Abstract 2789: Investigating the role of DNA methylation in pediatric choroid plexus tumors
Bibliographic record
Abstract
Abstract Choroid plexus tumors (CPTs) are rare neoplasms of the central nervous system most commonly found in the pediatric population. CPTs represent 1- 4% of all childhood brain tumors, with 10- 20% occurring during the first year of life. Within this family of tumors, choroid plexus carcinoma (CPC) is the malignant neoplasm which is categorized as a grade III tumor by the WHO. Choroid plexus papilloma (CPP) is a benign form classified as a grade I tumor, and atypical choroid plexus papilloma (aCPP) as a grade II tumor. Distinction between these tumor subtypes is essential for treatment stratification. Previous studies performed in our laboratory suggest that CPTs are highly unstable and harbor unique patterns of chromosome-wide gains and losses. To better understand the complexities of tumor biology of CPTs as well as to identify better molecular biomarkers to distinguish between aggressive and benign forms of CPTs we performed a genome-wide DNA methylation study using Illumina Human Methylation450 BeadChip. We analyzed genome-wide DNA methylation profiles from 34 CPT (14 CPCs, 5 aCPPs and 15 CPPs) samples. Differential DNA methylation analysis did not identify significant differences between aCPPs and CPPs, therefore we explored CPC-specific DNA methylation signature in comparison to CPPs. Using a median beta value difference of 0.3 or greater and an FDR adjusted p-value<0.05, we identified 3361 CpGs that showed significant difference in methylation between CPCs and CPPs or aCPPs. Two-way clustering performed using Pearson's correlation and average linkage for both the sample tree and the gene tree revealed segregation between the majority of CPCs and CPPs or aCPPs. Two main clusters were discovered within CPCs that were due to differences in TP53 mutation status. Pathway analysis on a 1328 gene set overlapping the 3361 CpGs using the IPA software revealed nine canonical pathways with GABA receptor on top of the list and several biofunction categories associated with cellular growth and proliferation that were significantly enriched in CPCs in comparison with CPPs or aCPPs. To identify minimal CPC specific signature, we applied a difference in DNA methylation of 40% and a p-value of 0.001. This increased statistical stringency led to the identification of 59 CpG sites encompassing 33 candidate genes. Of them, 3 genes were validated by pyrosequencing in the initial CPT cohort (n = 34) and a new replication cohort of n = 23 CPT samples. Next, we tested the sensitivity of the CPC specific DNA methylation signature against DNA methylation profiles of several brain tumor datasets extracted from GEO database and found that CPC-DNA methylation signature was highly specific. Our data suggest that dysregulation of epigenetic mechanisms contribute to the molecular events leading to tumor development and progression in CPC and that our DNA methylation based biomarker signature can have prognostic value for this disease and enable better treatment strategies. Citation Format: Malgorzata Pienkowska, Sanaa Choufani, Andrei Turinsky, Diana Merino, Ana Novokmet, Michael Brudno, Rosanna Weksberg, Adam Shlien, Cynthia Hawkins, Eric Bouffet, Uri Tabori, Richard Gilbertson, David Malkin. Investigating the role of DNA methylation in pediatric choroid plexus tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2789.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".