reflecting the overall host immune response toward the tumor. It is interesting to note that a repertoire of AAbs is shared by autoimmune diseases and cancer, suggesting that autoimmune conditions share many parallels with the humoral immune response to tumor-associated antigens (TAAs) [42]. Tolerance defects, inflammation, posttranslational modifications, and cell death can affect TAA immune presentation, contributing to cancer-related AAbs production. Recently, AAbs have become useful diagnostic, prognostic, and surveillance cancer biomarkers as they can be easily detected in the serum of cancer patient [43].
Genetic polymorphism is an alternative phenotype that appears to be widespread among the genes of the immune system and can correspond to an evolutionary adaptation of the host organism facing an environment in constant evolution. Several polymorphisms concerning genes that encode Janus kinase/signal transducer and activator of transcription (JAK/STAT), TLR genes, TNF-α, NF-κB, NOD2, autophagy-related protein 16 (ATG16), and receptors for the Fc domain of immunoglobulins (FcR), are involved in the immune responses in cancer development or affect the potency of certain anticancer therapies.
JAK/STAT-signaling pathway plays a key role in the regulation of cellular responses to cytokines (IFN-α, IFN-β, IFN-γ, and IL). It has been demonstrated that genetic polymorphism involved in JAK/STAT (STAT3 and STAT4) pathway is associated with the risk of non-Hodgkin lymphoma [44]. Moreover, polymorphisms in TLR genes may shift balance between pro- and anti-inflammatory cytokines in the host, contributing to the onset and progression of cancers. Recent evidence has implicated polymorphisms of FcRs in the efficacy of monoclonal antibody (mAb)-mediated therapy. Interestingly, the therapeutic effects of IgG1 mAbs (rituximab and trastuzumab) are partially mediated by the FcγR immune response, suggesting that polymorphisms of FcγRs may affect the potency of the mAb treatment [45].
The microbiota is composed of commensal bacteria and other microorganisms that live on the epithelial barriers of the host. Microbiota influences physiological functions including the maintenance of barrier homeostasis and the regulation of metabolism, hematopoiesis, inflammation, and immunity. Recent data demonstrated the involvement of microbiota in cancer initiation, progression, and dissemination. In addition, gut microbiota can modulate the response to chemotherapy, radiotherapy and immunotherapy, and susceptibility to toxic side effects. Therefore, targeting the microbiota may improve anticancer efficacy and prevent toxicity [46].
Immunity in humans can also be affected by environmental factors, including the presence of infectious agents, diet, exposure to sunlight (photoimmunity), and the intake of pharmaceuticals. Interestingly, during periods of decreased exposure to sunlight the human immune responses are associated with enhanced levels of IL-6 and C-reactive protein, which are linked to an increased propensity for autoimmunity. Therefore, it is acceptable to believe that low sunlight conditions may correlate with a more inflammatory systemic environment, leading to better responses to cancer immunotherapy [47].
Glasgow microenvironment score (GMS) is a cumulative prognostic score that combines Klintrup-Mäkinen (K-M) grade and tumor stroma percentage (TSP) and has an independent prognostic value. K-M grade semiquantitatively evaluates the peritumoral immune cell type and density at the invasive margin of the deepest point of tumor invasion using H&E-stained FFPE tissues. K-M grade is classified into (1) low-grade K-M: no increase or mild increase in inflammatory cells, and (2) high-grade K-M: prominent inflammatory reaction that forms a band at the invasive margin, or florid cup-like infiltrate at the invasive edge with destruction of cancer cell islands [48, 49]. K-M grade could be assessed by IHC-stained sections using CD3, CD8, CD45R0, and FoxP3 antibodies to evaluate immune T-cell type [49]. TSP evaluates the percentage of stroma using complete sections of the deepest point of tumor invasion. The proportion of stroma is calculated as the visible field at 10× objective, excluding areas of mucin and/or necrosis [50]. TPS is subsequently graded as low TSP (≤50%) or high TSP (>50%) [49]. The global GMS score is subdivided into three GMS categories: (GMS 0: high-grade K-M), (GMS 1: low-grade K-M/low-grade TSP), and (GMS 2: low-grade K-M/high-grade TSP) [51].
Microenvironment cell populations (MCP)-counter is a transcriptome-based computational method that quantifies the abundance of 10 stromal and immune cell populations in TME using a single-gene expression experiment. MCP-counter produces an abundance score for CD3+ T-cells (CD3D and CD5), CD8+ (CD8B) and CTLs (EOMES and GNLY), B lymphocytes (CD19, CD79A, and CD79B), NK cells (NKp46 and KIR genes), monocytic lineage (CSF1R), myeloid DCs (CD1), neutrophils (FCGR3B and CD66b), as well as fibroblasts (DCN and TAGLN) and ECs (CDH5). These scores can then be used for direct comparisons of the abundance of the corresponding cell type across samples within a cohort. MCP-counter was quantitatively validated by both using mRNA mixtures and IHC in FFPE tissues. This method can reproduce immunological and stromal prognostic classifications associated with overall survival in lung adenocarcinoma and colorectal and breast cancers [52]. However, the loss of spatial cell’s localization is one of limitations when using such transcriptomic technology. Thus, histological confirmation of MCP-counter seems to be necessary in cases where contamination of samples by surrounding non-tumoral tissues is unavoidable.
A transcriptomic classification of colorectal cancer has been recently proposed that stratifies colorectal cancer into intrinsic subtypes with different prognosis. This classification is subdivided into four consensus molecular subtypes (CMS): CMS1 (MSI-like subtype) that contains most microsatellite instability (MSI) tumors and BRAF mutations, CMS2 (canonical subtype) with high chromosomal instability (CIN), CMS3 (metabolic subtype) includes tumors with KRAS mutations and shows a disruption of metabolic pathways, and CMS4 (mesenchymal subtype) that concerns tumors with frequent CpG-island methylator phenotype (CIMP) [53]. Interestingly, a recent comparative study has demonstrated three microenvironmental signatures that correspond to each molecular subtype. The CMS1 was associated with the overexpression of genes specific to cytotoxic lymphocytes, and a good prognosis. Conversely, CMS4 revealed proinflammatory, proangiogenic, and immunosuppressive signature and was associated with poor prognosis. Finally, CMS2 and CMS3 showed almost similar TME profile and were associated with low immune and inflammatory signatures, and intermediate prognosis [54] (Table 2).
Consensus molecular subtypes (CMS) | Molecular characteristics [53] | MCP-counter signature [54] | Mechanisms of action | Prognosis | |
---|---|---|---|---|---|
CMS1 | MSI-like subtype |
| Overexpression of genes specific to cytotoxic lymphocytes | High expression of genes coding for T-attracting chemokines (CXCL9, CXCL10, and CXCL16) or TLS’s formation (CXCL13), Th1 cytokines IFNG and IL15 | Good prognosis |
CMS2 | Canonical subtype |
| Low immune and inflammatory signatures | Intermediate prognosis | |
CMS3 | Metabolic subtype |
| Low immune and inflammatory signatures | Intermediate prognosis | |
CMS4 | Mesenchymal subtype |
| Expression markers of lymphocytes and of cells of monocytic origin. Proinflammatory, proangiogenic, and immunosuppressive signature | High expression of myeloid chemokine CCL2, complement components, angiogenic factors (VEGFB, VEGFC, and PDGFC), and immunosuppressive molecules (TGFB1, TGFB3, LGALS1, and CXCL12) | Poor prognosis |
Cancer transcriptomic signature: molecular subtypes versus tumor microenvironment signature.
Comparatively, in triple-negative breast cancer, three TME subtypes using IHC analyses have been identified: (1) a first subtype with TLR9high expression by cancer cells, hypercellular stroma and numerous TILs overexpressing TLR9; (2) a second subtype with TLR9low expression by cancer cells, a predominantly paucicellular stroma, and rare inflammatory cells expressing TLR9 without TILs; and (3) a third subtype with TLR9low expression by cancer cells, a predominantly fibrotic and vascular stroma containing some immune and inflammatory cells [55].
Tumor microenvironment of metastasis (TMEM) score is an IHC-staining score assessed by three antibodies: anti-CD31, anti-CD68, and anti-panMena. The selected area should be identified by low power, focusing on representative high density and adequacy of tumor, and lack of necrosis, inflammation, and artifacts. TMEM is defined as a structure composed of the direct contact between an invasive pan Mena-overexpressing carcinoma cell, an endothelial cell (CD31+), and a perivascular macrophage (CD68+), with no discernible stroma between tumor cell and perivascular macrophage. Then, the number of TMEMs per 10 high-power fields (×400) is calculated to give a final TMEM score for each patient sample [56, 57]. In breast cancer, TMEM score is positively associated with risk of distant metastasis in ER+/HER2− patients [57].
A group of experts has proposed a step-by-step recommendation of how TILs should be evaluated by pathologists in breast carcinoma tissue samples [58], whether it can be on core biopsies or full surgical sections:
One section (4–5 μm, magnification × 200–400) per patient is considered to be sufficient.
Full sections are preferred over biopsies whenever possible.
TILs should be evaluated within the borders of the invasive tumor.
TILs should be reported as percentage for the stromal compartment (percentage of stromal TILs).
TILs should be assessed as a continuous parameter. The percentage of stromal TILs is a semiquantitative parameter for this assessment, for example, 80% stromal TILs means that 80% of the stromal area shows a dense mononuclear infiltrate.
All mononuclear cells (including lymphocytes and plasma cells) should be scored, but polymorphonuclear leukocytes are excluded.
Do not focus on hotspots: a full assessment of average TILs in the tumor area should be used.
Exclude TILs outside of the tumor border and around DCIS and normal lobules.
Exclude TILs in tumor zones with crush artifacts, necrosis, regressive hyalinization as well as in the previous core biopsy site.
Tumors can be classified into four groups based on their PD-L1 expression and the presence or absence of TILs [59, 60] (Table 3). The type of tumors that fit into each of PD-L1/TILs status depends on the genetic aberrations and oncogene drivers of these tumors. In melanoma, a high proportion of type I (~38%) and type II (~41%) tumors is observed, with the former having considerably the best prognosis [59]. Comparatively, pancreatic cancer has a lower level of PD-L1 expressed on tumor and immune cells [61]. By contrast, in non-small-cell lung cancer (NSCLC) where the oncogenes are more important drivers of tumor PD-L1 expression, the frequency of type III may be higher. In NSCLC, PD-L1 positivity is associated to adenocarcinoma and the presence of EGFR mutations, whereas PD-1 is associated with smoking status and the presence of KRAS mutations [62]. Additionally, increased levels of CD3 and CD8+ are associated with better outcome in NSCLC [63].
Expression groups | PDL-L1/TILs status | Significations |
---|---|---|
Group I | PD-L1+, with presence of TILs | Drives adaptive immune resistance |
Group II | PD-L1−, with no TIL | Indicates immune ignorance |
Group III | PD-L1+, with no TIL | Indicates intrinsic induction |
Group IV | PD-L1−, with presence of TILs | Indicates the role of other suppressor in promoting immune tolerance |
PDL-1/TILs score: tumors can be classified into four groups based on their PD-L1 expression and presence or absence of TILs.
Accumulating data suggest that two major categories of immune resistance within the TME may exist: (i) failure of T-cell trafficking due to low levels of inflammation and lack of chemokines for migration, and (ii) dominant suppression through immune-inhibitory mechanisms. The potential reasons explaining failed tumor rejection in the cases of T-cell-inflamed TME include extrinsic inhibition by PD-L1/PD-1 interactions and the suppression effect of Tregs [64].
Immunotherapies with checkpoint inhibitor PD-L1, which can inhibit T-cell function by binding PD-1 on T-cells, have shown encouraging results in patients with advanced NSCLC. Several agents such as pembrolizumab, nivolumab, atezolizumab, and durvalumab are approved or under clinical development for patients with metastatic NSCLC. Clinical trials have shown an association between the degree of clinical efficacy of these drugs and the level of PD-L1 expression by IHC. In two recent comparative trials, at least three PD-L1 IHC antibodies (22C3, 28–8, and SP263) are aligned regarding PD-L1 expression on tumor cells [65, 66]. A cancer cell is considered PD-L1 positive only when cell membrane is partially or completely stained. By contrast, an immune cell is considered PD-L1 positive if it features any PD-L1 staining: cell membrane or cytoplasm. PD-L1-positive immune cells are predominantly macrophages and lymphocytes. All assays revealed PD-L1 expression on immune cells, but with greater variance than expression on tumor cells. Alveolar macrophages are consistently stained with anti-PD-L1 antibody, serving as an internal positive control.
In NSCLC, PD-L1 tumor proportion score (TPS) is proposed to evaluate the IHC expression on tumor cells. The cutoffs of the different scoring criteria may be integrated into a six-step scoring system (Cologne Score: <1, ≥1, ≥5, ≥10, ≥25, ≥50%).
Currently, pathologists are confronted with two situations to evaluate TSP:
First-line metastatic NSCLC: Pembrolizumab is indicated in first-line setting as both monotherapy and combination therapy in metastatic NSCLC, which has TPS of ≥50%, with no EGFR or ALK genomic aberrations [67].
Second-line metastatic NSCLC: Pembrolizumab is indicated in second-line treatment of metastatic or locally advanced NSCLC, which has PD-L1 TPS of ≥1%. In this case, patients with EGFR or ALK genomic aberrations should have disease progression on therapy for these aberrations prior to receiving Pembrolizumab [61].
The above-cited data underline the importance of PD-L1 test as a biomarker in immunotherapy of NSCLC even in the first-line treatment. Nevertheless, the priority remains to harmonize the procedure of PD-1 testing and interpretation, which might require specific standardization. Therefore, pathologists have a major role to put in place the PDL-1 IHC test in routine practice and determine PDL-1 immunoscore on FFPE tissues.
Systemic anticancer therapies have evolved from chemotherapy through targeted therapies to immune agents and immunotherapy, which is now considered as the third paradigm in cancer treatment. Events from cancer immunity cycle and immune tolerance may serve as both predictive biomarkers and potential therapeutic targets. Immunotherapy is emerging as a novel therapeutic strategy promoting immune response against cancer cells and differing from traditional modalities that target tumor cells directly. Preclinical and clinical evidence provides the rationale for different promising immunotherapeutic approaches combining upregulation of immune responses and downregulation of immune tolerance, to edify a cancer immunity cycle or to re-activate a neutralized preexisting anticancer immune response [68].
Immunotherapies are most effective in patients with a T-cell-inflamed phenotype. Initially, immunotherapy using high-dose interleukin 2 and adoptive T-cell transfer allowed durable clinical benefit in patients with advanced malignancies. Currently, immune strategies have shifted to targeted manipulation of immune checkpoints. Immune checkpoints refer to multiple inhibitory and costimulatory pathways that counteract certain crucial steps of T-cell-mediated immunity to maintain self-tolerance and modulate the duration and amplitude of immune responses. Immune checkpoints are initiated primarily through T-cell inhibiting and stimulating receptors and their ligands, including CTLA-4 (CD152), PD-1 (CD279) and PD-L1 (CD274) or PD-L2 (CD273), among many others [41]. The CTLA-4 antibody ipilimumab was the first approved checkpoint inhibitor after it improved overall survival in patients with advanced melanoma in two randomized phase III trials. However, objective responses are low with ipilimumab monotherapy and 22% of patients with advanced melanoma survived at least 3 years after therapy. Greater clinical benefit has been observed with inhibitors targeting PD-1 or PD-L1 checkpoints. The anti-PD-1 inhibitors pembrolizumab and nivolumab have been recently approved by the US Food and Drug Administration (FDA) for patients with advanced unresectable melanoma, NSCLC, and metastatic renal-cell carcinoma, with objective responses in 40–45, 20, and 25% of patients, respectively. FDA approvals have been announced for nivolumab in patients with refractory Hodgkin’s lymphoma and for the anti-PD-L1 agent atezolizumab in patients with advanced bladder cancer. Furthermore, significant clinical benefit, including durable tumor responses and extension of progression-free and overall survival, has now been observed with other anti-PD-1 and anti-PD-L1 inhibitors in a wide spectrum of solid tumors and hematological malignancies [69, 70].
However, significant responses to immunotherapy only occur in a minority of patients. Attempts are being made to improve the activity of immunotherapies with novel combinatorial strategies and with biomarker optimization. Immuno-oncology drugs are thus currently evaluated and data from recent clinical phase I–III trials have highlighted the potential for combination therapies, including these immunomodulating inhibitory molecules (TIM-3, VISTA, LAG-3, IDO, and KIR) and costimulatory antibodies (CD40, GITR, OX40, CD137, ICOS) [41, 71, 72].
Selection of patients based on validated predictive biomarkers is an important issue that needs to be addressed. Although most of immunotherapies are dedicated to T lymphocytes and cell-mediated cytotoxicity, cancer immune response is a very complex process characterized by numerous reciprocal interactions between tumor cells, multiple immune/stromal cellular subtypes, soluble mediators, ECM, and blood vessels. A wide spectrum of biomarkers is thus required to guide anticancer immune strategies. Immunotherapeutic agents function through different mechanisms of action, including modulation of T-cell receptors (CTLA-4 and PD-1) and adoptive T-cell therapies that associate TILs, chimeric antigen receptors (CARs), and TCR-modified T-cells. Furthermore, tumor spatio-temporal heterogeneity is characterized by different antigenic profiles over time (before and after treatment) and topography (primitive and metastatic tumor) and numerous immunosuppressive mechanisms are promoted in the TME. Most importantly, discovering and optimizing immuno-oncology biomarkers could predict sensitivity or resistance to these immunomodulating molecules, identify their mechanisms of action, and define efficient combined therapies to rationally select patients. Thus, characterizing the anticancer immune response with multidisciplinary and multiparametric NGS and in situ technologies is pivotal to identify multiplex profiles that could allow patient’s stratification for optimal personalized immunotherapy [73].
According to the thematic hallmarks of anticancer immune response, a large spectrum of potential biomarkers that could predict response to immunotherapy have been recently identified, including (i) tumor foreignness: tumor immunogenicity, high mutational load, gene expression profiling, epigenetic modifications of immune genes, intra-tumor heterogeneity; (ii) immunosuppressive tumor metabolism: LDH and TGFβ levels; (iii) host immune status: total lymphocyte count, T-cell and B-cell repertoire, antitumor antibodies titers, preexisting autoimmunity; (iv) immune regulation: antigen presentation (CD40/CD40L), cancer cells reduced MHC expression, T-cell recognition, TCR repertoire diversity, IFNα and TNFα levels; (v) immune cells migration: T-cell trafficking chemokines (CCL5, CXCL9, and CXCL10), chemokines profile, VEGF levels, inflammatory signature; (vi) tumor immune infiltration: CD8+ TILs, FoxP3+ Tregs; (vii) T-cell cytotoxicity: granzyme A, perforin 1, and IFNγ levels; and (viii) immunosuppressive molecules: CTLA4, PDL1, PDL2, LAG3, TIM3, and IDO [73, 74].
These multiple predictive biomarkers present potential great interest in future practice to select patients for optimal immunotherapy: (i) PD-L1 expression in the TME may indicate increased sensibility to PD-1/PD-L1 checkpoint inhibitors; (ii) the presence of TILs suggests a preexisting antitumor immune response that can be reinitialized by immunotherapy; (iii) high tumor mutational load and neoantigens may be indicative of high tumor immunogenicity and sensitivity to immunotherapy; and (iv) the presence of immunosuppressive cells (immature DCs, MDSCs, TAMs, and Tregs), polarization of macrophages (anti-inflammatory M2 macrophages) and DCs (immunosuppressive/tolerogenic regulatory DCs), immunosuppressive molecules and immunoinhibitory cytokines may predict resistance to immunotherapy [72, 75].
Currently, only PD-L1 IHC assays have been validated for clinical utility, although several tumors, host, and environmental biomarkers are very promising candidate for patients’ stratification. NGS and in situ technologies investigating tumor-immune interactions include multiplex immunohistochemistry (multiplexed-IHC), whole-exome sequencing (WES), transcriptome analysis, proteomics, and flow cytometry. However, before clinical application, each of these potential biomarkers requires high-quality validation process, comprising assessment of basic assay performance, characterization of the performance of the assay, and validation in clinical trials.
Recent technological advances have provided new tools that will facilitate an in-depth understanding of the interaction between the immune system and tumor cells, particularly in the TME and will help guide the development of personalized cancer immunotherapies. Data generated from these innovative technologies (i.e., gene microarray, deep-sequencing technologies, mass cytometry, and multicolor IHC staining) are classified into three categories: (i) function (to evaluate the function of different immune cells), (ii) phenotype (to provide the frequency and status of these cells), and (iii) signature/pattern (to elucidate the potential mechanisms of action) [76].
Among these novel technologies, multiplex immunohistochemistry (multiplexed-IHC) appears as very effective and efficient method to identify on the same section and at the same time, several immune cell types, their location, and their state of activation, as well as the presence of immunoactive molecular expression. Multiplexed-IHC is a quantitative, image analysis-based method, using multicolor IHC on FFPE tissues, automated multispectral slide imaging, and advanced recognition software. When coupled with fluorophores (fluorescence multiplexed-IHC), this method takes advantage of light emission with different spectral peaks against a dark background (Figures 2A, B and 3). Fluorescence multiplexed IHC provides spatial localization and distribution of phenotypic and functional biomarkers within the TME and thus is highly beneficial in experimental research for exploring immune evasion mechanisms or finding potential biomarkers [77].
Fluorescent multiplex immunohistochemistry. (A) Breast cancer, section from tumor’s core, and (B) section from invasive margin of the same tumor. The sections are stained with cytokeratin (cancer cells, in green), CD45RO (memory lymphocytes, in red), CD4 (T helper cells, in orange) and FoxP3 (in blue).
Fluorescent multiplex immunohistochemistry. Tertiary lymphoid structure panel stained with CD20 (B cells, in green), CD3 (CD3+ T lymphocytes, in purple), DC-Lamp (mature DCs, in blue), CD21 (follicular DCs) and PNAd (high endothelial venule, in yellow).
After chemotherapy and targeted therapy, immunotherapy has become the third paradigm in cancer. Immunotherapy is a key component of the therapeutic strategies to control and potentially cure cancer. The complexity and heterogeneity of the interaction between the immune system and tumor cells, particularly in the tumor microenvironment, underlies the immune status (i.e., immunologically responsive or immunologically ignorant) of each tumor for every patient. These reciprocal interactions depend on the organ, the oncogenic processes, and their modification by treatments. Although immunomodulation by checkpoint inhibitors (targeting both CTLA-4 and the PD-1/PD-L1 axis) induced a durable tumor response in several malignancies, the use of PD-L1 immunohistochemistry alone has not been sufficient for ruling in or out the use of anti-PD-1 or anti-PD-L1 expression-based therapies. Therefore, characterization of recognized tumor antigens, effector T-cell function, and immune-suppressive mechanisms, TILs, T-cell receptor repertoire, and mutational or neoantigen burden should be aimed at creating an optimized model for predicting response to anti-PD-1 or anti-PD-L1 therapies. Furthermore, specific mechanisms of T-cell exclusion such as activation of the WNT/β-catenin-signaling pathway, microbiota status, and genetic polymorphism should be included in future biomarker development (Table 4).
A summary table describes the stratification factors implicated in the interactions between immune system, tumor microenvironment, and tumor cells, which can influence immunotherapy and therapeutic strategies (immunophenotype, tumor genome/epigenome, tumor microenvironment, Microbiota, environmental factors, host immunity and genetics). This table proposes global tumor microenvironment morphological-, immunophenotypical- and biological-based subtypes with linked immune biomarkers.
Accumulating evidences support that the optimal strategy for further immunotherapy development is combinatory regimens. The challenge of increasing the curative immune responses in a diverse population of patients will require multiple complementary therapeutic modalities to overcome the immunosuppressive tumor microenvironment. Thus, understanding the tumor microenvironment may offer opportunities to predict response to therapy and select the most appropriate immunotherapy for each patient. The recent availability of high-throughput next-generation sequencing and in situ technologies to quantify the different elements of the tumor microenvironment and understand their functionality opens the way for generalization of these approaches and the subsequent application of precision-personalized therapies based on these landscapes rather than on cancer subtypes only.
The authors declare that they have no competing interests.
Polycystic ovary syndrome (PCOS) is emerging as one of the most common endocrine disorders, affecting about 5–14% of the women of reproductive age and a leading cause of infertility [1, 2, 3]. In recent decades, there has been a wealth of evidence that the disease is a typical example of a female sex specific metabolic syndrome (MetS) due to IR, with obesity having an additional aggravating effect [4, 5, 6]. The interest in PCOS, from its first description in 1935 by Stein and Leventhal as a combination of bilaterally enlarged polycystic ovaries with manifest hirsutism, obesity, amenorrhea/oligomenorrhea, and infertility in a group of women [7], is an ever increasing one, becoming interdisciplinary, as affected girls and young women are at increased risk of cardiovascular disease (CVD) compared to age-matched healthy women [8, 9, 10]. This opinion is based mainly on the metabolic disorders established in PCOS (Table 1).
Overweight/obesity (android type) |
Insulin resistance/hyperinsulinemia |
Impaired fasting glucose (IFG)/impaired glucose tolerance (IGT)/ Diabetes mellitus (DM) type 2 |
Gestational diabetes mellitus |
Dyslipidemia (↓HDL-cholesterol; ↑ triglycerides) |
Arterial hypertension/Arterial hypertension during pregnancy |
Hypercoagulation |
Hyperuricemia |
Metabolic disorders in PCOS.
PCOS is a complex disorder that results from the interaction of diverse genetic and environmental factors. Heritable factors include polycystic ovarian morphology due to functional ovarian steroidogenic defects, hyperandrogenemia, IR, and insulin secretory defects. Acquired obesity is a major postnatal unfavorable factor [11] (Figure 1).
Mechanisms by which obesity may determine the obese PCOS phenotype (adаpted according to [11]).
The major atherogenic risk factor is IR, since excluding all other pathological abnormalities, weight included, the hyperinsulinemic women with PCOS have a 5-fold higher incidence of CVD risk factors than the normoinsulinaemic ones. But the latter, in turn, remain at a significantly higher CVD risk than their age-and BMI-matched healthy controls [12]. This fact supports the main impact of the disease itself. The role of hyperandrogenemia as an independent determinant of CVD risk in PCOS is controversial—there are studies supporting [13] and rejecting [14] the direct link; moreover, elevated androgen levels are interpreted by most authors as being secondary to an underlying IR [15, 16, 17].
Not only does the presence of hyperandrogenemia and IR/compensatory hyperinsulinemia in PCOS elucidate such important pathogenetic mechanisms of the disease, but some clinical observations show that, in fact, the late metabolic complications are more deleterious than the reproductive dysfunction itself. Moreover, recent data from a long-term prospective study indicate that hyperinsulinemia and IR tend to deepen spontaneously in PCOS women, even without worsening of the hyperandrogenism [18]. PCOS is a markedly heterogeneous disease that is why the results of numerous studies on CVD risk assessment in the PCOS women are controversial. This is due to some differences in research trial designs and the characteristics of PCOS women cohorts in terms of weight and anthropometric variables, presence or absence of IR, and other metabolic disorders [19].
Glucose tolerance in women with PCOS was systematically investigated for the first time by Dunaif et al. in 1987 [20]. A number of studies involving large populations of PCOS women reported incidence of carbohydrate disturbances higher than 40% (30–35%: IGT and 8–12%: overt DM type 2) [20, 21], which is considerably higher than the one observed in population studies of age-matched women. Overweight is a prerequisite for the development of carbohydrate disturbances, but Legro et al. [22] demonstrated that even lean PCOS women were exposed to a higher risk—31.1% had IGT and 7.5% were newly diagnosed with DM. In an Italian study, the incidence of carbohydrate disturbances was found to be lower—DM 2.5% and IGT 15.5% [23]. The Australian study of Dabadghao et al. reported incidence of overt DM 4% and IGT 15.6%, the latter correlating with age, family history, abdominal obesity, and the presence of MetS [24].
In general, the incidence of a newly diagnosed DM in targeted studies in PCOS women reaches 10%, the greater part of the affected women being in the third or fourth decade of life. Even the lower DM incidence in certain populations of PCOS women has proved to be significantly higher, as compared to the age-matched populations. For instance, in a study conducted at the University of Pittsburgh, DM type 2 was observed in 12.6% of PCOS women of an average age 42 years, against 1.4% observed in the corresponding healthy population [25]. In the Netherlands, DM was found in 2.3% of the normal-weight PCOS women, aged 45–54, but this incidence was four times higher, as compared to the control population [26]. Two studies of ours in Bulgaria [27, 28] found DM incidence of 1.4% in a group of 142 PCOS women and 1.1% in another group of 94 patients, which is a comparatively low figure. However, we must take into consideration that DM in PCOS women is manifested relatively later, at an age between 30 and 40, and the predominant part of our patients were of a younger age (average age 22 years). That is why special attention should be paid to the risk groups—the women with IFG and IGT (in our studies they were 4.9 and 7.4%, respectively) and especially those who are overweight or obese as well (5.8 and 6.4%, respectively) [27, 28].
The PCOS women are more predisposed to development of gestational DM as well [29]. On the other hand, women with gestational DM have a higher incidence of PCOS, diagnosed postpartum [30, 31], and this is associated with persistent carbohydrate disturbances occurring afterward [32], in contrast to the usual returning to the norm. A study involving diabetic PCOS women found that 55% of them had gestational DM during pregnancy [33].
Following the initial evidence of Burghen et al. concerning the presence of hyperinsulinemia in PCOS [34], many other investigators obtained similar results demonstrating that both lean and obese PCOS women are characterized by IR and hyperinsulinemia [35, 36]. It has been proved that PCOS women have a more marked IR as compared to age- and BMI-matched healthy women, the difference becoming greater with the increase in BMI [33, 34, 35, 36, 37]. The use of insulin sensitizers significantly improved the characteristic metabolic and endocrine features of PCOS, ovulatory function, menstrual cyclicity, and fertility [19, 38, 39, 40, 41].
It is generally accepted that obese PCOS women are insulin-resistant. The obese PCOS women have higher insulin levels and/or more marked IR, as compared to obese controls and normal-weight PCOS women [42]. Still debatable is the issue whether IR in PCOS depends on weight and/or the android redistribution of adipose tissue, or it is intrinsic to the disease, since there is evidence in both directions [35, 43, 44, 45, 46]. Studies differing in design have obtained similar results, showing that both obese and lean PCOS women have IR and hyperinsulinemia. Some of these differences are due mainly to nonstandardized criteria for diagnosing both PCOS and IR. Family history of DM type 2 is not always taken into account. In addition, there are certain racial and ethnic peculiarities, which become more and more distinct regarding not only the individual characteristics of PCOS, but also the insulin sensitivity and the MetS itself [47, 48, 49].
It is important to know that irrespective of the occurrence of multiple risk factors, DM develops only in the presence of impaired β-cell function. In addition to the reduced insulin sensitivity, secretory dysfunction in pancreatic β-cells has been found in PCOS [50, 51]. This β-cell defect—increased fasting and reduced postprandial insulin secretion—results in inability of the available insulin to compensate for the degree of resistance to its action. The reduced postprandial response to insulin in PCOS women resembles the defect typical of DM type 2 and is much more marked in those who have first-degree relatives with DM type 2. Weight reduction results in significant improvement in IR, but the β-cell defect persists [38], which presupposes that it may prove to be a primary abnormality in PCOS. This is supported by the fact that the β-cell is unable to compensate for the peripheral IR that occurs early in the course of PCOS. Thus, a reduced first phase of insulin secretion has been established in adolescent girls with PCOS, as well as reduced index of glucose disposal and increased liver glucose production [52].
As it was stated above, the PCOS women have an increased basal insulin secretion, although the insulin secretory response to glucose loading as a whole is inadequate, as compared to that of healthy women [43, 53]. On tissue level, IR develops in the liver, adipose tissue, and muscles of PCOS women. Hyperinsulinemia in PCOS is considered to be secondary to IR. The latter involves a new mechanism of marked defects in insulin-dependent glucose transport, with significant alterations in receptor dynamics [54]. Marked reduction in insulin sensitivity has been found in biopsy-obtained adipocytes of PCOS women, as well as a milder but also significant decrease in the maximum rate of insulin-stimulated glucose transport, secondary to the reduced expression of GLUT-4 glucose transporters [55]. Such defects are observed in DM type 2 and obesity, but in PCOS they are found even in the absence of carbohydrate disturbances, overweight, and alteration in the waist-to-hip ratio (WHR). Moreover, they do not correlate with sex hormone levels, which suggest that the impairment of insulin action is most likely primary [56].
With a view to further elucidate post-receptor defects, a reduction in insulin-dependent receptor tyrosine autophosphorylation has been found in isolated fibroblasts of about 50% of PCOS women, as well as an increase in noninsulin dependent receptor serine phosphorylation, i.e., receptor tyrosine kinase activity is inhibited. A factor extrinsic to the insulin receptor, probably serine/threonine kinase, induces serine phosphorylation of the insulin receptor, which results in signal inhibition [57, 58]. This defect leads to IR in the early stages of insulin-receptor-mediated signal transduction. This unique PCOS characteristic distinguishes it from the other clinical conditions with IR [57]. The resultant hyperinsulinemia, arising as it seems upon triggering of puberty, may involve the system of ovarian insulin-like growth factors (IGFs), influence the liver production of the IGF binding protein-1(IGFBP-1), and is probably a pathogenetic factor in the development of the disease [59].
Serine phosphorylation seems to modulate the activity of the key regulatory enzyme of androgen biosynthesis—Р450с17, present in both ovarian and adrenal steroidogenic tissue. In this way, serine phosphorylation enhances enzymatic activity and increases androgen synthesis [60]. It is interesting to note that serine phosphorylation of insulin receptor substrate-1 (IRS-1) is also the mechanism of the TNF-α-mediated IR in obesity [61]. Thus, one and the same defect—serine phosphorylation—is likely to result in both IR and hyperandrogenism. This is a very tempting hypothesis explaining the syndrome pathogenesis; unfortunately, it is valid for only a part (about 50%) of the population of PCOS women.
With view to establishing a defect in insulin action after binding to the receptor, Book and Dunaif [62] investigated the metabolic and mitogenic effects of insulin and IGF-1 in a culture of skin fibroblasts from PCOS women and healthy controls. The authors found that in PCOS, a selective defect was present involving insulin metabolic but not mitogenic signaling pathways; a similar defect was found in the action of IGF-1 (this fact shows that insulin and IGF-1 stimulate glycogen synthetase by one and the same post-receptor pathways) [62]. Poretsky et al. demonstrated that the inhibition of PI-3-kinase activity did not alter insulin-induced stimulation of progesterone production in cultures of human ovarian cells [63]. On the other hand, insulin-stimulated PI-3-kinase activity in skeletal muscles of PCOS women was damaged [64]. The results suggest that the insulin regulation of steroidogenesis and glucose metabolism uses different signaling pathways, the first one remaining functionally active and probably even overstimulated by the increased insulin levels in women with IR [4] (Figure 2).
Role of insulin in the pathogenesis of PCOS.
The hypothesis of presence of a post-receptor defect in insulin action in PCOS is consistent with the results of investigations performed on a molecular level, which have not found structural anomalies in the insulin receptor [65, 66, 67]. What exactly the defect is remains to be elucidated. An evidence provided by Ek et al. [68] suggests a selective impairment in the function of the protein kinase A-dependent hormone-sensitive enzyme lipase, which regulates the lipolytic response to catecholamines in visceral adipose tissue in lean PCOS women with mild IR. An abnormal post-receptor sensitivity to catecholamines has been observed while the antilipolytic sensitivity to insulin is preserved [68]. This anomaly differs from the impaired balance occurring in the MetS between the lipolytic β3- and the antilipolytic α2-adrenoreceptors [69]. It is still unclear whether this unique defect found in PCOS is primary or secondary to other factors, such as increased serum androgen levels.
Thus, the logical question arises—if IR and hyperinsulinemia play a major pathogenetic role in PCOS, why all women with hyperinsulinemia (e.g., with DM type 2) are not hyperandrogenic as well? IR and reproductive disturbances are probably indicative of single genetic defects and IR unmasks the syndrome in genetically predisposed individuals. Because of the fact that PCOS-related IR is a selective one and involves the metabolic but not the mitogenic and signaling pathways, we can explain the paradox of a persistent biological insulin action on reproductive processes on the background of systemic IR [70]. In general, studies have shown that predominantly PCOS women with both hyperandrogenism and chronic anovulation seem to be insulin-resistant. Women with only hyperandrogenism or a morphological finding of polycystic ovaries who have normal ovulation are less likely to develop IR [20, 71].
To sum up, insulin and LH act synergetically on the theca-cells of the polycystic ovary (hyperplasia of these cells is usually present) and activate Р450с17α1; thus, enhancing the biosynthesis of ovarian androgens and testosterone [72, 73, 74, 75]. A further adverse action of hyperinsulinemia on the ovaries of PCOS women includes arresting the development of the ovarian follicle up to 5–10 mm in size and preventing ovulation [71, 75]. Outside the ovary, insulin can act directly as a co-gonadotropin enhancing LH activity by stimulating the ovarian receptors for insulin and IGF, or indirectly by increasing the amplitude of LH serum pulses, enhancing the sensitivity to GnRH stimulation (Figure 3).
Schematic representation of the factors regulating ovarian steroidogenesis.
The possible mechanism of insulin-induced enhanced ovarian steroidogenesis is supported by the higher incidence of PCOS in women with DM type 1—the ovaries of the affected women are exposed to hyperinsulinemia resulting from the availability of exogenously administered insulin in the systemic circulation [76].
In approximately 40% of the women diagnosed with PCOS in conformity with the NIH (National Institute of Health) criteria, an IGT or DM type 2 developed as sequelae of IR in the fourth decade of life, the age and weight gain having an unfavorable effect on glycemic control [27, 77, 78, 79, 80]. In addition, a study based on the Rotterdam diagnostic criteria, 2003 reported IR in 71.54% of the studied PCOS women [81].
The incidence of IR, however, differed considerably among the various phenotypes—80.4% in the “classical” one (phenotypes А and В), 65% in the women with normal ovulation (phenotype С) and 38.1% in the group with normoandrogenemia (phenotype D). The classical phenotype and to a lesser degree the phenotype without ovulatory dysfunction were independently associated with IR, whereas in the normoandrogenic phenotype no IR was found [81]. This was confirmed by another study, showing that the number of women with PCOS and a HOMA-index >3.8 is considerably higher in the hyperandrogenic phenotypes, as compared to the normoandrogenic one [82]. That is why the nature and course of carbohydrate disturbances in women with different phenotypic presentations of PCOS require establishing a precise and timely diagnosis, as well as proper behavior by changing one’s lifestyle and dietary regimen, weight reduction whenever needed, with view to reducing the risk of developing DM and/or its complications.
The risk factors for developing carbohydrate disturbances in PCOS are presented in Table 2.
Confirmed | Possible |
---|---|
Age Obesity Abdominal deposition of adipose tissue Insulin resistance β-cell dysfunction Family history of DM type 2 | Chronic anovulation Hyperandrogenemia Dyslipidemia (hypertriglyceridemia) Ethnicity (certain risk populations) |
Risk factors for developing carbohydrate disturbances in PCOS.
Having in mind the incidence of carbohydrate disturbances in the general population of women aged 20–44 (7.8% for IGT and 1.0% for newly diagnosed DM type 2) and the average prevalence of PCOS (about 5%), it can be extrapolated that PCOS-associated IR contributes approximately to 20% of IGT and 40% of DM type 2 in women of reproductive age, which gives prominence to the social importance of this syndrome. In the light of this evidence in 2006, the Аmerican Association of Clinical Endocrinologists (ААСЕ) [83] recommended screening for presence of DM in all PCOS women after the age of 30, irrespective of weight—normal or overweight. Under certain risk circumstances, screening has been recommended before that age as well [83]. Considering the fact that DM type 2 can develop with age progression, the women who have had initially a negative result should be followed-up periodically.
The methods for evaluation of insulin sensitivity are presented in Table 3.
Euglycemic insulin clamp technique |
Minimal model—multiple determination from i.v. GTT |
Sensitivity insulin infusion tests
|
Insulin parameters in oral glucose tolerance test (oGTT)
|
Baseline insulin and derivative indexes, according to baseline blood sugar
|
Methods for evaluation of insulin sensitivity.
In the routine clinical practice a measurement of basal and during oGTT glucose and insulin levels is most frequently used.
The association between PCOS and obesity is complex. In the USA, it was reported that obesity affects from 30 to 75% of PCOS women [41, 84], which is higher than the percentage found in Europe [85, 86]. In a systematic review and meta-analysis of the literature, Lim et al. concluded that in PCOS women, as compared to the age-matched controls, a higher incidence of overweight and obesity was found [87]. In addition, the carriers of the syndrome of the Caucasian origin were found to be more overweight than their counterparts from the Asian origin [87]. These results are compromised to a certain extent by the fact that in most of the published studies, the patients have been selected from clinical practices on the basis of a subjective evaluation and local diagnostic methods. In general, overweight women are more often referred to a specialist for searching PCOS. However, in independent population samples, the incidence of obesity in PCOS does not seem as high as the one found in clinically targeted populations [27, 88]. Furthermore, PCOS incidence, based on the diagnostic criteria of NIH, is relatively stable throughout the world, irrespectively of the variable incidence of obesity in different populations [89].
In one of our studies, we found obesity in 51% and overweight of 22% in an unselected Bulgarian population of 142 women with PCOS [27]. Obesity incidence in our patients was higher than 38% found in women with PCOS from the island of Lesbos, Greece [90], and closer to the one found in England, where around 60% of the studied PCOS women were obese [71]. According to many studies including ours, PCOS women have higher ratio of central to peripheral redistribution of adipose tissue in comparison to controls [27, 91, 92, 93, 94]. Obesity, of visceral type mainly, plays a key role in developing and maintaining the syndrome [95, 96] and influences significantly its severity as well as metabolic and CVD risk, since it is a well-known risk factor for IGT and DM type 2, IR and dyslipidemia [91, 97, 98]. In this respect, insulin sensitizers may exert complex positive effects on both metabolic consequences and clinical manifestations of hyperandrogenism in women with PCOS [19, 47, 48, 83, 91] (Figure 4).
Role of insulin sensitizers in the treatment of PCOS.
Obesity may promote the onset and exacerbate other long-term sequalae of PCOS, including metabolic disorders, the occurrence of some types of carcinoma, potentiated by chronic unopposed estrogen secretion, and leads to further impairing of the quality of life (QOL), low self-esteem, and worsened social adaptation, which may even potentiate occurrence of mental disorders. Obese PCOS women have a more severe clinical picture with higher incidence of IR, hyperinsulinemia, carbohydrate and lipid disturbances, and hyperandrogenism. Many studies, including ours, have shown that overweight PCOS women possess a higher degree of IR in comparison to the lean ones [27, 91, 92, 93, 94].
Data exist that even normal-weight carriers of the syndrome show unfavorable abdominal redistribution of adipose tissue and IR [99]. In a study of young normal-weight PCOS women (mean age 15.9 ± 1.8 years, mean BMI 22.7 ± 2.3 kg/m2) Cree-Green et al. found reduced insulin sensitivity and mitochondrial dysfunction in the muscles, relative postprandial hyperinsulinemia, abnormal glucose disposal, and increased hepatic fat in comparison to healthy controls [100].
PCOS women frequently have decreased sex hormone-binding globulin (SHBG) levels, which may decrease further with obesity development [101]. In turn, SHBG was found to correlate positively with HDL-cholesterol and physical activity and negatively with obesity, central distribution of adipose tissue, triglycerides, IR parameters, and presence of DM type 2 [102].
In conclusion, the concomitant obesity, especially of an android type, is associated with an increase in the long-term metabolic risk in women with PCOS.
In the last decades, visceral adipose tissue is perceived as a source of biologically active substances—adipocytokines [103]. Commonly, PCOS women have increased amount of visceral adipose tissue and associated metabolic disorders. The influence of adipose tissue hormones on IR processes, carbohydrate, lipid, and atherogenic disorders in PCOS women is a subject of increased research interest [104].
Leptin is known to act as a chief “adipostat”—it suppresses the intake of food and water and leads to activation of catabolic metabolic pathways related to an increased production of energy. It improves the peripheral (liver and musculoskeletal) insulin sensitivity and affects β-cell function. It has been found that there is a positive correlation between plasma leptin levels and the amount of adipose tissue in the body. Leptin levels decrease rapidly during fasting and increase after food intake. Leptin is important not only for energy balance regulation and food intake but it also performs a function of metabolic and neuroendocrine hormone; participates in glucose metabolism, reproductive processes, interacts with the hypothalamic-pituitary-adrenal axis; influences thyroid hormone and growth hormone secretion; and even interferes with hematopoiesis and the immune system function. There are data for a strong association of circulating leptin and immunoreactive insulin (IRI) values and fasting plasma glucose, HOMA index, dyslipidemia, arterial hypertension that is independent or only partially dependent on obesity [103, 104].
A close relationship between IR and hyperleptinemia in PCOS women was found irrespective of their weight [105, 106] but the results are mostly controversial due to the differences in the studies designs and the lack of data on the independent effect of obesity, as well as the presence of various phenotype expressions of PCOS. In one of our studies [107], we found higher leptin levels with borderline significance in PCOS women in comparison to age-, weight-, waist circumference-, and WHR-matched healthy controls. Significant correlation of leptin was found with BMI, waist circumference, WHR, percentage of adipose tissue, as well as with basal insulin and HOMA-index in the PCOS group [107]. These findings were confirmed and complemented by our more recent studies of women with IR syndromes, including PCOS [108, 109]. Thus, leptin exhibited significantly positive correlation with BMI, WHR, percentage of adipose tissue, basal glucose and insulin, HOMA-index, total cholesterol, triglycerides, plasma atherogenic index, Castelli I, and Castelli II indexes. A significant negative correlation was found of leptin with Matsuda index, QUICKI-index, and adiponectin [108, 109].
Mohiti-Ardekani et al. [110] also found a positive correlation between free and total leptin levels and HOMA-index in PCOS women (r = 0.78, Р < 0.001; r = 0.84, Р = 0.003, respectively), as well as in healthy controls (r = 0.86, Р < 0.001; r = 0.69, Р < 0.001, respectively). Similar results were reported by authors from Australia [111], Brazil [112], Canada [113], Finland [114], Italy [115], Sweden [116], Turkey [117] and the USA [118, 119]. In a more recent study of PCOS women (mean age 34.30 ± 2.08 years, mean BMI 34.84 ± 4.77 kg/m2) and normally ovulating controls with comparable BMI (mean age 28.10 ± 4.61 years, mean BMI 33.59 ± 1.23 kg/m2) Nomair et al. [120] found higher leptin concentrations in PCOS women in comparison to controls (P = 0.005), significant differences being found in intergroup comparative analysis between the insulin-resistant and non-insulin-resistant PCOS women as well (P = 0.044). In women with PCOS a positive correlation between leptin and BMI (P = 0.049) was found. Authors also consider BMI and IR the two chief factors associated with leptin levels [120].
Our results, as well as those from the above-mentioned studies, are indisputable proof for the role of leptin in the pathogenesis of IR in PCOS.
Adiponectin is a model of an anti-inflammatory adipocytokine. A negative correlation was found between its serum levels and the degree of obesity, IR, IGT, dyslipidemia, and atherosclerosis [121, 122, 123]. The increased amount of visceral adipose tissue results in hypoadiponectinemia due to reduced expression of adiponectin genes. This leads to suppression of the insulin activity in the liver, muscles, and other peripheral tissues. Conversely, the high adiponectin levels are an independent factor for an increased insulin sensitivity and reduced risk for DM type 2. On the basis of the effects on insulin sensitivity and its anti-inflammatory properties, adiponectin is perceived as an antiatherogenic factor. The decreased adiponectin is also combined with increased production of pro-inflammatory proteins IL-6, С-reactive protein (CRP). A positive correlation between the reduced levels of the hormone and the development of ischemic heart disease has been registered [124]. It was established that adiponectin production is suppressed in conditions of IR—DM type 2 and obesity [125]. Low adiponectin levels in obesity are probably due to the process of “down”-regulation mediated by the increased amount of adipose tissue. In a study of a large population in Japan [126] and American Pima Indians [127], adiponectin levels were found to be in negative correlation with the indexes of IR even if the factors age and BMI were excluded.
Initial studies of the levels of this antiatherogenic adipocytokine in women with PCOS were conducted by Orio et al. [128] and Panidis et al. [129]. Orio et al. [128] determined serum adiponectin levels in 60 PCOS women (30 normal-weight and 30 overweight) and in 60 age- and BMI-matched healthy women. Adiponectin levels were significantly lower in obese women in comparison to normal-weight women in the PCOS group, as well as in the control group. A significant difference in adiponectin levels between PCOS women and healthy women was not found, its levels in both groups correlated negatively with BMI and HOMA-index. The authors concluded that adiponectin concentrations vary depending on the quantity of the adipose tissue and that insulin sensitivity does not play a key role in controlling adiponectin levels in PCOS women [128]. Although in other IR conditions adiponectin was found to be decreased, both cited studies reported that in normal-weight PCOS women with IR and hyperinsulinemia its levels did not differ from those in the controls [128, 129].
However, in PCOS women with severe IR, Sepilan et al. found that it was the insulin sensitivity but not the weight that was the chief determinant for adiponectin levels [130]. This fact was confirmed by one of our more recent studies [131], which revealed higher adiponectin levels in non-insulin resistant PCOS women in comparison to insulin resistant ones. In addition, insulin levels and HOMA-index proved to be higher in the group of obese PCOS women in comparison to BMI-matched controls while adiponectin levels were similar in both obese groups. On the other hand, adiponectin concentrations were significantly higher in PCOS women with normal BMI in comparison to those with obesity. In PCOS women a negative correlation between adiponectin and body weight, BMI, waist circumference, hip circumference, WHR, blood glucose at 60 and 120 min, IRI at 0, 60, and 120 min of oGTT, HOMA-index, triglycerides, triglycerides/HDL-cholesterol ratio, plasma atherogenic index, and leptin was found. We observed also a positive correlation of adiponectin with Matsuda and QUICKI indexes [109, 131]. Most probably the relation between adiponectin and IR is confined to the ability of this adipokine to stimulate glucose utilization and to reduce glucose production by the liver [71, 114]. The established significant correlation of adiponectin and androstenedione in PCOS which presupposes some interrelation between this hormone and ovarian steroidogenesis is very interesting and needs further elucidation [11, 131].
It is believed that the leptin/adiponectin ratio (L/A) correlates better with the degree of IR in comparison to leptin and adiponectin values taken separately. L/A is a powerful independent predictor of CVD, its values being strongly associated with the intima-media thickness and correlating positively with a number of other anthropometric, metabolic and clinical parameters [132]. In our studies we found significantly higher L/A values in PCOS women with IR in comparison to those without IR [109, 131]. This is yet another proof that insulin resistant PCOS women are with a higher CVD risk.
Resistin, described for the first time in 2001, is a protein rich in cystein, secreted by adipocytes, and it is suspected to carry out the relationship between obesity and DM. Due to its association with obesity, inflammatory process and IR, resistin is thought to be a potential biomarker for the MetS. Thus, the higher resistin levels found in patients with overt MetS in comparison to clinically healthy individuals support this theory [133]. However, data concerning the presence of significant dependencies between resistin levels and parameters of weight and insulin sensitivity in basal conditions and following weight reduction are controversial. Some authors found significantly higher resistin levels in obese individuals in comparison to individuals with normal weight [134], while others, including us [135], did not find significant differences [136, 137].
There is controversial data in studies among PCOS women concerning resistin levels in terms of a lack of association with the syndrome [138, 139], or an increase in PCOS [140]. Thus, Panidis et al. [139] did not confirm an active role of resistin in the pathogenesis of PCOS. The authors compared anovulatory PCOS women (obese and non-obese) and healthy controls with normal weight. Resistin was significantly higher only in the obese PCOS women in comparison to the other two groups irrespectively of the differences in insulin levels and the glucose/insulin ratio. Resistin did not correlate with any hormonal or metabolite index in our Bulgarian population of PCOS women with overweight [135]. Pangaribuan et al. [141] also did not find significant difference in serum resistin levels between PCOS women and controls. Meanwhile, the authors did not find significant correlation between resistin, BMI and HOMA-index [141]. Similar serum resistin levels in normal-weight women with or without PCOS were found by Seow et al. [142]. But resistin iRNA expression in adipocytes was twice as high in PCOS women. Probably, the overexpression of the resistin gene plays a role as a local factor [142].
Olszanecka-Glinianowicz et al. [143] studied the association of adiponectin and resistin with the process of IR in PCOS women and controls. All study participants were divided into two subgroups—obese and normal-weight. Comparable serum resistin concentrations between the two subgroups of PCOS women and controls were observed. No correlations between the adipokines, HOMA-index and androgen levels were found [143]. Lewandowski et al. published similar data [144]. The authors did not find a correlation between adiponectin and resistin with the parameters of IR (basal IRI, HOMA-index, QUICKI) [144]. Yilmaz et al. obtained different results—higher resistin in PCOS women in comparison to controls, however, they observed that resistin levels remained independent of the degree of IR and BMI [145] which supports the data of some of the above-mentioned studies.
The results from our studies in adipocytokines in PCOS [47, 48, 104, 107, 108, 109, 146] showed similar resistin levels in PCOS women and metabolically healthy obese women, higher resistin levels in insulin-resistant PCOS women in comparison to non-insulin resistant ones, lack of significant difference among the different subgroups of PCOS women, divided according to BMI. Resistin correlated positively with IRI at 0 and 120 min during oGTT, HOMA-index and negatively with Matsuda and QUICKI indexes [109, 146]. Wang et al. [147] registered significantly higher resistin levels in PCOS women (obese and normal-weight) in comparison to clinically healthy women. In similarity with our data, the authors reported a positive correlation of resistin with HOMA-index and a negative one with adiponectin [147]. Resistin in our study showed a positive correlation with IL-6 [109, 146]. Our data is peculiar in this aspect since it is considered that IL-6 is the main adipocytokine which regulates resistin levels. An in-vitro study showed that IL-6 production, as well as the one of other cytokines (IL-1 and TNFα), increased resistin expression in mononuclear cells [148].
A number of studies on the relation of resistin with obesity, IR, MetS, CVD risk in different age populations have been conducted so far, which, though being controversial in some respects, lead to further clarification of the role of resistin in the processes of atherogenesis [149, 150]. It was found that with the increase in the number of MetS components, serum levels of resistin and other pro-inflammatory markers increase as well [150]. These and a number of other results fully support the hypothesis on the relation between circulating resistin levels and the degree of IR. Having in mind that PCOS is considered a prototype of female specific MetS in young age populations, the role of resistin has to be clarified.
Visfatin—a protein derived from adipose tissue that is considered to have antidiabetic properties. Visfatin is isolated in the form of a cytokine which stimulates β-cell precursor maturation, therefore it was called pre-β cell colony-enhancing factor (PBEF) [151]. Visfatin stimulates glucose utilization from the adipocytes and myocytes and suppresses glucose release from liver cells, exhibiting the ability to bind to the insulin receptor and to activate it through inducing tyrosine phosphorylation. Acting as an insulin mimetic, visfatin can partially reduce IR, although it is found in much lower concentration in the circulation that insulin [152]. Visfatin participates in the process of adipocytes formation. A positive correlation between visfatin and the presence of obesity, increased visceral tissue, DM type 2 was found, and it was also elevated in patients with MetS [109, 131]. Taking in consideration these findings, the research interest in the changes in visfatin levels in PCOS is completely justified.
A meta-analysis [153] encompassing a study among 1341 women (695 with PCOS and 646 controls) showed higher visfatin levels in PCOS women without a significant correlation between it and BMI, HOMA-index, and testosterone. The authors concluded that high circulating visfatin can be perceived as a specific characteristic of PCOS, which even presupposes a role for this adipokine as a potential diagnostic biomarker for PCOS [153]. Kowalska et al. [154] found higher visfatin levels and a reduced insulin sensitivity in both normal-weight and obese PCOS women in comparison to healthy controls. Visfatin correlated negatively with parameters of IR, this correlation being well-expressed in normal-weight PCOS women, but missing in obese ones. It must be noted that in some circumstances visfatin does not succeed in exhibiting its beneficial effects on carbohydrate metabolism [154]. A hypothesis that the increase in serum visfatin levels is a secondary process aiming to prevent IR exists. On the other hand, insulin possesses the property to inhibit visfatin expression from the adipocytes so that the observed interrelationship could be explained as an inability of insulin to inhibit visfatin production in an already developed insensitivity to its action [155]. In this context, in the study of Kowalska et al. a positive correlation of visfatin with total testosterone and free androgen index (FAI) in the lean PCOS women was established [154]. The study of Tan et al. [156] also confirmed higher visfatin levels in PCOS women in comparison to age- and weight-matched healthy women. The researchers found a stimulated process of expression of visfatin mRNA and of the protein precursor of visfatin both in subcutaneous and visceral adipose tissue in PCOS women. Plasma visfatin levels were in a positive correlation with basal IRI (Р < 0.01), HOMA-index (Р < 0.01), testosterone (Р = 0.03), and estradiol (Р = 0.046). After performing a multiple regression analysis, the researchers found that the HOMA-index was the only predictive factor for visfatin levels. In contrast to plasma visfatin levels, the expressed visfatin mRNA in subcutaneous and visceral adipose tissue correlated positively with BMI and WHR [156].
In our Bulgarian studies [109, 131], we found higher serum visfatin levels in insulin-resistant PCOS women in comparison to non-insulin resistant ones. Visfatin levels in the PCOS women with obesity/overweight and in the BMI-matched metabolically healthy controls did not differ significantly. In our PCOS women, a negative correlation of visfatin with HDL-cholesterol and Matsuda index was found as well as a positive one with diastolic arterial pressure [109, 131]. In similarity to our results, Kowalska et al. reported a negative correlation between visfatin and HDL-cholesterol (r = −0.27, Р = 0.004) [154]. Such negative correlation (r = −0.349, Р = 0.013) was confirmed also by El-Said et al. [157] in insulin resistant PCOS women. In addition, the authors reported a positive correlation of visfatin with BMI, waist circumference, HOMA-index, FAI and a negative one with LH, total testosterone and sex hormone-binding globulin (SHBG). In this study, as well, visfatin was significantly higher in the PCOS women in comparison to the healthy women (72.94 ± 33.3 vs 54.69 ± 31.5 ng/mL, Р = 0.039) [157]. In contrast to our results and the ones mentioned so far, Gen et al. reported a positive correlation between visfatin and HDL-cholesterol in normal-weight PCOS women [158].
It appears that there is controversy with respect to the relation of visfatin with insulin sensitivity indexes in women with IR and namely with PCOS and MetS. The main action of visfatin is intended at prevention of IR development as it was already pointed out, and this can explain its increase in PCOS women. The negative correlation of visfatin with IRI and HOMA-index and respectively the positive one with atherogenic indexes QUICKI and Matsuda in women with overt MetS registered in our studies is in support of this suggestion. Visfatin secretion control is a subject of increased research interest that arises much debate. Up till now, the conducted clinical studies comprising insulin resistant individuals with obesity and MetS, exhibit controversial results. The changes in this adipocytokine in PCOS women with different phenotypes are still to be clarified in targeted studies.
Adipose tissue is an important source of factors of low-grade inflammation not only due to the production of various cytokines by the adipocytes themselves, but also because of tissue infiltration with pro-inflammatory macrophages. The adipose tissue macrophages are responsible for the production of almost the entire amount of TNF-α and a significant portion of IL-6 [159].
TNF-α is a cytokine, which interferes with the regulation of the amount of adipose tissue (inhibits the conversion of young immature fat cells into mature ones), the insulin action (disrupts the insulin receptor signal in peripheral cells) that causes post-receptor defect with subsequent development of IR [160]. The data regarding its relationship with IR are not consistent—some authors do not find any [161], but according to others insulin sensitivity is changing in parallel with the change in this cytokine levels [162, 163]. We also did not find a significant correlation of TNF-α and some parameters of IR in patients with various obesity morphotypes [135]. Basically, in our study, TNF-α levels were very similar in normal-weight women and in obese women, which corresponds to the data of Pincelli et al. [164]. It seems that the TNF-α levels, which we measure in the circulation, cannot reflect the degree of IR in obesity. It should be taken into account that this cytokine can have predominantly autocrine or paracrine action and can induce IR at a tissue level, since its concentrations in situ, at the level of adipose tissue, are much higher in comparison to the circulatory ones [165].
Elevated levels of TNF-α have been observed in PCOS women, which correlated positively with BMI and negatively with insulin sensitivity [166]. Gonzalez et al. [167] found elevated levels of TNF-α in normal-weight PCOS women as compared to controls. However, in all obese women in this study, despite the absence/presence of PCOS, TNF-α levels were similar. Direct correlation of TNF-α was detected with BMI, but with insulin such a correlation was found only in the healthy women. Apparently, factors other than obesity were the cause of TNF-α increase in normal-weight PCOS women. On the other hand, this cytokine did not correlate with testosterone, LH, and DHEA-S in the PCOS women [167].
We found significantly higher TNF-α concentrations in PCOS women compared to BMI-matched controls [109, 131]. In our studies, we did not establish a significant difference when comparing serum levels of TNF-α between insulin-resistant and non-insulin-resistant PCOS women. Higher levels of TNF-α were registered in obese PCOS women as compared to overweight PCOS women, but not to normal weight PCOS women. No correlations between TNF-α and the parameters of IR were established in the PCOS women [109, 131]. Contrary to our results are those of Soares et al., who did not detect a significant difference in the TNF-α levels between PCOS women and BMI-matched controls [168].
Data are controversial regarding the role of IL-6 in the development of IR. In general, it is considered that circulating levels of IL-6 are elevated in patients with obesity and IR. It is assumed, that persistent high levels of IL-6 in chronic inflammatory conditions (obesity and DM type 2) can cause disturbances in insulin sensitivity, while only periodically elevated IL-6 levels are associated with normal carbohydrate homeostasis [162, 169]. Lin et al. suggested that IL-6 may serve as an early chronic low-grade-inflammation marker in PCOS [170]. This hypothesis, also described by other authors [171, 172], launches the idea of an association of PCOS with increased CVD risk, and the strategies affecting the chronic low-grade inflammatory conditions, can be useful for coping with PCOS and related metabolic and atherogenic disorders [173].
Mohlig et al. [174] studied IL-6 and CRP in PCOS women and in age-matched controls, analyzing the influence of C-174G-IL-6 gene polymorphism on the IL-6 and androgens levels, and on the degree of obesity. The authors did not find elevated CRP and IL-6 levels in PCOS women (both lean and obese) compared to the controls. In PCOS women the anthropometric variables (BMI, WHR, amount of adipose tissue) and the parameters of IR, but not the markers of hyperandrogenic condition, showed significant correlation with IL-6 and CRP. In addition, a 6-month metformin treatment resulted in a significant decrease in the body weight, the amount of adipose tissue and total testosterone levels, but did not affect the levels of IL-6 and CRP. Using multivariate linear regression analysis, it was established that in PCOS women BMI but not HOMA-index constituted a dominant factor explaining 18% and 24% of the variations in IL-6 and CRP levels, respectively [174].
This fact was also confirmed by another study conducted in pre-menopausal women [175]. In the study of Mohlig et al. [174], no link between the C-genotype and the IL-6 and BMI levels was found. However, the heterogeneous GC genotype was associated with lower levels of androstenedione [174]. The C-174G polymorphisms of the IL-6 gene promoter could be expected to modify its activity in certain in vitro conditions [176]. In some studies, C-174G polymorphism was associated with higher levels of IL-6, with more pronounced IR, with a higher degree of obesity and with hyperandrogenism [177, 178].
Our studies [107, 108, 109, 131] showed similar levels of IL-6 between the groups of PCOS women—insulin-resistant vs non-insulin-resistant; normal weight vs overweight and obese. We did not establish a significant difference in IL-6 between PCOS women and controls. Our data are in conformity with those of Villuendas et al. in women with ovarian hyperandrogenism [179], but are in confliction with the results of another study of Kelly et al. in PCOS women [180].
In a study of Tarkun et al. [181] in PCOS women and age- and weight-matched healthy women, a comparative analysis of TNF-α and IL-6 was made, with an assessment of their role in IR pathogenesis. Higher concentrations of TNF-α and IL-6 were found in PCOS women compared to controls. A positive correlation was observed between TNF-α and BMI, waist circumference, triglycerides, basal insulin and HOMA-index (P < 0.001). IL-6 correlated positively with basal glucose and degree of IR (P < 0.05). The authors concluded that TNF-α and IL-6 have a pathogenetic role in the development of IR in PCOS [181].
In another study consisting of obese PCOS women, weight-matched healthy women and normal weight controls Vgontzas et al. [182] determined basal cytokine concentrations and conducted an 8-h nocturnal polysomnography searching for obstructive sleep apnea syndrome. Higher IL-6 plasma concentrations were observed in PCOS women as compared to obese and normal-weight controls (4.75 ± 0.5; 3.65 ± 0.4; 1.84 ± 0.3 pg/mL, respectively, P < 0.01). TNF-α levels were somewhat higher in the obese PCOS and control women compared to the normal-weight women, but the differences did not reach statistical significance (4.05 ± 0.3; 3.79 ± 0.2; 3.14 ± 0.2 pg/mL, respectively, P = 0.103). IL-6 and TNFα correlated positively with BMI (P < 0.01) in the obese healthy women, but not in the obese PCOS women. In addition, a stronger correlation of IL-6 and TNF-α levels with IR indexes (HOMA and QUICKI) was established in the PCOS women than in the obese controls. The authors came to the conclusion that IL-6 may be elevated in PCOS women, irrespectively of obesity and presence of obstructive sleep apnea, and may have a role in the process of IR in this syndrome [182].
Grimaldi Barcellos et al. [183] investigated the impact of PCOS and obesity on the levels of TNFα, IL-6 and CRP in young PCOS women and age- and BMI-matched women with a normal menstrual cycle without CVD risk factors (DM, dyslipidemia, arterial hypertension). The authors did not establish a significant difference in the levels of TNF-α, IL-6 and CRP between the PCOS women and the controls (2.1 vs 1.9 pg/mL, P = 0.397; 3.8 vs 5.7 pg/mL, P = 0.805, and 0.9 vs 0.5 ng/mL, P = 0.361). The TNF-α levels were similar between obese and normal-weight women. IL-6 and CRP were significantly higher in women with overweight/obesity than in normal-weight women (8.7 vs 2.0 pg/mL, P < 0.001, and 1.4 vs 0.2 ng/mL, P < 0.001). The authors concluded that obesity, but not PCOS itself, affects the levels of circulating markers of a chronic low-grade inflammation in young carriers of the syndrome without major CVD risk factors [183].
The pathogenesis of an inflammatory process development in MetS, and in particular in PCOS, has not yet been fully clarified. In scientific terms, the most logical and most widespread is the explanation that the higher amount of adipose tissue in case of obesity leads to increased excretion of IL-6 and TNF-α in the circulation, which in turn causes increased production of CRP by the liver. There is another hypothesis highlighting IR as the primary cause of the higher production of cytokines [184].
Women with PCOS, combining IR and hyperandrogenism are carriers of an unfavorable cardiovascular risk profile. However, data concerning the long-term risk of cardiovascular morbidity and mortality are scarce, controversial and this issue has not yet been addressed appropriately in targeted large prospective studies. However, since there is compelling evidence of the presence of MetS components and early stages of atherosclerotic processes in young PCOS women that are still reversible, it is essential that they must be diagnosed on the basis of the current knowledge in order to administer adequate complex treatment to prevent late consequences of IR.
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\\n\\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
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\\n\\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
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\\n\\nTERMINATION
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\\n\\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\\n\\nIntechOpen’s DUTIES AND RIGHTS
\\n\\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen, at its discretion, agrees to publish the Work attributing it to the Author and Co-Authors.
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\\n\\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\\n\\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\\n\\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
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\\n\\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
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\\n\\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\\n\\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
\\n\\nPolicy last updated: 2018-09-11
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\n\nCORRESPONDING AUTHOR'S GRANT OF RIGHTS
\n\nSubject to the following Article, the Author grants to IntechOpen, during the full term of copyright, and any extensions or renewals of that term, the following:
\n\nThe foregoing licenses shall survive the expiry or termination of this Publication Agreement for any reason.
\n\nThe Author, on his or her own behalf and on behalf of any of the Co-Authors, reserves the following rights in the Work but agrees not to exercise them in such a way as to adversely affect IntechOpen's ability to utilize the full benefit of this Publication Agreement: (i) reprographic rights worldwide, other than those which subsist in the typographical arrangement of the Work as published by IntechOpen; and (ii) public lending rights arising under the Public Lending Right Act 1979, as amended from time to time, and any similar rights arising in any part of the world.
\n\nThe Author, and any Co-Author, confirms that they are, and will remain, a member of any applicable licensing and collecting society and any successor to that body responsible for administering royalties for the reprographic reproduction of copyright works.
\n\nSubject to the license granted above, copyright in the Work and all versions of it created during IntechOpen's editing process, including all published versions, is retained by the Author and any Co-Authors.
\n\nSubject to the license granted above, the Author and Co-Authors retain patent, trademark and other intellectual property rights to the Work.
\n\nAll rights granted to IntechOpen in this Article are assignable, sublicensable or otherwise transferrable to third parties without the specific approval of the Author or Co-Authors.
\n\nThe Author, on his/her own behalf and on behalf of the Co-Authors, will not assert any rights under the Copyright, Designs and Patents Act 1988 to object to derogatory treatment of the Work as a consequence of IntechOpen's changes to the Work arising from the translation of it, corrections and edits for house style, removal of problematic material and other reasonable edits as determined by IntechOpen.
\n\nAUTHOR'S DUTIES
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\n\nAll payments shall be due 30 days from the date of issue of the invoice. The Author or whoever is paying on behalf of the Author and Co-Authors will bear all banking and similar charges incurred.
\n\nThe Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Work worldwide for the full term of the above licenses, and shall provide to IntechOpen, at its request, the original copies of such consents for inspection or the photocopies of such consents.
\n\nThe Author shall obtain written informed consent for publication from those who might recognize themselves or be identified by others, for example from case reports or photographs.
\n\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\n\nAUTHOR'S WARRANTY
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\n\nThe Author and Co-Authors confirm that: (i) the Work is their original work and is not copied wholly or substantially from any other work or material or any other source; (ii) the Work has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) Authors and any applicable Co-Authors are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) Authors and any applicable Co-Authors have not assigned, and will not during the term of this Publication Agreement purport to assign, any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\n\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\n\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\nTERMINATION
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\n\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\n\nIntechOpen’s DUTIES AND RIGHTS
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\n\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\n\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
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