Abstract 841: Meningioma subgroups associated with functional genomic elements defined by DNA methylation
Bibliographic record
Abstract
Abstract Although most meningioma are non-malignant, there is a high recurrence rate among atypical and anaplastic (malignant) meningiomas (grades II/III). In addition, malignant meningioma usually progresses after treatment. Recently, based on DNA methylation, two subgroups of meningioma were described with recurrence-free survival differences. Epigenetic deregulation at distinct genomic elements can affect changes in gene expression and alter the transcriptional profile of the cancer cells. We seek to understand the mechanisms of meningioma recurrence and progression after initial treatment. In order to address this, we will use DNA methylation data to identify candidate noncoding elements and their connection with genes that might explain differences in meningioma prognostic subgroups. Using published DNA methylation data we compared favorable and unfavorable meningioma subgroups and identified 3,045 differentially methylated probes (p< 0.0001, difference mean-methylation beta-value> 0.2). Focusing on probes within known functional genomics, we identified 18 highly conserved genomic enhancers known to be activated in cancer that can potentially drive meningioma recurrence. We next investigated links between these enhancers and their targeted genes by incorporating GeneHancer annotation. We found that the unfavorable subgroup of meningiomas presented hypomethylation within enhancer regions that have the potential to target PARK7, ARID4B, and FBH1. ARID4B was previously shown to be highly active in high-grade meningiomas. We also identified 16 enhancer regions that overlap known prognostic cancer enhancer, previously identified in other tumor types. Our findings were validated in independent cohort comprised by public and unpublished DNA methylation datasets. Our preliminary results are the first to suggest that DNA methylation changes can be used to identify noncoding regions associated with meningioma prognosis. Identification of noncoding regions associated with meningioma recurrence will provide knowledge of the role of epigenomics in the development of malignant meningioma and of opportunities for targeted therapy. Citation Format: Tathiane M. Malta, James Snyder, Michael Wells, Ana deCarvalho, Laila Poisson, Camila Souza, Gelareh Zadeh, Kenneth Aldape, Daniela Tirapelli, Carlos Carlotti, Yan Lee, Steven Kalkanis, Tobias Walbert, Houtan Noushmehr. Meningioma subgroups associated with functional genomic elements defined by DNA methylation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 841.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".