Disclaimer: This dissertation has been written by a student and is not an example of our professional work, which you can see examples of here.

Any opinions, findings, conclusions, or recommendations expressed in this dissertation are those of the authors and do not necessarily reflect the views of UKDiss.com.

Role of Tumour-Associated Macrophages (TAMs) in Lung Cancer Development

6642 words (27 pages) Dissertation

9th Dec 2019 Dissertation Reference this

Tags: MedicalCancer  The role of Tumour-Associated Macrophages (TAMs) in lung cancer development

It has been found that TAMs play a crucial role in cancer initiation by creating a mutagenic inflammatory environment, providing a fertile soil for cancer initiation and development (Qian, Pollard 2010). In lung cancer, it is well-known that chronic obstructive lung disease is associated with persistent colonisation with the bacterium Haemophilus influenza with subsequent increase of LC risk. This clinical evidence applies to animal models where the same virus lysate was used to induce lung cancer in the same way (Moghaddam et al. 2009). Smouldering inflammation is a term used to describe chronic inflammation which is associated with high risk of cancer due to persistent chronic irritation without removal of causative stimuli (Mantovani, Sica 2010). In this low-grade inflammation, the macrophage is a central player and has a vicious cycle of activation with other immune cells to keep the inflammatory process operated against causative agents (Balkwill et al. 2005).

Many researchers have investigated the mutagenic effect of chronic inflammation and have found that unstable reactive oxygen species and nitric oxide causes mutation and genetic instability in adjacent epithelial cells. Additionally, inflammatory cells including macrophages produce growth factors that influence the proliferation of mutant epithelial cells (Meira et al. 2008, Pang et al. 2007). The role of macrophages in tumour development is linked to its phenotype. This complex interaction between tumour cells and inflammatory cells plays an important role in cancer development. It has been found that during cancer initiation, the inflammatory macrophage is the activated one (M1), but when the cancer is initiated this inflammatory macrophage is switched into the alternative protumourigenic macrophage (M2 phenotype) (Gordon 2003). The M2 phenotype plays a crucial role in cancer progression, proliferation, and metastasis.  Tumour-Associated Macrophages (TAMs) and lung cancer progression and invasion

Evidence for a link between TAMs and lung cancer comes from finding an association between TAMs and poor lung prognosis (Takanami et al. 1999, Chen et al. 2005). Studies on animal model support this evidence of an association between TAMs and tumour progression. In a mouse model, Lin andhis research group found that mice with deficient macrophages by blocking the effect of CSF-1 have very low rates of tumour progression, while CSF-1 activation results in increased rates of tumour progression and metastasis by accumulating TAMs (Lin et al. 2001). In another study using human lung cancer tissue, TAMs were successfully isolated, and the mRNA of 9 genes were studied. They found that TAMs show phenotypic expression of MMP9 and VEGFA induced tumour progression in patients with NSCLC (Wang et al. 2011).  Tumour-Associated Macrophages (TAMs) and angiogenesis

The TME, as mentioned above, the following is initiation starts to switch TAMs from the tumouricidal M1 phenotype to the protumourigenic M2 phenotype. The protumourigenic M2 phenotype is a source of angiogenic factors including basic fibroblast growth factor, thymidine phosphorylase, urokinase-type plasminogen activator (uPA), and adrenomedullin (ADM) factors (Qian et al. 2011). A study used human breast tumour spheroids implanted into mice showed that the angiogenic response was measured three days after implantation, and those spheroids with macrophages had a higher level of blood vessel formation in comparison with non-macrophage spheroids (Bingle et al. 2006). It has also been found that VEGF-A secreted by TAMs is the key factor that initiates angiogenesis in the tumour, so replenishment of the TAMs with high levels of HIF-1α at tumour hypoxia sites induces the secretion of VEGF-A (Guruvayoorappan 2008).  Tumour invasion and metastasis induced by Tumour-Associated Macrophages (TAMs)

TAMs play an important role in tumour invasion and metastasis (Kato et al. 2010, Fromigue et al. 2006). It has been found that there is a vicious cycle of activation of tumour cells and macrophages, where tumour cells secrete CSF-1 which recruits macrophages and macrophages secrete EGF which enhances tumour cell invasion (Wyckoff et al. 2004). Furthermore, macrophages within the TME are the source of many proteolytic enzymes such as MMPs, which enhance the breakdown of extracellular matrix with subsequent influencing of tumour cell invasion and metastasis (Chanmee et al. 2014).

1.1.2     Targeting tumour-associated macrophages

It is believed that targeting TAMs could be a very promising approach for treatment and chemoprevention of cancer. TAMs are recruited to the tumour microenvironment from their precursors in the peripheral blood which is the circulating monocyte (Gordon, Taylor 2005). Furthermore, most studies have shown that these macrophages are of M2 phenotype rather than of M1 type that is to say TAMs have tumour immunosuppressive and progression effects rather than tumouricidal effects (Allavena et al. 2008, Solinas et al. 2009). Targeting molecular pathways regulating TAM polarisation also holds a bright future towards anti-cancer therapy. Therefore, a recent study by Tang et al. showed that there are four pathways for targeting TAMs (Tang et al. 2013). These are;

  1. Macrophage recruitment inhibition.
  2. Decrease TAM survival
  3. Stimulate the M1 phenotype
  4. Stop the M2 phenotype

Regarding macrophage recruitment inhibition, pathways through which the TAMs can be attracted to the TME are summarised in Figure (1.3). There are three recognised pathways:

C-C motif chemokine ligand 2 (CCL2) which is found to be highly expressed in tumours with high macrophage number and is usually associated with poor survival  (Gazzaniga et al. 2007, Mizutani et al. 2009, Qian et al. 2011, Zhu et al. 2011). Targeting this molecule could result in inhibition of macrophage recruitment. There are three main chemotherapeutic agents that are used to block this molecule: Trabectedin (Allavena et al. 2005), Siltuximab (Coward et al. 2011) and RS102895 (Jin et al. 2010a).

  1. Macrophage-colony stimulating factor (M-CSF), M-CSF is also overexpressed in tumours with high-density macrophages and associated with poor survival (Zhu et al. 2008). Recently, two inhibitors for this molecule have been identified which are: JNJ-28312141 and GW2580, which directly inhibit this molecule and cause a significant decrease in TAMs and hence suppression of tumour growth (Manthey et al. 2009, Kubota et al. 2009).
  2. VEGF, CXCL-12, and CCL5. These group of chemoattractants are significantly associated with TAMs so blocking their action through two pathways: the CXCL12/C-X-C motif chemokine receptor 4 (CXCR4) or the Placental Growth Factor (PIGF)/VEGFR-1 pathway that plays a role in decreasing TAMs (Dineen et al. 2008, Roland et al. 2009). An example of such an inhibitor is an anti-hypoxia inducible factor (HIF) because HIFs stimulate the expression of VEGF and CXCR4 to enhance angiogenesis in response to low oxygen levels. This causes stimulation of macrophage recruitment. Therefore its inhibition could result in reduced macrophage density (Imtiyaz et al. 2010).

The second way to deplete TAMs is by direct killing of them which can be achieved in two ways:

Inducing apoptosis either by chemical agents or by attenuated bacteria; an example of these chemical reagents are Bisphosphonates that are broadly used as macrophage depleting drugs (Rogers, Holen 2011). There are two categories of Bisphosphonates; Clodronate and Zoledronic acid. The first one is proving to be a very effective cytotoxic drug that specifically kills macrophages with subsequent regression of tumour growth. Zoledronic acid is very effective in depletion of TAMs, and it has further action on blocking differentiation of TAMs of the M2 phenotype that results in increasing the M1 tumouricidal effect (Tsagozis et al. 2008, Veltman et al. 2010, Coscia et al. 2010). The other way to directly kill these macrophages is by bacterial action. Some types of bacteria directly kill macrophages such as Shigella flexneri, Salmonella Typhimurium, Listeria monocytogens, Chlamydia psittaci and Legionella pneumonia (Suzuki et al. 2007, Farinha et al. 2005).

Inducing an immune response against macrophages by up-regulating macrophage molecules that could be targeted by cytotoxic T-cells and NK cells. There are two well-known molecules of this; legumain and CD1d. The first one is highly expressed in M2 phenotype TAMs and not the M1 phenotype TAMs. Therefore tumours with high expression of this molecule are usually aggressive tumours (Luo et al. 2006). It has been found that legumain, when used to vaccinate mice with tumours, results in the activation of the immune system against TAMs expressing these molecules especially cytotoxic CD8 T lymphocytes (Luo et al. 2006, Lewen et al. 2008), with subsequent regression in tumour size. Regarding CD1d it is a good target for NKT cells. Therefore, extensive investigations have been done to up-regulate this molecule in TAMs to facilitate killing by NKT cells (Chen, Ross 2007).

The third way for targeting the tumourigenic effect of TAMs within the tumour microenvironment is by enhancing the tumouricidal effect of the M1 phenotype, this could be achieved through three pathways (Tang et al. 2013, Solinas et al. 2009).

The nuclear factor-КB (NF-КB) pathway: Many studies have been found that upregulation of this signalling pathway results in up-regulation of T helper type 1 cytokines which enhance the M1 phenotype of TAMs (Biswas, Lewis 2010). Therefore nowadays scientists are interested in inactivation of this pathway to facilitate the tumouricidal macrophage function.

Signalling transducer and activator of transcription (STAT) pathway: there are different STAT molecules that transcriptionally control the macrophage phenotype. Of these, STAT1 is usually associated with the M1 phenotype while STAT 3 and six are usually associated with the M2 phenotype, that is to say, STAT1 is tumouricidal while STAT3 and STAT6 are not tumouricidal (Sica, Bronte 2007). Therefore up-regulation of STAT1 could be favourable for better prognosis of the patient while STAT3 and six are associated with a bad prognosis. This finding has led scientists to manufacture different drugs that upregulate STAT1. GM-CSF is a well-known immunotherapeutic agent used in human cancers as a therapeutic agent that induces the tumouricidal effect by enhancing the M1 phenotype in the TME (Eubank et al. 2009).

Finally, targeting the tumourigenic activity of M2 phenotype by direct blocking of its action. Could be made by three pathways:

  1. STAT3 inhibition, which is achieved by different drugs to block the immunosuppressive activity of the M2 phenotype. Examples of these blocking agents are WP1066 (Hussain et al. 2007), sunitinib and sorafenib (Xin-Yuan et al. 2012, Edwards, Emens 2010).
  2. STAT6 inhibition is very important in blocking the M2 phenotype activity of TAMs. There are three STAT6 inhibitors which are; AS1517499, leflunomide, and TMC-264. The action of these drugs on STAT6 is controversial. It is thought to achieve its action by phosphatidylinositol 3-kinase (PI3K) and Src homology 2-containing inositol-5′-phosphatase (SHIP).  Drugs block the M2 phenotype by either blocking PI3K or up-regulating SHIP (Weisser et al. 2011).

Several drugs could be used as inhibitors for the M2 phenotype such as Histidine-rich glycoprotein (HRG) which enhances the M1 phenotype by inhibiting PIGF which belongs to the VEGF family and enhances immunity and vessel normalisation (Rolny et al. 2011). The other drug is Copper chelate (CuNG) which results in IFN-y and IL-12 up-regulation with TGF-β down-regulation in TAMs (Chatterjee et al. 2009, Chakraborty et al. 2012). It had been found that cisplatin enhances tumouricidal activity by releasing proinflammatory mediators such as reactive oxygen species (Chauhan et al. 2009). 5, 6 Dimethylxanthenone-4-acetic acid (MDXAA) was found to up-regulate several cytokines from TAMs that enhance the tumouricidal activity of cytotoxic T cells (Jassar et al. 2005). Silibinin is another drug which was found to inhibit the angiogenesis process by inhibiting angiogenic cytokine production (Tyagi et al. 2009).

1.2       Tissue Microarrays (TMAs) for studying TAMs

Tissue Microarrays (TMAs) enable rapid and simultaneous analysis of biomarkers in archival tumour specimens from a large number of cases on one slide, while the traditional tissue sections involve the preparation of a single section per one donor block (Kallioniemi et al. 2000, Cregger et al. 2006). TMAs are built up by getting small core biopsies from representative areas of paraffin-embedded tumour tissues (donor blocks) and then inserting the donor cores on a recipient paraffin block (Fedor, De Marzo 2005). Usually, the sampling of the donor cores involves at least three cores per sample to cover the tumour heterogeneity (Banat et al. 2015, Shabo et al. 2009, Sickert et al. 2005). It has been found that there is a high rate of synchronised data of biomarker expression between TMAs and whole sections (Bentzen et al. 2008, Hendriks et al. 2003, Zu et al. 2005).

The major challenge with TMAs is to achieve an accurate interpretation by an experienced pathologist to guarantee an appropriate judgement of the level of biomarker expression (Permuth-Wey et al. 2009).  The traditional way to interpret immunostained TMAs or whole tissue sections is by visual scoring of immunohistochemistry, and this is quite time-consuming for evaluation of biomarkers for a large sample size as in TMAs and is prone to human error. However, with the rapid development of digital pathology, the interpretation of TMAs is becoming more robust with rapid and high throughput analysis possible (Bouzin et al. 2016).

Macrophages are usually irregular cells with dendritic cell projections that may appear differently on different sections. The use of TMAs for quantifying and phenotyping TAMs is quite limited, because of the difficulties in interpretation of TAMs whether manually or digitally. However, we found some studies investigated the prognostic significance of macrophages in some cancers like oral, and colorectal cancers using TMAs (Weber et al. 2016, Shabo et al. 2009, Sickert et al. 2005). In lung cancer, one publication evaluating macrophages in TMAs was investigating the prognostic role of macrophages together with interleukine-6 (IL-6) and colony stimulating factor-1 (CSF-1) in relation to NSCLC survival (Pei et al. 2014). In all these publications, there was no fully automated quantification of macrophages using digital pathology. Some of these studies did manual counting macrophages by at least two pathologist using high power fields representative areas (Pei et al. 2014, Shabo et al. 2009), or by doing semi-quantitative approach to score macrophages in tissue sections (Sickert et al. 2005).

Overall, we could not find any publication so far quantifying TAMs in lung adenocarcinoma TMAs and studying their micro-localisation in the tumour, stroma, and lumina using automated methods.

1.3       Digital pathology for scanning and analysis of immunostained sections to evaluate TAMs.

Molecular pathology has moved towards a digital era, whereby whole slide imaging and automated analysis of immunostained sections has become superior to traditional microscopical examination with the manual approach of stained sections.  For example, recently, the automated image analysis of HER-2/neu expression in immunostained breast cancer tissue sections has been confirmed by the American Society of Clinical Oncology and College of American Pathologist (Wolff et al. 2013). In spite of all the rapid developments in the concept of digital pathology and the wide range of softwares that are available, this automated approach to immunostained sections needs further more optimisation to become more applicable for routine clinical use (Bouzin et al. 2016). The routine approach to analysing the immunostained sections is still at present to use manual counting of stained cells, and more than one pathologist evaluates the staining intensity after examining stained sections visually using the traditional microscopes. This manual approach is always associated with a lot of individual variation. Therefore, it is vital to validate and standardise automated digital pathology approach to overcome these inter-observer variabilities (ibid).

1.3.1     Automated scanning of immunostained sections.

Automated digital analysis is of a great advantage because whole slide scanning delivers a high throughput digital analysis with high resolution (Bouzin et al. 2016). Digital scanning involves digital capture at different magnification, which is usually at either 20X or 40X.  This digital capture of tissue samples is either by line scanning or by tile scanning. Subsequently, these multiple digital lines or tiles like pictures are gathered and aligned digitally to give a final high throughput whole slide digital image (Hamilton et al. 2014).

Digitally scanned slides with whole image scanning produce large data files of around 3.6-14.5 GB, depending on the objective of scanning and the size of the section to be scanned (Krupinski et al. 2012). The speed and number of slide scanning vary between machines. Some high throughput scanners can scan around 300 slides in one run, and others are very small scanners which can scan up to 10 slides. Most of the scanners used for scanning take about 1-3 minutes and this again depends on the objective of scanning and the size of the tissue section to be scanned (Hamilton et al. 2014).

1.3.1     Automated analysis of immunostained sections.

The next step after scanning sections is to analyse them using different analysis software. Automated and digital interpretation of sections saves time and variations of manual interpretation between different observers. Automated digital analysis offers better throughput and performance over manual as automation saves time and effort in interpreting slides, and provides standardisation of analysis for all sections (Hamilton et al. 2014).

Studying the expression of cellular proteins whether nuclear, cytoplasmic or membranous and linking this expression into different diseases is an area of huge interest in diagnostic and research labs. It enables the classification of patients with certain diseases into molecular groups that could be treated differently according to their marker expression. Immunohistochemistry is still a very popular method in most diagnostic labs and is widely used for detection of different biomarkers. However, visual interpretation of immunohistochemistry is tricky sometimes and differs from one pathologist to another. Many studies carried out on different biomarkers and in different labs have shown huge variations in the interpretation of immunohistochemistry stained biomarkers such as P53, estrogen receptor ER, progesterone receptor PR, HER2 and Ki67 (McShane et al. 2000, Rhodes et al. 2002, Maisonneuve et al. 2014, Dowsett et al. 2011). For example, the evaluation of Her2 Neu expression as a marker of importance for breast cancer patients is subjected to 20% error rates as reported by Wolf et al. (Wolff et al. 2013).  The use of an automated image analysis may overcome problematic subjective variations. However, the use of an automated digital analysis and the interpretation of immunostained sections requires training to ensure the best results with this automated approach. Fluorescently stained sections use fluorophores instead of chromogens. Detection of photons emitted from fluorophores requires a special imaging system (Hamilton et al. 2014). In fluorescent imaging, there is a special detector where the photons strick this detector and there should be a linear relationship between the pixel values and the number of these photons, so the emission spectra for each filter represents the expression of a specific marker (ibid).

We could not find so far any published work about automated counting and phenotyping of TAMs in the human NSCLC microenvironment.

1.4         Statins

1.4.1     Statin chemical structure and pharmacokinetics

Statins are a family of drugs that are used in the medical field to lower the level of circulating unhealthy lipids (cholesterol, LDL, and VLDL). These include; atorvastatin, simvastatin, fluvastatin, lovastatin, pravastatin, and rosuvastatin. All of these are lipophilic apart from the last two which are hydrophilic. They are small molecule inhibitors of 3-hydroxy-3-methylglutaryl coenzyme-A (HMG-CoA) reductase (Demierre et al. 2005). Lovastatin, pravastatin and simvastatin are derived from fungi, while the rest of statins are synthetic products (Davidson M.H 2002).

The chemical structure of statins as in (Figure 1.5), includes three main parts; HMG-CoA correspondent that is covalently bound to a hydrophobic ring structure. HMG-CoA analogue is connected to another peripheral group that characterises the solubility properties of different statins (Gaw et al. 1999).

All statins have fast absorption rates, reaching its maximum levels (Tmax) within 4 hours of oral administration (Warwick et al. 2000). The rate of absorption of statins depends on the time of day they are administered whether day or night time (Cilla D.D. Jr, Gibson D.M., Whitfield L.R., Sedman A.J, 1996).

Some statins have lower effectiveness when taken along with food such as atorvastatin, fluvastatin and pravastatin (Radulovic L.L. et al. 1995, Smith et al. 2006, Pan H.Y. et al. 1993).

Lovastatin has much higher bioavailability when taken along with food (Garnett W.R, 1995). Simvastatin and rosuvastatin are not affected by food intake (Corsini A. et al. 1999, Davidson M.H 2002). All statins are bound to plasma proteins apart from pravastatin (Corsini A. et al. 1999). All the statins have a hepato-selectivity for the inhibition of HMG-CoA reductase enzyme since the liver is the major organ of cholesterol synthesis in the human body (Schachter 2005).

Z:LUNG CANCER2nd yearThesismethodologMETHODOLOGYstatain structure.JPG

Figure 1.5: Chemical structures of statins

(Schachter 2005).

1.4.2     Statin mechanism of action

Statins lower cholesterol level by two mechanisms; the first mechanism is by directly competing with the enzyme HMG-CoA reductase which is vital for the synthesis of cholesterol as they synthetic analogues to this substrate (ibid). The second mechanism is by increasing the hepatocyte cell surface expression of LDL cholesterol receptors hence there is more clearance of these lipids from the circulation (Pahan 2006). These important functions of statins in lowering LDL-cholesterol level have been used widely in the treatment and prevention of cardiovascular diseases (Cannon et al. 2004, Nissen et al. 2004). Because of statins’ protective effect against cardiovascular density and its safety, it is available as an over-the-counter drug instead of a prescribed drug in the UK (Marie France Demierre, Peter D. R. Higgins, Stephen B. Gruber, Ernest Hawkand Scott M. Lippman 2005). Nowadays, statins have been described to have multiple biological functions in addition to their classical role in lowering circulating lipids (Ito et al. 2006). Several studies reported that statins have the following activities; anti-inflammatory actions through lowering inflammatory cytokines and C-reactive protein (CRP) which is synthesised by liver cells in response to inflammatory process, antioxidant properties by increasing the bioavailability of nitric oxide, improvement in endothelial dysfunction through increasing nitric oxide production, and finally, through direct inhibition of MMPs, they result in atherosclerotic plaque stabilisation (Vishal Tandon, G Bano V Khajuria,A.Parihar S. 2005, Zhou, Liao 2010, Wang et al. 2008).

Zhou & Liao suggested in their study that statins have an important potential therapeutic role in many medical conditions such as sepsis, heart failure, stroke, Alzheimer’s disease and cancer (Zhou, Liao 2010).

As mentioned above, statins work on lowering cholesterol by a competitive inhibition of HMG-COA enzyme. Cholesterol is vital for maintaining cell membrane integrity and structure and as a precursor for the synthesis of steroid hormones and bile acid (Edwards, Ericsson 1999).

Statins, through their competitive inhibition of the mevalonate pathway, cause not just inhibition of cholesterol synthesis, but also the downstream products of this pathway such as farnesyl and geranylgeranylated proteins which are both essential for post-translational protein modification. This post-translational modification of cellular proteins is called (iso) prenylation. (Iso) prenylation involves the combination of the farnesyl or geranylgeranylated proteins with cellular proteins. This combination is mediated by two enzymes, farnesyltransferase and geranylgeranyl transferase, respectively. Ras proteins and many small GTP-binding proteins all need this prenylation process to be able to associate with the plasma membrane (Jackson et al. 1997) (Figure 1.6). The above effects of statins have made researchers study the impact of these drugs on many critical cellular functions in diseases including cancer.

Figure 1.6: The mechanism of action of statins.

Modified from (Buhaescu, Izzedine 2007).

1.4.3     Statins and the risk of lung cancer and other cancer types

There is a significant impact of statin use on cancer incidence that has been reported by many previous retrospective case-control studies (Poynter et al. 2005, Caldito, Fort 2005, Graaf et al. 2004, Blais et al. 2000, Demierre, Nathanson 2003).

The incidence of lung cancer and two other cancers (prostate and breast) was shown to be reduced by about half percent in those patients using statins (Caldito, Fort 2005, R. Singal, V. Khurana, G. Caldito and C. Fort 2005, Graaf et al. 2004). On the other hand, the important effect of statin use on the incidence of cancer was not found in three meta-analytic studies (Hebert et al. 1997, Bjerre, LeLorier 2001). A larger meta-analysis study with three other case-control studies found that statin use did not reduce the incidence of cancer (Dale et al. 2006, Watanabe et al. 1997). Another recent meta-analysis analysed 42 studies, 17 of which were randomised controlled clinical trials, 25 were observational studies to investigate the effect of statin use on cancer. They stated that there was no clinical evidence for the effect of statin use on (breast, colorectal, respiratory, gastrointestinal, genitourinary and prostate) cancer incidence. However, the same study reported that statin use was associated with a reduced risk of lung and prostate cancer in a review of twenty case-controlled studies (Kuoppala et al. 2008). A retrospective case-control study evaluated the association between statin use and lung cancer where they found a 45% reduction in lung cancer risk among statin users in comparison with non-statin users especially for those who were for more than six months on statin treatment (Boudreau et al. 2010). Vikas et al concluded in their study that the use of statins for six months or more reduce the risk of lung cancer by 55% (Khurana et al. 2007).

1.4.4     Statins and the survival of lung cancer and other cancer types

Previous studies showed an impact of statin use on the survival of different cancers (Nielsen et al. 2012, Ng et al. 2011, Jeon et al. 2015). However, the story of statin and survival in lung cancer is still not well explored. Recent epidemiological studies reported that there is evidence for the association between statin use and lower cancer-specific mortality from lung cancer, this study showed evidence for improved survival of early stage lung cancer patients on simvastatin treatment (Cardwell et al. 2015). Another study showed no advantage of using statins for the survival of advanced stage lung cancer patients (Han et al. 2011). Recently, another epidemiological study showed that among stage IV NSCLC patients, those on statin treatment had improved survival (Lin et al. 2016).

1.4.5     Anti-cancer mechanisms of statins

As mentioned above statins are cholesterol modulating agents that work mainly by competitive inhibition of the HMG-CoA reductase enzyme which is a rate-limiting enzyme in cholesterol synthesis. Statins are also of interest as anticancer drugs and their roles in cancer have been studied in different models to explore their effects in different types of cancers including; colon, breast, prostate, skin and lung cancer (Chan et al. 2003, Demierre et al. 2005).

Studies have shown that cancer cells need lipid components for their proliferation, cell cycle progression, membrane integrity and signalling. The isoprenoid pathway for synthesis of lipid is quite important for the viability of cancer cells. The inhibition of geranylgeranyl pyrophosphate (GGPP) and farnesyl pyrophosphate (FPP) production, which are the downstream products of the mevalonate pathway, are a rate-limiting step not just for cholesterol synthesis, but for cancer cell proliferation as well (Thurnher et al. 2012, Baenke et al. 2013, Clendening, Penn 2012).

Several cellular and animal model studies show the anti-cancer efficacy of statins when used singly or in combination with other classical chemotherapeutics. Recent studies have been found that statins affect different molecular pathways that inhibit cancer by either inhibition of cancer cell proliferation through inhibiting the Ras-MAPK and PI3K/AKT using Lovastatin (Yang et al. 2012). Furthermore, simvastatin was shown inhibit proliferation either singly or in combination with other classical chemotherapeutic agents through targeting several proliferation pathways such as HMG-CoA reductase, BCl-2 and Bax, ERK and mTOR (Kabel et al. 2013, Sadaria et al. 2011, Gober et al. 2003). Another research group found that simvastatin in combination with other targeted therapies agents such as Gefitinib and Erlotinib could induce apoptosis and inhibit cancer growth through targeting Akt, β-catenin, survivin and the cyclin D pathway (Atochina-Vasserman et al. 2013). Moreover, atorvastatin and lovastatin used in combination with another chemotherapeutic agent such as carboplatin and gefitinib could overcome classic chemotherapeutic drug resistance by targeting several cell proliferation drivers such as AKT and TIMP-1, MEK/ERK and PI3K/AKT and RAF, ERK1/2, AKTand EGFR (Yavasoglu et al. 2013).

In lung cancer, there are well-known effects of statins on lung cancer in different in vitro and in vivo models.  These effects of statins were in terms of inhibiting the proliferative and inducing apoptotic activities of cancer cells in both NSCLC and SCLC (Mantha et al. 2005a, Khanzada et al. 2006).  It has been found that lovastatin can inhibit the tumour growth in mice at the early promotional stage by suppressing the formation of tobacco-specific nitrosamine, 4-(N-methyl-N-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK)-induced lung tumours (Hawk et al. 1996). Mantha et al found an interesting inhibitory effect of statins on epidermal growth factor receptor (EGFR) expressing lung cancer cells. Furthermore, they found that the combined treatment of both lovastatin and gefitinib resulted in synergistic inhibition of AKT activation by enhanced EGFR suppression (Mantha et al. 2005b). All the above findings are very heartening for the possibility of the use of these statins as chemopreventive or chemotherapy agents in lung cancer.

1.4.6     Statins and cell proliferation drivers pathways

As mentioned above several in vitro and in vivo animal studies showed the effects of statins if used singly or in combination with other chemotherapeutic agents. We will highlight two important cell proliferation drivers which are the MAPK and PI3K/AKT/mTOR pathways.  Mitogen-Activated Protein Kinase (MAPK) pathway:

The MAPK pathway is also termed Ras-Raf-MEK-ERK pathway. It involves the transfer of signalling stimuli from cellular receptor to the nucleus (Figure 1.7). MAPK signalling pathway plays a critical role in cell proliferation and apoptosis. There are three families of MAPKs which are ERK (extracellular signal-regulated kinase), c-Jun NH2-terminal kinase (JNK); and p38-MAPK (Pearson et al. 2001). ERK1/2 are well-known MAPKs and are downstream of MEK1/2 activation, which are substrates for the RAF serine-threonine kinases (Crews et al. 1992). ERK1/2 activation promotes cell cycle activation, which explains the importance of ERK1/2 in proliferation (Cavallo 2009). Recently, Li Shang et alfound that simvastatin inhibits the expression of TGF-βII through inhibition of the ERK pathway in an in vitro model using A549 human cancer cell lines which raise again the possibility of using this drug to suppress the progression of lung cancer (Shang et al. 2015).  PI3K/AKT/mTOR Pathway:

This pathway is an important survival pathway that is activated in most cancers including NSCLC (LoPiccolo et al. 2008). Phosphatidylinositol 3- Kinase (PI3K) becomes activated after receptor tyrosine kinase (RTK) stimulation (Figure 1.7). PI3K phosphorylates PIP2 to PIP3 (phosphatidylinositol (3, 4, 5) triphosphate) at the cell membrane. PIP3 then recruits PDK1 (phosphatidylinositol-dependent kinase 1) and AKT to the membrane where PDK1 activates AKT. Activated Akt results in protection from apoptosis and increased proliferation by targeting a wide variety of substrates including mTOR, which is involved in regulation of translation. Ki-Eun Hwang et al found that simvastatin inhibits the Akt pathway with subsequent induction of apoptosis in A549 human lung cancer cell line (Hwang et al. 2011).

Figure 1.7: MAPK and PI3K pathways in cancer.

RTKs transfer the oncogenic stimulus resulting in activation of intracellular signalling RAS/RAF/MEK/ERK, and PI3K/AKT pathways. Modified from (Cohen NA. et al. 2017)

1.4.7     Statins and TAMs

In animal models, Cortez et al stated for the first time that targeting TAMs by splenectomised mice could result in lung cancer regression not only in advanced stages of cancer but also in the premalignant stage (Cortez-Retamozo et al. 2012). Statins are well known anti-inflammatory agents besides having cholesterol modulating activity; this anti-inflammatory effect is thought to be by chemokine and chemokine receptor depletion. The mechanism by which these drugs target chemokine function is not well described (Nielsen et al. 2012). It has been shown though several studies that statins have inhibitory effects on the expression of several chemokines and chemokine receptors at the mRNA level through the inhibition of mevalonate production that is to say this effect is HMG-CoA reductase-dependent.

These chemokines and receptors are; Monocyte Chemotactic Protein-1 (MCP-1), Macrophage Inflammatory Proteins (MIP-1 α and MIP-1β), CCR1, 2, 4 and 5 chemokine receptors (Fruscella et al. 2000, Bustos et al. 1998, Ueda et al. 1994, Waehre et al. 2003, Veillard et al. 2006).

This effect of statins on chemokines/receptors expression was further investigated by Veillard et al (Veillard et al. 2006), who showed that mevalonate inhibition is dose dependent and through the geranylgeranylation pathway. Specifically, when statins inhibit L-mevalonic acid production there is an inhibition of mevalonate conversion to geranylgeranyl pyrophosphate and farnesyl pyrophosphate, and they showed in their study that geranylgeranyl pyrophosphate inhibition rather than farnesyl pyrophosphate is the key molecule in chemokine inhibition. Furthermore, previous studies have demonstrated a cooperative interaction of the activator protein 1 (AP-1) and NF-κB in regulating gene transcription of chemokines such as IL-8 and MCP-1 (Foletta et al. 1998, de Winther et al. 2005). MIP-1 α has been shown to be regulated by NF-κB while MIP-1β is regulated by AP-1 (Grove, Plumb 1993). Other studies have demonstrated that statins by inhibition of NF-κB, inhibit the expression of chemokines MCP-1, IP-10, and IL-8 (Bustos et al. 1998, Fruscella et al. 2000, Ueda et al. 1994, Ortego et al. 1999). Furthermore, Niels et alshowed in their study that this inhibition of NF-κB by simvastatin is also through the geranylgeranylation pathway (Veillard et al. 2006). This inhibition of NF-κB is through the inhibitory production of RhoA which is required for signal transduction of NF-κB (Finco, Baldwin 1993), since RhoA, is a geranylgeranylated protein. Another group of scientists reported that statins have a stimulatory effect on the transcriptional repressor Oct-1 (Ortego et al. 2002) which is also geranylgeranylation pathway dependent (Veillard et al. 2006).

Recent data from our laboratory have shown an interesting effect of atorvastatin on tumour regression by targeting TAMs in a mouse model using a V600EBRAF driven lung cancer with a role of statins in TAM recruitment by targeting chemokine secretory pathways in a transcription-independent manner were identified. This study showed that atorvastatin blocked CCL6 mediated macrophage requirement to the TME (Kamata et al. 2015). This work has the raised the question of whether TAMs could be targeted by statins in the human settings. This project has forwarded on investigating the strategy of targeting TAMs by statins in the human settings.

Cite This Work

To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Related Services

View all

DMCA / Removal Request

If you are the original writer of this dissertation and no longer wish to have your work published on the UKDiss.com website then please: