Genomics Identifies Medulloblastoma Subgroups That Are Enriched for Specific Genetic Alterations
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Traditional genetic approaches to identify gene mutations in cancer are expensive and laborious. Nonetheless, if we are to avoid rejecting effective molecular targeted therapies, we must test these drugs in patients whose tumors harbor mutations in the drug target. We hypothesized that gene expression profiling might be a more rapid and cost-effective method of identifying tumors that contain specific genetic abnormalities. MATERIALS AND METHODS: Gene expression profiles of 46 samples of medulloblastoma were generated using the U133av2 Affymetrix oligonucleotide array and validated using real-time reverse transcriptase polymerase chain reaction (RT-PCR) and immunohistochemistry. Genetic abnormalities were confirmed using fluorescence in situ hybridization (FISH) and direct sequencing. RESULTS: Unsupervised analysis of gene expression profiles partitioned medulloblastomas into five distinct subgroups (subgroups A to E). Gene expression signatures that distinguished these subgroups predicted the presence of key molecular alterations that we subsequently confirmed by gene sequence analysis and FISH. Subgroup-specific abnormalities included mutations in the Wingless (WNT) pathway and deletion of chromosome 6 (subgroup B) and mutations in the Sonic Hedgehog (SHH) pathway (subgroup D). Real-time RT-PCR analysis of gene expression profiles was then used to predict accurately the presence of mutations in the WNT and SHH pathways in a separate group of 31 medulloblastomas. CONCLUSION: Genome-wide expression profiles can partition large tumor cohorts into subgroups that are enriched for specific genetic alterations. This approach may assist ultimately in the selection of patients for future clinical trials of molecular targeted therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it