Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The diagnosis of medulloblastoma likely encompasses several distinct entities, with recent evidence for the existence of at least four unique molecular subgroups that exhibit distinct genetic, transcriptional, demographic, and clinical features. Assignment of molecular subgroup through routine profiling of high-quality RNA on expression microarrays is likely impractical in the clinical setting. The planning and execution of medulloblastoma clinical trials that stratify by subgroup, or which are targeted to a specific subgroup requires technologies that can be economically, rapidly, reliably, and reproducibly applied to formalin-fixed paraffin embedded (FFPE) specimens. In the current study, we have developed an assay that accurately measures the expression level of 22 medulloblastoma subgroup-specific signature genes (CodeSet) using nanoString nCounter Technology. Comparison of the nanoString assay with Affymetrix expression array data on a training series of 101 medulloblastomas of known subgroup demonstrated a high concordance (Pearson correlation r = 0.86). The assay was validated on a second set of 130 non-overlapping medulloblastomas of known subgroup, correctly assigning 98% (127/130) of tumors to the appropriate subgroup. Reproducibility was demonstrated by repeating the assay in three independent laboratories in Canada, the United States, and Switzerland. Finally, the nanoString assay could confidently predict subgroup in 88% of recent FFPE cases, of which 100% had accurate subgroup assignment. We present an assay based on nanoString technology that is capable of rapidly, reliably, and reproducibly assigning clinical FFPE medulloblastoma samples to their molecular subgroup, and which is highly suited for future medulloblastoma clinical trials.
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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) | 0.000 | 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 it