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: [email protected]
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
Biomed Res J 2015;2(1):6-20
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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