1.5.1 MicroRNA Biomarkers for Diagnosis and Prognosis
The diagnosis of glioma is currently performed by MRI and subsequent histological examination of tumour sections which are obtained by surgery. Therefore the development of a non-invasive test for glioma diagnosis would be highly beneficial to patients and would potentially allow early diagnosis. Circulating miRNA could provide a minimally invasive diagnostic tool however the main focus of research has been on miRNAs for the prediction of prognosis and response to therapy. One study which investigated circulating miRNAs for the diagnosis of glioma was Manterola et. al. (2014) who identified a signature comprising of two miRNA; miR-320 and miR-574-3p,
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and one small non-coding RNA, RNU6-1 which were significantly associated with GBM diagnosis. This signature was identified by the isolation of microvesicles from the serum of patients and subsequent profiling using qRT-PCR and the signature was validated using a second independent cohort of patients (Manterola et al., 2014). MiR- 125b has also been identified as a potential diagnostic biomarker for glioma, analysis of serum samples obtained from patients using qRT-PCR identified down-regulation of miR-125b compared to control samples (Wei et al., 2014). Analysis of the diagnostic accuracy of miR-125b identified a sensitivity of 82 % for detection of glioma grades II- IV (Wei et al., 2014).
Evaluation of the miR-29 family, miR-29a, miR-29b and miR-29c, in the serum of glioma patients identified that this family of miRNAs had good sensitivity and specificity for the diagnosis of high-grade glioma (Wu et al., 2014). Serum samples from high-grade and low-grade glioma patients were analysed using qRT-PCR and the miR-29 family was found to be down-regulated in both high and low-grade glioma serum, with a greater down-regulation observed in high-grade samples (Wu et al., 2014). The authors concluded however, that the miR-29 family may not have sufficient sensitivity to detect early-stage glioma but could be useful in the identification of glioma progression (Wu et al., 2014).
A number of studies have identified miRNAs within tumour tissue for predicting the prognosis of glioma patients. Some miRNAs identified as prognostic biomarkers are outlined in Table 1.9. One such study identified miRNA expression patterns which could be used to identify subgroups of glioblastoma patients with differing prognosis (Niyazi et al., 2011). Tissue sections obtained from glioblastoma patients were analysed using microarray and identified two differing expression patterns of 30 miRNA which was used to group patients into ‘long-term’ or ‘short-term’ survivors (Niyazi et al., 2011). This study, however was limited by the small sample size of patients and was retrospectively performed.
Analysis of serum from astrocytoma patients identified three serum miRNAs; miR-19a- 3p, miR-106a-5p and miR-181b-5p, which were significantly correlated with survival (Zhi et al., 2014). Patients with an up-regulation of these three miRNAs exhibited a poorer survival rate than those with a down-regulation (Zhi et al., 2014).
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Table 1.9 Prognostic miRNAs and miRNA signatures identified in serum and tissue samples from glioblastoma.
MiRNA Source Expression Prognosis Reference miR-124 Frozen tissue Down-regulation Poor (Chen et al., 2015)
miR-155 Frozen tissue Up-regulation Poor (Sun et al., 2014)
30 miRNA signature FFPE tissue sections Up and down- regulation Long-term and short-term survival (Niyazi et al., 2011)
miR-335 Frozen tissue Up-regulation Poor (Jiang et al., 2012a)
miR-196a, miR-196b
Frozen tissue Up-regulation Poor (Guan et al., 2010)
Ten miRNA signature
TCGA dataset Seven up- regulated, three down-regulated
Good and poor prognosis
(Srinivasan et al., 2011)
Six miRNA signature
FFPE tissue Up and down- regulation
Good and poor prognosis
(Sana et al., 2014)
Six miRNA signature
Frozen tissue Up and down- regulation
Good and poor prognosis (Barbano et al., 2014) miR-19a-3p, miR-106a-5p, miR-181b-5p
Serum Up-regulation Poor Prognosis (Zhi et al., 2014)
1.5.3 MicroRNA biomarkers for response to therapy
In the treatment of glioma, chemoresistance can be a pivotal factor in the prognosis of a patient. The ability to predict response to treatment could improve prognosis by selecting the right treatment course as soon after diagnosis as possible and permit rapid adaption of treatment to the acquisition of chemotherapeutic and radio- resistance. This not only benefits the patient by improving their prognosis but also improves the cost-effectiveness of chemotherapeutics by using them only when they are expected to succeed. Furthermore, the use of predictive biomarkers in clinical trials could identify patients most likely to respond to new anti-cancer therapies, thereby accelerating the development of novel therapeutics (Carden et al., 2010). Currently, the gold standard for glioblastoma treatment is TMZ usually combined with
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radiotherapy. Only a small subset of patients respond to TMZ treatment, as patients with the functional O6-methyl guanine methyltransferase (MGMT) DNA repair protein reverse the guanine methylation caused by TMZ leading to chemoresistance and limited success of this drug (Zhang et al., 2012). MiR-181d could be used as a biomarker to identify patients who would respond the best to TMZ, because MGMT is a candidate target of miR-181d, and a higher expression of miR-181d correlates with a lower expression of MGMT and subsequently improved response to TMZ (Zhang et al., 2012).
Whilst miR-181d up-regulation may correspond to a better response to TMZ, up- regulation of miR-21 on the other hand, may predict poor response linked to the high rate of TMZ resistance which develops in patients. MiR-21 is one of the most frequently up-regulated miRNAs in glioblastoma and has been found to protect U87MG glioblastoma cells from TMZ-induced apoptosis (Shi et al., 2010). Inhibition of miR-21 in the resistant D54MG cell line enhanced chemosensitivity to TMZ (Wong et al., 2012). Both of these findings suggest that miR-21 could be used as a biomarker to predict or monitor the acquisition TMZ resistance in glioblastoma patients to enable quick adaptation in treatment strategy and maintain a good prognosis. Further to the role as a chemotherapeutic marker, miR-21 has also been shown to function in the acquisition of radio-resistance. Analysis of radio-resistance in a number of glioblastoma cell lines including U87MG and U373 showed that radio-sensitivity was closely related to the expression level of miR-21 (Gwak et al., 2012). The silencing of miR-21, using anti-miR-21 in radio-resistant malignant glioma cell lines led to the sensitisation of these cells to radiation (Gwak et al., 2012). Anti-miR-21 was found to sensitise U87MG and U373 cells through inactivation of the PI3K/Akt signalling pathway. Whilst these findings point towards a mechanism of acquired radio- resistance, they also highlight miR-21 levels could be an important predictor of acquired radio-resistance which if monitored would permit quick adaptation of treatment plans and effective treatment of the glioblastoma as it progresses.
As well as affecting TMZ and radio-resistance miRNA 21 along with miR-30b and 30c have been identified as regulators of TNF-related apoptosis-inducing ligand (TRAIL) induced apoptosis. These three miRNAs therefore, could affect the sensitivity of glioma cells to treatment with the TRAIL ligand (Quintavalle et al., 2012). A study of glioma cell
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lines and primary cultures found that miRNA-21, 30b and 30c were significantly up- regulated in TRAIL-resistant glioma cell lines (Quintavalle et al., 2012). Consequently TRAIL-sensitive cells were found to exhibit down-regulation of these three miRNA. The targets of these miRNA were found to include caspase-3 by the miR-30 family and Tap63 by miR-21. It was concluded that these miRNA regulate apoptotic programs within glioma cell lines. The results obtained also demonstrated a difference in miRNA expression between TRAIL sensitive and resistant cells (Quintavalle et al., 2012). These miRNA biomarkers for response to treatment and therapeutic resistance are outlined in Table 1.10.
Table 1.10 MicroRNA Biomarkers for Response to Therapy