\r\n \r\nCoverage included: \r\n- Preparation NiO catalyst on FeCrAl Subtrate Using Various Technique at Higher Oxidation Process \r\n- Electrochemical properties of carbon- supported metal nanoparticle prepared by electroplating methods \r\n- Fabrication of InGaN-Based Vertical Light Emitting Diodes Using Electroplating \r\n- Integration Of Electrografted Layers for the Metallization of Deep Through Silicon Vias \r\n- Biomass adsorbent for removal of toxic metal ions from electroplating industry wastewater \r\n- Resistant fungal biodiversity of electroplating effluent and their metal tolerance index \r\n- Experimental design and response surface analysis as available tools for statistical modeling and optimization of electrodeposition processes",isbn:null,printIsbn:"978-953-51-0471-1",pdfIsbn:"978-953-51-4991-0",doi:"10.5772/1913",price:119,priceEur:129,priceUsd:155,slug:"electroplating",numberOfPages:178,isOpenForSubmission:!1,hash:"18ec8cf0e50c5e8170a9d0b20af09b7f",bookSignature:"Darwin Sebayang and Sulaiman Bin Haji Hasan",publishedDate:"April 11th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/1455.jpg",keywords:null,numberOfDownloads:21498,numberOfWosCitations:25,numberOfCrossrefCitations:10,numberOfDimensionsCitations:23,numberOfTotalCitations:58,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 12th 2011",dateEndSecondStepPublish:"May 10th 2011",dateEndThirdStepPublish:"September 14th 2011",dateEndFourthStepPublish:"October 14th 2011",dateEndFifthStepPublish:"February 13th 2012",remainingDaysToSecondStep:"10 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"92970",title:"Prof.",name:"Darwin",middleName:null,surname:"Sebayang",slug:"darwin-sebayang",fullName:"Darwin Sebayang",profilePictureURL:"https://mts.intechopen.com/storage/users/92970/images/3175_n.jpg",biography:"Dr Darwin Sebayang was graduated from Rheinisch Westfaelische \nTechnische Hochschule Aachen- Germany (RWTH Aachen- Germany) on Light Structure. He is a professor in Faculty of Mechanical and Manufacturing Engineering at the Universiti Tun Hussein Onn Malaysia. The research focuses on light structure, engineering design and advance material and since five years ago he has been active on development of catalytic converter and exploring the application of electroplating of nickel to FeCrAl for catalytic converter.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Tun Hussein Onn University of Malaysia",institutionURL:null,country:{name:"Malaysia"}}}],coeditorOne:{id:"121404",title:"Prof.",name:"Sulaiman",middleName:null,surname:"Hasan",slug:"sulaiman-hasan",fullName:"Sulaiman Hasan",profilePictureURL:"https://mts.intechopen.com/storage/users/121404/images/system/121404.jpg",biography:"Professor Dr Sulaiman Haji Hasan has been teaching Manufacturing Engineering since 1980. Graduated in Bachelor of Manufacturing Engineering with Honours from the University of Birmingham , England in 1980, then Master in Advanced Manufacturing System and Technology in the University of Liverpool in 1987. In 1993 he pursued his PhD in Mechanical Engineering and Manufacturing at the University Of Birmingham and graduated in 1997. He has been teaching manufacturing in the Universiti Tun Hussein Onn and has also been a Dean for 8 years. He also supervises research and graduates student and also did some consultancy.He has authored and published more than 60 papers in the areas of specialization. He also advised curriculum and course implementation in private higher educational institutions in Manufacturing.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"505",title:"Electrochemistry",slug:"chemistry-physical-chemistry-electrochemistry"}],chapters:[{id:"35381",title:"Preparation of NiO Catalyst on FeCrAl Substrate Using Various Techniques at Higher Oxidation Process",slug:"preparation-of-nio-catalyst-on-fecral-substrate-using-various-techniques-at-higher-oxidation-process",totalDownloads:2378,totalCrossrefCites:3,authors:[{id:"92970",title:"Prof.",name:"Darwin",surname:"Sebayang",slug:"darwin-sebayang",fullName:"Darwin Sebayang"}]},{id:"35382",title:"Electrochemical Properties of Carbon-Supported Metal Nanoparticles Prepared by Electroplating Methods",slug:"electrochemical-properties-of-carbon-supported-metal-nanoparticles-prepared-by-electroplating-method",totalDownloads:2055,totalCrossrefCites:0,authors:[{id:"96667",title:"Prof.",name:"Seok",surname:"Kim",slug:"seok-kim",fullName:"Seok Kim"},{id:"127730",title:"BSc.",name:"Misoon",surname:"Oh",slug:"misoon-oh",fullName:"Misoon Oh"}]},{id:"35383",title:"Fabrication of InGaN-Based Vertical Light Emitting Diodes Using Electroplating",slug:"fabrication-of-ingan-based-vertical-light-emitting-diodes-using-electroplating",totalDownloads:4542,totalCrossrefCites:0,authors:[{id:"92949",title:"Dr.",name:"Jae-Hoon",surname:"Lee",slug:"jae-hoon-lee",fullName:"Jae-Hoon Lee"},{id:"139589",title:"Prof.",name:"Jung-Hee",surname:"Lee",slug:"jung-hee-lee",fullName:"Jung-Hee Lee"}]},{id:"35384",title:"Integration of Electrografted Layers for the Metallization of Deep Through Silicon 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\n
1. Introduction
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1.1. Glioblastoma
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Glioblastoma (GBM) is the most prevalent and most malignant (WHO grade IV) type of brain tumor in adults [1, 2]. In the United States, there are ~10,000 new cases diagnosed annually, and >50,000 patients living with the disease [2, 3]. The clinical responses of patients are particularly poor and vary greatly among individuals [4], and ~32% of all diagnosed cases survive less than a year [3]. This highly aggressive tumor develops either de novo (primary GBM), or as the result of the malignant progression from a lower-grade glioma (secondary GBM). In both cases, prognosis is very poor, and the median survival when radiotherapy and chemotherapy are combined is approximately 15 months [5]. Importantly, GBM is also characterized by extensive heterogeneity at the cellular and molecular levels. These tumors are highly diffuse, with extensive dissemination of tumor cells within the brain, which hinders complete surgical resection. These aggressive characteristics are associated with a remarkable resistance to therapies available today [6], which unfortunately are mostly palliative. In the context of their highest incidence of all malignant brain tumors in adults, their highly aggressive behavior and therapy-insensitive nature, which together account for a very poor prognosis of GBM patients, this chapter will focus specifically on GBM. In particular, it will review the different hypotheses of glioma/GBM-initiating cells, the major alterations at the levels of gene expression and signaling pathways found in GBM, as well as putative biomarkers of GBM prognosis, and current therapies currently available or under investigation for dealing with these tumors.
Figure 1.
Schematic representation of the differentiation process of neural stem cells into different cell lineages of the CNS and putative cells of origin of gliomas. Protein markers for neural stem cells, progenitors cells, and differentiated cells are indicated in boxes. The normal differentiation process (green arrows) originates three main types of cells in the mature CNS, including neurons and glial cells (particularly oligodendrocytes and astrocytes; ependymal cells are not represented). The most classical hypothesis on the origin of glioma cells is represented by orange arrows (differentiated glial cells are malignantly transformed through a dedifferentiation process). The most recent hypothesis postulating that gliomas originate from the direct transformation of neural stem cells or glial progenitor cells is represented by grey arrows.
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1.2. Glioma/GBM-Initiating cells
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The true cellular origin of gliomas, including GBM, is still a debatable question. It is generally accepted that identifying such tumor-initiating cells may allow a better understanding of tumor biology, and ultimately help in designing improved therapies for GBM. All human tumors arise from a series of molecular alterations that occur in a small number, or even single, founder cells. These tumor cells present a clonal nature due to the sequential accumulation of multiple rare genetic and epigenetic events. The critical importance of the tumor microenvironment in influencing tumor cells behavior and evolution has been recently recognized [7]. Indeed, the tumor microenvironment has been associated with the generation and maintenance of tumor heterogeneity; thus, understanding not only the surrounding microenvironment but also tumor heterogeneity, as well as their relationships, may be crucial in understanding the biology of these tumors. In the case of the brain tissue, a highly complex microenvironment with extreme phenotypic and functional diversity, the multiplicity of putative brain tumor cells of origin, and the variety of niches in which the malignant cells may evolve, is even more challenging. Thus, understanding this complexity is crucial to provide firm evidence for the cellular origin of gliomas [8-10]. Two different hypotheses for the origin of glioma cells, or tumor cells in general, have been proposed (Figure 1), as detailed below.
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One classical hypothesis postulates that cancer cells arise from the accumulation of alterations that occur in differentiated mature cells (glial cells in the case of glioma tumors, including GBM), which would result in a dedifferentiation of these cells along the carcinogenic process. This concept is supported, for instance, by the histological similarities between functional and differentiated glial cells and tumor cells from gliomas. In addition, before the experimental identification of the adult neural stem cells (NSCs), glial cells were the only known replication-competent population of cells in the adult brain, which further supported the idea that highly-proliferative glioma cells could derive from accumulated alterations in differentiated and proliferative glial cells. A landmark study supporting this theory showed that differentiated cells could be transformed into a pluripotent embryonic stem cell phenotype by using a cocktail of transcription factors [11]. However, this hypothesis has never been adequately tested, as there have been experimental limitations that preclude its validation, including: (i) the absence of good mature “astrocyte” markers in in vivo experiments [12], as it is now well known that the commonly used astrocyte marker GFAP is also expressed by adult NSCs; (ii) in vitro, the culture of mature astrocytes is particularly difficult; (iii) culturing astrocytes from neonatal mouse cortex has been described to contain also immature progenitor cells [13].
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The second and most recent hypothesis assumes that cancer cells arise from the accumulation of alterations that occur directly in stem cells, or progenitor (multipotent) undifferentiated cells, that are present in different tissues throughout the entire lifetime (neural stem cells or glial progenitor cells in the case of brain gliomas). According to this rationale, the tumorigenic process would not be accompanied by a dedifferentiation mechanism, as the molecular alterations would accumulate directly in undifferentiated cells [7-9, 14]. In support of this hypothesis is the concept of cancer stem cells (CSCs), which is a subpopulation of cells in the tumor that displays self-renewal capacity, and which can give rise to heterogeneous cancer cells that constitute the tumor. However, it should be noted that the concepts of CSCs and tumor-initiating cells have been frequently confused. The term ‘‘tumor-initiating cells’’ refers to the cells of origin of the tumor, whose alterations support tumor establishment and progression; in contrast, CSCs would more accurately be referred to as tumor-propagating cells, with stem cell-like properties, which are not necessarily the cells of origin [8, 14, 15]. A study by Chen and colleagues (2010) may help to distinguish these different cell populations and their role on tumor development, particularly in GBM [16]. They demonstrated a hierarchical organization of brain tumor-initiating cells by identifying subpopulations of clonal and long-term proliferating cells in GBM specimens. These subpopulations were shown to be hierarchically organized and to give rise to tumors with different molecular and histopathological features [16]. There are specific and very well delimited regions in the brain where neural stem cells and progenitor cells exist, particularly the subventricular zone (SVZ) of the fore brain lateral ventricles, and the subgranular zone (SGZ) in the dentate gyrus of the hippocampus [8-10]. It has been hypothesized that these are favorable regions where the process of gliomagenesis may originate, as these regions present an attractive microenvironment that has been described as propitious for the growth of stem cells, namely in the SVZ [8-10]. There is increasing experimental evidence that the SVZ is one of the most important regions of origin for malignant gliomas [10] as it may present ideal conditions for gliomagenesis, like the exposure to a transcription factor cocktail ideal for their growth. When compared to any other brain regions, stem cell-containing compartments have been shown to be more susceptible to tumor transformation [10], which additionally may argue in favor of this hypothesis of tumors arising from changes in stem/progenitor cells. Additionally, while it may be coincidence, there is a great similarity between the SVZ stem/progenitor cells and glioma cells. For instance, malignant astrocytic tumors in the brain typically appear close to the lateral ventricles [9, 10].
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In the recent years, the notable therapy resistance of gliomas, namely GBM, has been associated with the presence of glioma stem cells (GSCs). These cells present characteristics of stem cells, including: (i) self-renewal; (ii) multipotency, i.e., the capacity to differentiate into other cell lineages; and (iii) high replicative potential. GSCs are predicted to be difficult to target by anti-cancer therapeutics because they have a slow cell cycle, present high levels of proteins involved in drug efflux, and do not express or are dependent on particular oncoproteins for which targeted therapies are currently available [17]. GSCs were one of the first types of cancer stem cells isolated from solid tumors [18]. It was shown that as few as 100 GSCs could give rise to tumors that recapitulated the parental tumor when implanted in xenografted immunodeficient mice, whereas as many as 1,000,000 non-GSCs could not [18]. This suggests that neoplastic clones are maintained exclusively by a little fraction of cells with stem cell properties [18]. Of note, studies involving the use of GSCs face many difficulties, particularly in isolating such cells directly from biopsies, partly because of the high cellular heterogeneity composition of the specimen. On the other hand, currently there are no standardized methods available for cell sorting and assessment of “stemness” [8]. Indeed, there is a relevant discussion regarding the best methodology for culturing GSCs isolated from human GBM specimens. It has been argued by several authors that adherent monolayer cultures of glioma cells allow a more homogeneous exposure to the culture conditions (e.g., nutrients and oxygen levels) than nonadherent cultures, thus increasing the homogeneity of the cell population, reviewed in [8]. In contrast, the sphere-forming assay has been widely used for this purpose. The fidelity and benefits of these assays are still under debate. Thus, there is an exigency to standardize methods for identifying and isolating GSCs with unequivocal markers. It is believed that the use of NSCs markers is a good principle for identifying GSCs, as NSCs are now known to exist in very restricted areas of the brain, and can be unambiguously identified with specific markers [8]. Indeed, in the last decade, putative markers of GSCs have been identified, including Nestin, CD133, L1CAM, CD15, CD44, Id1, and integrin-α6 [8, 10, 14, 19-21]. Nonetheless, none of these markers is sufficient to, independently, identify specifically GSCs, implicating that a functional identification of GSCs (including their ability to (i) be tumorigenic in in vivo models, (ii) form neurospheres in culture; (iii) be multipotent) is still mandatory.
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2. Gene expression and signaling in GBM
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GBM, like other cancers, is a disease that presents several alterations, including DNA mutations, copy number aberrations, and chromosomal rearrangements, but also DNA and histones epigenetic modifications, ultimately resulting in alterations in the gene expression profiles [22]. Molecular studies from the last decades have identified critical genetic alterations that affect many key pathways involved in the regulation of typical cancer hallmarks, such as alterations in cell cycle, migration, proliferation, survival, angiogenesis, invasion and apoptosis [22]. While several alterations in signaling pathways occur in GBM, such as Wnt, Notch and Shh pathways (particularly relevant due to their associations with cancer stem-cells and resistance to radiochemotherapy) [23, 24], the most frequent aberrations in GBM occur in three critical signaling pathways: (i) retinoblastoma (RB), (ii) p53, and (iii) RTK/RAS/PI3K pathways [22, 25, 26], as detailed in Figure 2 and below.
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2.1. Retinoblastoma (RB) pathway
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Mutations in genes implicated in cell cycle regulation that allow cells to proliferate uncontrollably have been frequently identified in GBM, as in other human tumors [26-28]. The RB pathway, which is important in the G1/S transition, is aberrantly inactivated in GBM through the alteration of several genes and proteins [28].
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In a normal condition, the RB protein (encoded by RB1 gene, the first tumor suppressor gene described), a negative regulator of the cell cycle, is recruited to specific promoters through its interactions with E2F transcription factors. RB inhibits the transcription of genes by directly suppressing the transactivating function of E2F, and by recruiting factors that mediate transcriptional repression [27, 28]. E2F regulates the promoters’ activity of several genes related to (i) cell cycle, such as Cyclin E (CCNE) and A (CCNA), (ii) DNA replication, such as minichromosome maintenance complex component 7 (MCM7) and cell division cycle 6 (CDC6), (iii) nucleotide byosynthesis, such as ribonucleotide reductase (RRM), (iv) mitotic progression, such as Cyclin B1 (CCNB1) and cyclin-dependent kinase 1 (CDK1), and (v) apoptosis activation, such as apoptotic peptidase activating factor 1 (APAF-1) and caspases, such as caspase 3 (CASP3) [27, 28]. The interaction between RB and E2F can be disrupted due to the phosphorylation of the RB protein by Cdk4/6 kinases [27, 28]. To be active, these kinases are dependent of Cyclin proteins, namely CCND2 that competes for the binding site with Ink4 proteins [27]. Thus, the function of Ink4 is to prevent the formation of the active kinase complex (CCND2/Cdk4/6) [27]. This process is ultimately regulated by external signals, such as growth factors, which induce the cell to progress to the S phase [27].
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In GBM, the RB1 gene is frequently mutated [26]. However, the loss of function of RB is also reported to be a consequence of the amplification of CDK4 and CDK6, as well as by the inactivation of the INK4A/B (isoforms of CDKN2A/B) and INK4C (encoded by CDKN2C), which are inhibitors of Cdk4/6 [26]. Ultimately, these alterations lead to E2F accumulation and the consequent progression to S phase mediated by E2F-target genes [26, 27].
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2.2. p53 Pathway
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The TP53 gene encodes a protein (p53) that also controls the cell cycle by regulating target genes involved in cell cycle arrest, apoptosis and senescence [27]. Moreover, p53 has been named as the “guardian of the genome” because it leads to the arrest of cells with DNA damage in G1 phase, in order to promote DNA repair processes [29]. On the other hand, if irreparable genetic injuries occur, p53 induces cell death by activating the apoptotic machinery [29]. In normal unstressed cycling cells, some proteins, such as the ubiquitin ligase Mdm2, bind to p53 to promote its degradation via the ubiquitin/proteasome pathway [29-31]. The p53-mediated upregulation of MDM2 gene leads to a negative feedback that will maintain the levels of p53 very low in these cells [30, 31]. In this context, p53 loss of function may lead, for example, to uncontrolled growth and increased genetic instability. Its loss of function may be due to several reasons, including: (i) inactivating mutation [26], (ii) amplification of MDM2 and MDM4 [26, 31], and (iii) loss of function of ARF product encoded by CDKN2A, which interacts with and sequesters Mdm2 [26, 31, 32]. Unlike Mdm2, which degrades p53, Mdm4 inhibits p53 by binding to its transcriptional activation domain [31]. Moreover, Mdm4 also inhibits the degradation of Mdm2 [31].
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Figure 2
\n Common genetic alterations in GBM affect the RB, p53 and RTKs pathways. The aberrant deregulation of these pathways in GBM leads to alterations in cell cycle, migration, proliferation, angiogenesis, and apoptosis. Known proto-oncogenes or growth-promoting genes (shown in green), such as EGFR, PIK3CA (p110α) and AKT, are activated by mutations, overexpression and amplification, while tumor suppressor genes (show in red), such as PTEN, Arf and p53, are lost or inactivated by mutations, deletions, loss of heterozigosity, and epigenetic changes.
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2.3. Receptor Tyrosine Kinase (RTK) pathways
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GBM cells also commonly present a constitutive activation of cell growth signaling pathways by the overexpression of several mitogens and their specific membrane receptors [22, 24, 26, 33]. Glioma cells can also acquire mutations in the membrane receptors becoming independent of exogenous growth stimulation, increasing survival and motility [22, 24, 26, 33, 34]. In GBM, the deregulation of growth factor signaling occurs frequently by the amplification and/or activating mutations of RTKs [22, 26]. These play critical roles in several cellular processes, including cell growth, motility, survival and proliferation, and are tightly controlled by various physiological mechanisms (e.g., autocrine loops in which RTK ligands are produced in result of receptor activation) [26]. One of the most described RTK alteration in GBM is the deletion of exons 2-7 of epidermal growth factor receptor (EGFR) gene that results in the loss of the extracellular domain (EGFR-vIII mutant) [26]. Notwithstanding, other genetic alterations affecting EGFR, such as amplifications, activating point mutations that affect the extracellular domain, and other deletions in the region coding for the cytoplasmic domain, have also been described [26]. Moreover, alterations of other RTKs also occurs frequently in GBM, including: (i) overexpression of platelet-derived growth factor receptor (PDGFR) and its ligands PDGFA and PDGFB, suggesting an autocrine or paracrine loop activation, (ii) activating mutations in ERBB2 (member of the EGFR family), and (iii) activating mutations in hepatocyte growth factor receptor (MET) [22, 26, 33, 34]. RTKs mediate its functions by downstream effectors, namely phosphatidylinositol 3-kinase (PI3K), mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription (STAT) signaling cascades [34]. Although genetic alterations in RTKs may potentially activate these pathways, they can also be specifically activated due to other aberrations. Among them, the PI3K pathway is the most described in GBM and is involved in cell growth, proliferation, differentiation, motility and survival [26, 34]. The most frequent alterations involve inactivating mutations and homozygous deletions of PTEN [26]. This gene encodes the enzyme phosphatidylinositol (3,4,5)-trisphosphate 3-phosphatase, which removes a phosphate from phosphatidylinositol-(3,4,5)-triphosphate (PIP3), converting it to phosphatidylinositol-(4,5)-bisphosphate (PIP2) [22]. Thus, PTEN counteracts the action of the PI3K, which catalyzes the addition of a phosphate to PIP2 at the 3 position, converting it to PIP3 [22]. The accumulation of PIP3 recruits Akt to the plasma membrane. Here, Akt is activated by phosphorylation, promoting cell survival and proliferation [22]. The PI3K enzymatic complex is formed by 2 subunits, one regulatory protein (p85α), encoded by PIK3R1, and one catalytic protein (p110α), encoded by PIK3CA [22]. Note that other variants of this complex exist, but the referred subunits are the most expressed in GBM and the most widely-studied. Activating missense mutations and in-frame deletions have been detected in the PIK3CA [26]. One deletion was identified in the adaptor binding domain, raising the hypothesis that it may disrupt the normal interaction between p110α and its regulatory subunit, p85α [26]. Interestingly, in a few percentage of samples without activating mutations in the catalytic subunit, inactivating mutations were detected in the regulatory subunit [26]. This suggests a functional redundancy of these mutations as they individually activate PI3K. Again, the amplification of AKT3 gene, which encodes one of the Akt proteins, was described recently in a small fraction of GBM samples [26]. Other known mutation that ultimately leads to activation of PI3K and MAPK is the activating mutation of RAS [26]. RAS is indirectly activated by RTKs through the dissociation of a GDP molecule and association to a GTP. However, in a normal condition, this activation is quickly reverted from RAS-GTP to RAS-GDP. This RAS mutation impedes the dissociation of GTP, remaining RAS constitutively active. Neurofibromin 1 (NF1), which negatively regulates RAS signaling, is also downregulated in GBM by NF1 mutations or deletions, resulting in increased RAS signaling. Moreover, loss of expression of NF1 without evidence of genomic alteration was also observed [26]. In addition to its critical effects in cell growth, motility, and survival, the PI3K pathway seems to be also important in the activation of HOX genes, which were recently described to be important for the malignant phenotype of GBMs [35-37] (see section 3.3 for details).
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2.4. Crosstalk between RB, p53, and RTK pathways in GBM
\n
The development of new platforms of genome-scale screenings has allowed a more robust identification of the accumulation of genetic and epigenetic alterations. The Cancer Genome Atlas (TCGA) project, for example, was established with the aim of using genome-wide analysis technologies, which include DNA copy number, gene expression, DNA methylation, and nucleotide sequencing, to understand the molecular basis of cancer [26]. With this multiplatform profiling and using an integrative analysis, they identified a highly interconnected network of aberrations in GBM that include the pathways described above (RB, p53, RTKs and PI3K pathways) [26]. Interestingly, this integrative analysis showed a statistical tendency to mutual exclusivity for the specific alterations of components within each pathway. Nonetheless a great percentage of samples harbored aberrations in all signaling pathways [26], which is in agreement with the hypothesis that these pathways are a core prerequisite for GBM disease.
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2.5. Other key alterations in GBM
\n
In addition to the most common genetic alterations found in GBM, several other aberrations have been described. For example, mutations in IDH1 and IDH2 genes, which encode the metabolic enzymes isocitrate dehydrogenases were described. These reports suggest that these mutations lead to a new pro-oncogenic activity of IDH1/2 with the production of R(-)-2-hydroxyglutarate, an onco-metabolite [38, 39] (see section 3.2 for details). Other classes of proteins extremely important in GBM are DNA repair proteins, as they increase the probability of mutations. In fact, at least one of the MMR genes (MLH1, MSH2, MSH6 or PMS2) is mutated in hypermutated GBM samples [26], decreasing DNA repair competencies in these cells.
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2.6. Molecular subclasses of GBM
\n
Using an unsupervised hierarchical clustering analysis, Verhaak et al. [40] used TCGA data to successfully classify GBM into four subtypes - classical, mesenchymal, proneural and neural - improving and validating previous classifications of GBM [37, 41-47].
\n
The identity of the classical subtype was defined by displaying the most common genomic aberrations of GBM, with 93% of samples presenting amplifications in chromosome 7 paired with loss of chromosome 10, 95% showing high levels of EGFR amplification and/or expression, and EGFR-vIII activating point mutations. These amplifications of EGFR co-occurred with focal homozygous deletions targeting CDKN2A, which in turn was almost mutually exclusive with other alterations in RB pathway components, such as RB1, CDK4 and CCDN2. However, this subtype does not present TP53 mutations. Additionally, the Notch (NOTCH3, JAG1, LFNG) and Sonic hedgehog (SMO, GAS1, GLI2) signaling pathways, as well as the neural precursor and stem cell marker NES, were highly expressed in this subtype [40].
\n
The mesenchymal subtype presents a focal hemizygous deletions of 17q11.2 region that contains the NF1 gene. In fact, this deletion was associated with lower expression of NF1 in most cases. However, NF1 was also found to be mutated predominantly in this subtype, and sometimes this mutation co-occurred with PTEN inactivating mutations. Moreover, TRADD, RELB and TNFRSF1A genes, belonging to the tumor necrosis factor (TNF) superfamily, and genes encoding proteins from the NF-ĸB pathways, are highly expressed. Additionally, mesenchymal markers, such as CHI3L1 and MET, were expressed [40].
\n
The most relevant features of the proneural subtype were high levels of PDGFRA gene expression in combination with its focal amplification, and point mutations in IDH1. Importantly, these aberrations seem to be mutually exclusive. Loss of heterozygosity and inactivating mutations of TP53 were frequent in this subtype. While less frequent than in classical GBM samples, half of proneural samples also manifested amplification in chromosome 7, paired with loss of chromosome 10. PIK3CA and PIK3R1 activating and inactivating mutations, respectively, were observed mostly in samples without PDGFRA aberrations. Oligodendrocytic development genes, such as PDGFRA, NKX2-2 and OLIG2, were highly expressed. Lower expression of CDKN1A was observed, probably due to OLIG2 overexpression, which was described to be able to downregulate CDKN1A. Additionally, this subtype also presents the expression of proneural developmental genes, such as SOX genes, as well as DCX, DLL3, ASCL1 and TCF4 [40].
\n
In what concerns the neural subtype, few characteristics were reported, and it was almost merely classified based on neuron markers expression, including neurofilament light chain polypeptide (NEFL), gamma-aminobutyric acid A receptor (GABRA1), synaptotagmin I (STY1), and solute carrier family 12 (SLC12A5) [40].
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3. Molecular prognostic factors of GBM
\n
It is widely recognized that the molecular stratification of GBM patients may prove crucial in rationalizing treatment decisions, for which a set of molecular markers predictive of tumor response to specific therapies and/or patient outcome are required. The most well established prognostic factors in GBM patients include age, general performance status, tumor histological features and the extent of tumor resection [48]. Recently, several studies have identified biological and molecular features of GBMs that present prognostic value [37, 46, 49-58] and may help in therapeutic decisions. The work performed so far presents reasons for both optimism and caution regarding the improvements in the diagnosis and treatment of patients, but also demand validation in prospectively followed and in uniformly treated patients. Therefore, the focus remains in the identification of biomarkers that truly foster patient distinction in ways that may improve therapeutic decisions. The current most relevant prognostic biomarkers for GBM are summarized in Table 1, of which the most promising are briefly discussed below.
Selected molecular prognostic markers for glioblastoma.
\n
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3.1. MGMT promoter methylation
\n
Many studies have shown that the methylation status of MGMT (O6-methylguanine-DNA methyltransferase) gene is currently one of the most promising biomarkers of prognosis in GBM patients, although it has not yet reached broad clinical applicability [52, 60]. MGMT encodes a DNA-repair protein that removes alkyl groups from the O6 position of guanine, an important site for DNA alkylation. When DNA is left unrepaired, the lesions induced by chemotherapy trigger apoptosis and cytotoxicity [61]. Hegi and co-workers [52] showed that the epigenetic silencing of MGMT by promoter methylation leads to the loss of MGMT expression and reduced DNA-repair activity, resulting in increased sensitivity of the tumor cells to temozolomide (TMZ) treatment. In fact, they reported that this increased sensitivity is translated into differences in patient survival, as the methylation of the MGMT promoter is associated with longer overall survival (OS) in patients with GBM. Indeed, patients whose MGMT promoter is methylated and are treated with TMZ have an increased OS (median of 21.7 months), as well as a higher 2-year survival rate (46%), in comparison to patients treated with TMZ but with unmethylated MGMT promoter (median survival of 12.7 months and 2-year survival of 13.8%), suggesting that GBM patients whose tumors present MGMT expression do not benefit from TMZ treatment [52]. These results suggest MGMT promoter methylation as an independent and favorable predictive factor to patients’ response to TMZ therapy [52]. Despite these remarkable findings suggesting MGMT as a prognostic biomarker and as a specific predictor of response to TMZ-based chemotherapy, there is still a significant body of controversy surrounding them. Such controversy is mainly due to the heterogeneity of the patients enrolled in the study groups, as they present different glioma histologies, grades and treatment regimens, as well as the fact that different studies analyzed MGMT at different levels, including mRNA expression, methylation status and protein levels (as summarized in [62]). In an attempt to replicate Hegi’s findings, Costa and co-workers [62] analyzed a set of 90 GBM patients treated with postoperative TMZ-based chemoradiation regarding MGMT methylation. Despite a trend for longer overall and progression-free survival in GBM patients with MGMT promoter methylation, the differences did not reach statistical significance [62]. Moreover, sample classification as methylated or unmethylated for a certain gene is still controversial, as the relationship between the overall CpG island methylation, CpG methylation at individual sites, and their effects on gene silencing, is highly dependent on the location within the gene [63]. In this sense, Bady and co-workers [64] evaluated the relationship between the specific location of CpG methylation, MGMT expression and the outcome of patient in a population homogenously treated with alkylating agents. They reported two regions of methylated CpG’s that present strong association with patient longer survival, which negatively correlate with MGMT gene expression [64]. This is consistent with MGMT expression silencing via CpG methylation, resulting in sensitization to alkylating agents [64]. Similarly, Shah and colleagues also identified three regions of methylated CpGs on MGMT, associated with favorable patient progression-free survival, within a population of 44 GBM patients treated with radiotherapy and concomitant and adjuvant TMZ [65]. Nonetheless, the value of MGMT methylation status is also supported by a recent clinical trial that compares radiotherapy and TMZ treatment in elder patients, and reported an association between MGMT methylation and good outcome in the TMZ cohort, but not in the radiotherapy cohort [66]. Similarly, a meta-analysis performed by Olson and co-workers [67] that included 2018 patients from 20 different studies, showed that the silencing of MGMT was highly associated with improved OS in patients receiving chemotherapy as a part of the adjuvant treatment, a mild association in patients that received adjuvant radiotherapy, and no benefit in those submitted to surgery alone.
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3.2. IDH1 and IDH2 mutations
\n
Other important prognostic factors for GBM have been revealed by recent genomic studies and concern the presence of mutations in isocitrate dehydrogenase 1 and 2 genes (IDH1 and IDH2; IDH when referring to both) [26, 57, 68]. These are NADP-dependent enzymes that catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate, with the simultaneous production of NADPH [69]. The high-throughput sequencing of GBM revealed that IDH1 mutations occur in 12% of GBM, are somatic and heterozygous, and a consequence of the change of a guanine to an adenine at position 395 of the IDH1 gene (G395A), leading to the replacement of an arginine with a histidine at amino acid residue 132 of the protein (R132H) [57]. Similarly, sequence evaluation of IDH2 exons revealed a mutation in a histidine at amino acid residue 172 (R172), which is the exact analogue of the R132 residue in IDH1 [68]. Overexpression of IDH1\n \n R132H\n\n reduces the formation of α-ketoglutarate and increases the levels of HIF-1α [70]. As stated above, a recent study suggested that mutant IDH1 reduces α-ketoglutarate to R(-)-2-hydroxyglutarate, while converting NADPH to NADP+ [38, 39]. Even though the mechanism is yet to be clarified, it seems probable that the increased capacity to produce 2-hydroxyglutarate of cells presenting IDH1\n \n R132H\n mutation contributes to tumorigenesis [38]. IDH mutations are highly frequent in secondary GBM (up to 80%), but are rare in primary GBM (less than 10%) [68, 71]. IDH mutations are correlated with younger age at diagnosis, and with GBM patients’ longer survival when compared to patients with IDH\n wt genes [68, 72]. Mutations in IDH1 and IDH2 are mutually exclusive, which indicates that they might independently confer a growth advantage to mutated cells [73]. Moreover, IDH mutations generally associate with specific genetic and clinical characteristics when compared to gliomas that have IDH\n\n wt. In particular, it was shown that IDH mutations and amplification of EGFR in GBM are mutually exclusive events [74], and that the methylation of the MGMT gene promoter is often associated with IDH mutations [74, 75]. However, this association is yet to be clarified as it may represent a direct consequence of the activity of the mutant IDH, or an alternative marker for epigenetic changes in tumors presenting IDH mutations (reviewed in [76]). So, the deep understanding of the link between IDH mutations and common genetic events in GBM might furnish insights into their roles on gliomagenesis [40, 68]. Furthermore, a recent study evaluated the response of a series of 86 secondary GBM to TMZ treatment, and correlated several markers of GBM (including IDH mutations, 1p19q co-deletion, MGMT promoter methylation status, and TP53 expression) with progression-free survival and OS [77]. This study showed that IDH mutations were present in 73.4% of the analyzed patients, and that these mutations were associated with higher progression-free survival [77]. The authors also evaluated the response of patients presenting IDH mutations and MGMT promoter methylation, and found that patients presenting this combination had the best response to TMZ treatment, reporting also that IDH mutations seems to be a significant marker for positive chemosensitivity in secondary GBM [77].
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3.3. Molecular subclasses and prognostic value
\n
Strikingly, as stated above, mutations in IDH1 have been included in a GBM signature that allowed the division of GBMs into subtypes according to their recurrent genomic alterations [40] (see section 2.6 for details). The importance in the division of GBM into subtypes lies on the possible application of different therapeutic approaches, as treatments efficacy differs per subtype [40]. Aggressive therapy significantly delayed mortality in classical and mesenchymal subtypes, and a tendency to longer outcome was observed for the neural subtype, yet patients whose GBM present proneural features, associated with younger age, do not seem to benefit from highly aggressive therapies although presenting longer survival [40]. In this sense, some of the genetic events underlying the different GBM subtypes could become part of the clinical routine to rationalize therapeutic decisions, and ultimately lead to more personalized therapies for groups of patients with GBM.
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3.4. HOX genes signature
\n
Recent evidences have been revealing a remarkable resemblance between tumorigenic and developmental processes, indicating the relevance of molecular regulatory mechanisms crucial on normal development and on the tumorigenic process. Homeobox (HB) genes encode transcription factors that primarily play a crucial role during normal development, and are divided into two classes: class I comprises clustered homeobox (HOX) genes, and class II includes non-HOX genes, which are dispersed through the genome, and mainly serve as cofactors for HOX proteins [78]. During embryonic development, HOX genes are sequentially expressed from 3’ to 5’ along the anterior-posterior axis contributing to the temporospatial development of limbs and organs [79]. The mechanisms underlying HOX genes control in normal development occur according to three main principles: spatial collinearity, posterior prevalence, and temporal collinearity [80]. These were found to be altered in cancer as a consequence of three major mechanisms proposed by Abate-Shen [81]: temporospatial deregulation, gene dominance and epigenetic regulation. Different groups have been reporting the deregulation of these mechanisms in different HOX genes, and in different tumors (reviewed in [80]).
\n
The aberrant expression of HOX genes have been reported as crucial in several hallmarks of cancer, including increased proliferation, angiogenesis and invasion, and apoptosis resistance in leukemia and in several solid tumors [80, 82-85]. Interestingly, in recent years, HOX genes aberrant expression has been implicated in gliomagenesis. Abdel-Fattah and co-workers [86] evaluated the expression of all HOX genes in primary astrocytomas and in non-tumor brain specimens, reporting that some HOX genes are abnormally expressed in malignant astrocytomas. A subsequent report by Murat et al. [37] identified a HOX-dominated gene cluster, suggestive of a signature that displays srm cell-like self-renewal properties. These authors argue show that the expression of HOXA10 gene in GBM neurospheres is consistent with a role of HOX genes in glioma stem-like cell compartments [37]. Interestingly, the HOX-dominated gene signature arises along malignant progression to GBM, and is an independent predictive factor of chemo-radiotherapy resistance in patients [37]. Later, Costa and co-workers [35] showed that HOXA genes are predominantly activated in GBM, as compared to lower-grade gliomas and normal brain tissue, suggesting they may be a useful component of a molecular classification of gliomas. By analyzing expression microarrays data from 100 GBMs, they identified tumors with abnormal chromosomal domains of transcriptional activation, which comprise the HOXA cluster, and is reversibly regulated by the PI3K pathway [35]. Of all HOXA genes, HOXA9 expression was predictive of worse GBM patient outcome, and associated with pro-proliferative and anti-apoptotic functions, which may explain the unfavorable prognosis of GBM patients with HOXA9 reactivation [35]. More recently, Gaspar et al. [36], showed pediatric GBM cell lines that are resistant to TMZ present the coordinated expression of several HOX genes, of which HOXA9 and HOXA10 were highlighted as crucial effectors in this resistance [36]. In line with Costa et al. [35] report, Gaspar suggested that the HOX-enriched signature is regulated by the PI3K pathway, and interestingly, is associated with resistance to TMZ in pediatric GBM cell lines [36]. Moreover, pediatric patients with high-grade gliomas that express HOXA9 and HOXA10 presented shorter survival [36].
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3.5. CHI3L1 (YKL40) expression
\n
The molecular prognostic biomarkers currently available require the evaluation of tumor tissue in order to assess gene expression and promoter methylation levels. Moreover, tumor progression and treatment responses are monitored using imaging techniques, which do not distinguish the effects of treatment and tumor regrowth. In fact, patients who are submitted to magnetic resonance imaging (MRI) shortly after radiotherapy show increased volume of the tumor, which in up to 50% of the cases, is a consequence of the increased blood vessel permeability due to radiotherapy, an effect called pseudoprogession [87]. As it is difficult to distinguish between the therapeutic effects and real growth of the tumor [88], in addition to the impossibility of multiple tumor sampling during the course of the malignancy [89, 90], demand the establishment of less invasive prognostic and predictive markers. Serum markers that correlate with tumor biological properties might prove crucial in providing prognostic information and response to treatment, therefore allowing the proper adjustment of therapeutics, and improve care of patients with GBM. A study conducted by Tanwar [91] analyzed gene expression microarray data of tumor tissue from glioma patients, and showed that chitinase 3-like 1 (CHI3L1 or YKL40) was the most highly expressed among 10000 genes, when comparing to normal brain tissue [91]. The function of YKL-40 in gliomas and other tumors is yet to be fully clarified; however, it is thought to be involved in increased cell proliferation, differentiation, angiogenesis, decreased apoptosis, and extracellular matrix remodeling [92-94]. Interestingly, YKL-40 is secreted both by tumor cells and by tumor-associated macrophages in the bloodstream, therefore allowing its quantification in the blood. YKL-40 was found to be increased in the serum of patients with several solid tumor types, as breast, colorectal, ovary, small cell lung cancer and GBM (reviewed in [93]). Particularly in GBM, YKL-40 serum concentrations seem to be a strong predictor of an aggressive phenotype [53, 91], as the increased expression of YKL-40 appears to be associated with glioma grade, resistance to radiotherapy, shorter time to progression, and worse patient OS [53, 95-97]. However, to establish YKL-40 serum levels as a prognostic marker, there is still the need to perform further prospective studies that concern repeated determinations of YKL-40 levels before and after surgery. As YKL-40 can be reproducibly measured in the serum, and this biomarker is already well established for routine use, its inclusion in the clinical practice should be relatively straightforward, and might provide crucial information on tumor progression.
\n
In conclusion, the identification of molecular biomarkers that truly aid in the distinction of patients and therapeutic decisions still requires much effort. The integration of clinical and molecular data is becoming more frequent, and easier to perform and analyze, which will probably lead to more targeted and effective treatments. Moreover, it seems probable that sets of molecular biomarkers for GBM will be established in the next few years, and will become part of the clinical routine, leading to tailored therapies for subgroups of GBM patients. Importantly, the timely identification of patients who are not likely to respond to a certain therapy would allow their integration in clinical trials with novel therapies, but also to avoid the possible adverse side effects of a therapy that may not prove beneficial. Equally interesting, the establishment of molecular biomarkers of tumor therapy resistance may lead to a more guided and rational design of novel therapeutic agents and clinical trials for GBM patients. In the search for GBM patient individualized therapy, the discovery of particular tumor molecular features, as the status of MGMT promoter methylation status, the mutation status of IDH1 and IDH2, the expression of HOXA genes, and the serum levels of YKL-40, may prove crucial as initial building blocks of a panel of molecular biomarkers that may have real clinical implications. The challenge ahead is to discover further molecular markers of GBM, but also to integrate all the knowledge in an interdisciplinary way, considering different GBM subtypes, which altogether might allow a more rational and efficient fight against GBM.
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4. New molecular targets and treatments
\n
As described throughout this chapter, the molecular and cellular heterogeneity of GBM represents a major therapeutic challenge, but also offers a large number of opportunities to specific targeting of tumor cells’ alterations. Furthermore, the unsatisfactory prognosis of GBM patients, independently of the used treatment approaches, and the absence of a cure or significant advances in the treatment of GBM, are the major drivers of GBM therapeutics research.
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4.1. Classic therapeutics
\n
The current standard therapy for the treatment of GBM includes maximal surgical resection, followed by radiotherapy (RT) with concomitant and adjuvant administration of alkylating agents [98]. Administration of RT is usually given after the surgical removal of the tumor in order to eliminate residual tumor cells [99]. Alkylating agents act by introducing methyl groups in different positions in the DNA, resulting in DNA damage and specific cytotoxicity, that ultimately leads to apoptosis and cell death [100]. Before 1999, only nitrosourea-based chemotherapeutics were approved for the treatment of GBM, which includes oral lomustine (CCNU) and intravenous carmustine (BCNU) [101]. In 1999, FDA approved Gliadel® that consists in a polymeric biodegradable wafer that is able to release carmustine during 2-3 weeks after implantation in the gap where the tumor was removed during surgery [101-103]. Furthermore, in this same year, FDA granted accelerated approval to the imidazole derivative of the second-generation class of alkylating agents, TMZ, mainly because of its efficient absorption after oral administration and its ability to easily cross the blood-brain barrier [101, 104].
\n
TMZ was regularly approved by the FDA in 2005, and became the standard chemotherapeutic agent for the treatment of GBM [5]. The approval of TMZ was mainly due to the improvement in the OS of patients observed in a landmark study by Stupp et al. [5]. This clinical trial involving 573 patients with newly diagnosed GBM showed an increase in OS from 12.1 months to 14.6 months when patients were treated with RT plus TMZ comparing with RT alone [5]. In 2009, the 5-years retrospective analysis from this phase III clinical trial reported that, in addition to the improvement in OS, the 5-year survival rate was also higher in the group of patients treated with RT and TMZ, showing again the benefits of this treatment [105]. Nevertheless, some molecular mechanisms of resistance to this agent were identified, like the methylation status of the MGMT gene, which encodes a protein that repairs the damage induced by TMZ, and alkylating agents in general, resulting in chemoresistance [106].
\n
Besides TMZ, bevacizumab (BVZ, also known as Avastin®) was also conceded accelerated approval by the FDA in 2009 as monotherapy for patients with progressive GBM that did not respond to standard care (TMZ + RT) [101, 107]. This drug is a monoclonal antibody that targets VEGF, which is involved in the formation of new blood vessels [99]. Since GBM are highly vascularized tumors, this drug presented an attractive way to target tumor-associated increased angiogenesis [108]. When BVZ was combined with TMZ + RT for the treatment of newly diagnosed GBM patients in a phase II clinical trial, an improvement in OS (19.6 vs. 14.6 months) and progression-free survival (PFS, 13.6 vs. 6.9 months) was reported, when compared to the control cohort of the European Organization for Research and Treatment of Cancer-National Cancer Institute of Canada (EORTC/NCIC), in which patients were treated only with RT and TMZ [109]. BVZ also showed good radiographic responses in patients with recurrent GBM (71% and 35%, according to Levin and Macdonald criteria, respectively) when used first as a single agent, and later combined with irinotecan (topoisomerase I inhibitor) in a phase II clinical trial [110]. Although some exciting clinical results were described, several in vitro and in vivo studies have been unmasking unpredictable consequences of BVZ treatment. The treatment of intracranial xenograft mouse models of GBM with this VEGF inhibitor showed a decrease in the vascular network and contrast enhancement in MRI, but also a 68% increase in the infiltration of tumor cells trough the brain parenchyma [111, 112]. Furthermore, BVZ treatment increased the hypoxic microenvironment which is also implicated in increased invasion ability of tumor cells [24, 112].
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4.2. Novel molecular targeted therapeutics
\n
Conceptually, the development of targeted therapies for the treatment of GBM represents a significant advance in the search for a cure for this devastating disease. First, the specificity of these therapies has the potential to reduce toxic side effects. Second, the direct blockade of altered oncogenic signaling cascades may allow the reduction of tumor cell proliferation [113]. This next part will review some of the most promising therapeutic molecular and targeting strategies, including membrane proteins and growth factor receptors (e.g. RTK), and intracellular signaling pathways.
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4.2.1. Therapeutic targeting of membrane protein/growth factor receptors
\n
RTKs represent attractive targets for this therapeutic approach, since they are associated with GBM oncogenesis, and the binding of growth factors to these receptors activate signaling pathways that drive GBM cells survival and proliferation [113, 114] (see section 2.3 for information). There are two kinds of inhibitors for RTKs: (i) inhibitors targeting the intracellular tyrosine kinase domain (TKD), and (ii) monoclonal antibodies that can block RTK activation or target the RTK-expressing cells [115].
\n
EGFR
\n
As stated above, EGFR amplification, overexpression and mutation are frequent events in GBM cells and increased EGFR signaling is known to increase tumor proliferation, invasion ability, angiogenesis and blocking apoptosis [22, 116]. Several small molecule inhibitors targeting EGFR have been developed and approved for the treatment of particular cancers, as erlotinib and gefitinib in the treatment of advanced metastatic non-small cell lung cancer [24, 117]. This RTK can be targeted with a large number of inhibitors, like lapatinib (EGFR2, ErbB2), vandetanib (EGFR, VEGFR-2), PF-00299804 (EGFR, ERBB2 and ERBB4), BIBW2992 (EGFR, ERBB2, ERBB4), AEE 788 (EGFR, ERBB2, VEGFR), and monoclonal antibodies, as cetuximab (EGFR) and nimotuzumab (EGFR) [98, 116]; however, this section focus on the most reviewed and clinically tested drugs for the treatment of GBM (erlotinib, gefitinib and cetuximab). Erlotinib and gefitinib although, extensively tested in clinical trials for GBM patients (either already completed or currently ongoing), have not shown a significant benefit, and thus failed to reach clinical applicability (Table 2) [118]. The chimerical monoclonal antibody cetuximab (Erbitux) can also inhibit EGFR, and was shown to inhibit the mutant EGFR-vIII in glioma cells [119, 120]. Furthermore, preclinical studies using GBM xenograft models suggest that cetuximab could be effective for the treatment of invasive GBM [121]. The clinical evaluation of the administration of cetuximab in phase II trials for recurrent GBM patients has shown mixed results. The combination of cetuximab with BVZ and irinotecan resulted in 5% complete responses (CR), 21% partial responses (PR) and 40% of the patients with stable disease (SD), with only 9% of the GBM patients presenting signs of progressive disease (PD); the 6 months progression-free survival (6-PFS) of 33% obtained in this trial was also surprising [108]. In another phase II clinical trial for recurrent GBM patients, treatment with cetuximab showed worse outcomes, with a median time-to-progression (TTP) of only 1.9 months, and only 7.3% of the patients being progression free at 6 months after treatment [122].
\n
Most of the EGFR amplified GBMs also present expression of the mutant EGFR-vIII [116]. Since this mutated form of EGFR is absent in normal tissues, an immunotherapy-based approach to target EGFR-vIII was developed and is now under clinical trials (phase I, II and III) [118, 123]. This vaccine, called rindopepimut (CDX-110, PEPvIII) consists in a 14 aminoacids peptide that specifically recognizes EGFR-vIII, combined with an immunoadjuvant (keyhole limpet hemocyanin), that will potentiate an immune response against EGFR-vIII-positive tumor cells [124]. The clinical applicability of this vaccine was already tested in different clinical trials showing the benefits of this strategy (Table 2). Newly diagnosed GBM EGFR-vIII positive had a significant improvement in OS from 15.2 months (treated with TMZ + RT) to 23.2 months (CDX-110 + granulocyte macrophage-colony stimulating factor, GM-CSF, and TMZ, after RT), consistent with the benefit of this vaccine alone in other studies (OS 26 months vs. 15 months) [124, 125].
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PDGFR
\n
As referred previously, PDGFR is also frequently overexpressed in GBM [114]. As described for other RTK, PDGFR can also be blocked with different pharmacological inhibitors, such as imatinib mesylate (PDGFR, c-KIT, BCR-ABL), sunitinib (PDGFR, VEGFR, c-KIT), sorafenib (PDGFR, VEGFR, RAF), tandutinib (PDGFR, FLT3, c-KIT), vatalanib (PDGFR, VEGFR, c-KIT), IMC3G3 (PDGRFα), pazopanib (PDGFR, c-KIT, EGFR) or dasatinib (PDGFRβ, Src, BCR/Abl, c-KIT, ephrin A2) [98, 116]. However, this part will focus on the best characterized PDGFR inhibitor, imatinib mesylate (Gleevec or Livec), already evaluated in phase I/II clinical trials with GBM patients, which was originally FDA approved for the treatment of acute myeloid leukemia [106, 126]. In vitro treatments of GBM cells with imatinib have already shown inhibitory effects on cell proliferation, as a result of cell cycle arrest, increase apoptotic population and decreased clonogenic ability [127]. Its administration in mice models of GBM also showed an improvement in survival [128]. In clinical studies, imatinib mesylate was usually combined with hydroxyurea (HU), a ribonucleotide reductase inhibitor that blocks DNA synthesis [126, 129]. Treatment of recurrent GBM in phase II clinical trials was mostly disappointing, with 6-months PFS (6-PFS) of only 3% and 16% [130, 131]. Combination with HU, although showing a mild increase in OS and 6-PFS rates, again showed a lack of efficacy as compared to RT + TMZ [126]. The best result using imatinib was achieved in a phase I clinical trial for recurrent malignant glioma (MG), where imatinib was combined with HU and vatalanib (VEGFR inhibitor), with 24% of GBM patients revealing a radiographic partial response, 49% showing signals of stable disease, however 27% of the patients had progressive disease [132] (Table 2).
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VEGFR
\n
The therapeutic targeting of GBM-associated angiogenesis is already an approved strategy through VEGF inhibition with BVZ, but can also be achieved through inhibition of VEGF receptors using specific inhibitors, like cediranib, sorafenib, sunitinib, pazopanib, vandetanib, CT-332 (all VEGFR), XL-184 (VEGFR2, Met, RET, c-KIT, Flt3, Tie-2), semaxanib or AEE 788 [98, 116, 133]. For instance, cediranib (AZD2171) inhibits all VEGFR subtypes and was explored in phase I, II and III clinical trials [116]. The outcomes of cediranib (AZD2171) treatment in GBM patients are described as similar to the ones observed for BVZ, although only one of the completed trials has published results (Table 2) [116]. As reported for BVZ, also cediranib was associated with infiltrative cells not visible with contrast-enhanced MRI [112, 134]. In orthotopic mouse models of GBM, this VEGFR inhibitor induced alterations in the permeability and diameter of blood vessels, alleviating edema and increasing the survival of the mice [135].
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Met
\n
Met is an RTK for hepatocyte growth factor (HGF) that activates a series of signaling pathways, as referred above in section 2.3, similar to what is observed for EGFR or PDGFR activation, which ultimately leads to proliferative and invasive behaviors of cancer cells [106, 136]. In a series of 62 GBM patient samples, Met was found to be overexpressed and associated with poor prognosis, and with an invasive phenotype, supported by invasive multifoci lesions and expression of metalloproteinases 2 and 9 [137]. Inhibitors targeting Met include tivantinib, and cabozantinib (XL184) a potent inhibitor of several kinases, cabozantinib (XL184), which hase shown significant inhibitory effect on GBM tumor growth [138]. Furthermore, three phase I and II clinical trials for the evaluation of cabozantinib on the treatment of newly diagnosed GBM (monotherapy or combined with RT + TMZ) and recurrent GBM (monotherapy) (NCT00960492, NCT00704288 and NCT01068782) are now ongoing [118]. Another therapeutic approach to target HGF/Met axis is the use of the monoclonal antibody against HGF, rilotumumab (AMG-102), which was already tested during a phase II [116] clinical trial for recurrent GBM (Table 2); a second phase II trial to test the combination of rilotumumab with Avastin in patients with recurrent MG is now recruiting patients (NCT01113398) [118].
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Integrins
\n
Therapeutic targeting of the cell adhesion receptors integrins, which are transmembrane glycoproteins that attach cells to extracellular matrix proteins of the basement membrane or to ligands on other cells, have also proved to be a valuable therapeutic strategy for the treatment of GBM, with several recent clinical trials testing the success of the integrin inhibitor cilengetide (EMD 121974) as a monotherapy or in combination with RT + TMZ (Table 2) [139]. Cilengitide is an RGD (Asp-Gly-Asp) synthetic peptide that inhibits integrins αVβ3 and αVβ5 by receptor binding competition [139]. In vitro studies have shown an anti-angiogenic effect of this inhibitor by inhibiting proliferation and differentiation of endothelial progenitor cells, without affecting apoptosis [140]. In GBM cells, cilengitide exerted only a moderate loss of viability and was unable to sensitize GBM cells to radiotherapy and TMZ treatment [141]. Clinical studies with this drug have shown limited toxicity, but also reduced beneficial effect, when administered in newly diagnosed patients of GBM with RT+ TMZ (Table 2).
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4.2.2. Therapeutic targeting of intracellular signaling pathways
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PI3K/AKT/mTOR pathway
\n
As already mentioned the PI3K/Akt/mTOR pathway represents one of the most altered pathways in cancer, including GBM [113, 116]. Several inhibitors targeting different elements of this pathway are available and being tested both pre-clinically and at the clinical level. Enzastaurin is a specific inhibitor of protein kinase C (PKC) proteins, thus indirectly inhibiting Akt [104, 113, 142]. In preclinical studies, this inhibitor was able to suppress proliferation of GBM cells and tumor growth in GBM xenograft mice models [143]. In clinical studies, especially for recurrent GBM patients this drug failed to improve patient outcome, with PFS, OS and 6-PFS inferior to that of patients treated with lomustine in phase III clinical trials (Table 2) [144]. Inhibition of Akt can also be achieved using perifosine (KRX-0401), which affects the interaction of PIP3 with the PH domain of Akt [24]. When this drug was compared to mTOR inhibition in in vivo models with differential expression of PTEN, the treatment with perifosine did not alter tumor volume; on the other hand, treatment with mTOR inhibitor resulted in decrease tumor volume [145]. Furthermore, only a clinical trial phase II for patients with recurrent MG is under evaluation and no results are available until now (NCT00590954) [118]. A HIV type I (HIV-1) protease inhibitor called nelfinavir with applications in HIV infections is also able to downregulate Akt, and was proposed as an Akt inhibitor [146, 147]. Preclinical studies showed that treating GBM cells and xenograft mouse models with nelfinavir is able to sensitize tumor cells to RT and TMZ treatment [148]. Furthermore, this protease inhibitor decreases VEGF levels and angiogenesis, as well as HIF-1 expression levels and can cause endoplasmic reticulum stress and autophagy [146, 149]. Three phase I clinical trials to assess the toxicity of this treatment combined with RT + TMZ in newly diagnosed GBM are currently recruiting patients or active and ongoing (NCT01020292, NCT00694837, NCT00915694) [118].
\n
Several inhibitors of PI3K are also available, but the clinical evaluation of their efficacy is still very preliminary [150]. The class of pan-PI3K inhibitors (inhibit the catalytic p110 subunit) include LY294002, ZSTK474, and wortmannin. Derivatives of LY294002 and wortmannin, include SF1126 (LY294002 conjugated with an RGD peptide), PWT-458 and PX-866 (the first is a PEGylated derivate of wortmannin and the second is a wortmannin analog) [150]. From this group of specific PI3K inhibitors, only evaluation of PX-866 is proposed in a phase II clinical trial for the treatment of recurrent GBM patients, and is currently recruiting patients (NCT01259869) (Table 2) [118]. XL147 and GDC-0941 are also class I PI3K inhibitors, and IC877114 (targets p110δ) and TG100-115 (targets p110δ and p110γ) are PI3K isoform-specific inhibitors [150]. In turn, LY294002 was able to potentiate the citotoxicity of TMZ in glioma cells [151, 152]. Besides these agents that only target PI3K there are several dual PI3K/mTOR inhibitors, as PI-103, PI-540, PI-620, XL765, BEZ235 and BGT226 [150]. XL765 and XL147 were already tested in a phase I clinical trial with recurrent GBM patients (Table 2). Some preclinical studies support the theory of targeting these pathways in GBM therapeutics. Combination of LY294002 with the mTOR inhibitor rapamycin (or sirolimus) was able to diminish the self-renewal capacity of GBM cells and induce differentiation of cancer stem cell like cells (CSC); the same effect was achieved using a dual PI3K/mTOR inhibitor, NVP-BEZ235, which additionally reduced the ability of GBM CSLC to form tumors in vivo [153].
\n
For specific targeting of mTOR, several inhibitors were developed and tested clinically, like sirolimus (rapamycin), everolimus (RAD001) and temsirolimus (CCI-779) [106, 133]. All of these agents were already evaluated for the treatment of GBM in phase I and II clinical trials, but no significant improvements were seen (Table 2). A preclinical study showed that the outcome of mTOR inhibitory treatments could be efficiently monitored by Positron Emission Tomography (PET) based only in glucose and thymidine metabolism, through the uptake of [18F]FDG and [18F]FLT [154]. Furthermore, combination with other kinase inhibitors like AEE788 (inhibits both EGFR and VEGFR2) also showed some preclinical promising results, since its combination with everolimus (RAD001) resulted in increased effect on cell cycle arrest, proliferation and apoptosis, and impact tumor growth and survival in vivo [155]. This combination was tested in a phase I/II trial in 2006 for the treatment of recurrent GBM (NCT00107237) [118]. One liability of these therapies is that they only target mTORC1, and although this is the best characterized mTOR isoform, it is also known that full activation of PI3K/AKT pathway also requires mTORC2 [156]. Consequently, it is argued that dual inhibition of mTOR complexes 1 and 2 will be more efficient [156]. Preclinical studies have shown a significant decrease in tumor volume and growth in xenograft mouse models of GBM treated with PI3K/mTOR inhibitor AZD8055 [157]. Furthermore, there are three phase I clinical trials recruiting patients with MG (drug/trial reference: AZD8055/NCT01316809; CC-223/NCT01177397; OSI-027/NCT00698243) to test this possibility [118].
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RAS/RAF/MEK/ERK/MAPK pathway
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Another important pathway contributing to the neoplastic process is the one mediated by RAS/RAF/MEK/MAPK [106]. Inhibitors targeting members of this pathway include the farnesil transferase inhibitors of RAS, such as tipifarnib (Zanestra or R115777) and lonafarnib (Sarasar or SCH 66336) or multiple kinases inhibitors that target this pathway, like sorafenib [98, 116]. Some of the more significant clinical results of tipifarnib are summarized in Table 2. A phase I clinical trial to test the effectiveness of combining tipifarnib with TMZ and RT for newly diagnosed GBM or gliosarcoma is now ongoing (NCT00049387) [118]. Sorafenib is described as an inhibitor of EGFR, PDGFR and RAF, that can block MEK activation and, in preclinical studies, was able to induce apoptosis, and decreased proliferation of GBM cells [98, 158]. At the clinical level, it has been extensively studied in 12 clinical trials with completed, ongoing or recruiting status [118]; however, the results have still been somewhat different, with good results for newly diagnosed GBM and recurrent GBM, but when combined with BVZ for the treatment of recurrent GBM, it failed to improve survival, showing a high percentage of patients with progressive disease (Table 2) [118].
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Histone deacetylases (HDACs)
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Epigenetic events are crucial during the carcinogenic process, in which the chromatin state and remodeling are important mediators. Histone deacetylases (HDAC) are responsible for chromatin condensation and repression of transcription [159, 160]. Mechanistically they catalyze the elimination of acetyl groups from lysine residues in N-terminal tails of histone proteins [161]. The use of specific HDAC inhibitors has been described as an attractive opportunity to alter cancer-related epigenetic modifications [159]. These inhibitors are also reported as being able to block angiogenesis and invasion, promote cell cycle arrest and apoptosis, and to act as immunomodulators [116, 159, 160]. Valproic acid (VPA) is a short chain fatty acid, class I and IIa HDAC inhibitor, used as an anticonvulsant drug and frequently administered to treat glioma-associated seizures [159, 162, 163]. So, when the results of the EORTC/NCIC TMZ trial were analyzed taking in consideration the anti-epileptic drugs used, an interesting result showing a benefit in OS of the patients treated with TMZ + RT that were under VPA treatment was observed, suggesting that this drug could enhance the effects of TMZ + RT treatment [162]. VPA in combination with TMZ in vitro showed an increase in TMZ cytotoxicity, even for TMZ resistant cell lines, through downregulation of MGMT [164-166]; in vivo, this combination had also a benefit in tumor growth inhibition [164]; and increased the effects of γ-radiation in glioma cells [165]. Clinically, the evaluation of VPA for the treatment of GBM is proposed in two clinical trials: a phase II trial to evaluate the efficacy of VPA + RT followed by combination of VPA + BVZ for the treatment of children with high-grade gliomas (HGG) (NCT00879437) and a phase II trial to test the combination of VPA with TMZ and RT in adult HGG (NCT00302159) [118]. Vorinostat is also an inhibitor of class I and II HDACs, tested in several clinical trials now recruiting patients (NCT01378481, NCT01266031, NCT00731731, NCT01110876, NCT00555399) [118, 163]. The results of some of the already completed clinical trials for HDAC inhibitors as GBM therapeutics are in some way disappointing, with no radiographic responses observed when recurrent GBM patients treated with romidepsin and vorinostat failing to improve survival outcomes in different combinatory strategies (Table 2).
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\n Drug; Target(s)\n
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\n Clinical Trials/Population/Results\n
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\n Refs\n
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\n \n
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Erlotinib (Tarceva®); EGFR
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Phase I, and II clinical trials Acceptable toxicity and tolerable treatment with daily administrations of 150-200 mg/day dose Newly diagnosed GBM: combined with TMZ showed a PFS of 7.2 months and OS of 15.3 months; worse outcome for patients older than 70 years old; combined with standard care (RT + TMZ), the OS was 19.3 months, and correlated with MGMT promoter methylation and PTEN expression; combinations with other drugs are also under clinical trials (BVZ, administration after TMZ + RT, RT and erlotinib in younger patients) (NCT00124657, NCT00720356). Recurrent GBM: erlotinib as a single agent was not able to improve PFS compared to standard treatment (TMZ or carmustine + RT); combined with mTOR inhibitor sirolimus, treatment was well tolerated and OS was 33.8 weeks; combination with carboplatin showed a 30 weeks OS; Recurrent MG: combination with BVZ resulted in partial or total radiographic response for 48% of GBM patients and association with PFS; GBM tumors showing high levels of HIF-2α and VEGFR2 expression presented a worst prognosis. Recruiting or ongoing clinical trials combining erlotinib with isotretinoin, sirolimus and vorinostat, and also single agent administration for patients harboring the EGFR-vIII mutation (NCT01110876, NCT01103375, NCT01257594, NCT00509431). Nonprogressive GBM: as single agent, 1-year PFS was only 9% and less than 53% of 2 months, and less than 57% of the patients were alive after 1 year.
Phase I, and II clinical trials Recurrent GBM: as single agent, the treatment was well tolerated and resulted in OS of 39.4 weeks and PFS of 8.1 weeks. In a phase II study, OS did not overcome 8.8 months. Newly diagnosed GBM: 1-year OS (54.2%) and 1-year PFS (16.7%) were not significantly different from controls of other clinical trials.
Phase I, II, and III clinical trials EGFR-vIII-positive newly diagnosed GBM: given with GM-CSF, TTP of 14.2 months (vs. 6.3 months of historical controls) and OS of 26 months (vs. 15 months of historical controls); administration with TMZ also improved TTP (15.2 months vs. 6.4 months) and OS (23.2 months vs. 15.2 months); phase III trial (recruiting status) is projected to test the efficacy of rindopepimut with TMZ (NCT01480479). Newly diagnosed GBM: TTP was 10.2 months and OS was 22.8 months (vaccine given with DC); Phase II clinical trial is recruiting patients with relapsed GBM EGFR-vIII positive to test the efficacy of rindopepimut with BVZ (NCT01498328).
Phase I, II, and III clinical trials Newly diagnosed GBM: a phase II study with 20 patients showed a OS of 6.2 months. Recurrent GBM or MG: as single agent was well tolerated until doses of 800-1200 g/day, but very poor outcome with 6-PFS of 3%, only 2/34 patients with PR, and 6/34 with SD; when combined with HU, 6-PFS (27%) improved, but still very poor; combination with HU and vatalanib was well tolerated and resulted in OS of 48 weeks, PFS of 12 weeks and 6-PFS of 25%. In another phase II study the outcome of patients treated with imatinib as single agent was also (in newly diagnosed GBM) very poor (6-PFS: 16%); when combined with HU, imatinib also lacked efficacy. A phase III clinical trial showed no differences in TMZ resistant GBM patients treated with imatinbib + HU or HU alone (NCT00154375); phase II clinical trials combining imatinib with HU and Zactima were also performed but no results have been published (NCT00613054).
Phase I, II, and III clinical trials Recurrent GBM: as a single agent showed a PFS was 117 days and OS was 227 days (phase II); phase I trials to test cediranib + lomustine to treat GBM is already completed but without published results (NCT00503204); a phase III trial with the same combinatory approach for the treatment of recurrent GBM in currently ongoing (NCT00777153); recruiting trials include combination with gefitinib (NCT01310855) and with cilengitide (NCT00979862). Newly diagnosed GBM: all clinical trials are currently ongoing or recruiting – phase I and phase I/II cediranib + RT + TMZ (NCT01062425 and NCT00662506); phase I combination with BVZ (NCT00458731); phase I combination with gamma secretase inhibitor RO4929097 (NCT0130855).
Phase II clinical trial Recurrent GBM: when combined with prior BVZ treatment, did not affect PFS (4-4.1 weeks vs. 4.1-4.7 weeks), but OS was significantly different (3.4-3.6 months vs. 10.9-11.4 months).
Phase I, and II clinical trials Completed a phase I clinical study in patients with solid tumors (NCT00726583); Recruiting recurrent GBM patients for a phase II clinical trial (NCT01259869).
Phase I clinical trial Recurrent GBM: combination with a PI3K inhibitor XL147 already completed phase I trial (NCT0124460). Recruiting for a phase I trial for combination with TMZ to treat MG (NCT00704080).
Phase I, II, and III clinical trials Recurrent or progressive MG: in recurrent HGG, monotherapy had no significant impact in 6-PFS (7%); when compared with lomustine in a phase III clinical trial, no improvement in OS or PFS was achieved.
Phase I, and II clinical trials Phase I clinical trials showed that everolimus was well tolerated even when combined with RT + TMZ, BVZ or erlotinib. Changes in metabolism detected with FDG positron emission tomography days after administration of everolimus. \n Newly diagnosed GBM: combination with TMZ + RT + BVZ followed by BVZ + everolimus in a phase II clinical trial resulted in 57% PR, 1 CR, 18-months OS of 44%, and 18-months PFS of 29%.
Phase I, and II clinical trials Phase I trial showed that temsirolimus combined with TMZ and RT increased the risk of infectious diseases (3/25 fatal infections). Recurrent GBM: it was well tolerated as a single agent, and 36% radiographic responses were observed; 6-PFS was 7.8%, and OS was 4.4 months (phase II).
Phase I, and II clinical trials Recurrent GBM or MG: in tumors without PTEN, mTOR inhibition correlated with decreased proliferation of the tumors (phase I/II); combination with erlotinib (phase II) resulted in 47% SD, no CR or PR and 6-PFS of 3.1%; phase I/II trial combinatory treatment with erlotinib is currently ongoing (NCT00509431); phase I trial is recruiting patients to test combinatory treatment with vandetanib (NCT00821080). Recruiting patients with solid tumors to test combination with a vaccine (NCT01522820).
Phase I, and II clinical trials Newly diagnosed GBM: combined with RT and with or without TMZ, this treatment was well tolerated until doses of 300 mg (4-week cycle) (phase I). Administration with RT well tolerated until 200 mg/day, OS of 12 months and 1/9 PR, 4/9 SD, and 3/9 rapid progression. No significant improvement in survival with tipifarnib before RT (OS of 7.7 months). Recurrent GBM: treatment well tolerated, but 6-PFS (11.9%) and PFS (8 weeks) very poor, although one GBM patient remained progression-free for 36 months.
Phase I, and II clinical trials Newly diagnosed GBM: combination of TMZ and sorafenib after RT + TMZ showed 13% PR, 53% SD, and 28% PD. OS was 12 months, 1-year PFS was 16%, and PFS was 6 months (phase II); also tested in combination with erlotinib/tipifarnib/temsirolimus (NCT00335764). Recurrent GBM: combination with TMZ resulted in OS of 41.5 weeks, 1-year OS of 34.4%, PFS of 6.4 weeks (6-PFS: 9.4%); 3% of the patients had PR, 4.7% SD, and 50% PD (phase II); combination with BVZ was also tested (NCT00621686). Ongoing or recruiting clinical trials: NCT00734526 (phase I/II: sorafenib + RT + TMZ for the treatment of newly diagnosed GBM), NCT00884416 (phase I single agent HGG), NCT00329719 (phase I/II: combination with temsirolimus for recurrent GBM).
Phase I, II, and III clinical trials Well tolerated until doses of 2400 mg/m2\n Newly diagnosed GBM: when combined with RT + TMZ, the OS was 16.1 months and patients with MGMT promoter methylation tend to show a higher PFS and OS; clinical trials testing the efficacy of cilengitide with TMZ + RT in patients with or without MGMT methylation are now recruiting or ongoing (NCT00813943, NCT0068922). Recurrent GBM: as a single agent no complete responses were observed, but median OS was at least 6.5-9.9 months.
Phase I, and II clinical trials Progressive or recurrent GBM/MG: combination with bortezomib in a phase II trial resulted in very poor results (6-PFS 0%, OS 3.2 months, TTP 1.5 months); phase II monotherapy showed a 6-PFS of 15.2%, TTP of 1.9 months, PFS of 11.2 months, and OS of 5.7 months; Ongoing trials: phase I/II combination with BVZ and TMZ for recurrent MG (NCT00939991), phase I combination with TMZ for MG (NCT00268385), phase I combination with BVZ and irinotecan for recurrent GBM (NCT00762255).
Phase II clinical trial Recurrent MG: no radiographic responses, 72% PD and 28% SD; 6-PFS of 3%, PFS of 8 weeks, and OS of 34 weeks; 83% of the patients stopped treatment due to tumor progression, and 11% due to treatment toxicity.
Examples of clinical trials with molecularly targeted therapies directed to the most commonly altered signalling pathways in GBM.
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4.3. Novel therapeutic approaches
\n
As stated above, a small population of cells within the tumor, called cancer stem-cells, presents self-renewal capacity, ability to differentiate and initiate tumorigenesis, and express several markers of neural stem cells [24, 33, 116]. Furthermore, these cells are increasingly recognized as a niche of radiochemotherapy-resistant cells, making then attractive targets for new therapies [24, 49, 204]. There are several signaling pathways altered in cancer stem cells and that represent possible targets, such as PI3K, OLIG2, Shh, Wnt and Notch signaling pathways [24, 116].
\n
Another novel therapeutic strategy to treat cancer-related diseases is gene therapy (GT). GT was proposed for a long time as a molecular strategy that may help circumvent the non-specific cytotoxicity of the current pharmacological inhibitors, through specific delivery of suicide, pro-apoptotic, TP53, and other genes to tumor cells that, ultimately, lead to cancer regression or cure [106, 205]. GT can be performed delivering conditional or toxic transgenes using viral or non-viral delivery systems, including exosomes and stem cells [205, 206]. In GBM, the delivery of the thymidine kinase (TK) gene, produced by the herpes simplex virus type 1 (HSV1), in combination with the prodrug ganciclovir (GCV), using both retrovirus and adenovirus, was already tested at clinical level. The advantage of using retroviruses to deliver viral vectors is the specificity, since they target only highly proliferating cells. On the other hand, adenoviruses infect both quiescent cells and rapidly dividing cells [207]. A retroviralmediated delivery was already applied to newly diagnosed GBM patients until phase III clinical trial, but it was rejected after failing to improve survival compared to RT + TMZ [208]. Another promising strategy is the combination of viral vectors with factors that stimulate the immune system, as, for example, the delivery of the suicide gene HSV1 TK gene, with the cytokine IL-2. This strategy was already tested in 12 patients with recurrent GBM, where it was proved to be safe and well tolerable [209]. However, in terms of outcome, there were no patients with complete response and the PFS and OS were only 4.5 and 7.5 months, respectively [209].
\n
The induction of an immune response against tumor cells, called immunotherapy, is also a novel approach for the treatment of cancer, including GBM [210]. Immunotherapy can be performed with two different approaches: increasing the immune response to the tumor (active immunotherapy) with long term immunization, or delivering immune effectors to an immediate immune response (passive immunotherapy) [106]. Potent anti-tumor immunity is achieved through antigen-presenting cells, of which dendritic cells (DC) are the most promising [210, 211]. In a phase I clinical trial with 12 GBM patients (7 newly diagnosed GBM and 5 recurrent GBM) the administration of autologous DC vaccines showed that this treatment was well tolerated and minimally toxic. Additionally, it revealed promising outcome results, such as 2 long term-survivors (≥4 years) and OS of 23.4 months; however, the benefit in clinical outcomes were mainly observed in patients with stable disease and low levels of TGF-β2, who also had a higher number of infiltrating cytotoxic T-cells in the tumor bulk, suggesting that this treatment may favor particularly these patients [212]. In another phase I/II clinical trial with patients with recurrent GBM, it was found more beneficial the treatment with mature DC vs. non-mature DC, as well as intradermal and intratumoral administration of the DC pulsed with autologous tumor lysate, compared to intradermal approach alone [213]. The transfer of ex vivo maturated immune cells like effector T cells or lymphokine activated killer cells (LAK) is also under clinical evaluation for GBM immunotherapy [214].
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4.4. Current challenges and future trends
\n
As illustrated by the vast panoply of drugs and therapeutic strategies under investigation for the treatment of GBM, there is a major effort to develop more effective therapies to treat this highly malignant and therapy-insensitive disease. Unfortunately, the success of these new therapies has mostly been somewhat disappointing. Nevertheless, the efficacy of some of these approaches has yet to be determined. Of note, in addition to the strategies reviewed here, therapies targeting apoptotic elements (like Bcl-2, and inhibitor of apoptosis proteins), the mechanisms of resistance to TMZ (such as PARP and MGMT), or gene therapy to TP53, are also some examples of the search for an effective therapy for GBM [33]. Additionally, several developments were also made in helping surgeons with fluorescence-guided resection of the tumor and in radiotherapy [116]. In conclusion, the relevance of the effort to find a cure for GBM is unquestionable. However, despite the hard working search for a therapeutic strategy to reverse the poor outcomes of these patients, the standard treatment with TMZ and RT remains presently the best option. Future therapeutic trends for the treatment of GBM will have to: (i) include the new molecular classification of GBM; (ii) incorporate more efficient drug delivery systems to overcome blood-brain barrier restraints; and (iii) redirect the therapeutic choices to each patient, considering the specific molecular alterations of each tumor.
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\n \n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/42975.pdf",chapterXML:"https://mts.intechopen.com/source/xml/42975.xml",downloadPdfUrl:"/chapter/pdf-download/42975",previewPdfUrl:"/chapter/pdf-preview/42975",totalDownloads:2224,totalViews:379,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,dateSubmitted:"April 13th 2012",dateReviewed:"August 14th 2012",datePrePublished:null,datePublished:"February 27th 2013",dateFinished:"February 13th 2013",readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/42975",risUrl:"/chapter/ris/42975",book:{slug:"evolution-of-the-molecular-biology-of-brain-tumors-and-the-therapeutic-implications"},signatures:"Céline S. Gonçalves, Tatiana Lourenço, Ana Xavier-Magalhães, Marta Pojo and Bruno M. Costa",authors:[{id:"42804",title:"Dr.",name:"Bruno",middleName:null,surname:"Costa",fullName:"Bruno Costa",slug:"bruno-costa",email:"bfmcosta@ecsaude.uminho.pt",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Glioblastoma",level:"2"},{id:"sec_2_2",title:"1.2. Glioma/GBM-Initiating cells",level:"2"},{id:"sec_4",title:"2. Gene expression and signaling in GBM",level:"1"},{id:"sec_4_2",title:"2.1. Retinoblastoma (RB) pathway",level:"2"},{id:"sec_5_2",title:"2.2. p53 Pathway",level:"2"},{id:"sec_6_2",title:"2.3. Receptor Tyrosine Kinase (RTK) pathways",level:"2"},{id:"sec_7_2",title:"2.4. Crosstalk between RB, p53, and RTK pathways in GBM",level:"2"},{id:"sec_8_2",title:"2.5. Other key alterations in GBM",level:"2"},{id:"sec_9_2",title:"2.6. Molecular subclasses of GBM",level:"2"},{id:"sec_11",title:"3. Molecular prognostic factors of GBM",level:"1"},{id:"sec_11_2",title:"3.1. MGMT promoter methylation",level:"2"},{id:"sec_12_2",title:"3.2. IDH1 and IDH2 mutations",level:"2"},{id:"sec_13_2",title:"3.3. Molecular subclasses and prognostic value",level:"2"},{id:"sec_14_2",title:"3.4. HOX genes signature",level:"2"},{id:"sec_15_2",title:"3.5. CHI3L1 (YKL40) expression",level:"2"},{id:"sec_17",title:"4. New molecular targets and treatments",level:"1"},{id:"sec_17_2",title:"4.1. Classic therapeutics",level:"2"},{id:"sec_18_2",title:"4.2. Novel molecular targeted therapeutics",level:"2"},{id:"sec_18_3",title:"4.2.1. Therapeutic targeting of membrane protein/growth factor receptors",level:"3"},{id:"sec_19_3",title:"Table 2.",level:"3"},{id:"sec_21_2",title:"4.3. Novel therapeutic approaches",level:"2"},{id:"sec_22_2",title:"4.4. Current challenges and future trends",level:"2"}],chapterReferences:[{id:"B1",body:'\n \n \n \n Ohgaki\n H\n \n \n Kleihues\n P\n \n Genetic pathways to primary and secondary glioblastoma. 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D\n \n et al\n North Central Cancer Treatment Group Phase I trial N057K of everolimus (RAD001) and temozolomide in combination with radiation therapy in patients with newly diagnosed glioblastoma multiforme. Int J Radiat Oncol Biol Phys. 2011Oct 1;81\n 2\n 468\n 75\n \n '},{id:"B189",body:'\n \n \n \n Galanis\n E\n \n \n Buckner\n J. C\n \n \n Maurer\n M. J\n \n \n Kreisberg\n J. I\n \n \n Ballman\n K\n \n \n Boni\n J\n \n et al\n Phase II trial of temsirolimus (CCI-779) in recurrent glioblastoma multiforme: a North Central Cancer Treatment Group Study. J Clin Oncol. 2005Aug 10;23\n 23\n 5294\n 304\n \n '},{id:"B190",body:'\n \n \n \n Sarkaria\n J. N\n \n \n Galanis\n E\n \n \n Wu\n W\n \n \n Dietz\n A. B\n \n \n Kaufmann\n T. J\n \n \n Gustafson\n M. P\n \n et al\n Combination of temsirolimus (CCI-779) with chemoradiation in newly diagnosed glioblastoma multiforme (GBM) (NCCTG trial N027D) is associated with increased infectious risks. Clin Cancer Res. 2010Nov 15;16\n 22\n 5573\n 80\n \n '},{id:"B191",body:'\n \n \n \n Cloughesy\n T. F\n \n \n Yoshimoto\n K\n \n \n Nghiemphu\n P\n \n \n Brown\n K\n \n \n Dang\n J\n \n \n Zhu\n S\n \n et al\n Antitumor activity of rapamycin in a Phase I trial for patients with recurrent PTEN-deficient glioblastoma. PLoS Med. 2008Jan 22;5(1):e8.\n '},{id:"B192",body:'\n \n \n \n Lustig\n R\n \n \n Mikkelsen\n T\n \n \n Lesser\n G\n \n \n Grossman\n S\n \n \n Ye\n X\n \n \n Desideri\n S\n \n et al\n Phase II preradiation R115777 (tipifarnib) in newly diagnosed GBM with residual enhancing disease. Neuro Oncol. 2008Dec;10\n 6\n 1004\n 9\n \n '},{id:"B193",body:'\n \n \n \n Nghiemphu\n P. L\n \n \n Wen\n P. Y\n \n \n Lamborn\n K. R\n \n \n Drappatz\n J\n \n \n Robins\n H. I\n \n \n Fink\n K\n \n et al\n A phase I trial of tipifarnib with radiation therapy, with and without temozolomide, for patients with newly diagnosed glioblastoma. Int J Radiat Oncol Biol Phys. 2011Dec 1;81\n 5\n 1422\n 7\n \n '},{id:"B194",body:'\n \n \n \n Cloughesy\n T. F\n \n \n Wen\n P. Y\n \n \n Robins\n H. I\n \n \n Chang\n S. M\n \n \n Groves\n M. D\n \n \n Fink\n K. L\n \n et al\n Phase II trial of tipifarnib in patients with recurrent malignant glioma either receiving or not receiving enzyme-inducing antiepileptic drugs: a North American Brain Tumor Consortium Study. J Clin Oncol. 2006Aug 1;24\n 22\n 3651\n 6\n \n '},{id:"B195",body:'\n \n \n \n Moyal\n E. C\n \n \n Laprie\n A\n \n \n Delannes\n M\n \n \n Poublanc\n M\n \n \n Catalaa\n I\n \n \n Dalenc\n F\n \n et al\n Phase I trial of tipifarnib (R115777) concurrent with radiotherapy in patients with glioblastoma multiforme. Int J Radiat Oncol Biol Phys. 2007Aug 1;68\n 5\n 1396\n 401\n \n '},{id:"B196",body:'\n \n \n \n Hainsworth\n J. D\n \n \n Ervin\n T\n \n \n Friedman\n E\n \n \n Priego\n V\n \n \n Murphy\n P. B\n \n \n Clark\n B. L\n \n et al\n Concurrent radiotherapy and temozolomide followed by temozolomide and sorafenib in the first-line treatment of patients with glioblastoma multiforme. Cancer. 2010Aug 1;116\n 15\n 3663\n 9\n \n '},{id:"B197",body:'\n \n \n \n Reardon\n D. A\n \n \n Vredenburgh\n J. J\n \n \n Desjardins\n A\n \n \n Peters\n K\n \n \n Gururangan\n S\n \n \n Sampson\n J. H\n \n et al\n Effect of CYP3A-inducing anti-epileptics on sorafenib exposure: results of a phase II study of sorafenib plus daily temozolomide in adults with recurrent glioblastoma. J Neurooncol. 2011Jan;101\n 1\n 57\n 66\n \n '},{id:"B198",body:'\n \n \n \n Nabors\n L. B\n \n \n Mikkelsen\n T\n \n \n Rosenfeld\n S. S\n \n \n Hochberg\n F\n \n \n Akella\n N. S\n \n \n Fisher\n J. D\n \n et al\n Phase I and correlative biology study of cilengitide in patients with recurrent malignant glioma. J Clin Oncol. 2007May 1;25\n 13\n 1651\n 7\n \n '},{id:"B199",body:'\n \n \n \n Reardon\n D. A\n \n \n Fink\n K. L\n \n \n Mikkelsen\n T\n \n \n Cloughesy\n T. F\n \n \n O\n Neill\n \n \n A\n Plotkin\n \n S, et al. Randomized phase II study of cilengitide, an integrin-targeting arginine-glycine-aspartic acid peptide, in recurrent glioblastoma multiforme. J Clin Oncol. 2008Dec 1;26\n 34\n 5610\n 7\n \n '},{id:"B200",body:'\n \n \n \n Stupp\n R\n \n \n Hegi\n M. E\n \n \n Neyns\n B\n \n \n Goldbrunner\n R\n \n \n Schlegel\n U\n \n \n Clement\n P. M\n \n et al\n Phase I/IIa study of cilengitide and temozolomide with concomitant radiotherapy followed by cilengitide and temozolomide maintenance therapy in patients with newly diagnosed glioblastoma. J Clin Oncol. 2010Jun 1;28\n 16\n 2712\n 8\n \n '},{id:"B201",body:'\n \n \n \n Friday\n B. B\n \n \n Anderson\n S. K\n \n \n Buckner\n J\n \n \n Yu\n C\n \n \n Giannini\n C\n \n \n Geoffroy\n F\n \n et al\n Phase II trial of vorinostat in combination with bortezomib in recurrent glioblastoma: a north central cancer treatment group study. Neuro Oncol. 2012Feb;14\n 2\n 215\n 21\n \n '},{id:"B202",body:'\n \n \n \n Galanis\n E\n \n \n Jaeckle\n K. A\n \n \n Maurer\n M. J\n \n \n Reid\n J. M\n \n \n Ames\n M. M\n \n \n Hardwick\n J. S\n \n et al\n Phase II trial of vorinostat in recurrent glioblastoma multiforme: a north central cancer treatment group study. J Clin Oncol. 2009Apr 20;27\n 12\n 2052\n 8\n \n '},{id:"B203",body:'\n \n \n \n Iwamoto\n F. M\n \n \n Lamborn\n K. R\n \n \n Kuhn\n J. G\n \n \n Wen\n P. Y\n \n \n Yung\n W. K\n \n \n Gilbert\n M. R\n \n et al\n A phase I/II trial of the histone deacetylase inhibitor romidepsin for adults with recurrent malignant glioma: North American Brain Tumor Consortium Study 03-03. Neuro Oncol. 2011May;13\n 5\n 509\n 16\n \n '},{id:"B204",body:'\n \n \n \n Eramo\n A\n \n \n Ricci-vitiani\n L\n \n \n Zeuner\n A\n \n \n Pallini\n R\n \n \n Lotti\n F\n \n \n Sette\n G\n \n et al\n Chemotherapy resistance of glioblastoma stem cells. Cell Death Differ. 2006Jul;13\n 7\n 1238\n 41\n \n '},{id:"B205",body:'\n \n \n \n Verma\n I. M\n \n \n Weitzman\n M. D\n \n Gene therapy: twenty-first century medicine. Annu Rev Biochem. 2005\n 74\n 711\n 38\n \n '},{id:"B206",body:'\n \n \n \n Kroeger\n K. M\n \n \n Muhammad\n A. K\n \n \n Baker\n G. J\n \n \n Assi\n H\n \n \n Wibowo\n M. K\n \n \n Xiong\n W\n \n et al\n Gene therapy and virotherapy: novel therapeutic approaches for brain tumors. Discov Med. 2010Oct;10\n 53\n 293\n 304\n \n '},{id:"B207",body:'\n \n \n \n Castro\n M. G\n \n \n Candolfi\n M\n \n \n Kroeger\n K\n \n \n King\n G. D\n \n \n Curtin\n J. F\n \n \n Yagiz\n K\n \n et al\n Gene therapy and targeted toxins for glioma. Curr Gene Ther. 2011Jun;11\n 3\n 155\n 80\n \n '},{id:"B208",body:'\n \n \n \n Lesniak\n M. S\n \n Gene therapy for malignant glioma. Expert Rev Neurother. 2006Apr;6\n 4\n 479\n 88\n \n '},{id:"B209",body:'\n \n \n \n Colombo\n F\n \n \n Barzon\n L\n \n \n Franchin\n E\n \n \n Pacenti\n M\n \n \n Pinna\n V\n \n \n Danieli\n D\n \n et al\n Combined HSV-TK/IL-2 gene therapy in patients with recurrent glioblastoma multiforme: biological and clinical results. Cancer Gene Ther. 2005Oct;12\n 10\n 835\n 48\n \n '},{id:"B210",body:'\n \n \n \n Vanneman\n M\n \n \n Dranoff\n G\n \n Combining immunotherapy and targeted therapies in cancer treatment. Nat Rev Cancer. 2012Apr;12\n 4\n 237\n 51\n \n '},{id:"B211",body:'\n \n \n \n Yong\n R. L\n \n \n Lonser\n R. R\n \n Immunotherapy trials for glioblastoma multiforme: promise and pitfalls. World Neurosurg. 2012May;77(5-6):636-8.\n '},{id:"B212",body:'\n \n \n \n Liau\n L. M\n \n \n Prins\n R. M\n \n \n Kiertscher\n S. M\n \n \n Odesa\n S. K\n \n \n Kremen\n T. J\n \n \n Giovannone\n A. J\n \n et al\n Dendritic cell vaccination in glioblastoma patients induces systemic and intracranial T-cell responses modulated by the local central nervous system tumor microenvironment. Clin Cancer Res. 2005Aug 1;11\n 15\n 5515\n 25\n \n '},{id:"B213",body:'\n \n \n \n Yamanaka\n R\n \n \n Homma\n J\n \n \n Yajima\n N\n \n \n Tsuchiya\n N\n \n \n Sano\n M\n \n \n Kobayashi\n T\n \n et al\n Clinical evaluation of dendritic cell vaccination for patients with recurrent glioma: results of a clinical phase I/II trial. Clin Cancer Res. 2005Jun 1;11\n 11\n 4160\n 7\n \n '},{id:"B214",body:'\n \n \n \n Jackson\n C\n \n \n Ruzevick\n J\n \n \n Phallen\n J\n \n \n Belcaid\n Z\n \n \n Lim\n M\n \n Challenges in immunotherapy presented by the glioblastoma multiforme microenvironment. Clin Dev Immunol. 2011;2011\n \n '}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Céline S. Gonçalves",address:null,affiliation:'
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimarães, Portugal
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimarães, Portugal
'},{corresp:"yes",contributorFullName:"Bruno M. Costa",address:"bfmcosta@ecsaude.uminho.pt",affiliation:'
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimarães, Portugal
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Ramazan Yigitoglu",authors:[{id:"54960",title:"Dr.",name:"Pınar",middleName:null,surname:"Atukeren",fullName:"Pınar Atukeren",slug:"pinar-atukeren"},{id:"163866",title:"Dr.",name:"M.Ramazan",middleName:null,surname:"Yigitoglu",fullName:"M.Ramazan Yigitoglu",slug:"m.ramazan-yigitoglu"}]},{id:"43971",title:"Management of Brain Tumor in Pregnancy — An Anesthesia Window",slug:"management-of-brain-tumor-in-pregnancy-an-anesthesia-window",signatures:"Hala M. Goma",authors:[{id:"42540",title:"Dr.",name:"Hala",middleName:"Mostafa",surname:"Goma",fullName:"Hala Goma",slug:"hala-goma"},{id:"162798",title:"Prof.",name:"Amr",middleName:null,surname:"Abo Ela",fullName:"Amr Abo Ela",slug:"amr-abo-ela"}]},{id:"44038",title:"Interdisciplinary Surgical Management of Orbital and Maxillo- Ethmoidal Complex Disorders",slug:"interdisciplinary-surgical-management-of-orbital-and-maxillo-ethmoidal-complex-disorders",signatures:"Jarosław Paluch, Jarosław Markowski, Jan Pilch, Agnieszka Piotrowska – Seweryn, Robert Kwiatkowski, Joanna Lewin-Kowalik, Czesław Zralek and Agnieszka Gorzkowska",authors:[{id:"46014",title:"Prof.",name:"Jarosław",middleName:null,surname:"Markowski",fullName:"Jarosław Markowski",slug:"jaroslaw-markowski"},{id:"55851",title:"Dr.",name:"Jarosław",middleName:null,surname:"Paluch",fullName:"Jarosław Paluch",slug:"jaroslaw-paluch"},{id:"165065",title:"Dr.",name:"Agnieszka",middleName:null,surname:"Piotrowska - Seweryn",fullName:"Agnieszka Piotrowska - Seweryn",slug:"agnieszka-piotrowska-seweryn"},{id:"166896",title:"Prof.",name:"Joanna",middleName:null,surname:"Lewin-Kowalik",fullName:"Joanna Lewin-Kowalik",slug:"joanna-lewin-kowalik"},{id:"166897",title:"Dr.",name:"Włodzimierz",middleName:null,surname:"Dziubdziela",fullName:"Włodzimierz Dziubdziela",slug:"wlodzimierz-dziubdziela"},{id:"166944",title:"Dr.",name:"Maciej",middleName:null,surname:"Kajor",fullName:"Maciej Kajor",slug:"maciej-kajor"},{id:"166955",title:"Dr.",name:"Czesław",middleName:null,surname:"Zralek",fullName:"Czesław Zralek",slug:"czeslaw-zralek"},{id:"166968",title:"Dr.",name:"Robert",middleName:null,surname:"Kwiatkowski",fullName:"Robert Kwiatkowski",slug:"robert-kwiatkowski"},{id:"167226",title:"Prof.",name:"Jan",middleName:null,surname:"Pilch",fullName:"Jan Pilch",slug:"jan-pilch"}]},{id:"43805",title:"The Epidemiology of Paediatric Brain Cancer — Descriptive Epidemiology and Risk Factors",slug:"the-epidemiology-of-paediatric-brain-cancer-descriptive-epidemiology-and-risk-factors",signatures:"Adrianna Ranger",authors:[{id:"40865",title:"Dr.",name:"Adrianna",middleName:null,surname:"Ranger",fullName:"Adrianna Ranger",slug:"adrianna-ranger"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"66118",title:"Combating Alarm Fatigue: The Quest for More Accurate and Safer Clinical Monitoring Equipment",doi:"10.5772/intechopen.84783",slug:"combating-alarm-fatigue-the-quest-for-more-accurate-and-safer-clinical-monitoring-equipment",body:'\n
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1. Introduction
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Highly reliable, precise, user-friendly, and cost-effective clinical alarm systems are critical to efficient functioning of health-care facilities [1, 2, 3]. Despite tremendous progress over the past few decades, the “perfect solution” remains elusive, with focus being placed primarily on clinical indications and appropriateness of use for the existing equipment and monitoring frameworks [3, 4, 5, 6]. Beyond the concept of “false alarm,” suboptimal implementation of clinical monitoring systems can have much more profound and potentially dangerous consequences [7, 8, 9]. One such consequence, and the primary topic of this chapter, is the phenomenon of alarm fatigue (AF). It is defined as the decrease of clinician response caused by excessive alarms, sensory overload, and desensitization, in addition to other occupational and environmental variables [9, 10, 11]. Among contributing factors are also high staff workload, long shift hours, and work environments with high noise levels, all of which contribute to the “desensitization effect” associated with AF [10, 12].
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Hospital patient care units tend to be high-paced and potentially unpredictable environments, with complex workflows. Multiple simultaneous interactions between patients, families, and health-care staff may create an added element of chaos [13, 14]. To help nurses and other staff cope with their many responsibilities, various audible and visual alerts have been implemented to prompt immediate response and clinical assessment of patients [15]. These alerts are relayed from patient monitoring devices, which provide continuous flow of vital sign data with a high degree of sensitivity. The advanced technology used in these surveillance systems has provided a significant amount of physiological data at low cost while being particularly helpful by facilitating the monitoring of critically ill patients to identify deviations of vital signs (e.g., heart rate, respiratory rate, blood pressure, and pulse oximetry) from normal ranges [16]. However, when various clinical alarm systems are superimposed on the need for constant vigilance in the setting of highly challenging and often chaotic environment of the typical clinical unit, the stage is set for the emergence of AF and other forms of cognitive lapses [17, 18, 19].
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The prevalence of various monitoring modalities has increased significantly, with most health-care institutions utilizing some broadly defined combination of different alarm systems. As the use of these systems became more widespread, a major flaw became evident: the excessive amount of triggered alarms was contributing to unintended consequences, both in terms of patient outcomes and staff fatigue/dissatisfaction [8, 20, 21]. The high rate of nonactionable alarms, where immediate action is not required on the behalf of clinicians, was especially problematic [22]. In fact, the increasing frequency of “false alarms” has a significant desensitization effect on hospital staff, whereby some alarms may be erroneously “dismissed by assumption” as being “noncritical” [23]. This desensitization leads to both increased response times and decreased, or even lack of, clinician response. In the setting of a busy hospital, it is commonplace to hear constant chimes and beeps, each coming from different machines and indicating different “alarm conditions” (Figure 1). It should be more of an expectation that clinicians become desensitized to extraneous stimuli given the constant sensory bombardment coupled with the need for vigilance and differential interpretation of each alarm [25, 26]. When further compounded by heavy clinical workloads and long shifts, it becomes a matter of “statistical probability” before a critical alarm is missed [27, 28, 29]. Given the effect of this potentially dangerous phenomenon on both quality and safety of patient care, closer scrutiny of AF and related concepts is warranted. In this chapter, we will present a vignette-based discussion outlining fairly typical AF scenarios. Opportunities for improvement, including equipment, personnel, and systems-based considerations, will then be provided.
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Figure 1.
Conceptual model for daily observed alarms at a typical acute care hospital. Data shown in proportion to different scales, from individual patient to entire institution, showing the true magnitude of the problem (source: Ref. [24]).
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2. Primary research methods
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For the purposes of this chapter, the authors performed a thorough literature search using PubMed, Google Scholar™, and Bioline International. Primary search terms included “alarm fatigue,” “health-care alarms,” “patient monitoring,” “provider burnout,” as well as secondary terms consisting of various combinations of primary search terms. From over 47,000 unique search results, we distilled 73 most pertinent references immediately relevant to this document. Finally, additional sources that were cited across our primary search results were added, for a total of 101 references included in the final manuscript.
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3. Patient monitoring: different types and modalities
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A diverse number of patient monitors are widely used across various health-care settings [30, 31, 32]. When employed correctly, they provide potentially valuable, actionable, and real-time information about a patient’s clinical status. Different monitoring devices are intended to measure different parameters, potentially allowing for rapid assessment of a patient. This is especially relevant in the context of the current discussion of AF and more specifically the domain of alarm trigger accuracy [32, 33]. As clinical monitoring becomes more sophisticated and better integrated, remote (off-site) implementations also become possible [34, 35, 36]. The subsequent discussion will outline major types of monitoring equipment and alarms, including ventilation/oxygenation, hemodynamic, and pressure point alert systems.
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3.1. Ventilation/oxygenation alarms
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In general, primary ventilation/oxygenation alarms (VOA) include capnography and pulse oximetry, respectively. More broadly, respiratory parameter monitoring indicates the patient’s oxygen saturation, respiratory rate, and end-tidal carbon dioxide [33, 37]. The use of VOA has been particularly important for critically ill patients who require mechanical ventilatory support. In such applications, the monitor is designed to be exquisitely sensitive to detect even the slightest changes in a patient’s oxygenation or ventilation status [38]. As demonstrated in Clinical Vignette #1 later in the chapter, an alarm may be triggered following the detection of a very small respiratory parameter “excursion,” regardless of its clinical significance or magnitude of the observed change in the patient’s actual clinical status. In this context, apnea and minute volume warnings are among the most common alarms triggered, with majority of such occurrences deemed clinically irrelevant upon further interrogation [39]. Moreover, many VOA triggers can be attributed to artifactual sources (e.g., patient movement, interruption of blood flow by inflating blood pressure cuff, and even atmospheric pressure variations) [37]. Thus, providers should be educated accordingly to ensure that the above considerations are appropriately factored into final clinical determinations and decisions.
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3.2. Hemodynamic alarms
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Hemodynamic alarms (HA) monitor a variety of parameters, of which the most common ones include heart rate, systolic/diastolic/mean blood pressure, and various other intravascular pressure measurements via both invasive and noninvasive approaches [37, 40]. Hemodynamic monitoring has become a useful tool for the bedside assessment of patients in a number of clinical scenarios, from routine telemetry applications to advanced intravascular catheter utilization. There is some degree of predictability based on measured parameters, especially when trend determination and volume responsiveness are being considered [41, 42]. Hemodynamic monitors are particularly important in the setting of an unstable (or potentially unstable) patient, similar to the one described in Clinical Vignette #3 later in the chapter. In such capacity, HAs can help facilitate rapid intervention and prompt correction of emergent issues. Still, HAs are far from perfect, with significant shortcomings in their discriminatory capabilities. More specifically, HAs are unable to identify a patient as “stable” or “unstable,” especially when physiologic compensatory processes mask any underlying instability or in the setting of rapid change in hemodynamic status [43]. Thus, when using any particular monitoring modality, there is no substitute for an astute clinician who is able to effectively correlate HA findings with the clinical reality [44, 45, 46].
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3.3. Bed and chair pressure sensors
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Bed and chair pressure sensor (BCPS) alarms are utilized across many hospitals and other health-care facilities to help reduce mechanical falls among patients who experience ambulatory or balance difficulties [47, 48]. Falls typically occur as patients attempt to mobilize and/or ambulate without the required assistance of trained health-care staff [49]. Consequently, the use of BCPS alarms serves to alert staff—typically by a pressure-sensitive mechanism—when a patient attempts to move from a bed or chair without assistance. However, the weight-sensitive pads are easily triggered by very slight patient movement, resulting in a significant number of false alarms [50, 51]. This challenge was readily apparent in Clinical Vignette #3 later in the chapter, as the majority of BCPS alerts were likely due to the patient merely shifting slightly in the bed, and not by an actual attempt to independently mobilize and/or ambulate. Unfortunately, the one true positive alarm became lost in “a sea of false negatives.” The practicality of BCPS alarms is also diminished by the inability of staff members to immediately assess/respond to the triggered alarm. Instances have been noted in which the alarm signal is transmitted after the event already transpired, as patients tend to fall immediately upon leaving the bed or chair [52].
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In summary, the above-referenced monitor/alarm types have become an important part of the modern health-care fabric. Despite their ubiquitous use and great potential for constructive and practical clinical application, each type of device carries inherent flaws that providers must be aware of. Detailed knowledge of the risk-benefit equation associated with each device and clinical alarm type is important not only for patient safety but also required to help improve the quality and accuracy of the next generation of monitoring devices.
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4. Patient monitor alarm design
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Patient monitors are designed to have high sensitivity to predefined changes in various measured parameters, including vital signs, respiratory/ventilator status, and patient movements. However, the major drawback associated with high alarm sensitivity is the poor specificity and inherently disproportionate number of nonactionable (or nonclinical) alarms triggered [22, 53, 54]. Depending on the specific alarm and clinical setting, the estimated in range of “false positives” may be as high as 80–99% of all triggered alarms [8]. Broadly speaking, nonactionable alarms can be categorized as false alarms, nuisance alarms, and technical alarms (Figure 1). To elaborate further, false alarms occur in the absence of an actual patient or system trigger and typically result from a measurement artifact [55]. Technical alarms mandate the provider to attend to some operational aspect of the monitoring system, such as when readjustment of monitor leads/sensors is required [21]. Nuisance alarms are defined as clinically insignificant alarms that may interfere with patient care [10]. In aggregate, these nonactionable alarms are a major cause of the overall desensitization of hospital staff that may ultimately result in AF (Figure 2).
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Figure 2.
Schematic representation of the classification of alarm types triggered by various patient monitoring systems, including both actionable and nonactionable alerts (source: Ruskin [8]; Gorges [66]; and Tsien [67]).
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Furthermore, to be effective, the alarms transmitted by monitoring systems must trigger some degree of cognitive response in health-care providers. This equates to introducing stress and the need for constant vigilance, both of which further heighten the risk of AF [56, 57]. When multiple clinical competing priorities collide, it becomes increasingly difficult for a provider to proactively address all ongoing problems, thus forcing them to resort to only partially addressing acute issues while at the same time disrupting other (parallel) activities due to multitasking [58, 59, 60, 61]. Consequently, an ideal alarm should be perfectly audible and easily recognized by health-care providers working within the patient care unit [8], while at the same time minimizing the amount of stress imposed on the responding clinical staff.
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The increasingly complex environment of modern health-care systems has led to several important considerations regarding the practical application of monitoring systems. For example, space-related issues deserve special mention, with overly crowded clinical units creating an abundance of alarm-related stimuli and geographically larger clinical units presenting a barrier to prompt patient access. Elevated acuity and high patient throughput are also important considerations in this context [62].
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Furthermore, technological advancements facilitated the development of increasingly sophisticated alarm systems, with novel features designed to decrease the nuisance factor of the alert mechanism while preserving the level of overall clinical vigilance [63, 64]. These are intended to provide a range of alarm tones that allow care providers to easily identify and prioritize alarms, typically as high, medium, or low priority. However, the implementation of such systems (e.g., IEC 60601-1-8 standard) has presented challenges in terms of recognizability of melodic alarm tones. More specifically, nurses found it difficult to accurately identify all of the melodic tones signifying high-priority alarms, in addition to the potential for confusion between certain alarm pairs [65]. An example of such phenomenon is presented in Clinical Vignette #1 below, where two sets of tones were too difficult for the nurse to readily differentiate, rendering the alarm feature ineffective. Consequently, it is important for systems to have some degree of built-in learnability and flexible discriminative ability, with continued refinement, development, and testing of each clinical alarm, both alone and in tandem with other competing alarms [65]. Without exception, any observed deficits in patient monitor effectiveness and/or safety should prompt an immediate critical evaluation of both technical and clinical aspects of its implementation and function.
A 62-year-old female was admitted to the local hospital 5 days ago due to chronic obstructive pulmonary disease (COPD) exacerbation. She was diagnosed with COPD several years prior and remained stable with no history of exacerbations until 1 week ago when she developed a progressively worsening cough. Soon after her symptoms worsened, she began to feel shortness of breath that was not relieved by rest. At this point, her family insisted she go to the hospital for evaluation. Upon arriving in the emergency department, short-acting bronchodilators and oral corticosteroids were administered with only mild symptomatic improvement. Given the patient’s dyspnea at rest, as well as decreased oxygen saturation of 86%, she was admitted to the pulmonology unit. Supplemental oxygen and intravenous corticosteroids were administered.
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At admission, continuous pulse oximetry monitoring was started. The patient’s hypoxemia seemed to improve slightly over the next 4 days, with oxygen saturation climbing to 88–90% range. Still, the patient’s ventilatory monitor sent alarm signals to the hospital staff several times an hour due to high respiratory rate and episodic oxygen desaturations. Alarm signals were transmitted as either a single low tone (respiratory rate) or a double alarm (desaturations), alternating between low and medium tones. The difference of alarm tone indicated the range in which the patient’s oxygen saturation was measured, but the assigned night-shift nurse found the tones to be too difficult to distinguish and would routinely just perform an in-person check of the saturation level upon entering the room. Throughout the first two nights, the same nurse responded to the alarms in a timely fashion, only to find the patient stable and with no signs of acute distress. Assuming that alarms are unlikely to represent any actionable clinical events, the same nurse then began to silence the sounds and began checking on the patient hourly. In the early morning hours of the fourth day, the nurse silenced the alarm once again, intending to assess the patient once the remainder of her rounding routine was completed. When the nurse finally came to the patient’s room an hour later, she found the patient unresponsive and cyanotic. A rapid assessment showed an oxygen saturation of 79%. The patient was immediately intubated, transferred to intensive care unit, and mechanical ventilation was initiated.
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5.2. Clinical Vignette #2: 65-year-old male transferred to inpatient unit following a total knee arthroplasty
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A 65-year-old male with a history of osteoarthritis of the right knee and refractory pain underwent preoperative evaluation by an orthopedic surgeon. Given his adequate performance status and lack of comorbidities, the patient was determined to be a suitable candidate for total right knee arthroplasty. The surgical procedure was uneventful, with appropriate antibiotic and venous thrombosis prophylaxis administered perioperatively. Following a brief recovery in the postanesthesia care unit, the patient was transferred to the inpatient floor with expected discharge within 5 days postsurgery. Due to the nature of his surgery and apparent fall risk, the patient’s room was fitted with weight-sensitive bed and chair alarms. During the first 3 days, he remained relatively sedated due to the frequent administration of pain medications. However, as the patient began to regain strength, his analgesia regimen was tapered. On day 4, the concurrent increase in patient’s movement began to trigger his bed monitor to the point where the on-call nurse was receiving nearly constant alarm notifications. Multiple times, the nurse entered to assess the patient only to find him resting comfortably without apparent attempt to leave his bed. Later that night, after leaving the patient’s room, the nurse was unexpectedly assigned to three additional patients due to an unplanned absence of a coworker. As the nurse hurried to assess the new patients, the bed monitor transmitted yet another alarm signal. Annoyed by the repeated negative alarms, the nurse disabled the alerts from the bed monitor, intending to check in after tending to her newly assigned patients. When she finally returned to the patient’s room, she found him sprawled on the floor and writhing in pain. The patient, emboldened by his rapid recovery, had attempted to ambulate to the bathroom without assistance and lost his balance in the process. The intense pain prevented him from reaching the call button on the hospital bed, so he was forced to lie on the floor in pain for approximately 1 h. A subsequent skeletal survey revealed a left hip fracture, which required additional surgery, prolonged hospital stay, and the need for inpatient rehabilitation stay due to temporary disability involving bilateral lower extremities (e.g., right knee arthroplasty and left hip injury).
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5.3. Clinical Vignette #3: 71-year-old male with history of multiple myeloma admitted for right lower extremity swelling associated with minor pain
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A 71-year-old male with a history of multiple myeloma was admitted to the urgent care center after noticing sudden onset of right lower extremity swelling associated with minor pain. The patient began induction therapy for multiple myeloma approximately 1 year prior, achieving adequate disease control. He was subsequently transitioned to maintenance treatment, which he continued for the past 6 months. Evaluation in the urgent care center with venous duplex studies revealed a deep venous thrombosis (DVT). Because of the patient’s established history of malignancy, the triage clinician opted for hospital admission and therapeutic anticoagulation. While being transferred to the inpatient unit, unfractionated heparin anticoagulation was started. Per standard protocol, monitoring equipment was hastily fitted to the patient for noninvasive measurement of his blood pressure and heart rate. Overnight, the patient remained stable, with some resolution of lower extremity of pain despite persistent swelling. The on-call physician assessed the patient during morning rounds and ordered to repeat venous duplex for the afternoon to evaluate for resolution/progression of the DVT. Of note, throughout the night and into the morning hours, the patient’s hemodynamic monitor had been sending intermittent alarm signals. With the first few alarms, the charge nurse promptly responded and quickly assessed the patient for any signs of instability or distress. However, as the shift progressed, the nurse increasingly dismissed repeated signals as “false alarms” due to a recurring pattern of mildly elevated blood pressure and heart rate secondary to episodic extremity pain. Because the inpatient unit continued to be understaffed during the morning shift, the charge nurse decided to disable the patient’s repeated monitor alarms after the patient was assessed during morning rounds and found not to have any acute issues. It was hoped that this decision would eliminate the distraction of the nuisance alarms. However, during the patient’s routine afternoon assessment, the rounding physician noted cold and diaphoretic extremities with markedly increased swelling. Interrogation of the monitor system revealed progressive bradycardia and hypotension over the past hour. An emergency CT angiogram showed a massive pulmonary embolism, prompting immediate thrombolytic therapy and patient transfer to intensive care. Despite aggressive management, the patient’s shock became refractory, culminating in his death several hours later.
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5.4. Summation of Clinical Vignettes: finding common threads
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The three hypothetical clinical scenarios outlined above share a common theme: dedicated monitoring systems implemented to ensure early detection of clinical deterioration and thus patient safety were utilized either ineffectively or incorrectly. In all three vignettes, a confluence of factors (environment, patient, medical personnel) subsequently led to AF and then adverse patient outcomes. In the following sections, we will further discuss the phenomenon of alarm fatigue, focusing on its impact on daily clinical practice.
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6. Alarm fatigue
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After the general introduction of AF earlier in the chapter, the authors will now discuss this important concept in greater detail. The phenomenon of AF is multifaceted and includes increased clinician response time with simultaneous decreased response rate that is mainly attributed to excessive stimuli from clinical alarms [8]. Depending on patient acuity and clinical monitoring requirements, typical bedside health-care personnel may be exposed to as many as 1000 alarms during a single shift, of which as many as 95% can be nonactionable and thus do not require immediate clinical determination [8, 66, 67]. Given the multitude of clinical alarms, a provider has to sort through during a typical hospital shift, there will be a natural tendency to potentially dismiss certain alarms as insignificant through rationalization. This phenomenon is described in the literature as the natural human behavioral reaction to “deprioritize signals” that have often been proven to be either false or misleading. Thus, staff may begin reflexively disabling or silencing alarm systems, which could effectively mask other alarms that may be clinically significant [68, 69]. To some extent, this behavioral pattern was seen in all three Clinical Vignettes, where the actionable alarm was masked by the vast number of nonactionable alarms that preceded it. Ultimately, the resulting delay in response or inadequate response puts patient safety at risk and may result in morbidity and/or mortality [70, 71]. Technologically advanced physiologic monitors bring a lot of promise, both in terms of earlier and more sensitive detection of patient deterioration (or other clinically significant event); however, the sensory overload and desensitization associated with AF will likely continue to present a major opportunity for improvement.
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Certain other factors have been implicated in the increased incidence and severity of alarm fatigue, including greater staff workload, higher patient acuity, and the complexity of the modern health-care environment [10]. Nurses serve as key frontline staff in most clinical settings and play a pivotal role in overseeing patient care and monitoring. Moreover, nurses are subject to significant occupational stress that can be attributed to multiple causes, including heavy workloads [72]. This stress, as outlined in previous sections of this chapter, certainly influences AF by forcing nurses to instantaneously adjust their work activities (and priorities) according to perceived importance of near constant clinical alarm activity. Our Clinical Vignette #2 illustrated the difficult task of ongoing patient triage, with the nurse having to prioritize between the three newly admitted patients and all of her other assigned patients. This constant need for clinical vigilance and prioritization is potentially disruptive to typical workflow, especially when high task complexity is involved. It can also contribute to the development of burnout [73]. Nurses have expressed the internal conflict between having to ignore the constant alarms simply to maintain sufficient focus to finish their routine tasks [74]. It is not surprising that increasing workload or task complexity has been associated with both suboptimal job performance and inconsistent alarm response [10]. Furthermore, the effort of acknowledging, evaluating, and responding to an alarm significantly increases the overall time commitment and workload of the nurses, which further perpetuates the trend of decreased alarm response and task performance [8].
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Because multiple factors contribute to AF, many existing models struggle to fully account for (and address) clinician behavioral patterns seen with AF [75]. At the same time, it should be noted that AF is not unique to clinicians. In fact, a similar phenomenon has also been seen among human operators utilizing automated monitoring systems, such as aircraft pilots and nuclear power plant operators. The excessive number of alarm activations leads to the tendency of operators to ignore alerts, particularly when the monitoring system produces a high rate of false alarms or alerts [75]. For these operational environments, it has also been suggested that increased primary and secondary task workloads have a compounding effect on alarm response degradation that may occur in the setting of low alarm system reliability [76]. Similar to the clinical setting, AF can be associated with serious safety risks and represents a similar barrier to the practical application of automated monitoring systems in other fields (Figure 3).
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Figure 3.
The word cloud demonstrating the multifaceted phenomenon of alarm fatigue.
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7. Potential outcomes of alarm fatigue
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Significant percentage of nonactionable alarms in the typical modern clinical environment can lead to the development (and subsequent habituation) of AF. As previously mentioned, AF can be characterized by alarm desensitization, mistrust of alert accuracy/utility, and delay of caregiver response (or even lack thereof). Commonly seen reactions to AF include the deactivation and silencing of systems or adjustment of alarm parameters to decrease the number of alarms. Such reactive behaviors have the potential to result in missed critical alarms, leading to patient morbidity or even mortality. In fact, patient safety considerations associated with AF are among the top items of Emergency Care Research (ECRI) Institute’s Health Technology Hazards list [77, 78]. The subject of AF has been extensively studied, primarily due to its high prevalence across essentially all health-care settings. The underreporting of alarm-related events has been recognized as a challenge, and it should be noted that recorded incidents likely reflect only a small proportion of actual events. Available records from the Joint Commission’s Sentinel Event Database show 98 alarm-related occurrences between January 2009 and June 2012 (Figure 4). Of these reported events, several common alarm system issues (Figure 5) were directly connected to events leading to injury or death (Table 1) [79].
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Figure 4.
Alarm-related events and subsequent results from January 2009 to June 2012 (source: Joint Commission’s Sentinel Event Database).
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Figure 5.
Major contributing factors of alarm-related events (source: Joint Commission’s Sentinel Event Database).
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Event
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Falls
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Delays in treatment
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Delays in ventilator use
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Medication errors
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Table 1.
Common alarm-related events leading to injuries or deaths.
Additionally, the US Food and Drug Administration’s Manufacturer and User Facility Device Experience (MAUDE) database has identified 566 alarm-related patient deaths between January 2005 and June 2010 [79]. Reports detailing alarm-related events have prompted thorough investigation into AF and possible strategies to address this important phenomenon in the clinical setting.
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8. Quality improvement
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Considering the potential for very serious clinical consequences of AF, quality improvement measures have been proposed to help reduce both nonactionable alarm occurrences and the incidence of AF. Successful quality improvement projects must address multiple facets of the overall problem, including root causes that lead to AF (Figure 6). For example, poor usability and lack of user-centered devices have the potential for elevating clinical personnel stress levels, creating unnecessary workload and interjecting workflow inefficiencies into an already tense environment [81].
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Figure 6.
The different aspects of alarm fatigue that can be addressed through different quality improvement approaches (source: Ref. [80]).
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Potential solutions for reducing the incidence of AF include multipronged approaches consisting of staff education, equipment (hardware and software) enhancements, and implementation of more efficient clinical protocols or guidelines [82, 83, 84]. From an educational perspective, it is important to ensure adequate staff education, equipment training, and closer team collaboration to improve patient safety within the existing framework [8, 85]. In addition to staff education, hospital policies have been developed and implemented to more clearly define which staff members are able to change alarm settings, as well as how such changes should be made and documented. Many of these polices have also delegated the responsibility of performing clinical alarm monitoring rounds to a staff member in order to allow for continued review of the application of patient monitoring systems [86, 87, 88].
\n
To address the issues of staff workload, two potential approaches have been proposed. The first approach consists of secondary notification systems. The second option involves the use of dedicated staff to oversee alarms. A secondary notification system involves a specialized network interface that algorithmically facilitates the decision process regarding which alarms will be further communicated or escalated to pertinent downstream clinical staff. Further, this system would also enable the automatic escalation of an alert to another clinician, should the primary recipient fail to acknowledge the alarm within a designated timeframe. The use of staff to oversee alarms, while an expensive option, can give additional support to care providers in the form of dedicated personnel whose responsibility is to continuously monitor patient data trends and alarms from a central station [58].
\n
No matter the solution, all the quality improvement processes require a multidisciplinary approach to address the causes and effects of AF. Only through collaborative efforts can substantial change be accomplished to reduce the number of alarm-related events in health care. In addition to the quality improvement measures taken by hospitals, technological advances have also led to more efficient and practical application of patient monitors in the clinical setting. These advances are directed at the reduction of nonactionable alarms with the goal of decreasing the alarm desensitization associated with AF. The importance of adequate information technology support, including better device designs, must be emphasized. As increasingly efficient and complex monitoring equipment is introduced into the clinical realm, certain phenomena, such as the emergence of “unpredictable code,” may adversely affect computer performance (including the ability to effectively recognize important data patterns) and lead to clinical alerts being missed despite the fact that alert-specific data were clearly and provably present [89].
\n
\n
\n
9. Technological advances in patient monitors
\n
In general, clinical monitoring is based on a careful balance between sensitivity and specificity of alarm signal recognition, as well as the associated threshold setting required to trigger “alert condition” [90, 91]. Increasing monitor sensitivity helps ensure that truly significant events are not missed, primarily using single-parameter alarms and default thresholds [8]. However, as a trade-off this increases the incidence of nuisance alarms that are nonactionable. This issue may be remedied by the development of “smart alarm systems” that use algorithmic approaches to evaluate multiple parameters prior to determining whether the detected change is truly critical, and only then sending an alert to the operator [15]. This improvement in device specificity would result in significantly fewer false alarms and therefore reduce AF. At the same time, the challenges of “unpredictable code” and “interrupted or corrupt data” have been noted and may represent an important safety issue due to the potential for missing data or data misinterpretation, especially when using memory-intensive applications on devices that are continually operating for prolonged periods of time [89, 92, 93, 94, 95].
\n
The ideal patient monitor would have high sensitivity, as well as high negative predictive value for life-threatening clinical scenarios. This would result in excellent “event detection rate” while reducing the number of false and nuisance alarms. Still, any improvement of sensitivity/negative predicative value for monitors must be accompanied by corresponding adjustment to specificity/positive predictive value, ensuring that clinically significant events are captured efficiently [33]. The accomplishment of the above goals may be possible using the application of artificial intelligence (AI) in monitoring systems, wherein AI would be incorporated into logic-based, decision-making systems. The ultimate goal would be the development of clinical monitoring capabilities that reflect and mirror human cognitive/decision-making processes [37]. In the context of this chapter’s Clinical Vignettes, the application of such AI-based systems might be helpful in minimizing the number of nonactionable alarms, thus reducing the subsequent AF associated with adverse clinical events. So far, the utilization of AI has been explored in several different applications (Table 2).
\n
\n
\n
\n
\n\n
\n
System
\n
Description
\n
Application
\n
\n\n\n
\n
Rule-based expert systems
\n
Application of expert knowledge from a compiled database to new context and simulation of expert decisions
\n
Development of a highly specific patient monitor system with electronic access to data available in a multichannel patient monitor and data management system to detect cardiac disturbances [37, 96]
\n
\n
\n
Neural networks
\n
Utilization of artificial neural networks to predict disease presence based on advanced information
\n
Development of neuronal network used to detect myocardial infarction early on in patients admitted for chest pain [37, 97]
\n
\n
\n
Fuzzy logic
\n
Diffuse processing of exact data that does not indicate an explicit conclusion
\n
Development of a monitor system able to diagnose simulated cardiac arrest via evaluation of EKG, capnography, and arterial blood pressure [37, 98]
\n
\n
\n
Bayesian networks
\n
System used for the estimation of event occurrence based on causal probabilistic networks
\n
Application of system for decision support in cardiac event detection [37, 99]
\n
\n\n
Table 2.
Applications of artificial intelligence in the development of intensive care monitoring.
Given the proliferation of advanced monitoring equipment, AF continues to be a major patient safety issue across modern health-care systems. While technological advances show great promise in improving patient care, significant barriers to more optimal implementations exist, including the ongoing struggle to balance the need for high sensitivity versus the excessive number nonactionable clinical alarms. The high frequency of clinical alerts, especially when combined with heavy clinical workload, is known to have negative effects of hospital staff, including alarm desensitization and subsequent delay and/or lack of caregiver response. The resultant AF poses a serious risk to patient safety and has been associated with significant adverse events, including the need for additional or prolonged hospital care, excess attributable morbidity, and even mortality. Prevention of AF requires a multipronged approach consisting of quality improvement measures, staff training, better equipment management (e.g., monitor threshold adjustments) to reduce false alarms, and focus on optimizing staff workload.
\n
\n\n',keywords:"alarm fatigue, clinical alarms, clinical monitoring, monitoring equipment, patient safety",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66118.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66118.xml",downloadPdfUrl:"/chapter/pdf-download/66118",previewPdfUrl:"/chapter/pdf-preview/66118",totalDownloads:745,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 15th 2018",dateReviewed:"January 28th 2019",datePrePublished:"March 12th 2019",datePublished:"September 18th 2019",dateFinished:"March 12th 2019",readingETA:"0",abstract:"As the demand for health-care services continues to increase, clinically efficient and cost-effective patient monitoring takes on a critically important role. Key considerations inherent to this area of concern include patient safety, reliability, ease of use, and cost containment. Unfortunately, even the most modern patient monitoring systems carry significant drawbacks that limit their effectiveness and/or applicability. Major opportunities for improvement in both equipment design and monitor utilization have been identified, including the presence of excessive false and nuisance alarms. When poorly optimized, clinical alarm activity can affect patient safety and may have a negative impact on care providers, leading to inappropriate alarm response time due to the so-called alarm fatigue (AF). Ultimately, consequences of AF include missed alerts of clinical significance, with substantial risk for patient harm and potentially fatal outcomes. Targeted quality improvement initiatives and staff training, as well as the proactive incorporation of technological improvements, are the best approaches to address key barriers to the optimal utilization of clinical alarms, AF reduction, better patient care, and improved provider job satisfaction.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66118",risUrl:"/chapter/ris/66118",signatures:"James Nguyen, Kendra Davis, Giuseppe Guglielmello and Stanislaw P. Stawicki",book:{id:"7447",title:"Vignettes in Patient Safety",subtitle:"Volume 4",fullTitle:"Vignettes in Patient Safety - Volume 4",slug:"vignettes-in-patient-safety-volume-4",publishedDate:"September 18th 2019",bookSignature:"Stanislaw P. Stawicki and Michael S. Firstenberg",coverURL:"https://cdn.intechopen.com/books/images_new/7447.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. Stawicki"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",fullName:"Stanislaw P. Stawicki",slug:"stanislaw-p.-stawicki",email:"stawicki.ace@gmail.com",position:null,institution:{name:"St. Luke's University Health Network",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Primary research methods",level:"1"},{id:"sec_3",title:"3. Patient monitoring: different types and modalities",level:"1"},{id:"sec_3_2",title:"3.1. Ventilation/oxygenation alarms",level:"2"},{id:"sec_4_2",title:"3.2. Hemodynamic alarms",level:"2"},{id:"sec_5_2",title:"3.3. Bed and chair pressure sensors",level:"2"},{id:"sec_7",title:"4. Patient monitor alarm design",level:"1"},{id:"sec_8",title:"5. Clinical Vignettes",level:"1"},{id:"sec_8_2",title:"5.1. Clinical Vignette #1: 62-year-old female presenting with chronic obstructive pulmonary disease (COPD) exacerbation",level:"2"},{id:"sec_9_2",title:"5.2. Clinical Vignette #2: 65-year-old male transferred to inpatient unit following a total knee arthroplasty",level:"2"},{id:"sec_10_2",title:"5.3. Clinical Vignette #3: 71-year-old male with history of multiple myeloma admitted for right lower extremity swelling associated with minor pain",level:"2"},{id:"sec_11_2",title:"5.4. Summation of Clinical Vignettes: finding common threads",level:"2"},{id:"sec_13",title:"6. Alarm fatigue",level:"1"},{id:"sec_14",title:"7. Potential outcomes of alarm fatigue",level:"1"},{id:"sec_15",title:"8. Quality improvement",level:"1"},{id:"sec_16",title:"9. Technological advances in patient monitors",level:"1"},{id:"sec_17",title:"10. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Kesselheim AS et al. Clinical decision support systems could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation. Health Affairs. 2011;30(12):2310-2317\n'},{id:"B2",body:'Oppenheim MI et al. Design of a clinical alert system to facilitate development, testing, maintenance, and user-specific notification. In: Proceedings of the AMIA Symposium. Bethesda, Maryland: American Medical Informatics Association; 2000\n'},{id:"B3",body:'Konkani A, Oakley B, Bauld TJ. Reducing hospital noise: A review of medical device alarm management. 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A knowledge-based alarm system for monitoring cardiac operated patients—Technical construction and evaluation. International Journal of Clinical Monitoring and Computing. 1993;10(2):117-126\n'},{id:"B97",body:'Baxt WG, Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet. 1996;347(8993):12-15\n'},{id:"B98",body:'Goldman JM, Cordova MJ. Advanced clinical monitoring: Considerations for real-time hemodynamic diagnostics. In: Proceedings of the Annual Symposium on Computer Applications in Medical Care. 1994. pp. 752-756\n'},{id:"B99",body:'Laursen P. Event detection on patient monitoring data using causal probabilistic networks. Methods of Information in Medicine. 1994;33(1):111-115\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"James Nguyen",address:null,affiliation:'
Medical School of Temple University/St. Luke’s University Health Network, USA
Department of Medicine, Section of Pulmonary and Critical Care Medicine, St. Luke’s University Health Network, USA
'},{corresp:"yes",contributorFullName:"Stanislaw P. Stawicki",address:"stawicki.ace@gmail.com",affiliation:'
Department of Research and Innovation, St. Luke’s University Health Network, USA
'}],corrections:null},book:{id:"7447",title:"Vignettes in Patient Safety",subtitle:"Volume 4",fullTitle:"Vignettes in Patient Safety - Volume 4",slug:"vignettes-in-patient-safety-volume-4",publishedDate:"September 18th 2019",bookSignature:"Stanislaw P. Stawicki and Michael S. Firstenberg",coverURL:"https://cdn.intechopen.com/books/images_new/7447.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. 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Rapid onset of drug action, avoidance of first-pass metabolism, and high immunocompetence of mucosa are some of the important features for mucosal drug and vaccine delivery. The use of mucoadhesive drug delivery systems, systems with fast dissolving properties, and nanomaterials with mucus penetration properties are examples of successful strategies to achieve effective mucosal drug and vaccine delivery. Non-keratinized mucosa of the oral cavity, the nasal and vaginal mucosa represent favorable sites of drug administration. Polymer nanofibers have attracted much attention because of remarkable characteristics such as a large surface area to volume ratio and high porosity. Nanofibers have been extensively used for different biomedical applications including wound dressing, tissue engineering, and drug delivery. Among their fabrication methods, the introduction of electrospinning technique was an important step toward achieving the goal of large scale industrial production of nanofiber-based drug delivery systems used in mucosal applications. This chapter provides an overview on all aspects of mucosal drug and vaccine delivery using nanofibers.",signatures:"Josef Mašek, Eliška Mašková, Daniela Lubasová, Roman Špánek, Milan Raška and Jaroslav Turánek",authors:[{id:"42706",title:"Dr.",name:"Jaroslav",surname:"Turanek",fullName:"Jaroslav Turanek",slug:"jaroslav-turanek",email:"turanek@vri.cz"},{id:"172158",title:"Dr.",name:"Josef",surname:"Mašek",fullName:"Josef Mašek",slug:"josef-masek",email:"masek@vri.cz"},{id:"273113",title:"Dr.",name:"Eliška",surname:"Mašková",fullName:"Eliška Mašková",slug:"eliska-maskova",email:"maskova.e@vri.cz"},{id:"273115",title:"Dr.",name:"Daniela",surname:"Lubasová",fullName:"Daniela Lubasová",slug:"daniela-lubasova",email:"daniela.lubasova@tul.cz"},{id:"280858",title:"Dr.",name:"Roman",surname:"Špánek",fullName:"Roman Špánek",slug:"roman-spanek",email:"roman.spanek@tul.cz"},{id:"280859",title:"Prof.",name:"Milan",surname:"Raška",fullName:"Milan Raška",slug:"milan-raska",email:"milan.raska@upol.cz"}],book:{title:"Nanomaterials",slug:"nanomaterials-toxicity-human-health-and-environment",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"7153",title:"Prof.",name:"Gustavo",surname:"Morari Do Nascimento",slug:"gustavo-morari-do-nascimento",fullName:"Gustavo Morari Do Nascimento",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/7153/images/system/7153.jpg",biography:"Dr. Gustavo Morari do Nascimento is a professor at the Federal University of ABC, Brazil. He has strong experience in many fields related to the spectroscopic and microscopic characterization of nanomaterials. He obtained a doctoral degree from the University of São Paulo (USP) in 2004 with a thesis on spectroscopic characterization of nanocomposites formed by conducting polymers and clays. He spent 2007–2008 working on a post-doctoral degree at Massachusetts Institute of Technology (MIT) in the resonance Raman study of double-walled carbon nanotubes doped with halogens under the guidance of the legendary Mildred S. Dresselhaus. Back in Brazil, Dr. Morari do Nascimento spent three years (2009–2011) at the Federal University of Minas Gerais (UFMG) working with the synthesis of nanostructured carbon modified with molecular magnets. Currently, his research focus is on using different spectroscopic techniques for molecular characterization of chemically modified, carbon-nanostructured materials and polymer nanocomposites. He employs Resonance Raman and surface enhanced Raman spectroscopy (SERS) coupled to microscopy techniques added to X-ray absorption techniques at the National Synchrotron Light Laboratory in his investigation.",institutionString:"Universidade Federal do ABC",institution:{name:"Universidade Federal do ABC",institutionURL:null,country:{name:"Brazil"}}},{id:"42706",title:"Dr.",name:"Jaroslav",surname:"Turanek",slug:"jaroslav-turanek",fullName:"Jaroslav Turanek",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"172158",title:"Dr.",name:"Josef",surname:"Mašek",slug:"josef-masek",fullName:"Josef Mašek",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"173479",title:"Dr.",name:"Paulo",surname:"Dias",slug:"paulo-dias",fullName:"Paulo Dias",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Santa Catarina",institutionURL:null,country:{name:"Brazil"}}},{id:"173488",title:"BSc.",name:"Daniela Sousa",surname:"Coelho",slug:"daniela-sousa-coelho",fullName:"Daniela Sousa Coelho",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"259805",title:"Associate Prof.",name:"Xiaoniu",surname:"Yu",slug:"xiaoniu-yu",fullName:"Xiaoniu Yu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"273113",title:"Dr.",name:"Eliška",surname:"Mašková",slug:"eliska-maskova",fullName:"Eliška Mašková",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"280858",title:"Dr.",name:"Roman",surname:"Špánek",slug:"roman-spanek",fullName:"Roman Špánek",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"280859",title:"Prof.",name:"Milan",surname:"Raška",slug:"milan-raska",fullName:"Milan Raška",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"284913",title:"Dr.",name:"Qiwei",surname:"Zhan",slug:"qiwei-zhan",fullName:"Qiwei Zhan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
\n"}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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