Molecular subtypes, stem cells and heterogeneity: Implications for personalised therapy in glioma

Journal of Clinical Neuroscience xxx (2015) xxx–xxx

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Review

Molecular subtypes, stem cells and heterogeneity: Implications for personalised therapy in glioma Andrew Morokoff ⇑, Wayne Ng, Andrew Gogos, Andrew Kaye Departments of Surgery, Neurosurgery, Royal Melbourne Hospital, Level 6 Centre for Medical Research, Grattan Street, Parkville, VIC 3050, Australia

a r t i c l e

i n f o

Article history: Received 14 December 2014 Accepted 14 February 2015 Available online xxxx Keywords: Glioblastoma Glioma stem cells Mesenchymal Molecular profiling Personalised medicine Proneural

a b s t r a c t We discuss a number of recent developments that have led to new concepts regarding the biology of gliomas. Collective tissue banking, large-scale genomic, transcriptomic and methylomic expression profiling, and discoveries such as isocitrate dehydrogenase gene mutation and the C-phosphate-G island methylation phenotype have improved glioma classification schemes. Furthermore, the discovery of glioma stem cells has both enhanced and complicated our understanding. Gene signatures describing a proneural versus mesenchymal subtype within glioblastoma multiforme is reflected in both parental tumour as well as glioma stem cells and correlates with differential prognosis and response to radiation and chemotherapy. Finally, we discuss how these factors integrate with the known heterogeneity within brain cancers and the implications of this for the development of personalised therapy. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The era of modern neurosurgery was ushered in when Rickman Godlee removed the first brain tumour in 1884, however, it soon became apparent that even radical surgery such as hemispherectomy, as attempted by Dandy, made little difference to survival for most brain tumour patients. In 1926, Harvey Cushing and Percival Bailey published the first grading scheme for gliomas linked to clinical outcome and based on the light microscopy histologic appearance of tumour cells that morphologically resembled cell types in the central nervous system [1]. They recognized the diffuse, invasive nature of these tumours that made them surgically incurable, particularly the most aggressive and common form known as glioblastoma multiforme (GBM). Over the subsequent 50 years, radiotherapy and chemotherapy treatments were trialed, but only led to marginal improvements in survival. In 2005, Stupp et al. reported that concurrent radiotherapy and temozolomide chemotherapy, with subsequent adjuvant temozolomide, led to a modest 2.5 month increase in median survival but importantly, a significant increase (from 10% to 26%) in the proportion of patients surviving 2 years. This has now been universally adopted as the standard of care [2]. In order to better understand GBM and the other gliomas, various classification systems were refined, further culminating in the currently used World Health Organization (WHO) scheme which was first published in 1979 and last updated in 2007 [3]. Early efforts to survey the genetic abnormalities of glioma were based on gross ⇑ Corresponding author. Tel.: + 61 3 9035 8586; fax: +61 3 9347 6488. E-mail address: [email protected] (A. Morokoff).

chromosomal abnormalities as well as specific detection of known gene alterations such as epidermal growth factor receptor (EGFR), retinoblastoma tumor suppressor protein (RB1) and plateletderived growth factor receptor alpha (PDGFRA) [4]. Later studies were array-based comparative genomic hybridization but only included small cohorts of patients [5]. These findings began to be incorporated in the 2000 edition of the WHO scheme and now many gene alterations are well recognized in GBM. For instance, mutation of the extracellular domain in the amplified EGFR gene resulting in a constitutively active receptor known as EGFRvIII is present in around 25% of GBM. Such alterations, however, are still not part of the diagnostic criteria of the WHO system. The clinicopathologic stratification of primary (de novo) GBM from secondary GBM, the latter developing from lower grade astrocytomas, has been recognised for many years [6]. In 2008, the Cancer Genome Atlas (CGA) project, a multi-institutional cooperative effort including several hundred tumour samples, compiled DNA mutation and copy number analysis, methylation alteration and gene expression data with clinical outcome to try to identify specific gene subsets and molecular pathways linked to survival. The initial report from this big data approach confirmed many known alterations in GBM including phosphatase and tensin homolog (PTEN), TP53, EGFR, retinoblastoma 1 (RB1), neurofibromin 1 (NF1), ERBB2 and phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) or phosphoinositide-3-kinase catalytic subunit alpha (PIK3CA) mutations [7]. A key discovery for GBM classification was the observation of a high frequency of mutations (10%) in the isocitrate dehydrogenase genes (IDH1/2). Subsequently, IDH1 mutations were found to match closely with the secondary

http://dx.doi.org/10.1016/j.jocn.2015.02.008 0967-5868/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Morokoff A et al. Molecular subtypes, stem cells and heterogeneity: Implications for personalised therapy in glioma. J Clin Neurosci (2015), http://dx.doi.org/10.1016/j.jocn.2015.02.008

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GBM subgroup and were extremely common in low grade astrocytomas and oligodendrogliomas [8,9]. Large-scale gene analysis has also allowed similar subclassification of medulloblastoma identifying five subgroups that are now being incorporated into the design of clinical trials [10]. Methylation alterations epigenetically affecting gene expression have come to light over the last few years as an important factor influencing gene pathways in cancer. Widespread hypomethylation of the cancer genome occurs concomitantly with hypermethylation of promoter CpG islands resulting in gene silencing. Specifically, methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) gene promoter results in defective DNA repair and is associated with improved response to alkylating chemotherapy agents in GBM [11]. Although the WHO scheme has proven extremely useful, it is based on subjective criteria and relies exclusively on histopathological features. It does not correlate perfectly with treatment response and outcome and suffers from bias due to limited tissue sampling. In this review, we focus on recent attempts to subclassify gliomas in order to refine the understanding of the heterogeneity of the disease, particularly from the viewpoint of identifying genes, cell types or pathways of interest that might represent targets for personalised therapy. 2. Primary versus secondary GBM The distinction between primary and secondary GBM was first suggested by the pioneering German neuropathologist HansJoachim Scherer over 70 years ago [12]. Secondary GBM develops from lower grade astrocytomas and oligodendrogliomas, is seen in younger age groups, is more likely to be associated with seizures and has a better prognosis whereas primary GBM arises de novo in an older population and is more rapidly aggressive. Early identification of DNA alterations elucidated that TP53 mutation and EGFR overexpression appeared to be mutually exclusive between primary and secondary GBM [13]. In addition to amplification of EGFR, primary GBM typically shows loss of PTEN whereas secondary GBM often contains loss of chromosome 19q along with TP53 [6,14,15]. These observations implied that the two subgroups likely arise from separate evolutionary pathways in the development of GBM, however, the suite of gene alterations between each is not well defined enough to be diagnostically or clinically useful. Since 2008, the discovery of IDH1 mutations added extra molecular information to our understanding of secondary GBM and this is discussed further below. Table 1 shows a summary of the major clinical and genetic differences between primary and secondary GBM. 3. GBM subtypes based on expression profiling A number of attempts have been made to group GBM into subtypes based on patterns of large-scale genomic and expression data since the technology became available over the last decade. A key publication by Phillips et al. in 2006 profiled 76 samples from the MD Anderson Center and 31 from The University of California, San Francisco by DNA microarray and found three subclasses designated mesenchymal (MES), proliferative (PROLIF) and proneural (PN) based on the dominant gene expression patterns within each group [16]. Nearly all WHO Grade III tumours were classified as PN and these were younger on average than MES tumours and had a better prognosis. A key feature of these subclasses was the mutual exclusivity of the PN and MES subtypes which may represent two distinct disease entities. GBM was one of the first tumour types to be analysed by the CGA cooperative research effort once a substantial number of tissue samples had been collected [7]. Based on an 840 gene

Table 1 Differences between primary and secondary glioblastoma multiforme for clinical and genetic factors Variables

Clinical factors Population, % Median age, years Clinical history Seizures, % Overall survival, months Oligodendroglial component, % Necrosis Location Genetic factors IDH1 mutation, % TP53 mutation, % ATRX mutation, % EGFR amplification, % CDKN2A deletion, % PTEN mutation, % 19q loss, % 1p/19q loss, % 10p loss, % 10q loss, % Neurosphere culture, CD133+ expression Expression profile

Primary glioblastoma multiforme

Secondary glioblastoma multiforme

94 60 Short 30 6–12 18

6 40 Long 70 12–36 42

Very common Widespread

Less common Frontal lobes

4–7 17–35 4–7 36–45 31–52 23–25 6 2–8 47 70 Yes

88 60–88 57–80 0–8 19–20 4–12 54 0–13 8 63 No

Mesenchymal, classical

Proneural

+ = positive, CDKN2A = cycline-dependent kinase inhibitor 2A, EGFR = endothelial growth factor receptor, IDH1 = isocitrate dehydrogenase 1, PTEN = phosphatase and tensin homolog.

signature, Verhaak and colleagues described four subtypes of GBM: classical, proneural, mesenchymal and neural [17]. There was considerable (though imperfect) overlap with the PN and MES groups of Phillips et al., however, the PROLIF group was subdivided into classical and neural types by Verhaak et al. It has been suggested that these intermediate groups may simply reflect a fraction of tumours from either PN or MES groups that have increased cell cycling and proliferation [18]. Alternative schemes have also been reported such as that by Gravendeel et al. who studied the expression profile of 276 samples and found clustering into six subtypes that correlated better with survival than histology [19]. Toedt et al. in Heidelberg examined 131 gliomas using array-based comparative genomic hybridization analysis and 74 by expression profiling. Low grade astrocytoma, anaplastic astrocytoma and secondary GBM were grouped based on gene copy number similarities whereas primary GBM appeared to be a genetically distinct entity [20]. They also described a 304 gene signature that clustered tumours into subtypes similar to those of Phillips et al. with PROLIF/MES being distinct from PN types. Efforts to define the signalling pathways and gene expression patterns associated with the subtypes are ongoing but a current summary of the various factors that have been associated with each subtype profile is shown in Table 2. Although the exact number of subtypes and their validity is not settled, the opposing picture of the PN versus MES subtype is evident. The PN subtype is strongly associated with secondary GBM, younger age, lack of enhancement on MRI and better prognosis [16,21–23]. The PN pattern is closely associated with oligodendroglial features, in particular 1p/19q chromosome deletion. Early mutation of TP53 is likely a master regulator of the PN subtype and directs a remarkably consistent pattern of genetic alterations as the tumour evolves [24–26]. It also appears that the proneural subtype might be more dependent on Notch signalling and, thus, responsive to gamma secretase inhibition [27].

Please cite this article in press as: Morokoff A et al. Molecular subtypes, stem cells and heterogeneity: Implications for personalised therapy in glioma. J Clin Neurosci (2015), http://dx.doi.org/10.1016/j.jocn.2015.02.008

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Table 2 Factors associated with glioma subtype classifications

4EBP1 = eukaryotic translation initiation factor 4E binding protein 1, ALT = alanine transaminase, ASCL1 = Achaete-scute homolog 1, BMP = bone morphogenetic protein, CDKN2A = cyclin-dependent kinase inhibitor 2A, Chr = chromosome, CIC = capicua transcriptional repressor, COX2 = cytochrome C oxidase subunit 2, DCX = doublecortin, DLL3 = delta-like 3, G-CIMP = CpG island methylator phenotype, CHI3L1 = chitinase 3-like protein 1, CXCR4 = chemokine receptor 4, EGFR = endothelial growth factor receptor, FAT1 = protocadherin fat homolog 1, FUBP1 = far upstream element binding protein 1, GABRA1 = gamma aminobutyric acid A receptor alpha 1, GBM = glioblastoma multiforme, HH = hedgehog, IDH1 = isocitrate dehydrogenase 1, IGFBP2 = insulin-like growth factor binding protein 2, MERTK = MER proto-oncogene tyrosine kinase, MET = hepatocyte growth factor, mTOR = mechanistic target of rapamycin, MGMT = O-6-methylguanine-DNA methyltransferase, MYC = myelocytomatosis viral oncogene homolog, NEFL = neurofilament light protein encoding gene, NES = nestin-coding gene, ND = no data, NF-jB = nuclear factor kappa light chain enhancer of activated B cells, NF1 = neurofibromin 1, NSC = neural stem cells, OLIG2 = oligodendrocyte transcription factor 2, PI3K = phosphoinositide-3-kinase, PDGFRA = platelet-derived growth factor receptor alpha, PIK3CA = phosphoinositide-3-kinase catalytic subunit alpha, PIK3R1 = phosphoinositide-3-kinase regulatory subunit 1, SLC12A5 = solute carrier family 12 member 5, SOX2 = sex determining region Y box 2, SYT1 = synaptotagmin 1 encoding gene, TCF4 = transcription factor 4, TERT = telomerase reverse transcriptase, TGFb = transforming growth factor, TNF = tumour necrosis factor, TSC2 = tuberous sclerosis 2, VEGFR = vascular endothelial growth factor receptor, WHO = World Health Organization, YKL40 = CHI3L1 encoding gene.

Other recently reported gene associations with the PN subtype include alternative lengthening of telomeres [28]. In contrast to PN, the MES subtype tumours tend to be more invasive, displaying angiogenesis, necrosis and contrast enhancement. MES genes seen in GBM include NF1, vimentin, CD44, matrix metalloproteinase (MMP) and YKL-40 [29]. It is controversial whether epithelial to mesenchymal transition (EMT), a process known to drive migration and metastasis in various carcinomas, occurs in GBM, however a number of recent publications implicate signalling through transforming growth factor beta (TGF-b), stromal cellderived factor 1 (SDF-1) or chemokine receptor type 4 (CXCR4), Snail, aldehyde dehydrogenase 1 family member A3 and other pathways as a driver for EMT in glioma [29–33]. The MES phenotype also depends on the transcriptional regulator TAZ [34] and can be replicated by expressing the CCAAT-enhancer binding protein beta/signal transducer and activator of transcription 3 (C/EBP-b-STAT3) module in neural stem cells [35]. Overall, it is likely that complex genetic pathway dysregulation underlies these subtypes.

4. Low grade gliomas, IDH1 and CpG island methylator phenotype (G-CIMP) Using the 840 gene classifier from Verhaak et al., almost all low grade gliomas in the CGA and The Repository of Molecular Brain Neoplasia Data (REMBRANDT) databases appear to correlate with the PN subtype [36,37]. This might reflect the fact that the PN subtype is linked to the presence of an oligodendroglioma progenitor cell expression pattern [26]. Although first identified by large-scale screening of GBM, the IDH mutation was subsequently recognised to be much more prevalent in low grade gliomas and is also a

defining feature of secondary GBM [38–42]. It is also seen in acute myeloid leukaemia, chondrosarcoma and other tumours, however, it is not found in pilocytic astrocytoma nor ependymoma and only rarely in medulloblastoma. The mutation is most commonly caused by a single point mutation in the IDH1 gene leading to an arginine for histidine substitution in the protein. IDH1-R132H, as well as the rarer homologous IDH2 mutation, both led to a marked reduction of normal enzymatic activity and the accumulation of 2-hydroxyglutarate (2HG), a metabolite which bears a close structural similarity to glutamate [42,43]. Interestingly, IDH1 mutated tumours are more likely to present with seizures but the molecular mechanism for this is not understood [44,45] The presence of IDH1 mutation appears to be an independent positive prognostic marker for survival [40,42,46]. Evidence for its role as an early tumour-initiating mutation is based on the fact that IDH1 mutation is more common than either TP53 mutation, which leads development down the astrocytic tumour pathway, or chromosome 1p/19q loss which likely drives the oligodendroglial pathway. It is also observed that both of these almost never occur in the absence of an IDH1 mutation. An exception is the rare instances where IDH1 mutations occur in astrocytomas in patients with Li–Fraumeni syndrome who carry a germline mutation in TP53 [47]. The capicua transcriptional repressor (CIC), telomerase reverse transcriptase (TERT) and far upstream element binding protein 1 (FUBP1) genes have also been reported to have key mutations in the oligodendroglial pathway whereas ATRX mutation is frequently seen in association with IDH1 and TP53 in astrocytomas and oligoastrocytomas [48–51]. It is possible that IDH1 mutation occurs in the NG2+ progenitor cells that give rise to both astrocytic and oligodendroglial lineages [52]. There is recent evidence for an IDH1 role in tumourigenicity,

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that mutation of IDH1 alters DNA methylation and strongly correlates with the G-CIMP in glioma [53]. The G-CIMP was first described in human colorectal cancer in 1999 and is associated with tumours in the proximal colon, B-Raf proto-oncogene (BRAF) mutations and microsatellite instability [54]. Wide-scale methylation of gene promoters is expected to target tumour suppressor genes although the pattern of genes involved in tumourigenesis requires further investigation. Methylation screening across an expanded CGA sample set has identified the G-CIMP profile in around 8% of all gliomas [55,56]. However, compared to GBM, GCIMP status is approximately 10-fold more prevalent in lower grade astrocytomas (45%) and oligodendrogliomas (93%) and is prognostic in low grade gliomas [55]. Interestingly, G-CIMP status appeared to remain stable over time when 15 recurrent gliomas were examined [55]. All IDH1 mutations appear to have the G-CIMP pattern and there is also major overlap between the G-CIMP group and the PN subtype. The vast majority of G-CIMP tumours (87.5%) fall into the PN subtype, whereas only 30% of PN tumours are classified as G-CIMP. The G-CIMP group is associated with a significantly younger median age at diagnosis than the rest of the PN group, probably accounting for the better prognosis of the PN group [56]. Study of the cellular effects of IDH1 mutation has been elusive because of the difficulty of establishing cell lines expressing the mutated protein, although one has been reported [57]. Contradicting reports have suggested that IDH1-R132H has a pro- or anti-proliferative effect on cells lines but it does appear to confer increased sensitivity to radio- and chemotherapy [58– 60]. In one study, about 40% of IDH1 mutant tumour explants formed tumours in mice, maintained IDH1 mutation expression and some developed additional driver mutations including in Akt, hepatocyte growth factor (MET), PTEN loss or PDGFRA, suggesting that mutation of IDH1 can form a stable basis for tumour progression [61]. IDH mutations have the capacity to disrupt the normal homeostasis of cellular reduction-oxidation reactions causing exposure of the cell to additional oxidative stresses. In fact, it has been shown that IDH1-R132x mutations simultaneously decrease production of alpha-ketoglutarate (a-KG) whilst inducing the production of 2-hydroxyglutarate which has the capacity to drive reduction reactions. However, the complexity of the IDH1– G-CIMP interaction is demonstrated through reports of IDH1 mutations correlating with the induction of hypoxia-inducible factor-1alpha (HIF-1a). Here, reductions in a-KG levels result in reduced activation of HIF-1a degrading enzymes and enable the activation of the HIF-1a pathway. Despite the lack of clarity on its cellular effects, active development of anti-IDH1 targeted therapies is ongoing, but a key question is whether inhibiting the function of the IDH1 mutant protein is an adequate strategy in the face of established genomic methylation changes [62]. The close correlation between IDH1 mutation and secondary GBM has led many to suggest that it should be adopted as the definition of that tumour subtype. Routine immunochemistry screening for IDH1 mutation is now commonplace in many pathology departments since the availability of a reliable antibody. On traditional criteria, only around 3% of primary GBM are IDH1 mutation positive. Similarly, only a fraction of secondary GBM do not contain an IDH1 mutation [63]. The former may represent true secondary GBM that escaped early diagnosis of the precursor lesion, whilst the latter may have initially been misclassified as anaplastic astrocytoma. However, for prognostic purposes, rather than relying on IDH1 status alone, combinations of markers may be needed. For instance, a two gene combination of IDH1 mutation plus MGMT methylation is better at predicting outcome in GBM than either marker alone [64,65]. For low and intermediate grade gliomas, a signature based on key mutations that distinguishes three groups (IDH1/ATRX, IDH1/CIC/FUBP1 and other) was shown to have strong

prognostic correlation [48]. Another similar signature including ATRX, IDH1 and the proliferation marker Ki-67 has been reported [66]. Finally, much interest is focused on the possibility of noninvasive detection of IDH1 using magnetic resonance spectroscopy or circulating 2HG levels in serum [67–69]. It remains to be seen whether these diagnostic markers will gain utility in clinical practice in the future.

5. Glioma stem cells and GBM subtypes Stem cells in brain tumours were first identified over a decade ago and their behaviour and genetic profiles were seen to mimic those of neural stem cells, which had previously been identified in the subventricular and other zones of the brain [70,71]. Some authors prefer the less committing terminology brain tumour initiating cells, or stem-like cells, as opposed to brain tumour stem cells or glioma stem cells (GSC), as we will refer to them here. Relying on a functional set of definitive features, cell lines derived directly from brain tumour samples were able to be grown as 3D gliomaspheres in serum-free non-adherent culture. They showed persistent self-renewal, the ability to differentiate into various neural lineages and had the ability to recapitulate GBM-like tumours when xenografted into brains of immunodeficient mice. Expression of neural stem cell markers such as CD133, Sox2, Musashi and Nestin is seen. CD133 appeared initially to distinguish those cells that could efficiently form tumours in mice whereas the CD133-negative population could not [71]. Since then, many reports have suggested that CD133 expression in GBM correlates with increasing glioma grade and worse prognosis [72–75]. The relationship between the GSC and the molecular subtypes of Phillips et al. and Verhaak et al. is not well understood, but expression profiling of GSC cultured in vitro from primary GBM appears to divide them into two groups: Type I (PN signature) cells resemble foetal neural stem cells (NSC), are CD133-positive and CD15-positive and grow as gliomaspheres whereas Type II (MES signature) cells are more similar to adult NSC lines, are CD133negative and CD44-positive, more invasive and show semi-adherent growth [76–79]. It is not clear whether an intermediate GSC with a classical subtype exists between these two groups or whether this may be a rapidly proliferating progenitor-like cell type [80]. The nature of a stem cell is that it can undergo asymmetric division resulting in one copy of the parent cell (self-renewal) plus a nonstem daughter cell that can differentiate along a functional pathway. Therefore, it is not surprising that both CD133-positive and CD133-negative cells are found in gliomaspheres and GBM, the former cell type giving rise to the latter. The CD133-positive cells are probably more tumourigenic based on traditional criteria [81], however, CD133-negative cells also fulfil stem cell criteria under certain conditions, can form tumours [79] and can even produce CD133-positive progeny in vivo [82]. These observations suggest that CD133 expression and stem cell-like behaviour is not a stable feature but actually a fluid and adaptable process, highly dependent on local environmental factors such as hypoxia in the stemcell niche [83]. Therefore, the use of CD133 as the sole marker of stem cell-likeness or tumour aggressiveness is probably not appropriate. Other factors such as signalling by Snail may drive the switch between cells behaving in a tumourigenic manner with stem cell-like features such as sphere formation or an invasive manner [84]. In colloquial terms, this dual potential of GBM cells has been referred to as the ‘‘grow or go’’ hypothesis. In an interesting recent study that underlines the adaptability of GSC, Baysan et al. compared parental tumours, GSC cell cultures and mouse xenografts and found significant differences in the transcriptome and methylation profile in the cell lines whereas the xenografts and parental tumours were more similar [85]. More

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importantly, cells taken from xenografts could revert to the in vitro pattern rapidly when placed back into culture, showing the importance of the microenvironment. Nevertheless, because GSC cell lines appear to resemble the original tumour more closely than traditional glioma cell lines, they are potentially considered more ideal for drug testing [86]. Many biological questions remain, however, such as why stem cells are only able to be cultured successfully from high grade GBM and not from lower grade PN tumours or secondary GBM, especially given that the CD133-related gene signature correlates highly with the PN subtype. Growth under culture conditions may require a set of gene mutations or epigenetic dysregulation that is only found in higher grade gliomas. 6. Glioma stem cells as a target for therapy If GSC are responsible for tumour propogation they should be the focus of novel targeted therapies, rather than the traditional concept of removing or treating as much of the tumour bulk as possible. This has led to a push to identify the specific signalling pathways that stem cells depend on. Receptor tyrosine kinases such as EGFR, vascular endothelial growth factor receptor (VEGFR) and PDGFR have been known to play a central role in glioma biology. For instance, the well recognised constitutively active EGFR mutant EGFRvIII increases proliferation and promotes anti-apoptotic signalling. However, paradoxically it has been observed that EGFRvIII was associated with better prognosis, its expression level was lower in recurrent GBM and the EGFRvIII-negative cells were resistant to temozolomide [87]. Interestingly, when present, the EGFRvIII mutant is preserved in GSC lines but is lost in traditional cell lines [88]. It is possible that EGFRvIII may drive stem cells towards a rapidly proliferating progenitor cell subpopulation that subsequently loses EGFRvIII expression and responds to temozolomide, rather than maintaining the stem cell compartment which is relatively quiescent and resistant [89]. This, together with the development of cellular adaptive resistance, may help explain why anti-EGFR therapies such as cetuximab have generally failed in GBM, despite holding much theoretical promise, and why there is now intense interest in other signalling pathways such as MET and PI3K/Akt that may be involved in promoting the stem cell phenotype [90,91]. In fact, CD133 forms a physical construct that helps stabilise the p85 regulatory subunit of the PI3K enzyme at the cell membrane and contributes to enhanced PI3K activity [92]. Other studies have highlighted the importance of the Hedgehog, Notch and the canonical Wnt signalling pathways [93,94]. The chemokine receptor CXCR4 also appears to be a key driver of GSC, described as promoting either the PN or MES phenotype [95–97]. Furthermore, even though its significance is in doubt and its function is not fully elucidated, there is interest in directly targeting CD133 in order to reverse stem cell characteristics [98]. Many other targeted therapeutic strategies against stem cell pathways, or aimed at promoting differentiation, are currently being developed. 7. Prognostic value and therapeutic resistance of GBM subtypes Although identifying glioma subtypes has been intriguing from a cancer biology point of view, their usefulness as clinical markers of survival has not been settled. The CGA analysis found no clear association between the subtypes and survival (except for the GCIMP group) [56]. Other studies of long-term survivors have shown all four subtypes of Verhaak et al. to be evenly represented [99]. In a study of 94 gliomas, MGMT methylation, IDH1 mutation and the PN expression signature were overrepresented in long-term survivors, however, the IDH1 wild-type tumours (primary GBM) with long-term survival included a mix of subtypes [100].

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MGMT methylation is a recognised marker of response to temozolomide and radiotherapy and may be useful to guide treatment decisions in the elderly GBM population [11,101], however, this appears to be true for the classical but not the PN subtype [56,102]. Additionally, GSC, at least as defined by CD133 expression, have also been thought to represent a tumour cell subpopulation that is resistant to chemotherapy [103–105] and radiotherapy, especially in vivo [106,107]. However, some recent evidence suggests that the traditional stem cell markers (Nestin, Sox2) and the PN subtype actually correspond to a radiotherapy-sensitive subpopulation whereas the radiotherapy-resistant cells were defined by CD44 expression and the MES subtype [108]. A MES transition gene signature associated with CD44 expression has been associated with a worse prognosis and post-radiation relapse [29,109,110]. Furthermore, in Phillips et al.’s study, the PN subtype in initial tumours was seen to transition to the MES type after recurrence [16]. Biologically, radio-resistance is also seen to be related to PN GSC transitioning to a MES phenotype that has improved ability to repair DNA damage via activation of nuclear factor kappa light chain enhancer of activated B cells (NF-jB) signalling [111]. Thus, there is a growing evidential trend for the MES signature association with worse prognosis but it remains to be seen whether this becomes as strong a marker as MGMT or IDH1. 8. Intra-tumour heterogeneity and genomic evolution It is certain that gliomas progress via the acquisition of more mutations, driven by factors affecting genomic instability. By examining recurrent GBM, further alterations, including EGFR amplification, and TP53 and PTEN mutations are frequently seen at relapse [112,113]. The traditional stochastic or clonal evolution model of cancer posits that all tumour cells have equal opportunity to acquire damaging mutations that lead to dominant growth of that clone as the tumour progresses through time. This implies that within each tumour heterogeneic cell populations harbouring different cancer genomes, transcriptomes and methylomes will be found. Indeed, the presence of genetically distinct subpopulations within gliomas was demonstrated 30 years ago [114]. Recently, using a strategy of multiple sampling of spatially separated areas within GBM during fluorescence guided surgery, clonal variation across the whole tumour was confirmed in 60% of patients represented by at least two Verhaak et al. subtypes within the same tumour. Additionally, distinct evolutionary lineages were described [115]. The genomic subclone(s) that gives rise to the GSC subpopulation is currently not known. If tumours develop from slow turnover or quiescent stem-like cells, these cells would be expected to harbor the original background mutation pattern with further mutations accumulating in the rapidly proliferating progenitor offspring cells, perhaps PN-type, during the rapid growth phase, and subsequently in a more invasive MES phenotype at late recurrence. Another possibility is that various subpopulations can act as or de-differentiate into a stem cell. In fact, genomically different GSC populations can be identified within the same tumour [116]. If there is so much heterogeneity within GBM even GSC-targeted treatments might inevitably fail. Although it is an attractive idea, it is currently uncertain whether large-scale gene profiling will become useful in clinical practice. A further problem is the cost in both money and time of current technologies to provide the data, as well as the complex bioinformatics associated with the analysis. Although this is becoming exponentially faster and cheaper by the year, a rapid, cost-effective bench-top method is still not available in the clinic. Surrogate approaches are being investigated such as a simplified immunohistochemistry panel using only EGFR, PDGFRA and TP53 expression that has been suggested to be highly accurate in

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dividing glioblastomas into the classical, PN and other groups [102]. Another study utilised a similar set of markers but included combined expression of CD44/MER proto-oncogene tyrosine kinase (MERTK) as indicative of the MES subtype [117]. MicroRNA profiling also shows promise as a further method to provide subclassification [118]. It must also be remembered that any subclassification system is based on one snapshot view of a tumour and gives no information about how the cancer will evolve over time. As discussed above, recurrent GBM tends to show a shift towards the MES subtype but this is not always the case [17,115]. Furthermore, it has been recently demonstrated that the mutational profile of recurrent gliomas is often not fully represented in the primary tumour and that temozolomide selects out hypermutated clones that drive malignant progression [119]. These facts complicate the potential of personalised medicine approaches based on genomic screening of the initial tumour. 9. Conclusion New genomic technologies such as wide-scale expression and methylome profiling, together with cooperative efforts to collate large samples of tumour specimens have enabled a vast data approach to unlocking the genetic landscape of glioma. In order to make sense of this new data, it will be necessary to develop a deeper understanding of cancer stem cell biology and how it relates to the evolution of gene mutations as well as gene expression. Identification of subtypes and novel markers such as IDH1 have shown promise as an addition to traditional histopathological grading for improving prognostic and predictive ability. However, since diagnosis still relies on piecemeal surgical sampling at the time of initial resection, it has a limited scope to capture the heterogeneity that has been shown to exist across the whole tumour both spatially and throughout the time course of tumour evolution. Furthermore, analogous to the stock market or the weather, the complex changes during tumour progression in response to therapy may not be intrinsically predictable. In order for the concept of personalised cancer treatment to become a reality, multiple surgical sampling strategies across space and time, and the development of validated imaging biomarkers or repeated blood sampling for circulating biomarkers, will be required. Conflicts of Interest/Disclosures The authors declare that they have no financial or other conflicts of interest in relation to this research and its publication. Acknowledgements The authors would like to thank the Royal Melbourne Hospital Neuroscience Foundation and the Royal Australasian College of Surgeons for support. References [1] Bailey P, Cushing H. A classification of the tumors of the glioma group on a histogenetic basis with a correlated study of prognosis. Philadelphia: JP Lippincott; 1926. [2] Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352:987–96. [3] Louis DN, Ohgaki H, Wiestler OD, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007;114:97–109. [4] Bello MJ, de Campos JM, Kusak ME, et al. Molecular analysis of genomic abnormalities in human gliomas. Cancer Genet Cytogenet 1994;73:122–9. [5] Godard S, Getz G, Delorenzi M, et al. Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes. Cancer Res 2003;63:6613–25.

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