Notice bibliographique
Résumé
The most common tumors seen in daily neuro-oncology practice are meningiomas, which account for more than one-third of all primary CNS tumors.1 The majority of meningiomas are WHO grade I (∼80%), which are generally noninvasive, localized, and slowly growing tumors, followed by atypical meningiomas (WHO grade II; 15%–20%), and malignant meningiomas (WHO grade III; 1%–4%).2 Meningiomas show heterogeneous histology in addition to a variable clinical course, even in benign WHO grade I meningiomas.2 Grade I meningiomas occasionally exhibit signs of recurrence, which cannot be explained by histologic features alone, and this phenomenon is well-recognized in clinical practice. The treatment of a symptomatic and growing meningioma is primarily surgical resection, and the likelihood of recurrence is directly related to the extent of surgical resection. Radiotherapy is a therapeutic option for recurrent, grade II and III meningiomas,3–6 while chemotherapy presently has a minimal role in the management of meningiomas.7,8 The genomic and molecular landscape of meningiomas has received some attention, with interest in understanding the biology of these tumors raising hope to inform therapeutic targets. A variety of chromosomal alterations have been described, with deletions of chromosomes 1 and 14 having independent prognostic value for prediction of relapse-free survival.9 The tumor suppressor gene, neurofibromin 2 (NF2) located on chromosome 22, is mutated or lost in a substantial proportion of cases. Corroborating this biology is the propensity of neurofibromatosis type 2 patients to be diagnosed with meningiomas. The availability of next-generation sequencing has allowed more complete investigations of the genomic makeup of meningiomas in a number of recent studies.10,11 Using exome-sequencing, mutations in TRAF7, KLF4, SMO, and AKT1 were identified in addition to NF2. Some clinicopathologic correlations were observed, with SMO and AKT1 mutations enriched in skull base meningiomas, which were more likely to be grade I. Phosphatidylinositol 3-kinase is composed of an 85 kDa regulatory subunit and a 110 kDa catalytic subunit. PIK3CA represents the catalytic subunit, utilizing ATP to phosphorylate phosphatidylinositols. Mutations in PIK3CA have been identified in a variety of cancers, including gliomas.12–14 Mutations in PIK3CA activate PI3 kinase, and hotspot mutations in PIK3CA that are common and lead to constitutive activation include H1047R, E542K, and E545K. The role of PIK3CA mutations in meningioma have been implicated in tumor progression but are not clearly established. A prior study reported point mutations in the kinase domain (exon20) in 2 cases of atypical and malignant meningiomas.15 A third case of point mutation in PIK3CA was reported in a patient with malignant meningioma.16 In the study published in this issue of Neuro-Oncology by Abedalthagafi et al, 150 meningiomas, including 104 WHO grade I, 41 grade II, and 5 grade III meningiomas were studied using high-resolution array-comparative genomic hybridization (aCGH), and well as screening for mutations in AKT1, KLF4, NF2, PIK3CA, SMO, and TRAF7. Of the 150 samples, 55 were NF2- mutated, and a total of 85 samples showed loss of chromosome 22. As described previously by other groups, mutually exclusive events between NF2 mutations and mutations in AKT1, KLF4, SMO, and TRAF7. Among the 95 NF2–wild type samples, 23 had mutations in TRAF7, 10 cases had KLF4 (K409Q) mutations, 9 had AKT1(E17K) mutations, and 6 showed mutations in SMO. New to this study was the characterization of PIK3CA mutations in the context of these previously established alterations. Overall PIK3CA mutations were found in 7 cases; 4.7% of all and 7.4% of non-NF2 meningioma cases. The PIK3CA mutations included 3 H1047R and 1 E545K as well as additional mutations. Notably, PIK3CA mutations were mutually exclusive of mutations in AKT1 and SMO but coexisted with TRAF7 mutations. PIK3CA-mutated tumors tended to be grade I and occurred in the skull base. The authors also examined common copy number alterations (CNAs) in meningioma, and evaluated these by using a “cytogenetic abnormality score” (CAS), based on a sum of the number of CNAs in the individual tumor. All or most of the cases with mutations in AKT1, SMO, and PIK3CA had a CAS of 0, indicating a “quiet” genome. Interestingly, one of the PIK3CA-mutant tumors was an outlier, in that it did not arise from the skull base, showed some aggressive histologic features, and also exhibited numerous CNAs commonly found in WHO grade II meningiomas including losses of 1p 10, 14, 18, and 2 with a CAS equal to 5, which suggested a distinct pathobiology compared with the majority of PIK3CA-mutated tumors. Combining the PIK3CA- and AKT1-mutant tumors together, activating alterations of the PI3K signaling pathway were seen in 16 of the 95 tumors in which an NF2 mutation was not present. This study is the first to focus on how a key relevant and potentially targetable growth signaling pathway correlates with the mutational landscape and grade of meningiomas. Current investigations are underway to target SMO and NF2 mutations in meningioma, and this paper provides additional evidence that perhaps PI3kinase, a known oncogene, can be used as a target for meningioma. The authors correctly point to the future by suggesting the possibility of blood-based detection of PIK3CA mutations in meningioma, which could potentially lead to the possibility for neoadjuvant therapy, specifically in difficult-to -approach skull base regions that could potentially reduce morbidity from surgery and/or radiotherapy for these lesions. As with most contributory studies, this work raises new questions that will build upon these findings. What are the mutations and genomic alterations that contribute to WHO malignancy grade, tumor recurrence, and resistance to radiotherapy? Can targeting PIK3CA in radiation-refractory cases prevent or delay tumor recurrence? What is the relation of PIK3CA mutation and other mutations with previously described DNA CNAs and additional molecular changes exhibited by these tumors? With the increased interest in meningiomas spurred by this and other groups, elucidation of new possibilities for therapeutic approaches are likely to be unveiled in the coming years.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,013 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».