Molecular classification predicts response to surgery and radiotherapy
Notice bibliographique
Résumé
Meningiomas are the most common primary brain tumor in adults. Surgery is the first-line treatment for large or symptomatic tumors.1,2 Despite their presumed “benign” designation, some tumors can recur early after aggressive surgical resection. Radiation therapy is sometimes used to control the growth of tumors for a subset of patients, though again there remains a critical group of patients that do not respond to repeated treatments leading to considerable morbidity and sometimes even early mortality. This heterogeneity in outcome is not entirely captured by the traditional standard-of-care World Health Organization (WHO) classification and grading system. As a result, there has been a tremendous effort to better understand the biology of meningiomas, to improve classification, and to identify clinically relevant biomarkers that can guide management for patients. Many groups have independently developed molecular classification and prognostic systems that improve prognostication beyond the standard of care WHO grade.3–10 We previously described 4 consensus molecular groups of meningioma that had distinct biology and divergent clinical outcomes: Immunogenic (MG1) and NF2-wildtype (MG2) meningiomas with favorable outcomes as well as Hypermetabolic (MG3) and Proliferative (MG4) meningiomas that have a shorter time to recurrence after resection.3 Others have also described similar groups of meningiomas shortly thereafter.5 While the molecular descriptors of meningioma have led to an improved understanding of biology with the resolution of some heterogeneity in outcomes, the impact of surgical resection and radiation therapy in the context of molecular classification remained unclear. A study published in Nature Medicine by Wang et al. aimed to address these gaps by classifying over 1600 meningiomas into all existing major molecular classification and prognostic systems in addition to 2021 WHO grade (which incorporates CDKN2A/B homozygous deletion and TERTp mutation as molecular criteria for WHO grade 3 meningiomas) to evaluate the role of surgical resection and radiotherapy in the context of molecular biomarkers.11 Propensity-score matching was performed when comparing treatment effects to account for key baseline differences between meningiomas that may affect the extent of resection or outcome after radiation therapy. Several important findings emerged from this study. Firstly, although gross total resection remains paramount in all meningiomas regardless of molecular group, its benefits in delaying progression or recurrence are somewhat tempered in the most biologically aggressive Proliferative meningiomas. However, and importantly, complete surgical resection in the same aggressive Proliferative meningioma appears to provide a significant overall survival benefit. Resection of dural attachments along with the tumor itself (ie, Simpson grade 1 resection) has historically been considered to be the gold standard for complete resection. However, its modern relevance has been called into question given the fact that these areas may not always contain neoplastic tumor cells and inclusion of the dural tail in radiotherapy treatment plans does not consistently improve tumor control. This study found that regardless of molecular classification, additive dural treatment (either dural resection or thermocoagulation) was associated with improved progression-free survival compared to no treatment of the dural attachment (Simpson grade 3). However, there was no significant difference between whether the dura was excised (Simpson grade 1) or simply thermocoagulated in situ (Simpson grade 2), suggesting that perhaps the latter, less aggressive strategy could similarly deplete neoplastic meningioma cells in the tumor-adjacent dura. Lastly, perhaps the most important discovery by Wang et al.11 is that the investigators found that the addition of adjuvant radiotherapy (RT) improved outcomes in molecularly defined Immunogenic, Neurofibromatosis-2 (NF2)-wildtype, and Hypermetabolic meningiomas, but not in Proliferative cases, suggesting these latter cases may be RT-resistant. This is an important finding as radiation is often reserved to control aggressive tumors, and Proliferative meningiomas were identified in tumors from each WHO grade. Importantly, these findings were consistent when prospective RT-treated cases from the RTOG-0539 phase 2 clinical trial were reclassified into molecular groups and matched to retrospective control cases. These RT-treated cases from the RTOG-0539 clinical trial were then used to discover DNA methylation and gene expression signatures of RT-response. These signatures were utilized to build molecular predictive models that were specific to RT-response, setting it apart from other prognostic models of overall outcome.10 The predictive models were superior to standard classifications used today to estimate response to treatment, and as with meningioma classification, the incorporation of multiple molecular platforms, in this case, DNA methylation and gene expression, improved the predictive accuracy of progression-free survival (PFS) post-RT over either platform alone. These findings and their implications for meningioma treatment should be interpreted in the context of their limitations. Although the investigators leveraged propensity-score matching to balance baseline covariates between treatment groups to mimic a randomized clinical trial, most cases utilized in this study were retrospective cases collected across multiple institutions over decades. Furthermore, reporting of Simpson grade is highly reliant on the treating surgeon as radiography review alone will not be able to differentiate between Simpson grade 2 and 3 resections, or even Simpson grade 1 and 2 resections. Further validation in prospective cohorts with standardized reporting and classification, including for trials that may utilize more advanced imaging (eg, defining extent of resection (EOR) using 177Lu-DOTATATE PET) to better define the extent of resection, can be helpful. On the RT side, the predictive RT models in this study were built and trained using the uniformly treated, prospective RTOG-0539 meningiomas, and then validated on retrospective RT-treated meningiomas. Further validation using prospective RT-treated cases such as those from the ongoing EORTC-1309 (Radiation versus Observation following surgical resection of Atypical Meningioma (ROAM) trial) and NRG BN-003 may be helpful for iterative improvement in performance as needed. For now, however, these findings provide evidence for how a unified molecular taxonomy for meningioma can meaningfully define treatment responses following surgery and in select cases, receipt of adjuvant RT and calls for the design of future molecularly informed clinical trials, particularly for RT-resistant Proliferative meningiomas.
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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».