MB-03 * FUNCTIONAL CHARACTERIZATION OF NOVEL BIOMARKERS IN SELECTING FOR SUBTYPE SPECIFIC MEDULLOBLASTOMA PHENOTYPES
Bibliographic record
Abstract
Major research efforts have focused on characterization of brain tumor propagating cells from a variety of cancers. Elucidating cell surface marker profiles for selective isolation of this population, which could include stem or progenitor cells, will be imperative toward development of targeted therapies. Medulloblastoma (MB) is the most common primary malignant pediatric brain cancer and consists of 4 molecular subgroups: Wnt, Shh, Group 3 and Group 4. Given the cellular heterogeneity within and between MB variants, cell surface marker profiles will likely be subtype-specific. We employed an unbiased high throughput flow cytometry screen to identify differentially expressed cell surface markers in high vs. low self-renewing Shh MB phenotypes. The top 25 differentially expressed markers were refined by evaluating levels in Shh relative to the other molecular variants in 3 transcriptome datasets representing 548 patient samples. Four markers, CD271/p75NTR, CD106/VCAM1, EGFR and CD171/NCAM-L1 showed consistent differential expression in the Shh subtype relative to the other variants and this was validated in additional cell lines. In addition, immunohistochemical staining specifically for CD271 in paraffin embedded sections of primary MB patient samples validated higher expression of CD271 in Shh MB compared to the other MB variants as well as high levels in the external granular layer of the fetal cerebellum. Further functional characterization of CD271 was performed as proof of principle to demonstrate the power this unique approach has in identifying novel markers associated with Shh MB tumorigenesis. Gain and loss of function studies performed in vitro as well as in vivo analysis suggested that CD271 selects for progenitors, as opposed to more primitive stem cell populations, and that CD271 levels are inversely correlated with expression of representative Shh pathway genes. This work highlights an integrated approach for identification and subsequent functional validation of novel cell surface markers representing diverse MB phenotypes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".