Perspectives and Challenges in Molecular-Based Diagnostics and Personalized
Treatment for Recurrent High-Grade Gliomas
Darius Kalasauskas
1
, Mirjam Renovanz
1
, Sven Bikar
2
, Anton Buzdin
3,4,5
, Ather Enam
6
, Sven Kantelhardt
1
, Alf Giese
6
and Ella L Kim
1*
1
Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, Mainz, Germany
2
Star SEQ GmbH, Mainz, Germany
3
National Research Centre Kurchatov Institute, Centre for Convergence of Nano, Bio, Information and Cognitive Sciences and Technologies, Moscow, Russia
4
First Oncology Research and Advisory Centre, Moscow, Russia
5
Rogachyov Federal Research Centre of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
6
Department of Surgery, Section of Neurosurgery, Aga Khan University Hospital, Karachi, Pakistan
*
Corresponding author: Ella L Kim, Clinic for Neurosurgery, Laboratory for Experimental Neurooncology, Johannes Gutenberg University Medical Centre,
Langenbeckstrasse 1, 55131 Mainz, Germany, Tel: +496131178211; E-mail: ella.kim@unimedizin-mainz.de
Received date: March 27, 2017; Accepted date: April 18, 2017; Published date: April 24, 2017
Copyright: © 2017 Kim EL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Glioblastoma is the most common and most malignant type of intrinsic brain tumor in adults. The standard of care
for glioblastoma consists of surgical debulking followed by combined radiochemotherapy. The clinical efficacy of
standard therapies for newly diagnosed glioblastomas is rather modest with the highest survival rate at 5-years
being less than 10%. Inevitable recurrence after cytotoxic therapies poses the major challenge in the clinical
management of high grade gliomas. For recurrent glioblastomas, there is no standard therapy with lack of level one
evidence for treatment efficacy. Recent evidence indicates that post-therapy recurrence in gliomas is a consequence
of a plethora of molecular and cellular factors including intratumoural heterogeneity, functional hierarchy of distinct
types of glioma cells, dynamic changes in the molecular landscapes and cellular composition of the tumour during
therapy and the impact of particular treatment modalities. There is an emerging consensus that molecular
distinctions within and between individual tumours is an important factor determining clinical outcomes.
Consequently, integrated approaches based on the combination of molecular profiling with traditional methods such
as immunohistochemical phenotyping, karyotyping and/or non-quantitative methylation-specific PCR have emerged
as a promising venue towards increasing the predictive value of diagnostics for malignant brain tumors. The high
level of inter-and intra-tumoural molecular diversity in gliomas underscores the need of integrating high throughput
molecular profiling and pharmacogenomics into a diagnostic paradigm for gliomas and raises the possibility that
molecular-instructed personalized treatments may provide clinical benefit to patients with glioblastoma, particularly in
the setting of post-treatment recurrence. Here we discuss potential prospects and challenges of patient-tailored
diagnostics and personalized treatment strategies for recurrent glioblastomas.
Keywords: Glioblastoma; Glioblastoma recurrence; Glioma stem
cells; Intratumoural heterogeneity; Molecular diagnostics; High
throughput profling; Personalised medicine
Personalized Approach to Cancer Treatment
Personalized cancer therapy is the most promising also most
challenging area of research in modern oncology. Refecting this fact,
PubMed search by terms “personalized cancer therapy”, “precision
cancer treatment” or “individualized cancer therapy” yields 4619, 4982
and 3821 entries as of March 2017. Te underlying concept is that
molecular distinctions between individual tumours/tumour subtypes
within the same cancer can provide important instructions for
delineating most efective treatment schemes particularly for cancers
poorly responding to conventional treatments. Consequently,
developing patient-tailored individualised therapy (IT) has become
recognized as a promising venue improving the accuracy of diagnosis
and stratifcation of treatment schemes for patients with treatment-
refractory cancers [1-3]. A direct relationship between the level of
genetic complexity and poor efcacy of therapeutic strategies based on
the “one-treatment-for-all” principle can be seen in glioblastoma, a
genetically complex cancer characterized by a high degree of molecular
and cellular heterogeneity. Te promise of high-throughput molecular-
based diagnostics as a strategy for more accurate patient stratifcation
and prognostic staging has been recognized as a matter of urgent
priority for improving the efcacy of glioblastoma therapy [4,5]. In this
article, we discuss the current state, perspectives and specifc
challenges in molecular characterization and development of
personalized approaches for treatment-refractory glioblastoma.
Glioblastoma: General Facts
Glioblastoma is the most common, most aggressive and inevitably
lethal brain tumor in adults [6]. A dismal prognosis in conjunction
with limited treatment options place glioblastomas among most
challenging human cancers today. Age and gender are the most
signifcant risk factors. Glioblastoma is most frequently diagnosed at a
later age (median age at diagnosis 64 years old), occur at slightly
higher rates in men than women and have the highest rate of incidence
in Caucasians [7-9]. Although a higher risk of developing gliobalstoma
has been associated with radiation exposure and germ line mutations
causing neurofbromatosis (NF1 and NF2), Li Fraumeni syndrome
(TP53), melanoma-astrocytoma syndrome (CDKN2A), tuberous
sclerosis (TSC1/TSC2), Turcot syndrome (mismatch repair genes) and
Kalasauskas et al., J Carcinog Mutagen 2017, 8:2
DOI: 10.4172/2157-2518.1000290
Review Open Access
J Carcinog Mutagen, an open access journal
ISSN:2157-2518
Volume 8 • Issue 2 • 1000290
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ISSN: 2157-2518 ISSN: 2157-2518