Prognostic impact of genetic alterations and methylation classes in meningiomas
Anna S. Berghoff 1 | Thomas Hielscher 2 | Gerda Ricken 3 | Julia Furtner 4 |
Daniel Schrimpf 5.6 | Georg Widhalm 7 | Ursula Rajky 1 | Christine Marosi 1 |
Johannes A. Hainfellner 3 | Andreas von Deimling 5.6 | Felix Sahm 5.6 | Matthias Preusser 1
1division of oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
2division of Biostatistes, German Cancer Research Center (DKFZ, Heidelberg, Germany
3institut of Neurology, Medical University of Vienna, Vienna, Austria
4department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
5Clinical Cooperation Unit Neuropathology, German Consortium Cancer, German Cancer Research Center, Heidelberg, Germany
6Deniment of Neuropathology, Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
7Deniment of Neurosurgery, Medical University of Vienna, Vienna, Austria
Correspondence
Matthias Preusser, Division of Oncology, Department of Medicine I, Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
Email: matthias.preusser@meduniwien.
ac.at
Funding Information
Else Kröner Fresenius Stiftung; Deutsche
Krebshilfe, Grant/Award Number:
70112956 and 70110983
Summary
Meningiomas are classified according to their histological characteristics, but genetic and epigenetic characteristics appear as relevant biomarkers for the prediction of results and can supplement histomorphological evaluation .
We have studied the relevant mutations for meningiomas, their correlation with DNA methylation groups and patient survival duration . Samples fixed in formalum and included in paraffin of 126 patients with meningiomas (Grade I of WHO 52/126; 41.3%; Grade II of WHO: 48/126; 38.1%; Grade III of WHO: 26/126; 20.6%) were studied.
We have analyzed the NF2, Traf7, KLF4, Arid, SMO, AKT, Promoter of TERT, PIK3CA and SUFU mutations using panel sequencing and correlated with the DNA (CM) of DNA determined using 850K Epic Arrays. The genotype of the Trakl mutation was characterized by the presence of one of the following mutations: Traf7, AKT1 and KLF4. Survival data, including progression -free survival (SSP) and overall survival (SG), have been extracted from the examination of the files.
Mutations were highlighted in 90/126 (71.4%) specimens, the most frequent changes being those of NF2 (39/126; 31.0%), Traf7 (39/126; 31.0%) and KLF4 (25/126; 19.8%). Two or more changes were observed in 35/126 (27.8%) specimens.
While TRAKL was mainly found in mild cm, NF2 was associated with clever CM (p <0.05). The genotype of the Traf7, KLF4 and Trakl mutation was associated with an improvement in the SSP and the SG (p <0.05). The methylation of the PERT promoter, intermediate and clever MC were associated with a decrease in SSP and SG (p <0.05).
The methylation group has shown better prognostic discrimination for the SSP and the SG (C-INENDEX 0.77/0.75) than each of the individual mutations (C-INENDEX 0.63/0.68).
In a multivariate analysis correcting age, sex, CM and the Grade WHO, none of the individual mutations, with the exception of Tert, has remained a significant independent prognostic factor for the SSP.
Molecular profiling including mutational analysis and DNA methylation classification can facilitate a more precise prognostic assessment and the identification of potential targets for personalized therapy in patients with meningioma.
KEYWORDS
meningioma, methylation classes, mutation, prognosis
1 | INTRODUCTION
Méningiomes are the most common primary intracranial tumors . Although the majority of cases have a benign clinical evolution, aggressive cases with an altered overall survival exist and require an adaptation of the therapeutic approach (1).
Diagnostic difficulties are frequent in meningiomas because the diversity of histological characteristics and biological behavior is a key characteristic. Different histological models can coexist within the same sample, calling into question the diagnostic interpretation and the prognostic evaluation which results from it as the basis of therapeutic approaches (2, 3).
Overall, the current edition of the WHO classification defines 15 different meningiomes subtypes:
- 9 variants of Grade I MENINGIOMS WHO which are on average associated with a slow growth rate and a benign biological behavior;
- 3 histological variants of MENINGIOMS from Grade II of the WHO, characterized by an increased risk of recurrence;
- 3 histological variants of MENINGIOMA from Grade III of WHO, which are associated with an aggressive clinical evolution and high recurrence rates (4).
If the prognostic role of the WHO classification for the prediction of the results is obvious on a cohort basis, individual patients can have a divergent clinical evolution of the classification. It is important to note that the rank of WHO is currently the basis of post-neurchurgeal therapeutic decisions : additional radiotherapy can be envisaged for higher grade meningiomas in order to prevent a local recurrence (1).
However, adjuvant radiation is associated with side effects and should only be applied if a risk of clinically relevant progression exists. Recently, several distinct studies have identified genetic alterations associated with the clinical evolution of meningiomas as a basis for a more precise diagnostic assessment (5).
Simple mutations of AKT1, Traf7, KLF4 and SMO as well as the Trakls mutation genotype (defined by the presence of one of the following elements: SMO, AKT1, KLF4, Traf7 or a combination of AKT1/ Traf7 or KLF4/ Traf7) have proven to be associated with clinical factors and generally occur in the meningomes Grade I of WHO (6, 7). While the mutations of AKT1 and SMO are associated with a rather altered progression survival in certain studies (8, 9), a study on the complete genotype of the Trakls mutation has shown that they were associated with a favorable progression survival (10).
The meningiomas with NF2 mutant are more likely to be atypical than the Méningiomes of the Trakls group (7, 8, 11). In addition, the incidence of changes from the promoter of Tert has proven to be higher in recurring and higher grade meningiomas, as well as associated with shorter progression survival (12, 13). Recently, these genetic aberrations have been correlated with methylation classes (CM) and a method of classification of tumors based on methylation as a basis for the diagnosis and future treatment of meningiomas was proposed (14-16). Here, we have studied the correlation between relevant mutations for meningiomas with CM and clinical evolution in a retrospective series of meningiomas.
Key points
- The meningiomas are heterogeneous in terms of prognosis, even within a given rank, requiring a therapeutic approach adapted to the prognosis.
- Molecular markers have been suggested to improve the accuracy of the prediction of the result, but the number of studies on DNA methylation is still limited and the reports on the prognostic role of changes are contradictory.
- We validate the association of relevant mutations for meningiomas as well as methylation classes with clinical parameters.
- The methylation classes, Traf7, KLF4, NF2, mutations of the Promoter of TERT, and the type of Trakl mutation were associated with progression -free survival.
-In order to assess the power of the markers offered so far, we have enriched our cohort, on the one hand, for the high-grade meningioma meningiomas (rank Oms II and III), because the impact of adjuvant radiotherapy is particularly controversial in these cases, and on the other hand, for the subtypes of Grade I of the WHO likely to host the genotype Trakls mutation.
2 | Methods
2.1 | Cohort of patients
Patients with a mastologically proven meningioma diagnosis were identified from Neuro-Bibank, Institute of Neurology, Medical University of Vienna. We have enriched our cohort, on the one hand, for high-grade meningiomas (Grade II and III of WHO) because the impact of adjuvant radiotherapy is particularly controversial among these, and on the other hand, for the subtypes of Grade I of WHO likely to host the genotype of Trakls mutation (6, 7, 17).
All specimens have been examined by a certified neuropathologist to confirm the histological diagnosis. The samples fixed in formalum and included in paraffin (FFPE) were examined macroscopically for sufficient quantity and under the microscope for tumor cell content. Clinical data, including histological diagnosis, WHO classification, progression and survival durations have been recovered by examining files.
The progression/ recurrence was defined on the basis of the written report of the radiology consultant and documented in the patient's file. The reassessment of images by magnetic resonance (MRI) was not possible because most patients received cranial MRI outside the center. The cranial discount was carried out 3 months after the surgical intervention, followed by another MRI 6 months later, then an MRI per year except in the event of the appearance of symptoms. If no recurrence or progression is obvious after 5 years, the interval between the stages is brought to 2 years. Only patients with full follow -up were included .
The study was approved by the local ethics committee of the University of Medicine in Vienna with the approval number 078/2004.
2.2 | Panel methylation and sequencing classes
The methylation analysis using the results of the Epic 850K (Illumina, San Diego, CA, USA) was available from a previous analysis and was carried out as described (14). In addition, the sequencing of the panel for the genes which we know has an impact on meningiomas, namely NF2, Traf7, KLF4, SMO, AKT1, the promoter Tert, Arid, SUFU and PIK3CA, was carried out according to the methods previously published (14). The bookstores were generated from an enrichment panel and sequenced on an illumina Nextseq 500 in fine pairs mode (12). All genetic variations of the exome or almost exome (splicing site) have been included, while the enthroned sequences, with the exception of the promoter TERT, and the polymorphisms whose incidence is> 1/100,000 in the databases have been excluded. Germinal DNA was not available. Mononucleotide variants and the small remaining insertions/deletions after these filtering criteria are subsequently called “mutation” in the text. The genotype of the Trakls mutation was defined by the presence of at least one of the following mutations: Traf7, AKT1, KLF4, or/and SMO (10). The changes from the promoter TERT C228T and C250T were combined in a single group. In addition, ARID1A, ARID1B and ARID2 mutations were combined in the Arid mutation group. See table S1 for detailed information on exact mutations. The sources data of this manuscript are not accessible to the public.
2.3 | Statistical analysis
The methylation classes were defined using an unopensed classification. It is important to note that the classes were available in a previous publication and not newly defined (14). Fisher's exact test was used to assess the differences between groups in categorical variables. Progressing survival (SSP) has been defined as the number of months elapsed between meningioma surgery and the radiological diagnosis of progression/recurrence or death, according to the first possibility. Patients were censored to the last information on progression. Global survival (SG) has been defined as time until death. Patients were censored for the latest information on survival. The distribution of survival times was estimated by the Kaplan-Meier method and the log-trip was used to compare the groups. The proportional risk model of COX has been for the univariable and multivariable analysis of the SSP and the SG. For each mutation, a multivariable model separated from COX has been adjusted taking into account the grade OMS, age, sex and methylation group. Firth correction was used in the event of a complete separation. The Harrell concordance index (C-INENDEX) was used to assess predictive discrimination. The values of 0.05 or less were considered significant. Due to the exploratory design and generating hypotheses of this study, no adjustment for multiple tests has been applied (18).
3 | RESULTS
3.1 | Characteristics of patients
One hundred and twenty-six meningiomas specimens of 126 patients [94/126 (74.6 %) women] whose median age was 59 years (interval 6-86 years) at the time of meningioma surgery were available for analysis. The median SSP was 27 months with 32 events. For the SG, the median duration of follow -up was 101 months with 27 deaths and a survival rate at 5 years of 83%. Out of 39 patients with grade 2 meningioma according to WHO, 27/39 (69.2%) had atypical meningioma and 12/39 (30.7%) presented other rare types of Grade II of WHO. The SSP (p = 0.890) and the SG (p = 0.150) did not differ between atypical meningiomas and the other rare types of Grade OMS II.
Table 1 lists the other characteristics of patients.
3.2 | Presence of mutations relevant to meningioma
Ninety of the 126 meningioma specimens (71.4%) had at least one relevant mutation for meningioma, while no mutation could be detected in 36/126 (28.6%) Méningioms specimens. The most frequently affected genes were NF2 (39/126; 30.9%) and Traf7 (39/126; 30.9%), followed by KLF4 (25/126; 19.8%) and one of the ARID genes (18/126; 14.3%). The mutations of the AKT1 genes (6/126; 4.8 %), of the Promoter TERT (4/126; 3.2 %), SUFU (2/126; 1.6 %) and PIK3CA (1/126; 0.8 %) have however rarely observed. SMO mutations were absent from the analyzed cohort (Figure 1A).
Two or more changes were highlighted in 40/126 (31.7%) specimens of meningiomas. Due to the absence of SMO mutations, we only included patients with one of the following mutations in the genotype of the Trakl mutation: Traf7, AKT1, KLF4 (10). The genotype of the Trakl mutation was highlighted in 42/126 (33.3 %). COOCCECTION OF TRAF4 and KLF4 mutations was the most frequently observed combination because all patients with KLF4 mutations also had a Traf4 mutation (p <0.001). The NF2 mutations were almost mutually exclusive with the genotype of the Trakl mutation, because a single patient had overlap (p <0.001).
Table 1 Characteristic of patients
Characteristic whole cohort (n = 126)
n %
Age of diagnosis, years (extended) 59.0 (6–86)
Gender
Man 32 25.4
Woman 94 74.6
Histology
Anaplastic meningioma 25 19.8
Atypical meningioma 36 28.6
Choroid meningioma 12 9.5
Secretory meningioma 24 19.0
Rhabdoid meningioma 1 0.8
Psammomatous meningioma 21 16.7
MENINGIOME Microocystic 3 2.4
Transitional meningioma 4 3.2
WHO classification
I 52 41.3
II 48 38.1
III 26 20.6
Location
Convexity 10 7.9
Basal 28 22.2
Front 21 16.7
Occipital 3 2.4
Posterior pit 6 4.8
Parietal 3 2.4
Temporal 3 2.4
Spinal 10 7.9
Missing 42 33.3
Progression/death (SSP events)
Yes 32 25.3
No 94 74.6
Median survival without progression,
month (extent)
27 (13–36)
Alive during the last follow -up
Yes 99 78.6
No 27 21.4
Median survival since meningioma 101 surgery (90-112)
month (extent)
3.3 | Correlation between relevant mutations for meningioma and methylation classes
The presence of mutations relevant to meningiomas has been correlated with methylation classes as described previously (14). The frequency of methylation classes in the current cohort is presented in Table 2 and Figure 1B.
The genotype of the Trakl mutation is significantly more frequently observed in mild (62.5%) CM than in intermediate (4.5%) or malignant (0%; p <0.001). KLF4 and Traf7 mutations were also more frequent in benign cm (39.1 %; 59.4 %) than in intermediate cm (0 %; 2.3 %) or malignant cm (0 %; 0 %; p <0.001). Consequently, the genotype of the Trakl mutation was more frequent in mild (62.5 %) CM than in intermediate (4.5 %) or malignant (0 %; p <0.001). NF2 mutations were significantly more frequently observed in malignant (50.0%) CM than in mild (18.8%) and intermediate (40.9%; p <0.001). In addition, the mutations of the PRETURTER TERT have been more frequently observed in malignant CM (11.1 %) than in mild cms (0 %) and intermediate CM (4.5 %; p <0.04). No significant association with CM and AKT1, the genotype of the Arid mutation, PIK3CA or the SUFU mutation was observed (p> 0.05).
3.4 | Correlation of relevant mutations in meningioma and the methylation class, on the one hand, and progression -free survival and global survival, on the other hand
All the mutations relevant to meningioma with sufficient prevalence have been tested for their association with progression -free survival and overall survival. In the univariable analysis the presence of a Traf7 and KLF4 mutation as well as the genotype of the Trakl mutation were associated with a better prognosis of SSP and SG at least 90 % and 95 %, respectively (p <0.05; Tables 3 and 4; Figures 2A-C and 3A-C).
The transfer of the promoter of NF2 and TERT was associated with a deterioration of the SSP and the prognosis of the SG, with a median SSP of 29 and 5 months, and SG levels at 5 years of 63 % and 25 % (p <0.05; tables 3 and 4; 2D, E and 3D figures). The methylation classes, the WHO classification and the age at the time of the diagnosis were associated with the SSP and the SG (Figures 2F and 3F; p <0.05).
The methylation group has shown better prognostic discrimination for the SSP and the SG (C-INDEX 0.77/0.75) than each of the individual mutations (C-INNEX 0.63/0.68; Tables 3 and 4). In addition, the methylation group has shown better prognostic discrimination for the SSP than a model based on the sequencing panel (trakl, nf2, tert; C-index 0.69; p = 0.052) but not for SG (C-INNEX 0.74; p> 0.05; Tables 3 and 4). Compared to the WHO classification, the methylation group showed a better prognosis for the SSP (0.77 vs. 0.69; p = 0.055) but not for the SG (0.75 vs. 0.76; p> 0.05; Tables 3 and 4). In the multivariable analysis only the mutation of the promoter of Tert (HR 4.34; 95 % CI 1.08-17.42; p = 0.04) but none of the other individual mutations remained an independent prognostic factor of the SSP after adjustment for age, sex, CM and grade WHO. In addition, none of the individual mutations has remained an independent prognostic factor for the SG after adjustment according to age, sex, the CM index and the WHO grade (p> 0.05). On the other hand, CM has always remained a significant prognostic factor for SSP and SG (p <0.05).
Table 2 methylation class and presence of relevant mutations for meningioma
Whole cohort (n = 126)
n %
Methylation class
CM Benin 64 51
CM intermediate 44 35
Cm smart 18 14
Relevant mutations for meningioma
NF2 39 30.9
Traf7 39 30.9
KLF4 25 19.8
ARID 18 14.3
AKT1 6 4.8
TERT 4 3.2 promoter
Pik3ca 1 0.8
SUFU 2 1.6
SMO 0 0
Table 3 Univauria regression analysis E COX and C-INENDEX for progression-free survival
Risk ratio 95% CI value P C-INNEX value P for comparison with cm
Methylation class 0.77
Benin Reference
Intermediary 6.25 1.92–10.76 <0.001
Malin 22.94 7.45–70.63 <0.001
KLF4 0.11 0.01–0.81 0.03 0.57 <0.001
Traf7 0.20 0.06–0.64 0.001 0.62 <0.001
NF2 1.98 0.98–3.99 0.06 0.59 <0.001
TERT promoter 12.13 3.32–44.30 <0.001 0.55 <0.001
Genotype TRAKL 0.19 0.06–0.63 0.01 0.63 <0.001
Arid Mutation 0.97 0.37–2.85 0.95 0.52 <0.001
AKT1 1.60 0.01–12.35 0.76 0.51 <0.001
Panel (trakl + nf2 + tert) 0.68 0.052
Age (by increase 10 years) 1.36 1.00–1.85 0.049 0.63 0.03
WHO Classification 0.69 0.055
I reference
II 1.07 0.40–2.85 0.90
II 4.71 2.01–11.06 <0.001
Table 4 Univauria regression analysis of COX and C-INDEX for overall survival
Risk ratio 95% CI value P C-INNEX value P for comparison with cm
Methylation class 0.75
Benin Reference
Intermediary 4.80 1.56–14.79 0.01
Malin 13.26 4.15–42.42 <0.001
KLF4 0.15 0.02–1.10 0.06 0.58 <0.001
Traf7 0.08 0.01–0.63 0.016 0.64 0.003
NF2 4.67 2.09–10.44 <0.001 0.68 0.23
Promoter Tert 5.45 1.62–18.33 0.01 0.55 <0.001
Genotype trakl 0.08 0.01–0.63 0.01 0.65 0.01
Change arid 1.03 0.36–3.00 0.95 0.50 <0.001
AKT1 0.44 0.00–3.12 0.51 0.52 <0.001
Panel (Trankl
+NF2+TERT) 0.74 0.86
Age (by edge 10
years of increase) 1.94 1.36–2.76 <0.001 0.71 0.53
WHO Classification 0.76 0.90
I reference
II 3.03 0.82–11.24 0.10
II 15.04 4.32–54.39 <0.001
See Figure 1,2 and 3 in the original document.
4 | DISCUSSION
Meningiomas be a clinical challenge in modern neuro-oncology, as the selection of patients is essential for planning a personalized treatment and adapted to the risk. Here, we validate that separate prognostic sub-groups can be defined by the presence of molecular drivers and methylation classes (14). Future clinical processing trials should take into account the inclusion of molecular information in order to study therapeutic potential in distinct meningioma subgroups.
Relevant mutations for meningioma were present in 90/126 (71.4%) specimens, including NF2, Traf7, KLF4, SMO, AKT1, the promoter Tert, ARID, SUFU and PIK3CA mutations in frequencies similar to those of previous studies (6, 11, 12, 14-21). In accordance with previous publications, we were able to validate the overlap of certain relevant mutations for meningiomas such as AKT1 and KLF4 with Traf7 mutations (19, 21). The Trakls mutation as well as the changes of the promoter of TERT, KLF4 and Traf7 presented in our cohort a statically significant association with the prognosis of survival, as was in previous independent cohorts (10-12). A recent 469 meningiomes study suggested a higher 22x recurrence rate in aggressive subgroups (NF2, PI3K, HH, Traf7) compared to others (KLF4, POLR2A, SMARCB1) (22). In addition, KLF4K mutations have been shown to lead to increased HIF paths as a new potential therapeutic therapy (23). This cohort also provided the strong association previously described between the KLF4/Traf7 mutations and the secretory subtype, while the association between the mutations of AKT1 or SMO with the location at the base of the skull and the meningothelial histology was not significant in our series, perhaps because of the limited number of affected cases. It is important to note that a complete panel of mutations is necessary to determine distinct genetic subgroups of meningioma, because some overlaps exist but are rarely mutually exclusive in a cohort containing meningiomas of grade I to III of the WHO (6, 10-12). In addition, we have been able to validate that the methylation classes are significantly correlated with the presence of mutations specific to meningiomas, as well as with clinical characteristics, including progression -free survival (14). Indeed, analysis of methylation classes is a promising method for the diagnosis of brain tumors in addition to routine histological analysis, as it could reveal certain important molecular alterations for prognosis (24). As expected, the rank of WHO was also associated with the survival duration in our cohort, thus stressing the importance of histological characteristics for the prognosis for prognostic evaluation. However, cooche competition of several histological characteristics in the same specimen can introduce a bias and inaccuracies (4, 25). Indeed, it has recently been demonstrated that the classification of WHO is suffering from sub-optimal inter-observer reproducibility and a low prognostic effect in the upper grade meningiomas (26). Genetic and epigenetic analysis could help give a more objective, reliable and reproductive prognostic assessment (5, 14).
We have selected high-grade meningiomas (WHO II and III) as well as less frequent histological subtypes because the impact of adjuvant radiotherapy is particularly controversial in this cohort with high recurrence rates, up to 39% -58% (1, 4). The ROAM/ EORTC-1308 study is currently trying to determine whether early adjuvant radiotherapy reduces the risk of tumor recurrence after a complete surgical resection of atypical meningioma (17). The WHO classification of meningiomas is currently the subject of discussions due to the wide range of clinical behavior observed from the MENINGIOMS of Grade I and II of WHO (1). Consequently, the expansion of prognosis work seems to be of particular interest in order to provide a stratification based on a molecular marker in future clinical trials. Indeed, the molecular characteristics including the mutations of relevant meningomes and the methylation classes could be used in future trials to redefine the populations of patients with a particular risk of local recurrence and allow a risk in meningioma in order to avoid both over- and subcontracting in a personalized context (5).
Although we have been able to validate the importance of relevant mutations for meningioma and their association with methylation classes and survival times, our data set must face certain limits. A considerable limitation is certainly that we have not been able to predefine the progression/recurrence uniformly. The data on the progression was recovered by a retrospective examination of the files and a central re-evaluation of the neuro-imagery was not possible. Due to the frequent realization of MRI images outside the center, only the written declaration was available, the original MRI was not available and, consequently, the guidelines for assessing the response could not be applied (27). However, our survival data benefit from the high patient membership in our centers, because no patient has been lost sight of.
Nevertheless, we wanted to contribute to the clarification of the role of trakl mutations and compare them with the previous results on the correlation between defined methylation classes and relevant mutations for meningioma. Here, we were able to validate the role of trakls mutations as being correlated with the result in our large set of independent data, but also detect the superior prognostic role of CM. Thus, the data supports the basis of the concept of “integrated” diagnostic as proposed in the revision of classifications of WHO 2016 for SNC tumors, also for meningioma (4). These results are added to the previous studies suggesting that the DNA methylation scheme as a predictor of the outcome of the meningiomas (15, 28, 29). Based on all the discoveries published previously, we were able to stratify six biological groups (CM Ben-1, 2, 3, Int-A, B, Evil) and three clinical CM combined (Benin, Intermediate, Malin) (14). In addition, unlike previous studies, we were able to correlate genetic alterations with particular methylation profiles, which allowed us to have a more complete overview of the molecular alterations at the origin of the recurrence of meningiomas. However, other studies are necessary to examine the value of the mutation or methylation classes relevant to meningiomas as a stratification factor in prospective clinical trials.
In conclusion, we have been able to validate the prognostic impact as well as the correlation with the clinical characteristics of the most frequent mutations relevant for meningioma, and correlated these markers with methylation classes, which could be used in future clinical trials for patient stratification.
THANKS
We thank Astrid Kovanda for his help in the allocation of clinical data and Maximilian Mair for his help in the preparation of the manuscript. The manuscript was supported by the research budget of the Medical University of Vienna and the University of Heidelberg, by German aid against cancer (70112956 and 70110983) and by the project "Translational Neuropathology" of the Else Kröner Fresenius Stiftung (EKFS) and the “Translational Neuropathology” project (2015_A60). FS is a member of the Else Kröner Excellence Programm of the Else Kröner-Fressenius Stiftung (EKFS).
Interest
Anna Sophie Berghoff has support for research from Daiichi Sankyo and fees for conferences, consultations, or participation in an advisory advice from Roche Bristol-Meyers Squibb, Merck, Daiichi Sankyo, as well as travel assistance from Roche, Amgen and Abbvie. Matthias Preusser has received fees for conferences, consultation, or participation in advisory council on the part of the following lucrative companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contast, Glaxosmithkline, Mundipharma, Roche, BMJ Journals, Medmedia, Astra Zeneca Abbvie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, Tocagen. & Dome, Tocagen. The following lucrative companies supported clinical trials and contract research carried out by Matthias Preusseer with payments carried out in his institution: Böhringer-Ingelheim, Bristol-Myers Squibb, Roche, Daiichi Sankyo, Merck Sharp & Dome, Novocure, Glaxosmithkline, Abbvie. All other authors do not report any conflict of interest regarding this publication publication. FS: speakers' Office Illumina, Agilent, Medac, Sab Abbvie.
Contributions from authors
Anna S. Berghoff: study design, data collection, data interpretation, manuscript writing, approval of the final version of the report. Thomas Hielscher: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. GERDA RICKEN: Data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Julia Furtner: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Daniel Schrimpf: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Georg Widhalm: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Ursula Rajky: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Christine Marosi: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Johannes A. Hainfellner: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Andreas von Deimling: data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Felix Sahm: design of the study, data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript. Matthias Preusser: design of the study, data collection, data interpretation, writing of the manuscript, approval of the final version of the manuscript.
Data availability declaration
The data is available on request from the corresponding author request.
Orcid
Anna S. Berghoff https://orcid.org/0000-0001-9379-6797
Matthias Preusser https://orcid.org/0000-0003-3541-2315
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Additional information
Additional information is available online in the “Additional Information” section.
Table S1 Detailed information of exact mutations in the Méningiomes analyzed cohort
How to quote this article: Berghoff AS, HielScher T, Ricken G, et al. Prognostic Impact of Genetic Alterations and Methylation Classes in Meningioma.
Brain Pathology. 2022; 32: E12970. https://doi.org/10.1111/bpa.12970
