Abstract 4591: Whole genome sequencing of SHH medulloblastomas predicts molecular groups of responders and non-responders to SMO-inhibition .
Bibliographic record
Abstract
Abstract Recently, we and others analyzed large series of pediatric medulloblastomas (MB) and identified a number of recurrent aberrations in each of the four major subgroups (WNT, SHH, Group 3 and Group 4). However, due to their infrequent occurrence not much is known about genetic aberrations in adult MBs. We now have analyzed a large series of adult SHH MBs (age ≥ 16) by whole genome sequencing and compared them with pediatric SHH MBs. We sequenced tumor and blood of 26 adult SHH MBs and compared them with 30 pediatric SHH MBs. A replication cohort of 28 adult and 29 pediatric SHH MBs was sequenced for 414 genes by targeted sequencing. We also performed expression profiling and DNA methylation analyses on most of these cases. Whole genome sequencing revealed a clear correlation between mutation rate and age of the patient (r = 0.69). Furthermore, the genomic data showed that SHH MBs can be split up in three distinct subgroups: infants, children and adults. They all have mutations in the SHH pathway, mostly in PTCH1 (infants and adults), SUFU (infants) and SMO (adults). Children almost completely lack the classical upstream SHH pathway mutations but instead have more downstream aberrations such as MYCN and GLI2 mplifications, and have frequent TP53 mutations, often in the germline, whilst all of these were extraordinarily rare in infants and adults. We also identified several recurrent mutations in adult MBs that were not found in pediatric MBs. Moreover, the three SHH subgroups were different in their transcriptome and methylome with the TP53 mutated SHH MBs in children being more similar to adult MBs. Collectively, this data will help to better understand the biology of pediatric and adult SHH-medulloblastoma, but most importantly may help selecting patients for targeted SHH inhibition and new treatment strategies for TP53 mutated SHH MBs in a clinical setting. Citation Format: Marcel Kool, David TW Jones, Natalie Jaeger, Paul A. Northcott, Volker Hovestadt, Ulrich Schueller, Marc Remke, Yoon-Jae Cho, Scott Pomeroy, Michael D. Taylor, Roland Eils, Andrey Korshunov, Peter Lichter, Stefan M. Pfister. Whole genome sequencing of SHH medulloblastomas predicts molecular groups of responders and non-responders to SMO-inhibition . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4591. doi:10.1158/1538-7445.AM2013-4591
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".