Abstract 2346: Meningioma molecular classification predict response to surgical resection and adjuvant radiotherapy: An integrated clinicomolecular analysis
Bibliographic record
Abstract
Abstract Importance: Meningiomas are the most common primary brain tumor and a heterogeneous disease with a need to study response to surgery and adjuvant RT in the context of contemporary molecular classifications. Objective: The gold standard for treatment of symptomatic meningiomas has been surgical resection including complete removal of tumor and its surrounding dural margin, although the necessity of different degrees of marginal resection is controversial. Adjuvant radiotherapy (RT) is generally reserved for aggressive or subtotally resected (STR) meningiomas. However, uncertainty exists regarding optimal case selection for RT and response to treatment is variable. We aimed to examine the effects of treatment (surgery/RT), molecular, and clinical factors on meningiomas in the context of molecular classifications. Design, Setting, and Participants: In this retrospective analysis, a 20-year cohort of 2490 patients with meningiomas were assembled across 10 institutions. Molecular data (DNA methylation, RNA sequencing) was generated for 1645 cases. Propensity score matching (PSM) was performed for key baseline covariates including WHO grade and molecular classifications, mimicking a randomized, molecular pathology informed clinical trial. Main Outcome and Measures: Progression-free survival (PFS), and overall survival (OS) were assessed based on extent of resection (EOR), Simpson grade, and receipt of adjuvant RT. Results: Gross total resection (GTR) was associated with improved PFS across all molecular groups. The PFS benefit of GTR was lessened in molecularly-defined proliferative meningiomas (HR 0.69, 95%CI 0.50-0.95), but OS benefit was enhanced (HR 0.52, 95%CI 0.30-0.93). Surgically treating the dural margin (Simpson grade 1/2) was associated with improved PFS compared to removal of tumor alone (Simpson grade 3) particularly for immunogenic (HR 0.16, 95%CI 0.03-0.82) and NF2-wildtype meningiomas (HR 0.24, 95%CI 0.11-0.53). Adjuvant RT was associated with improved PFS across all molecular classifications. Molecular groups could reliably predict response to RT, even when controlling for WHO grade and EOR with immunogenic and NF2-wildtype meningiomas benefitting most while proliferative meningiomas are largely RT-resistant (HR 0.96, 95%CI 0.67-1.37). Conclusion: This study is the first to investigate interactions between contemporary molecular classifications and response to both surgical resection and adjuvant RT in a large, multi-institutional cohort of molecularly defined, clinically annotated meningioma cases. Citation Format: Justin Z. Wang, Alexander P. Landry, Vikas Patil, Andrew Ajisebutu, Christopher D. Wilson, Aaron Cohen-Gadol, Ghazaleh Tabatabai, Silky Chotai, Jill Barnholtz-Sloan, Rebecca Yakubov, Mark Wilson, Ramneet Kaloti, Alexander Rebchuk, Damian A. Almiron Bonnin, Eric C. Holland, Stephen Yip, Felix Sahm, Kenneth Aldape, The International Consortium on Meningiomas, Farshad Nassiri, Gelareh Zadeh. Meningioma molecular classification predict response to surgical resection and adjuvant radiotherapy: An integrated clinicomolecular analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2346.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".