Gene Expression Profiling from Formalin-Fixed Paraffin-Embedded Tumors of Pediatric Glioblastoma
Bibliographic record
Abstract
PURPOSE: Gene expression profiling has proved crucial for understanding the biology of cancer. In rare diseases, including pediatric glioblastoma (pGBM), the lack of readily available fresh frozen (FF) material limits the feasibility of this analysis, as well as its validation, on independent data sets, a step needed to ensure relevance, mandating the use of alternate RNA sources. To overcome the limitation of material number and to validate results we obtained on FF pGBM, we did microarray analysis on RNA extracted from formalin-fixed, paraffin-embedded archival samples from pGBM and control brains, wherein we had no control on the fixation process. EXPERIMENTAL DESIGN: RNA from 16 pGBM and 3 control brains was extracted and linearly amplified. Reverse transcription-PCR on housekeeping and formerly identified tumor-associated genes and microarray analysis were done on this RNA source. Results were validated by immunohistochemistry. RESULTS: Despite extensive RNA degradation, microarray analysis was possible on 16 of 19 samples and reproduced the pattern of results obtained on FF pGBM. Gene lists and ontology subgrouping were highly concordant in both sample types. Similar to the findings on FF samples, we were able to identify two subsets of pGBM based on their association/lack of association with evidence consistent with an active Ras pathway. CONCLUSIONS: Archival formalin-fixed, paraffin-embedded tissues are an invaluable resource as they are the most widely available materials often accessible in conjunction with clinical and follow-up data. Gene expression profiling on this material is feasible and may represent a significant advance for understanding the biology of rare human diseases.
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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 | 0.002 | 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".