Abstract 2505: Predicting prognosis in PFA ependymoma and group 3/4 medulloblastoma methylomes using interpretable epigenetic models
Bibliographic record
Abstract
Abstract Introduction: Our goal is to develop interpretable, prognostic models for Group 3/4 medulloblastoma (G3/4 MB) and PFA ependymomas (PFA-EP) using brain tumor DNA methylomes. Our models use prior knowledge of genome regulation and may identify cellular processes that help develop molecular therapies. We previously developed a classifier algorithm, netDx, which uses similarity networks to integrate heterogeneous patient data and predict outcome1. netDx demonstrates excellent performance and allows measures to be grouped into biologically meaningful features (e.g. grouping genes into pathway features). Ependymoma Methods: We predicted survival in PFA-EP using 569 methylomes2 (Illumina 450K). Using netDx, we evaluated a model that grouped CpG-level methylation into sets reflecting 25 cell types from the developing human cerebellum, 3 chromatin states, EZH2 binding sites, and brain super enhancers (80:20 train/test split, feature selection >=8/10; 10 splits). This model was compared to a baseline lacking prior knowledge. Preliminary Results: Organizing methylomes by prior knowledge significantly improves prognostic prediction (Table 1, p < 5x10-3, one-sided WMW). Features that predict prognosis are consistent with known dysregulation in PFA-EP2 (Table 1). G3/4 MB Methods: We predicted binarized survival using 285 methylomes3 (Illumina 450K), using identical methods as above. Preliminary results: Tumor methylomes carry predictive signal for survival prediction (Table 1), consistent with previous findings. We are currently evaluating the effect of including prior knowledge. Interim Conclusion: Prior knowledge can improve survival prediction in PFA-EP and identifies features reflecting tumor biology. We are interested in extending interpretable modeling to other tumours.1. Pai et al. (2019) Mol Sys Biol. 15. 2. Pajtler et al. (2018) Acta Neuropathol. 136. 3. Northcott et al. (2017) Nature 547. Table 1. Average model performance and top features. Dataset Predictor design AUPR (mean+/- SD, 10 train/test splits) Predictive features PFA-EP; 252 good/317 poor survivors2 DNAm, no prior knowledge 64.2 +/- 2.3 n/a * Covariates: CXOrf67 mutation status, sex DNAm, prior knowledge* 67.5 +/- 3.5(p < 5x10-3; one-sided WMW test compared to baseline model) CXOrf67mut, methylation in: {H3K9me3 sites; marker genes for ependymal cells of choroid plexus; marker genes for multiple interneuron classes} G3/4 MB: 146 good/139 poor survivors3 DNAm, no prior knowledge 0.61 +/- 6.3 n/a ** Covariates: age, sex DNAm, prior knowledge** In progress Citation Format: Indy Ng, Alexander Fricke, Shraddha Pai. Predicting prognosis in PFA ependymoma and group 3/4 medulloblastoma methylomes using interpretable epigenetic models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2505.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".