Abstract B71: Molecular heterogeneity and novel oncogenic fusions in <i>RELA-</i> and <i>YAP1-</i>negative supratentorial ependymoma
Bibliographic record
Abstract
Abstract Introduction: One of the DNA methylation-based molecular subgroups of supratentorial ependymoma (ST-EPN), designated ST-EPN-RELA, mostly harbors fusions of the uncharacterized gene C11orf95 and RELA (ST-EPN-RELA). Rarely, no C11orf95-RELA fusion is detected in tumors predicted to belong to the ST-EPN-RELA group. With this study we aimed to refine the molecular classification of ST-EPN and to identify alternative oncogenic mechanisms in the absence of a classic fusion type. Methods and Materials: In an unbiased approach, t-Distributed Stochastic Neighbor Embedding was applied to 53,468 DNA methylation profiles from brain tumors, other cancer types, and control tissues. Only samples clustering with a reference set of ST-EPN-RELA were selected for further analyses (n=614), including RNA- and/or DNA-panel sequencing, histopathologic reevaluation, and immunohistochemistry for L1CAM. Fusions were validated using RT-PCR on total RNA and Sanger sequencing. Clinical data were analyzed retrospectively for 150 patients. Results: We identified one large and four satellite clusters. The large cluster (n=479; designated ST-EPN-RELA 1) and one of the satellite clusters (n=12; ST-EPN-RELA 2) predominantly contained samples with a calibrated score ≥ 0.9 for ST-EPN-RELA based on the current version of the Heidelberg Brain Tumor Classifier. Samples of the three other satellite clusters (n=41, n=17, and n=25 samples) contained 65.9%, 88.2%, and 96.0% of samples with a calibrated score < 0.9 for any methylation class, and were thus predicted as unclassifiable. These clusters were provisionally designated ST-EPN-RELA-like A, B, and C, and initial histologic diagnoses showed a wide spectrum of rare morphologies beside EPN, e.g., sarcoma and teratoma. Within clusters ST-EPN-RELA-like A and C, sequencing revealed fusions of C11orf95 with different partner genes, including MAML2 (n=14), MAML3 (n=2), and NCOA2 (n=7), while ST-EPN-RELA-like B included classic C11orf95-RELA fusions (n=11) in samples with initial diagnoses other than EPN. Copy number variation analysis showed clear differences between the clusters. L1CAM-positivity was observed in all groups. Within the cluster ST-EPN-RELA 1, samples separated according to fusion types, 1 versus 2/3. Analysis of clinical data showed significant differences in overall survival between cases with confirmed C11orf95-RELA fusion type 1 (n=25, median OS=88 months) and type 2/3 (n=20, median OS=67 months). Clinical data collection for the satellite clusters is currently ongoing. Conclusion: Molecular refinement of ST-EPN-RELA revealed novel subgroups harboring fusions of C11orf95 with numerous fusion partners different from RELA, which will be included in the next update of the Heidelberg Classifier. Preliminary analysis suggests differences in clinical outcome related to the fusion type. Findings of this study will improve diagnostic accuracy and clinical management and need to be considered when developing targeted treatment strategies against ST-EPN. Citation Format: D.R. Ghasemi, K. Okonechnikov, A. Korshunov, M. Sill, T. Zheng, J.M. Huebner, K.K. Maass, J. Benzel, M. Snuderl, J. Gojo, U. Schüller, N.U. Gerber, I. Stoler, P. Hernáiz-Driever, T. Milde, D. Sturm, R. Chapman, R.G. Grundy, A. von Deimling, D. Kawauchi, D.T.W. Jones, M. Kool, S.M. Pfister, F. Sahm, K.W. Pajtler. Molecular heterogeneity and novel oncogenic fusions in RELA- and YAP1-negative supratentorial ependymoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B71.
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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) | 0.001 | 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".