INTRODUCTION

Glioblastoma

Brain tumors account for about 85–90% of all

primary central nervous system (CNS) tumors.

Worldwide, approximately 343,175 new cases

of brain and other CNS tumors were diagnosed

in the year 2012 (http://www.cbtrus.org/).

Glioblastoma or glioblastoma multiforme

(GBM) is the most lethal and clinically

challenging of brain tumors. Most patients die

of their ailment in less than a year (Stupp et al.,

2005). Some of the reasons for high fatality are

the complex nature and diffuse character of the

tumor itself and the high rate of disease

recurrence. As the name infers, it is

multiforme microscopically showing regions

of pseudopalisading and hemorrhage,

multiforme genetically with various genetic

alterations leading to its aggressive nature.

The standard of care for treatment of GBM

includes surgical resection followed by

radiation and chemotherapy. The addition of a

chemotherapeutic agent, Temozolamide in

recent years changed the median survival of

for GBM patients to 14.6 months from 12.1

months with surgery and radiotherapy (Stupp

et al., 2005). Also, currently there is no

Key words: Glioblastoma, Omics, Genomics, Transcriptomics, Epigenomics, Proteomics, Metabolomics. *Corresponding Author: Anita Tandle, Radiation Oncology Branch, National Cancer Institute, 10 Center Drive Magnuson Clinical Center Room B3-B100, Bethesda MD 20892, USA.Email: tandlea@mail.nih.gov

Advances in Omics Technologies in GBM

Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda MD, USA

Glioblastoma multiforme (GBM) is one of the most lethal human cancers and poses a great challenge in the

therapeutic interventions of GBM patients worldwide. Despite prominent recent advances in oncology, on

an average GBM patients survive 12–15 months with conventional standard of care treatment. To

understand the pathophysiology of this disease, recently the research focus has been on omics-based

approaches. Advances in high-throughput assay development and bioinformatic techniques have provided

new opportunities in the molecular analysis of cancer omics technologies including genomics,

transcriptomics, epigenomics, proteomics, and metabolomics. Further, the enormous addition and

accessibility of public databases with associated clinical demographic information including tumor

histology, patient response and outcome, have profoundly improved our knowledge of the molecular

mechanisms driving cancer. In GBM, omics have significantly aided in defining the molecular architecture of

tumorigenesis, uncovering relevant subsets of patients whose disease may require different treatments. In

this review, we focus on the unique advantages of multifaceted omics technologies and discuss the

implications on translational GBM research.

Uday B. Maachani, Uma Shankavaram, Kevin Camphausen, Anita Tandle*

Biomed Res J 2015;2(1):6-20

Review

standard of care available for recurrent disease

and most of the patients die. Hence there is an

urgent need to develop molecular targeted

therapy for this devastating disease.

Some of the molecular alterations

responsible for GBM progression and

therapeutic resistance includes genetic and

epigenetic alterations, activation of stem cell

pathways, and changes in the tumor

microenvironment and cellular metabolism.

However, the functional consequences of

many of these alterations are largely unknown

in GBM tumorigenesis (Frattini et al., 2013;

Schonberg et al., 2013).

Omics

With the sequencing of the human genome, the

study of biological systems underwent a major

genomic revolution. The major technological

breakthroughs in high-throughput assay

development, technological advancements in

instrumentation and bioinformatics data

analysis have reshaped how we view the

cancer genome (Vucic et al., 2012). “Omics”

refers to the study of cancer as a whole entity

focusing on the various micro- and macro-

molecules. It includes (but not limited to)

DNA mutations, copy number changes,

epigenetic changes like DNA methylation,

transcriptome analysis and whole-genome

DNA/RNA sequencing. The omics-based

recent approaches including genomics,

transcriptomics, epigenomics, proteomics and

metabolomics have unveiled the molecular

mechanisms behind various cancers and

assisted in identification of next-generation

molecular markers for early diagnosis,

prognosis, predictive of response to treatments

and predisposition to gliomas (Chin, 2013;

Cho, 2010) (Fig. 1). The publically available

multi-omics databases collected by

International Cancer Genome Consortium

Figure 1: Omics in glioblastoma.

Biomed Res J 2015;2(1):6-20

Maachani et al. 7

(ICGC) and The Cancer Genome Atlas

(http://cancergenome.nih.gov/) a network

group using a sample cohort of several

hundred clinical specimens of GBM further

elaborated the molecular processes funda-

mental to GBM pathogenesis (Hudson et al.,

2010; Verhaak et al., 2010).

Genomics/Transcriptomics

Early work on gene expression analysis of

gliomas employed DNA microarrays and

attempted to correlate mRNA signatures with

the grades of gliomas and their clinical

behavior to aid in overall prognosis and

treatment response of patients (Kim et al.,

2002; Mischel et al., 2004). Transcriptomics is

the study of RNA transcripts that are produced

by the genome, under specific circumstances

or in a specific cell using high-throughput

methods, such as microarray analysis,

allowing the identification of genes that are

differentially expressed in distinct cell

populations. Recent multi-omics (genomics,

transcriptomics and proteomics) data

integration studies have utilized patient

derived samples and cell lines to reveal

heterogeneity among the primary GBM,

suggesting additional molecular subclasses:

neural, proneural, classical and mesenchymal

(Verhaak et al., 2010). These subtypes were

defined on the basis of distinct gene signatures

and also characterized by different molecular

alterations and activated pathways (Brennan et

al., 2013; Verhaak et al., 2010). The proneural

subtype was mostly characterized by

abnormalities in platelet-derived growth

factor receptor-α (PDGFRA) or in isocitrate

dehydrogenase 1 (IDH1); whereas mutation of

the epidermal growth factor receptor (EGFR)

was found in the classical subgroup, and

mutations in neurofibromin 1 (NF1) were

common in mesenchymal tumors. The neural

subtype seemed to be similar to the classical

subtype but with a higher frequency of TP53

mutations (Brennan et al., 2013). Cytogenetic

and molecular studies have also identified a

number of recurrent chromosomal

abnormalities and genetic alterations in

malignant gliomas, as well as novel

candidates, particularly in GBMs. The

identification of molecular subtypes has

revealed a set of core signaling pathways

commonly activated in GBM (Table 1)

(Furnari et al., 2007) and could be used in

molecular targeted therapies (Table 2).

Epigenomics

Epigenetic changes involve enzymatic

modifications of DNA and associated histone

proteins to regulate gene expression. In recent

years these changes have been recognized as

important causes of phenotypic changes in

human cancers (Esteller, 2007). The

epigenetic changes are dynamic in nature and

play an important role in gene expression and

DNA structure. Epigenetic alterations,

especially those related to changes in histone

acetylation, are a recent focus for therapeutic

Biomed Res J 2015;2(1):6-20

8 Advances in omics technologies in GBM

drug targeting in clinical trials. Genomic-array

(microarray) techniques studying DNA

methylation have identified frequent

promoter-associated hypermethylation of

specific loci accompanying tumor suppression

in GBM (Sturm et al., 2014), such as

(CDKN2A), RB1, PTEN, TP53 (Amatya et

al., 2005; Baeza et al., 2003; Costello et al.,

1996; Nakamura et al., 2001) and other

previously unrecognized regulatory genes

EMP3, PDGFB (Alaminos et al., 2005; Bruna

et al., 2007). Most significantly, O-6-methyl-

guanine-DNA methyltransferase (MGMT)

promoter hypermethylation was identified

occurring in ~45% of adult patients with GBM

(Brennan et al., 2013; Esteller et al., 1999).

MGMT hypermethylation leads to gene

silencing, and reduced gene expression levels

which compromises its ability to repair

damaged DNA by alkylating agents like

Temozolomide (Felsberg et al., 2011). Thus,

gene methylation could be used as a biomarker

Table 1: Role of Omics in biomarkers identification and disease prognosis

GBM Biomarkers Role in GBM prognosis

EGFR amplification

EGFRvIII mutation

EGFR amplification is the most common event in primary GBM, with EGFRvIII being the most

prominent mutated receptor tyrosine kinase receptor occurring in ~50% of GBM cases that

overexpress EGFR (Verhaak et al., 2010). A potential predictive biomarker for molecular

therapies.

PDGFRA PDGFRA is mainly mutated and expressed in abnormally high amounts in proneural tumors

(Verhaak et al., 2010) and associated with poor prognosis in IDH1 mutant GBM (Brennan et al.,

2013).

TP53 mutation TP53 gene although mutated, has no predictive or prognostic role. Can distinguish tumor grade

(Brennan et al., 2013).

1p/19q Co-deletion 1p/19q co-deletion is the most common genetic alteration in oligodendroglioma tumors and is

associated with favorable response to chemotherapy, radiation and survival (Alaminos et al.,

2005).

MGMT promoter

methylation

Promoter methylation of MGMT gene, inactivates DNA repair function (Esteller et al., 1999). It is

the first predictive epigenetic biomarker with a putative diagnostic role in detecting

pseudoprogression. MGMT methylation helps in molecular stratification of patients for

Temozolomide therapy (Malstrom et al., 2012).

VEGF VEGF is considered to be the driving factor of tumor angiogenesis and has been identified in

64.1% GBMs. It is a strong predictor of survival, in patients with gliomas (Reynes et al., 2011)

PTEN A gene level biomarker with poor survival outcomes for GBM (Baeza et al., 2003). PTEN is deleted

in 50–70% of primary and 54–63% of secondary GBM. Also mutated in 14–47% primary GBM.

Mutation is linked to resistance to targeted EGFR inhibitors in GBM (Deberardinis et al., 2008).

IDH1/2 mutation IDH1 mutation is now recognized as an important driver in the etiology of low-grade and

secondary brain tumors (48). Has prognostic value in WHO grade III and IV GBM. Accumulation of

oncometabolite 2-hydroxygluatrate (2HG) considered as metabolomic imaging biomarker for

mutant IDH1 gliomas (Chen et al., 2014).

Biomed Res J 2015;2(1):6-20

Maachani et al. 9

to predict sensitivity to chemo- radiotherapy

(Malmström et al., 2012; Wick et al., 2012).

Further, based on DNA methylation patterns,

proneural subtype is classified into CpG island

methylator phenotype (CIMP) positive and

CpG–CIMP-negative GBM subsets which

strongly correlates with IDH1 gene mutation

status (Noushmehr et al., 2010; Turcan et al.,

2012). Glioma CIMP (G-CIMP) is a powerful

determinant of tumor aggressiveness

(Brennan et al., 2013; Riemenschneider et al.,

2010). These epigenomic and other multi-

omic analyses have revealed several

mutations, altered proteins, miRNA

expressions and pathways associated with

GBM pathogenesis and prognosis.

Table 2: Molecular targeted therapies for glioblastoma

Other current strategies tested in GBMs

Pathways targeted Agents Molecular targets

Epidermal Growth Factor Pathway

EGFR is amplified and frequently mutated in ~50% of GBMs and is

overexpressed in many malignant gliomas. Therefore could be used

as a therapeutic targeted agent in GBM patients.

Erlotinib (Roche)

Gefitinib (AstraZeneca)

Kinase inhibitors of

EGFR

VEGF Pathway

Targeting vascular endothelial growth factor (VEGF) pathways to

induce anti-angiogenic effects in the treatment of malignant gliomas

has been in focus for past few years.

Bevacizumab (Avastin;

Genentech)

Recombinant human

neutralizing monoclonal

antibody to VEGF

Vatalanib (Novartis) Kinase inhibitor of

VEGFR/PDGFR

Cediranib (AstraZeneca) pan-VEGFR inhibitor

Transforming Growth Factor β (TGF-β) Pathway

TGF-β is a multifunctional cytokine, which regulates glioma cell

motility, invasion, and immune surveillance. Several small molecule

inhibitors of TGF-β receptors have shown antitumor efficacy in

preclinical models of gliomas.

Trabedersen (Antisense

Pharma)

Anti-sense TGF-β2

mRNA

PI3K–AKT–mTOR Pathways

PI3K pathways regulate several malignant phenotypes including

antiapoptosis, cell growth, proliferation, and invasion. Activated PI3K

phosphorylates several downstream effectors, including AKT. mTOR

is a major player connecting multiple pathways downstream from

AKT.

Rapamycin (Sirolimus)

Temsirolimus (Sirolimus)

Everolimus (Novartis)

inhibitors of m-TOR

PKC Pathways

Protein kinase C (PKC) is a serine/threonine kinase that regulates

cell proliferation, invasion, and angiogenesis.

enzastaurin (Eli-Lilly) PKC-β inhibitor with

activity against glycogen

synthase kinase 3β

Note: Several of the above agents are being evaluated in clinical trials as monotherapies or in combination with other

treatment modalities such as chemotherapy or radiation in patients with malignant gliomas.

Biomed Res J 2015;2(1):6-20

10 Advances in omics technologies in GBM

Proteomics

Proteomic profiling represents the large-scale

examination of protein expression, post-

translational modification, and understanding

how different proteins interact with each other.

Using various bioinformatics techniques, the

information can be unified into protein

networks. Currently, histopathology

represents the gold standard for the typing and

grading of gliomas and depends largely on

certain architectural similarities of tumor cells

with normal glial cells (Riemenschneider et

al., 2010; Tohma et al., 1998). We feel that the

underlying disease pathology would result

into differential proteomic profiling of

diseased tissue and the surrounding disease-

free normal tissue. Recent technological

advances in proteomics has allowed analysis

of glioma patient biopsies, proximal fluids,

cerebrospinal fluid (CSF) and cyst fluid,

plasma, glioma cell lines. This has allowed a

comprehensive proteomic profiling of glioma

biology to aid the traditional histopathology in

improving our understanding of glioma

processes and to better evaluation of drug

responses to treatment (Somasundaram et al.,

2009). The techniques involve evaluation of

protein arrays, including antibody and aptamer

arrays. This allows simultaneous detection of

multiple proteins/phosphoproteins. These

high throughput techniques can be used for

efficient biomarker validation, treatment

monitoring and can be translated into clinical

applications in an affordable manner. Various

plasma/serum biomarkers have been

identified earlier for GBM including YKL-40,

GFAP and matrix metalloproteinase-9

(Jayaram et al., 2014). Reynes et al. (2011)

reported inflammatory markers (C-reactive

protein, IL-6 and TNF-) and angiogenesis

markers such as VEGF and soluble VEGF

receptor 1 to be significantly elevated in the

plasma of GBM patients. Jung et al. (2007)

identified GFAP as a discriminatory serum

biomarker for GBM. Similarly, serum

osteopontin (OPN), validated using IHC and

ELISA in GBM patients, was shown to

correlate with poor prognosis

(Sreekanthreddy et al., 2010). In an extended

effort, the TCGA group also generated protein

expression data from 214 GBM patient

samples using a high throughput antibody-

based reverse phase protein arrays (RPPAs)

(Brennan et al., 2013) revealing several

mutations, altered genes, proteins and their

pathways underlying GBM pathophysiology

(Dong et al., 2010).

Some of the challenges in using protein

profiling more commonly in characterizing

and quantifying accepted protein biomarkers

includes high costs, lengthy production times

and most importantly lack of high specificity

antibodies. Moreover, the proteomic approach

has the potential to identify novel diagnostic,

prognostic, and therapeutic biomarkers for

human gliomas. The application of proteomics

in neuro-oncology is still in its developing

stage. Please refer recent reviews by Whittle et

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Maachani et al. 11

al. (2007) and Niclou et al. (2010) for more on

the current status of glioma proteomics and its

clinical applications.

Metabolomics

Nearly a century ago, Otto Warburg made a

seminal observation that even in the presence

of adequate oxygen cancer cells metabolize

glucose by aerobic glycolysis, termed as

Warburg effect (Warburg et al., 1924; 1927).

Moreover, very recently disease-related

altered cellular metabolism has come into

forefront of cancer research. Now, there is

increasing evidence that the underlying

genetic alterations contributing to glioma

pathogenesis is also responsible for altered

cellular metabolism (Parsons et al., 2008).

Metabolomics refers to the global quantitative

assessment of endogenous enzyme kinetics,

cellular biochemical reactions, and synthesis

of cellular metabolites within a biologic

system, (Boros et al., 2005; Griffin and

Shockcor, 2004). Although considerable

progress has been made in understanding

GBM biology through genetic analysis, little is

known about the underlying metabolic

alterations in glioma. In recent years, several

biochemical and biophysical techniques such

as Mass Spectrometry (MS), liquid- and gel-

chromatography, Magnetic Resonance

Spectroscopy (MRS), Nuclear Magnetic

Resonance (NMR), have helped in profiling

global metabolomic signatures in cancers

including glioma (Dunn et al., 2005; Serkova

and Niemann, 2006). Several key differences

in metabolite profiles have been identified in

GBM cancer cells when compared to normal

controls, providing a novel insight into GBM

tumorigenesis (Spratlin et al., 2009). As

metabolomics reflect underlying altered

genotype-phenotype, it can be used as a

predictive biomarker for measure of efficacy

and as a pharmacodynamic marker, for both

traditional chemotherapy and hormonal

agents. Using the 1H-NMR spectra and neural

networks, human glioma cell cultures can be

separated into drug-resistant and drug-

sensitive groups before treatment with

nitrosourea treatment (El-Deredy et al., 1997).

Frequent genetic alterations in glioma such as

MYC amplification, PTEN deletion or protein

loss and EFGR amplification are associated

with multiple downstream metabolic targets

(Deberardinis et al., 2008). IDH1 and IDH2

metabolic genes are mutated in ~12% of

primary gliomas, 86% of grade II and III

gliomas and secondary glioblastoma through a

gain-of-function mutation that alters the

enzymatic activity of the protein product,

which results in the production of 2-

hydroxyglutarate (Dang et al., 2009). The

detection of 2-HG metabolic product has been

proposed to be a potential tool for in vivo

distinction of secondary from primary

glioblastomas (Esmaeili et al., 2013). More

recently Chen et al. (2014) showed that

IDH1-mutant glioma growth is facilitated by

overexpression of glutamate dehydrogenase 2

Biomed Res J 2015;2(1):6-20

12 Advances in omics technologies in GBM

gene (GLUD2) and it could be targeted for

growth inhibitory effects. Hence,

metabolomics applications in a clinical

perspective may have a favorable impact on

glioma grade, metabolic state and treatment

stratification of glioma patients.

Other Omics: microRNAs

In recent years, microRNAs (miRNAs) have

emerged in the forefront of cancer molecular

biology. MicroRNAs are key post-

transcriptional regulators that inhibit gene

expression by promoting mRNA decay or

suppressing translation (Iorio and Croce,

2012). Experimental and clinical evidence

supports that miRNAs play pivotal role in

cancer gene regulation, proliferation,

apoptosis and metastasis (Cho, 2011; Iorio and

Croce, 2012). The functional role of miRNAs

was first discovered in human gliomas (Li et

al., 2013). Several miRNA expressions are

found to be dysregulated in GBM. TCGA

group identified alterations in 149 miRNAs

(Dong et al., 2010) and an expression

signature comprising 10 miRNAs with

prognostic prediction (Srinivasan et al., 2011).

miR-128, miR-342 and miR-21 are known to

play both oncogenic and tumor suppressive

roles and are being explored as possible

markers for GBM (Dong et al., 2010;

Srinivasan et al., 2011). More recently several

lines of evidence have implicated over-

expression of miR21 with chemo and

radioresistance of GBM cells. Its expression

levels have been associated with glioma grade

and as a candidate independent marker for

overall survival (Chao et al., 2013; Wu et al.,

2013). Thus, integrative omics analysis has

revealed the importance and scope of

translational repression in microRNA-

mediated GBM pathogenesis. Please refer to

additional reviews (Karsy et al., 2012; Nikaki

et al., 2012; Sana et al., 2011) for more

detailed coverage on miRNA expression and

function in GBM.

Omics data integration methods

The post-human genome project era has

generated enormous heterogeneous and large

data sets. As vast gene profiling datasets and

technologies are being developed, they have

created an unprecedented need to develop

technologies to process the data in a

meaningful way. The efforts have yielded

meaningful results in cancer biomarker

discovery, protein interactions and genotype

to phenotype correlations (Park et al., 2005).

However, current omics technologies cannot

model interactions between multiple

molecules by analyzing individual genes,

proteins or metabolites. This is often not very

effective due to the complex and

heterogeneous nature of human cancers.

Cancer is a complex biological system and

requires a better understanding of the disease's

complexity at systems-level (Faratian et al.,

2009; Hu et al., 2013). Pathway and network

based methods have taken more important role

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Maachani et al. 13

in analysis of high-throughput data, that can

provide a global and systematical way to

explore the relationships between biomarkers

and their interacting partners (Wang et al.,

2015). Integration of data from multiple omic

studies can not only help unravel the

underlying molecular mechanism of

carcinogenesis but also identify the signature

of signaling pathway/networks characteristic

for specific cancer types that can be used for

diagnosis, prognosis and designing tumor

targeted therapy.

Most recently, attempts at integration of

multiple high-throughput omics data have

concentrated on comparing data acquired

using various experimental conditions/

platforms to explore functional and regulatory

associations between genes and proteins

(Faith et al., 2007; McDermott et al., 2009).

This has culminated into combining functional

characterization and quantitative interactions

extracted from various biomolecules such as

DNA, mRNA, proteins and metabolites (Chen

et al., 2011; Coban and Barton, 2012; Mitchell

et al., 2013) (Fig. 1). Some analysis utilizes

pathways in the form of connected routes

through a graph-based representation of the

metabolic network (Blum and Kohlbacher,

2008). Other approaches focus on the

functional module of protein interaction

network and analyze experimental data in the

context of pathways using multiple source

omics data (Wang et al., 2012; Blazier and

Papin, 2012; Federici et al., 2013). Although

currently there are tools available to process

large datasets generated by one platform, it is

expected that soon tools combining data

across multiple platforms will be available to

researchers. This will help in integrating

research results into a framework of whole

biological systems to support translation of

research into clinical applications.

Omics advantages in GBM therapy

So far clinical translation of an effective GBM

therapy has been hindered by multiple factors,

including diffuse infiltration at the time of

diagnosis, significant cellular heterogeneity

(both intratumoral and intertumoral),

difficulty in crossing the blood-brain barrier

by effective drugs, and the role of tumor

progenitor cells in reestablishment of resistant

disease following chemo and radiotherapy.

Current standard treatment of GBM consists

of attempted gross total surgical resection

followed by concurrent temozolomide and

radiation therapy (RT) (Clarke et al., 2010).

Although, RT provides good local control, it is

not very beneficial in controlling the disease

recurrence. In case of GBM, majority of

patients die from recurrent disease, as

currently there is no effective therapy for

recurrent GBM. Therefore, the addition of

systemic chemotherapy to RT can help in

controlling recurrence and offering an

additional radiosensitization benefits in GBM,

benefiting both definitive and palliative

strategies for disease management (Clarke et

Biomed Res J 2015;2(1):6-20

14 Advances in omics technologies in GBM

al., 2010; Stupp et al., 2006). So far non-omics

studies have identified few GBM targets at the

protein level, but fail to see an overall role of

molecules in signaling pathways, protein-

protein interactions, and role in metabolic

processes. Unfortunately so far only one drug

has been identified (Temozolomide) which

can radiosensitize GBM patients. Thus, non-

omics techniques will compliment whole

genomic/epigenomics/metabolomics approach

of omics technologies. Without publically

available databases, the surge of preclinical

and clinical information seen in the GBM field

over last few years, would have not been

possible. As omics studies expand our

understanding of the molecular pathways

driving GBM tumorigenesis, more druggable

targets will be identified to treat GBM patients.

Also, understanding of ionizing radiation at

the level of molecular biology will lead to

development and production of targeted

radiosensitizers. Temozolomide is currently

the only radiosensitizing agent used for GBM

with class I evidence of benefit (Mrugala and

Chamberlain, 2008). It is a novel oral

bioavailable second-generation alkylating

agent. At physiologic pH it undergoes

hydrolysis to its active form methyl traizeno-

imidazole carboxamid (MTIC). The

mechanism of action of MTIC, is to transfer a

methyl group to the middle guanine in a GGG

sequence to convert it to O6-methylguanine.

Temozolomide exerts its antineoplastic

activity by interfering with repair of damaged

DNA after radiation treatment (Mrugala and

Chamberlain, 2008). In a recent randomized

trial, concomitant and adjuvant

Temozolomide chemotherapy with radiation,

significantly improved progression free

survival from 12.1 months to 14.6 months, for

GBM patients (Clarke et al., 2010; Stupp et

al., 2005). The consequent analysis of these

patients by Hegi et al. (2005) reported that

patients with methylated MGMT gene

promoter were benefited from this treatment

compared to patients with unmethylated

MGMT promoter. The MGMT promoter

methylation silences the gene function

required to reverse the O6-guanine

methylation and therefore cannot counteract

the action of Temozolomide. Thus, omics has

been helpful in predicting tumor response to

Temozolomide and to guide clinical decision

making. The other most common types of

chemotherapies for GBM under investigation

include targeted molecular therapies,

antiangiogenic therapies, immunotherapies,

gene therapies, radiation-enhancement

therapies and drugs to overcome resistance

(Table 2).

Challenges and Prospective

Oncogenic transformation is a complex,

multistep process that differs widely between

and even within cancer types. Advances in the

large scale omics technologies have led to

identification of promising GBM disease

biomarkers. The major challenge is how to

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Maachani et al. 15

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CONFLICT OF INTEREST

The authors claim no conflict of interest.

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