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CHI3L1, NTRK2, 1P/19Q and IDH Status Predicts Prognosis in Glioma

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CHI3L1, NTRK2, 1P/19Q and IDH Status Predicts Prognosis in Glioma cancers Article CHI3L1, NTRK2, 1p/19q and IDH Status Predicts Prognosis in Glioma 1,2 1, 3,4, 5 Elise Deluche , Barbara Bessette y , Stephanie Durand y , François Caire , Valérie Rigau 6, Sandrine Robert 1,7 , Alain Chaunavel 1,7, Lionel Forestier 3, François Labrousse 1,7, Marie-Odile Jauberteau 1,8, Karine Durand 1,7 and Fabrice Lalloué 1,* 1 EA3842 CAPTuR, Faculty of Medicine, University of Limoges, 2 Rue du Docteur Marcland, 87025 Limoges, France; [email protected] (E.D.); [email protected] (B.B.); [email protected] (S.R.); [email protected] (A.C.); [email protected] (F.L.); [email protected] (M.-O.J.); [email protected] (K.D.) 2 Department of Medical Oncology, Limoges University Hospital, 2 rue Martin Luther King, 87042 Limoges, France 3 Bioinformatics Team, BISCEM Platform, CBRS, University of Limoges, 2 rue du Docteur Marcland, 87025 Limoges, France; [email protected] (S.D.); [email protected] (L.F.) 4 EA7500 PEREINE, University of Limoges, 123 av. Albert Thomas, 87060 Limoges, France 5 Department of Neurosurgery, Limoges University Hospital, 2 rue Martin Luther King, 87042 Limoges, France; [email protected] 6 Department of Neuropathology and INSERM U1051, Hospital Saint Eloi—Gui de Chauliac, 80 av. Augustin Fliche, 34090 Montpellier, France; [email protected] 7 Department of Pathology, Limoges University Hospital, 2 rue Martin Luther King, 87042 Limoges, France 8 Department of Immunology, Limoges University Hospital, 2 rue Martin Luther King, 87042 Limoges, France * Correspondence: [email protected]; Tel.: +33-5-55-43-59-29 These authors contributed equally to this work. y Received: 8 February 2019; Accepted: 12 April 2019; Published: 15 April 2019 Abstract: The aim of this study was to identify relevant biomarkers for the prognosis of glioma considering current molecular changes such as IDH mutation and 1p19q deletion. Gene expression profiling was performed using the TaqMan Low Density Array and hierarchical clustering using 96 selected genes in 64 patients with newly diagnosed glioma. The expression dataset was validated on a large independent cohort from The Cancer Genome Atlas (TCGA) database. A differential expression panel of 26 genes discriminated two prognostic groups regardless of grade and molecular groups of tumors: Patients having a poor prognosis with a median overall survival (OS) of 23.0 9.6 months ± (group A) and patients having a good prognosis with a median OS of 115.0 6.6 months (group B) ± (p = 0.007). Hierarchical clustering of the glioma TCGA cohort supported the prognostic value of these 26 genes (p < 0.0001). Among these genes, CHI3L1 and NTRK2 were identified as factors that can be associated with IDH status and 1p/19q co-deletion to distinguish between prognostic groups of glioma from the TCGA cohort. Therefore, CHI3L1 associated with NTRK2 seemed to be able to provide new information on glioma prognosis. Keywords: glioma; IDH status; CHI3L1; NTRK2; prognosis 1. Introduction Gliomas are the most common primary brain tumors of the central nervous system. Overall age-adjusted incidence rates vary from 4.67 to 5.73 per 100,000 persons [1]. Gliomas are classified according to World Health Organization (WHO) criteria [2]. However, despite the undeniable Cancers 2019, 11, 544; doi:10.3390/cancers11040544 www.mdpi.com/journal/cancers Cancers 2019, 11, 544 2 of 16 contribution of this classification, patients with tumors that have the same histologic appearance may still have different outcomes due to molecular heterogeneity [3]. One way to improve the clinical management of gliomas is to identify new molecular biomarkers that distinguish more homogeneous subgroups of patients regardless of histological tumor characteristics, which could further refine the prognostic value of the current biomarkers, 1p19q chromosome arm loss and IDH mutation. These biomarkers could also improve our knowledge of glioma biology. IDH mutations are strongly implicated in tumor initiation and progression [4], and the presence or absence of IDH mutations contributes to glioma prognosis [5]. However, while IDH mutations are a hallmark of diffuse low-grade glioma (LGG), as they occur in 70 to 90% of astrocytomas and oligodendrogliomas (WHO grades II and III), they are also identified in 10% of glioblastomas (GBMs) (WHO grade IV) [2], which are associated with a better prognosis as compared to IDH wild-type GBM [6–8]. Despite the prognostic benefit associated with IDH mutations, and regardless of the glioma subtype, differences in outcomes among IDH-mutant tumors have been observed [9]. Deletion of 1p19q is a characteristic of oligodendrogliomas, where it occurs concomitantly with IDH mutation. It is a strong independent prognostic biomarker associated with improved survival [10]. This glioma group, although relatively homogeneous on the molecular level, could also have different outcomes [11]. Efforts have been focused on classifying gliomas according to molecular aberrations and resultant new genetic signatures that could help to optimize the clinical management [12,13]. To improve prognosis prediction, other genes associated with IDH and 1p19q status need to be identified. So, in this study we analyzed 96 genes considered as relevant based on our previous work [14]. These potential candidate prognostic biomarkers are usually conventional genes involved in different mechanisms implicated in gliomagenesis, such as signaling pathways, hypoxia, angiogenesis, or cancer stem cell markers. More rarely, these genes are involved in the glycosylation process or in neurotrophin receptor-dependent signaling. Aberrant glycosylation is a common feature of different tumor types and is generally associated with deregulation of cell adhesion or migration, which results in tumor processes such as metastasis and invasion. Our previous study allowed us to present evidence that eight glycosylation-related genes were upregulated in the most aggressive and undifferentiated glioblastoma cells [14]. In parallel, the family of tyrosine receptor kinases (TRKs) and p75NTR are already known to be involved in tumor cell survival. For instance, TrkB and TrkC receptors promote the growth of brain tumor-initiating cells, and p75NTR promotes glioma invasion [15–17]. Finally, we recently showed that some autophagic factors and neurotrophin pathways cooperate to contribute to tumor cell aggressiveness [18]. Thus, genes encoding Trks and glycosylation-related genes could be of interest to complement the current IDH mutation-based classification. In this study, we screened expression levels of a set of genes to identify those linked to prognosis and analyzed whether they could refine prognosis according to IDH and 1p19q status in glioma. 2. Results 2.1. Clinical and Histological Characteristics The analysis was performed in radiotherapy- and/or chemotherapy-naive glioma surgical specimens. Sixty-four patients (26 women and 38 men; median age: 47.5 years; range: 22–81) were included. The main clinical and histological data from this treatment-naive population are shown in Supplementary Table S1. The population comprised patients with gliomas ranging from grade II to grade IV (grade II, n = 18; grade III, n = 25; grade IV, n = 21). Thirty-six tumors exhibited the IDH mutation and 28 were IDH wild-type; 14 tumors had 1p19q deletion. Cancers 2019, 11, x 3 of 16 Cancers 2019, 11, 544 3 of 16 2.2. Hierarchical Clustering of Studied Cohort to Define Two Distinct Prognostic Groups 2.2. HierarchicalUsing a 96-gene Clustering set of with Studied hierarchical Cohort to Define clusteri Twong Distinct identification, Prognostic two Groups distinct groups were identified (data not shown). To better identify differentially expressed genes between the two groups, univariateUsing aanalysis 96-gene and set multivariate with hierarchical analysis clustering (principal identification, component twoanalysis) distinct were groups performed. were identifiedUnivariate (data analysis not detected shown). 29 To genes better of identifyinterest ( dip <ff erentially0.05) (Supplementary expressed genesFigure between S1, left), thewhereas two groups,principal univariate component analysis analysis and multivariatedetected 30 analysisgenes of (principal interest (Supplementary component analysis) Figure were S1, performed. right). By Univariatecombining analysisthese two detected techniques, 29 genes we identified of interest a (rep <levant0.05) 26-gene (Supplementary panel (Supplementary Figure S1, left), Figure whereas S1): principalNine genes component involved in analysis neurotrophin detected pathways 30 genes (AKT3, of interest EGF, AREG, (Supplementary ERBB4, neuregulin Figure 2, S1,neuregulin right). By3, combiningneuregulin these4, BRAF, two internexine-a), techniques, we six identified genes encoding a relevant neurotrophins 26-gene panel (Supplementaryand their receptors Figure (NGF, S1): NineNTSR2, genes NTRK1, involved NTRK2-FL, in neurotrophin NTRK2-T1, pathways NTRK3), (AKT3 three, EGF glycosylation-related, AREG, ERBB4, neuregulin genes (CHI3L1, 2, neuregulin KLRC3, 3, neuregulinST3GAL5), 4 ,twoBRAF genes, internexine-a implicated), six in genes autophagy encoding (PARK2, neurotrophins PINK1), and three their genes receptors known (NGF to ,occurNTSR2 in, NTRK1hypoxia, NTRK2-FL and angiogenesis, NTRK2-T1 (VEGF-A,, NTRK3 VEGFR-2,), three glycosylation-related VEGFR-3) and three genesglioma (CHI3L1 markers, KLRC3 (OLIG2,, ST3GAL5 NANOG,), twoSYP). genes implicated in autophagy
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