OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2022.13624 OL-25-01-13624 Articles Molecular profile and clinical features of patients with gliomas using a broad targeted next generation-sequencing panel RomanidouOurania 1 * ApostolouParaskevi 2 * KouvelakisKyriakos 3 TsangarasKyriakos 4 EliadesAlexia 4 AchilleosAchilleas 4 LoizidesCharalambos 4 LemesiosChristos 4 IoannidesMarios 4 KypriElena 4 KoumbarisGeorge 4 PapadopoulouKyriaki 5 PapathanasiouAthanasios 2 RigakosGeorgios 6 XanthakisIoannis 7 FostiraFlorentia 2 KotoulaVassiliki 8 FountzilasGeorge 5 9 10 PatsalisPhilippos C. 4 11 Department of Medicine, Medical Oncology Unit, Giannitsa General Hospital, 58100 Giannitsa, Greece Molecular Diagnostics Laboratory, InRASTES, National Centre for Scientific Research Demokritos, 15341 Athens, Greece Section of Biostatistics, Hellenic Cooperative Oncology Group, Data Office, 11526 Athens, Greece NIPD Genetics Ltd., 2409 Nicosia, Republic of Cyprus Laboratory of Molecular Oncology, Hellenic Foundation for Cancer Research/Aristotle University of Thessaloniki, 54006 Thessaloniki, Greece Third Department of Medical Oncology, Hygeia Hospital, 15123 Athens, Greece Oncology Department, European Interbalkan Medical Center, 55535 Thessaloniki, Greece Department of Pathology, School of Health Sciences, Faculty of Medicine, Aristotle University of Thessaloniki, 54006 Thessaloniki, Greece Aristotle University of Thessaloniki, 54006 Thessaloniki, Greece Department of Medical Oncology, German Oncology Center, 4108 Limassol, Republic of Cyprus School of Medicine, University of Nicosia Medical School, 2417 Nicosia, Republic of Cyprus Correspondence to: Professor Philippos C. Patsalis, NIPD Genetics Ltd., 31 Neas Engomis, Engomi, 2409 Nicosia, Republic of Cyprus, E-mail: p.patsalis@nipd.com

Contributed equally

 01 2023 08 12 2022 25 1 38 13092022 02112022 Copyright © 2022, Spandidos Publications 2022

Gliomas are the most common malignant primary brain tumors characterized by poor prognosis. The genotyping of tumors using next generation sequencing (NGS) platforms enables the identification of genetic alterations that constitute diagnostic, prognostic and predictive biomarkers. The present study investigated the molecular profile of 32 tumor samples from 32 patients with high-grade gliomas by implementing a broad 80-gene targeted NGS panel while reporting their clinicopathological characteristics and outcomes. Subsequently, 14 of 32 tumor specimens were also genotyped using a 55-gene NGS panel to validate the diagnostic accuracy and clinical utility of the extended panel. The median follow-up was 19.2 months. In total, 129 genetic alterations including 33 structural variants were identified in 38 distinct genes. Among 96 variants (single nucleotide variants and insertions and deletions), 38 were pathogenic and 58 variants of unknown clinical significance. TP53 was the most frequently mutated gene, followed by PTEN and IDH1 genes. Glioma patients with IDH1 mutant tumors were younger and had significantly longer overall survival compared to patients with wild-type IDH1 tumors. Similarly, tumors with TP53 mutations were more likely observed in younger patients with glioma. Subsequently, a comparison of mutational profiles of samples analyzed by both panels was also performed. Implementation of the comprehensive pan-cancer and the MOL panels resulted in the identification of 37 and 15 variants, respectively. Of those, 13 were common. Comprehensive pan-cancer panel identified 24 additional variants, 22 of which were located in regions that were not targeted by the MOL panel. By contrast, the MOL panel identified two additional variants. Overall, the present study demonstrated that using an extended tumor profile assay instead of a glioma-specific tumor profile panel identified additional genetic changes that may be taken into consideration as potential therapeutic targets for glioma diagnosis and molecular classification.

 gliomas tumor targeted next generation sequencing genetic variants somatic molecular profiles prognostic biomarkers molecular diagnosis precision medicine HeCOG research HE R_17/15 NIPD Genetics Limited and by a HeSMO Grant This work was supported by an internal HeCOG research grant (grant no. HE R_17/15), by NIPD Genetics Limited and by a HeSMO Grant. Introduction

Tumors of the brain and Central Nervous System (CNS) are uncommon. Of these tumors, gliomas represent approximately one-third of all primary CNS tumors and the majority of malignant CNS tumors with a yearly incidence of about 6/100000 (1). Patients with gliomas have a varying 5-year survival rate, a high mortality rate, and a poor prognosis depending on histology and grading. The poorest prognosis is for glioblastoma, which has a 5-year relative survival rate of 6.8 (2,3).

Traditionally, the classification of tumors has been based on histopathological features and immunochemistry, but in 2016 the WHO (World Health Organization) took molecular characteristics into account in its reclassification of tumors, and in 2021 it underlined molecular markers after additional update classifications (4–6).

Gliomas have been found to have several genetic changes that maybe used as diagnostic, prognostic, predictive, and potentially therapeutic biomarkers. The most commonly reported genetic variations include mutations in ATRX, BRAF, CDKN2A/B, IDH, NF1, RB1, TERT, and TP53 genes as well as MGMT promoter methylation status, copy number alterations such as 1p/19q codeletion, EGFR amplification and combined gain of chromosome 7 and loss of chromosome 10 (7+/10-), and rearrangements (7–9).

Subsequently, the advent of next-generation sequencing (NGS) technologies over the past ten years, has significantly influenced the field of molecular oncology in gliomas, demonstrating a significant variability by age, gender, and ethnicity (10,11).

Implementation and broad use of multigene panels on Formalin-Fixed, Paraffin-Embedded (FFPE) tissues enable the cost-effective study of many genes and detection of genetic abnormalities by massively parallel sequencing.

To date, a large number of investigations encompassing commonly affected and new candidate genes have been performed to more precisely categorize gliomas and identify distinct prognostic categories that may have treatment implications, with varied results being obtained (12,13). Therefore, the aim of the present study is to further elucidate and determine the prevalence of somatic mutations by implementing a broad 80-gene panel among 32 patients with gliomas, while reporting their clinicopathological features and outcomes. Subsequently, 14 of 32 tumor samples were examined using another multigene panel to evaluate the accuracy and clinical utility of the broad panel.

 Materials and methods

This study initially included 48 patients diagnosed with high-grade gliomas and 48 available FFPE tissue samples selected in the clinical centers affiliated with Hellenic Cooperative Oncology Group (HeCOG). Sixteen out of these 48 tumor samples did not meet the quality control criteria for further analysis. Subsequently, thirty-two tumor paraffin blocks from 32 patients were examined. The median age of cancer diagnosis among the patients was 54.9 years (range: 25.2-77.8 years). Clinical data were obtained from the HeCOG data office. Written informed consent was obtained from all individuals prior to the use of their biological material for research purposes as specified in the Declaration of Helsinki. Tumor blocks were stored and centrally processed in the Laboratory of Molecular Oncology (MOL; Hellenic Foundation for Cancer Research/Aristotle University of Thessaloniki) for histology review, including confirmation of tumor tissue on the section; comparison to local typing; histologic grade; areas with necrosis; microvascular proliferation; assessment of tumor areas for macrodissection. The study was approved by the Bioethics Committee of the Aristotle University of Thessaloniki School of Medicine (AUTH #No 2/February 4, 2015) and by Cyprus National Bioethics Committee (EEBK/EΠ/2016/54, 22/4/2021).

<sec> <title>Tissue processing, DNA extraction and NGS genotyping

Tumor dense areas were marked on H&Es and microdissected manually from 10 µm unstained FFPE sections prior to DNA extraction. FFPE samples from 48 patients diagnosed with gliomas were processed. DNA was isolated from FFPE tissue sections using the GeneRead DNA FFPE Kit (Qiagen GmbH, Hilden, Germany) in accordance with manufacturer's instructions. DNA was quantified with a fluorometric-based assay for FFPE tissue-derived DNA (Qubit flex fluorometer, Qubit dsDNA high sensitivity assay, ThermoFisher Scientific, Carlsbad, CA, USA). Sixteen samples that did not meet the minimum acceptable quality control (QC) criteria (a minimum of 10 ng of DNA and a minimum DNA concentration of 1 ng/µl) were excluded from the analysis. Therefore, 32 FFPE samples from 32 patients were included and analysed.

Tumor genotyping was performed in the Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory, using a comprehensive, commercially available, pan-cancer tumor profile panel of 80 genes (NIPD Genetics, Nicosia, Cyprus, http://nipd.com/products/oncology/foresentia/), from which Single Nucleotide Variants (SNVs), Insertions and Deletions (InDels), Copy Number Alterations (CNAs) and Rearrangements were detected in accordance with manufacturer's instructions (Table SI). Targeted genomic loci were captured using an in-solution hybridization method (NIPD Genetics, Nicosia, Cyprus) (Supplementary materials and methods). Notably, 14 of 32 tumor samples were also genotyped on Ion Personal Genome Machine (Thermo Fisher/IonTorrent) at the MOL with a custom Ampliseq panel (IAD68363_167), targeting mutations in 55 genes, as previously described (14). Library construction with the MOL panel was performed with standard protocols, using 20 ng DNA per sample and the Ampliseq primers along with the Ampliseq Library Kit v.2.0 and Ion Xpress barcodes (Life Technologies, Carlsbad, CA). Resulting libraries, once normalized to 15 ng/ml, were clonally amplified on the One-Touch-2 instrument, enriched on the OneTouch ES station and sequenced on the Ion Personal Genome Machine sequencer. For data retrieval, base calling was performed on the Torrent Server with Torrent Suite v.4.4.2. Consequently, variants were annotated with Ion Reporter v.4 and accepted for analysis if they had P-value <0.0001; >100 amplicon reads; position coverage >100; variant coverage >40 for position coverage of 100; variant allele frequency (VAF) >5% and Indels without GC stretches. Finally, only tumor samples with mean depth >150 and at least 5 variants were considered eligible and included in the study (14). The study design is presented in Fig. 1.

 Bioinformatics analysis

Sequencing data were de-multiplexed with bcl2fastq (v.2.16.0) and paired-end DNA sequencing reads were processed to remove adapter sequences and poor-quality reads. The remaining sequences were aligned to the human reference genome build (hg19) using the Burrows-Wheeler alignment algorithm (15). Duplicate read entries were removed and aligned reads files were converted to a binary (BAM) format (16). Variant calling was performed using a versatile somatic variant caller (17) and variant annotation was performed using the ENSEMBLE Variant Effect Predictor (VEP) (18). Subsequently, variants were filtered on the basis of variant mapping and read quality (amplicon coverage >100; and base coverage >50). Moreover, variants that fulfilled selection criteria for rarity (minor allele frequency <0.1% based on dbSNP, 5000 Exomes and ExAC) and were identified in coding regions and splice junctions were included in the study. The identified variants were classified and interpreted in accordance with ClinVar and COSMIC databases (19,20). Gene-level CNAs were detected using an in-house bioinformatics pipeline that implements a circular binary segmentation method (21). Rearrangement calling was performed by utilizing discordant pair and split-read alignments following local assembly, realignment, and an in-house filtering pipeline to refine the set of candidate events (22–24).

 Statistical analysis

The objectives of the study were descriptive. Categorical variables were summarized using frequencies and percentages while continuous variables using median (min, max). Since Greece lacks a national Tumor Registry, we proportionally estimated the number of glioma patients to the country's approximate 10 million residents using information from glioblastoma (GBM) patients in Malta, a country that is geographically close to Greece. More specifically, a recent study reported that glioblastoma has an incidence rate of 4.5 cases per 100,000 population in Malta (25). Therefore, we assumed that there were 450 glioma patients in Greece and 48 patients would generate a confidence interval of maximum width +/- 14.07% for the percentage estimates. Associations between categorical and continuous variables were examined using the Kruskal-Wallis test whilst associations between categorical variables were examined using the chi-squared test. Overall survival was defined as the time elapsed from cancer diagnosis until death or last contact. The Kaplan-Meier method was used to estimate the survival rate since diagnosis. Univariable Cox regression was used to generate hazard ratios which were presented alongside 95% C.I. The long-rank test was used to examine differences in event-free probability between patient subgroups. Significance level was set to 0.05 for all significance tests. The data management and statistical analysis were mainly performed using the SAS software (version 9.4). The Python 3 and the R 4.2.0 programming languages were employed to build different types of charts for data visualization. The R packages readxl and GenVisR were used to produce the genetic variants' map.

 Results <sec> <title>Patient characteristics

A total of 32 patients with high-grade gliomas were involved in the present study, of which 10 (31.3%) were female and 22 (68.7%) were male. The median age of cancer diagnosis was 54.9 years of age, with ages ranging from 25.2 to 77.8 years of age. Among the 31 patients who had a known type of surgery, biopsy was carried out on 16.1% (5/31) while the vast majority of patients underwent subtotal tumor excision (67.8%; 21/31). In our cohort, tumors were predominantly glioblastomas (81.2%; 26/32) whereas 12.5% (4/32) were grade 3 astrocytomas. Of 30 cancer patients with information on the tumor side, tumors were located in the right or in the left hemisphere with the same ratio (each 46.7%; 14/30) and two tumors in both hemispheres (6.6%; 2/30). All information is summarized in Table I.

 Genomic landscape of somatic variants in gliomas

In total, 96 variants (SNVs and InDels) were identified in 30 of the 32 (93.75%) tumors in 34 genes, namely APC, AR, ATM, ATRX, BARD1, BRCA1, CDKN2A, CHEK2, DICER1, EGFR, ERBB2, ERBB3, FANCA, FGFR1, FGFR2, IDH1, KEAP1, MLH1, MRE11, MSH2, NF1, NTRK1, NTRK3, RB1, PALB2, PIK3CA, POLD1, POLE, PTEN, RAD51D, STK11, TMPRSS2, TP53 and ZNF276. The number of identified variants per sample ranged from 1 to 14. Among the 30 tumors with variants, 6 (20%) had only one, 9 (30%) had two and 15 (50%) had three or more variants. Two tumor samples (6.25%; 2/32) demonstrated no variants in the targeted genomic regions. In total, the analysis revealed 38 pathogenic variants and 58 variants of unknown clinical significance (VUS) of which 5 were in-frame insertions/deletions. Among 38 pathogenic variants in 23 tumor samples in 13 genes, 19 were missense, 7 were frameshift, 6 were nonsense and 6 affected a conserved splice-site. TP53 mutations were the most prevalent (10/38; 26.3%), followed by PTEN (9) and IDH1 (4) (23.7 and 10.5% respectively). Four out of ten TP53 pathogenic variants were located at mutational hotspot codons 248 and 273. Notably, all the IDH1 mutations involved the p.(R132H) one. In EGFR, all pathogenic variants were located in the extracellular domain. The distribution of pathogenic variants per gene is shown in Fig. 2A.

Twenty-two out of 32 tumor samples (68.75%) showed evidence of structural variants (SVs) (Table SII). Overall, 33 SVs were identified of which the copy number deletions (53.2%; 17 out of 32 patients) were more frequent. Copy number amplifications involved 40.7% (13 out of 32 patients), followed by rearrangements (1 out of 32 patients; 3.1%) (Fig. S1A). Copy number deletions were identified in CDKN2A (46.9%; 15 out of 32 patients) and PTEN (9.4%; 3 out of 32 patients) whereas gene amplifications were identified most frequently in EGFR (31.3%; 10 out of 32 patients) followed by MYC (6.3%; 2 out of 32 patients), FGFR3 and KIT (3.1%; 1 out of 32 patients for each gene). Interestingly, a FGFR3-TACC3 rearrangement was also identified in one patient (1/32; 3.1%) concurrently with FGFR3 amplification (Fig. S1B). And also interestingly, 8 out of 22 patients with copy number alterations had concurrent CNAs, predominately CDKN2A deletions with EGFR amplifications (Fig. S1C).

Finally, it was noteworthy to mention that 15 out of the 32 tumors had both SNVs/Indels and SVs. The distribution of genetic alterations encompassing SNVs, InDels, and SVs per gene and per tumor is demonstrated in Fig. 2B.

 Comparison of mutational profiles derived from a comprehensive pan-cancer gene panel and EMO panel

To evaluate the newly implemented targeted sequencing panel, we compared the results from analysis of 14 tumor blocks genotyped with both the comprehensive pan-cancer and the MOL panels. Implementation of the comprehensive pan-cancer and the MOL panels resulted in the identification of 37 and 15 variants, respectively. Of those, 13 were common. Comprehensive pan-cancer panel identified 24 additional variants, 22 of which located in regions that were not targeted by the MOL panel. On the contrary, the MOL panel identified two additional variants. Of these, one was located in a non-targeted region by a comprehensive pan-cancer panel. Interestingly, six variants that were identified only by the comprehensive pan-cancer panel were pathogenic, whereas two variants identified only by the MOL panel were pathogenic.

Subsequently, 18 tumor blocks were genotyped with a comprehensive pan-cancer panel only. A total of 59 genetic variants were identified, of which 43 were located in regions targeted by the comprehensive pan-cancer panel only, while the remaining 16 variants were detected in regions by both panels. Notably, twenty-one of all detected variants were pathogenic.

 Associations between clinicopathological features and mutations in IDH1, PTEN and TP53 genes <sec> <title>IDH1 mutational status

Patients with IDH1 positive tumors were diagnosed with brain tumors at a statistically significant younger age than those with IDH1 wild-type tumors (34.4 vs. 56.3 years, P<0.05). Moreover, IDH1 mutational status was significantly associated with specific type of surgery (x2 P<0.05). The majority (75%; 3/4) of patients with IDH1 positive tumors carried out biopsy whereas 25% (1/4) underwent subtotal excision. In contrast, the majority (74.1%; 20/27) of patients with IDH1 wild-type tumors underwent subtotal surgery, while 18.5% (5/27) performed total excision and 7.4% (2/27) carried out biopsy. Additionally, half of the IDH1 positive tumors were glioblastomas, one was grade 3 astrocytomas and one was anaplastic oligodendroglioma. IDH1 mutant tumors were located either in the right or left hemisphere with the same proportion (50% each). This was also the case with the IDH1 wild-type tumors (46.2% each). All data are summarized in Table II.

 PTEN mutational status

Tumors with PTEN mutations (SNVs and InDels) were identified in older patients when compared to PTEN wild-type tumors (59.6 vs. 52.1 years) although this finding was not statistically significant. The vast majority (77.8%; 7/9) of patients with PTEN mutant tumors underwent subtotal surgery, while all involved glioblastomas. No associations were observed between PTEN mutational status and the side of the tumor (Table II).

 TP53 mutational status

Tumors with TP53 mutations were more frequently detected in younger patients, when compared to TP53 wild-type tumors (42.6 vs. 58.7 years). One third of patients (3/9; 33.3%) with TP53 positive tumors carried out biopsy. Most ofTP53 mutant tumors were glioblastomas (8/9; 88.9%). According to available information (n=30), five patients with TP53 mutant tumors developed left-sided and four right-sided tumors, respectively (Table II).

 Associations of the mutational status between IDH1, PTEN and TP53 genes

Wild-type TP53 was present in IDH1 wild-type tumors only (P<0.05). Similarly, wild-type TP53 was more frequently identified in PTEN wild-type than PTEN mutated tumors. Additionally, PTEN mutations did not co-occur with IDH1 mutations. All the data are summarized in Table III.

 Patient outcomes

Herein, was assessed whether having a mutant tumor on a specific gene had an impact on patients' overall survival (OS). Notably, all the patients received standard of care treatment. The median follow-up of patients from diagnosis until death or last contact was 19.2 months and during this time period 29 deaths (90.6%; 29/32) occurred and three patients were alive (9.4%; 3/32). Our analysis showed that patients with IDH1 mutant tumors had a statistically significant longer survival when compared to patients with IDH1 wild-type tumors, although due to small size, a confidence interval was not available (Fig. 3).

 Discussion

In the present study, we explored the genomic landscape of 32 tumor tissues from patients with high-grade gliomas by massively parallel sequencing using an 80 multigene panel. Subsequently, we examined the correlation between our results and patient outcomes as well as histopathological characteristics. Additionally, implementing two distinct panels on 14 common samples, we evaluated the molecular profiles of tumors.

Our findings showed that 96 genetic alterations (SNVS and InDels) were identified in the tumor samples. Of these, 38 were pathogenic and scattered among 13 distinct genes. IDH1, PTEN, and TP53 pathogenic variants exhibited high incidence in mutational spectrum, with TP53 being the most prevalent. These results were in line with those of other studies since the p53 pathway was deregulated in many cancers and these three genes, along with EGFR, were the most frequently altered genes in gliomas (26–29).

In the present study, the most frequently detected mutations at codons 248 and 273 of the TP53 gene were also found in TP53 positive tumors, but at a decreased frequency compared to other series (30,31). Additionally, among IDH1 mutated tumors, the p.(R132H) variant was solely found. Given that more than 90% of all IDH1 mutants have the present variant, this finding is consistent with other reports (32). Genetic alterations were also detected in genes that were involved in cell cycle regulation, DNA damage response pathways, the MAPK/PI3K pathway, the receptor tyrosine kinase pathway and telomere maintenance including ATRX, CDKN2A, NF1 andRB1. These genes could also serve as therapeutic targets for the design of a more individualized treatment protocol (33).

Furthermore, the use of the comprehensive pan-cancer panel allowed for the detection of structural variations. The identification of CDKN2A deletions and EGFR amplifications is consistent with previous research since both genetic changes are frequent in glioblastomas and have previously been linked to a poor prognosis (34). Additionally, an FGFR-TACC3 rearrangement was identified; these genomic events are present in approximately 2–3% of glioblastomas. Clinical studies and case reports have provided some preliminary evidence that suggests that this result could serve as an actionable therapeutic target in advanced solid tumors. Erdafitinib, a pan-FGFR tyrosine kinase inhibitor, produced one stable disease and two partial responses in three glioblastoma patients with FGFR-TACC3 positive tumors in two phase I studies (35,36).

We also assessed the extended panel's clinical usefulness and accuracy. We presented a comparison of the results from the tumor analysis using both the comprehensive pan-cancer panel and the MOL panel. We observed that there was a high concordance (86.7%; 13/15) between the two panels since 13 variants were common. It's noteworthy that a significant portion (22/24; 91.6%) of additional variants that were identified by the comprehensive pan-cancer panel occurred in regions that were not targeted by the MOL panel, emphasizing the necessity of implementing a broader panel with additional targeted regions.

In this study, a mutation in exon 14 of the EGFR gene was found in the tumor of a glioblastoma patient, using the comprehensive pan-cancer panel; this exon was not included in the MOL panel. Although numerous prior studies involving EGFR tyrosine kinase inhibitors have failed to demonstrate anti-tumor efficacy in tumors with EGFR mutations, there is still continuing research using anti EGFR-immunotherapy approaches, such as antibody-based strategies and vaccines (37,38).

Subsequently, variant calling parameters; such as relatively low variant allele frequency (VAF; ≤5%) and/or low base coverage (≤50%), explained why despite the fact that two genetic alterations although were targeted by the MOL panel, they were not detected and reported.

Moreover, a mutation in the LZTR1 gene's Kelch domain was only detected by the MOL panel due to the absence of the LZTR1 gene from the extended panel. This could be considered a limitation of the comprehensive pan-cancer panel, because it has been demonstrated that LZTR1 mutations facilitate glioma sphere development and self-renewal, these mutations could be considered as a potential therapeutic target for glioma (39,40). Additionally, the IDH1 p.(R132H) mutation was identified by the MOL panel but not by the comprehensive pan-cancer panel, although it was targeted by the latter, and sufficient quality control parameters were reached, ruling out the likelihood of decreased assay sensitivity. This disagreement may be explained by the fact that both assays used DNA that was isolated from two separate sets of FFPE sections from the same FFPE block, demonstrating intra-tumor heterogeneity (41,42). Finally, our findings showed that SVs could be identified using NGS data if the suitable NGS platform and multigene panel were implemented. This is essential for the comprehensive molecular characterization of tumor profiles.

Therefore, the implementation of a pan-cancer panel enables the identification of a larger number of mutations as well as rare genomic events, thus providing more therapeutic choices for patients with gliomas. Consequently, these findings may help with glioma patient diagnosis, prognosis, eligibility for clinical trial enrollment, and treatment as previously described (12,28,43).

The application of broad panel sequencing also provided insightful information about clinicopathological traits and patient outcomes. We found that IDH1 positive tumors were more frequently detected in younger patients, when compared to IDH1 wild-type tumors. Patients with IDH1 mutated tumors were characterized by better prognosis compared to patients with IDH1 wild-type tumors which is consistent with previous studies (44–46). Patients with IDH1 mutant tumors mainly underwent biopsies rather than tumor resections; that were more frequently diffused and cannot be completely removed.

Moreover, we observed that patients with TP53 positive tumors were more likely to be diagnosed with cancer at a younger age than patients with wild-type TP53 tumors, as expected (47). Interestingly, PTEN mutations were mutually exclusive with IDH1 mutations and wild-type TP53 tumors were presented in IDH1 wild-type tumors only (14,26,48). However, these data should be regarded with caution due to the small examined number of tumors.

All patients in our study have received standard-of-care and have long term follow up that reflects actual survival. This, gives our study a benefit in identifying prognostic molecular biomarkers for glioma patients.

These observations are consistent with other reports and established findings as mentioned above.

Furthermore, both platforms verified the tumor genotyping results for the common samples. This study also demonstrates the importance of collaboration between many institutions and clinics in the acquisition of tumor blocks, along with comprehensive clinical information, histology reports, and patient outcomes. On the other hand, there are certain restrictions with the present study.

First of all, the small sample size restricts the statistical power of our research, necessitating careful interpretation of the results. Second, tumors were chosen based on the quantity and quality of available tissue, thereby bringing selection bias into the study.

Overall, utilizing FFPE samples in clinical and research contexts, the use of a broader sequencing panel enables the simultaneous identification of several genetic alterations in a wide range of genes across gliomas, improving tumor molecular characterization and classification. The application of the new technology improves clinical outcomes by facilitating accurate molecular diagnosis as well as the identification of known and candidate prognostic biomarkers that could serve as possible therapeutic targets for the personalized treatment of glioma patients.

 Supplementary Material Supporting Data Supporting Data Acknowledgements

Not applicable.

 Availability of data and materials

We have submitted our data to the ‘Figshare’ Repository. They are available from Romanidou, Ourania (2022): Accession no. figshare. Dataset. Version 1 25.10.22, 13:15 first online date https://doi.org/10.6084/m9.figshare.21394077.v1 and the link https://doi.org/10.6084/m9.figshare.21394077.

 Authors' contributions

OR, PA, VK, GF and PCP conceptualized the study. KK, AA, CLo, CLe, AP and FF analyzed the data. KT, AE, KP and VK performed the experiments. OR, MI, EK, GK, GR, IX, GF and PCP acquired the data. KT, AP and FF validated the reproducibility of results. KT and KP designed the methodology of next generation sequencing. AA, ChrL and AP software development. AE, AA and ChaL curated the data. MI, EK and GK supervised the study. PCP was project administrator and acquired funding. OR, PA, AE and GF wrote the original draft. PA, KK, KT, AA, CLo, CLe, MI, EK, GK, KP, AP, GR, IX, FF, VK and PCP wrote or revised critically the manuscript. KP and AE confirm the authenticity of all the raw data. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

 Ethics approval and consent to participate

The study was approved by the Bioethics Committee of the Aristotle University of Thessaloniki School of Medicine (approval no. 2/February 4, 2015) and by Cyprus National Bioethics Committee (approval no. EEBK/EΠ/2016/54; 22/4/2021).

 Patient consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

 Abbreviations CNAs

copy number alterations

 CNS

central nervous system

 FFPE

formalin-fixed, paraffin-embedded

 HeCOG

Hellenic cooperative oncology group

 InDels

insertions and deletions

 MOL

laboratory of molecular oncology

 NGS

next generation sequencing

 OS

overall survival

 P/LP

pathogenic/likely pathogenic

 SNVs

single nucleotide variants

 SVs

structural variants

 VEP

variant effect predictor

 VUS

variants of unknown clinical significance

 WHO

World Health Organization

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An illustration of the study's design, demonstrating the patients' enrolment and analysis of samples at each stage. FFPE, formalin-fixed, paraffin-embedded.

Mutational profiles of gliomas tumors. (A) Distribution of mutations (SNVs and InDels) among cancer genes. (B) Map of SNVs/InDels and SVs per gene and per type and per tumor. Del, deletion; Dup, duplication; InDels, insertions and deletions; SNVs, single nucleotide variants; SVs, structural variants.

Kaplan-Meier Curve with respect to overall survival based on the mutational status of IDH1 gene in the entire cohort. IDH, isocitrate dehydrogenase; OS, overall survival; HR, hazard ratio.

Clinical and histopathological characteristics of patients diagnosed with gliomas.

Characteristic Value
Age at diagnosis, years
  Median (minimum-maximum) 54.9 (25.2-77.8)
Sex
  Female 31.3% (10/32)
  Male 68.7% (22/32)
Type of surgery
  Biopsy (<75% of the tumor) 16.1% (5/31)
  Subtotal (75–99% of the tumor) 67.8% (21/31)
  Total excision 16.1% (5/31)
  Unknown 1
Histology
  Grade 3 astrocytoma 12.5% (4/32)
  Glioblastoma 81.2% (26/32)
  Othera 6.3% (2/32)
Hemisphere
  Bilateral 6.6% (2/30)
  Left 46.7% (14/30)
  Right 46.7% (14/30)
  Unknown 2
Necrosis
  No 21.9% (7/32)
  Yes 78.1% (25/32)
Hemorrhage
  No 56.3% (18/32)
  Yes 43.7% (14/32)
Endothelial hyperplasia
  No 21.9% (7/32)
  Yes 78.1% (25/32)

Other: One glioblastoma tumor with oligodendroglioma elements and one anaplastic oligodendroglioma tumor. Values presented as Mean ± SD, Median (P25, P75), Median (min, max) or N (column %). Percentages were calculated with respect to the total number of patients with the known relevant information.

Associations between clinicopathological characteristics and the mutational status of several genes.

IDH1 mutation PTEN mutation TP53 mutation
Characteristic Eligible No Yes P-value No Yes P-value No Yes P-value
Age at diagnosis, years 32 56.3 34.4 0.003a 52.1 59.6 0.167 58.7 42.6 0.065
(40.3,77.8) (25.2,42.1) (25.2,77.8) (40.3,69.2) (40.3,77.8) (25.2,59.1)
Sex 32 0.28 0.87 0.66
  Female 10 (35.7) 0 (0.0) 7 (30.4) 3 (33.3) 7 (30.4) 3 (33.3)
  Male 18 (64.3) 4 (100.0) 16 (69.6) 6 (66.7) 16 (69.6) 6 (66.7)
Surgery specify 31 0.02a 0.49 0.57
  Biopsy (<75% of the tumor) 2 (7.4) 3 (75.0) 3 (13.6) 2 (22.2) 2 (8.7) 3 (33.3)
  Subtotal (75–99% of the tumor) 20 (74.1) 1 (25.0) 14 (63.6) 7 (77.8) 17 (73.9) 4 (44.4)
  Total excision 5 (18.5) 0 (0.0) 5 (22.7) 0 (0.0) 3 (13.0) 2 (22.2)
  Unknown 1 (4.3) 0 (0.0)
Histology 32 0.15 0.45 0.99
  Grade 3 astrocytoma 3 (10.7) 1 (25.0) 4 (17.4) 0 (0.0) 3 (13.0) 1 (11.1)
  Glioblastoma 24 (85.7) 2 (50.0) 17 (73.9) 9 (100.0) 18 (78.3) 8 (88.9)
  Other/Unknown 1 (3.6) 1 (25.0) 2 (8.7) 0 (0.0) 2 (8.7) 0 (0.0)
Hemisphere 30 0.99 0.83 0.83
  Bilateral 2 (7.7) 0 (0.0) 2 (9.1) 0 (0.0) 2 (9.5) 0 (0.0)
  Left 12 (46.2) 2 (50.0) 9 (40.9) 5 (62.5) 9 (42.9) 5 (55.6)
  Right 12 (46.2) 2 (50.0) 11 (50) 3 (37.5) 10 (47.6) 4 (44.4)
Necrosis 32 0.20 0.36 0.46
  No 5 (17.9) 2 (50.0) 6 (26.1) 1 (11.1) 6 (26.1) 1 (11.1)
  Yes 23 (82.1) 2 (50.0) 17 (73.9) 8 (88.9) 17 (73.9) 8 (88.9)
Hemorrhage 32 0.61 0.40 0.99
  No 15 (53.6) 3 (75.0) 14 (60.9) 4 (44.4) 13 (56.5) 5 (55.6)
  Yes 13 (46.4) 1 (25.0) 9 (39.1) 5 (55.6) 10 (43.5) 4 (44.4)
Endothelial hyperplasia 32 0.20 0.36 0.46
  No 5 (17.9) 2 (50.0) 6 (26.1) 1 (11.1) 6 (26.1) 1 (11.1)
  Yes 23 (82.1) 2 (50.0) 17 (73.9) 8 (88.9) 17 (73.9) 8 (88.9)

P<0.05. Percentages were calculated with respect to the total number of patients with the known relevant information

Associations of the mutational status between IDH1, PTEN and TP53.

A, IDH1 mutation
Mutation No Yes P-value
TP53 mutation 0.25
  No 22 (78.6) 2 (50.0)
  Yes 6 (21.4) 2 (50.0)
PTEN mutation 0.55
  No 20 (71.4) 4 (100.0)
  Yes 8 (28.6) 0 (0.0)
B, PTEN mutation
Mutation No Yes P-value
TP53 mutation 0.18
  No 15 (65.2) 8 (88.9)
  Yes 8 (34.8) 1 (11.1)
IDH1 mutation 0.54
  No 20 (87.0) 9 (100.0)
  Yes 3 (13.0) 0 (0.0)
C, TP53 mutation
Mutation No Yes P-value
PTEN mutation 0.18
  No 15 (65.2) 8 (88.9)
  Yes 8 (34.8) 1 (11.1)
IDH1 mutation 0.017a
  No 23 (100.0) 6 (66.7)
  Yes 0 (0.0) 3 (33.3)

P<0.05.