Abstract 3687: An integrative genomics approach identifies distinct molecular and epigenetic subgroups of pediatric glioblastoma
Bibliographic record
Abstract
Abstract Pediatric glioblastoma (GBM) belongs to the comparably small group of childhood malignancies for which cure is still an exception. Histologically indistinguishable from their adult counterparts, they carry a similar dismal prognosis. Whereas genetic and epigenetic properties have been extensively studied in adult tumors, little is known about the molecular characteristics of pediatric GBM, although some reports indicate that it is likely a different entity in terms of tumor biology and molecular genetics. Thus, this study aimed to elucidate disease-defining molecular lesions by determining genomic, transcriptomic and epigenetic alteration profiles. Using an integrative genomics approach combining multiple screening strategies, we investigated primary tumor samples from 55 childhood GBM for copy-number aberrations (CNA), transcriptomic and epigenetic changes, complemented by sequencing analysis of TP53, IDH1/2 and further candidate genes. Methylome analysis revealed the existence of five separate clusters of childhood GBM with distinct molecular and clinico-pathological features. Methylation patterns correlated with novel recurrent, subgroup-specific driver mutations unique to the pediatric population, and with clearly distinguishable transcriptomic profiles. Integration of methylation and gene expression data suggested that different tumor subgroups are derived from at least two distinct precursor-cell populations, one of them without any signs of neural lineage commitment. Furthermore, distinct clusters were highly associated with the presence of balanced (13%) or aneuploid (33%) genomic profiles or with cases displaying highly-rearranged genomes (11%), or various high-level focal amplifications (43%) of known and novel oncogenes. Similar to adults, CNA frequently targeted GBM core signaling pathways such as RTK/PI3K, p53 and RB signaling. TP53 loss-of-function mutations were present in 46% of pediatric GBM. IDH1 mutations were detected in only six patients (11%), but these tumors displayed concerted hypermethylation at a large number of loci, resembling a CpG island methylator phenotype (CIMP). Relevant findings are being validated by immunohistochemistry or FISH analysis in an independent, large-scale cohort representing 130 uniformly-treated pediatric GBM. This study, one of the largest cohorts of pediatric GBM investigated for molecular alterations to date, describes frequent genetic and epigenetic features of this devastating disease and further emphasizes and differences between adult and pediatric GBM. The identification of distinct molecular subgroups and commonly altered pathways will help to characterize molecular biomarkers for improved prognostic assessment and risk-adapted treatment stratification, and may facilitate the development of suitable in vitro and in vivo models for defining novel therapeutic strategies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3687. doi:1538-7445.AM2012-3687
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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".