BI-02 * FUNCTIONAL CHARACTERIZATION OF NOVEL BIOMARKERS IN SELECTING FOR SUBTYPE SPECIFIC MEDULLOBLASTOMA PHENOTYPES
Bibliographic record
Abstract
Major research efforts have focused on the isolation and characterization of brain tumor stem cells, or propagating cells (BTPC) in a variety of brain cancers. Elucidating cell surface marker profiles that can be used to selectively isolate this cellular population is an imperative first step in the development of targeted therapies. Medulloblastoma (MB) is the most common form of pediatric brain cancer. MB is divided into 4 molecular subgroups; Wnt, Sonic Hedgehog (SHH), Group 3 and Group 4. Given the variable results obtained for currently utilized markers, as well as the cellular heterogeneity within and between MB sub-groups, it is likely there are additional surface marker profiles capable of selecting for sub-type specific MB BTPCs. We set out to identify novel surface marker combinations capable of selecting for TPCs in SHH MB. We employed the new BD Bioscience Lyoplate screening platform to compare 242 human cell surface marker levels across high and low self-renewing SHH MB sub-clones. The top 25 markers were refined by evaluating expression levels in Shh vs Group 3,4 and Wnt variants in transcriptome datasets representing 548 patient samples. Four markers, CD271, CD106/VCAM1, EGFR and CD171/NCAM-L1 showed consistent differential expression in the SHH subtype relative to the other variants. Flow cytometry validation in additional cell lines confirmed these findings. As a proof of principle, functional characterization of CD271 in SHH MB in vitro and in vivo was performed. Using fluorescence activated cell sorting and gain/loss of function studies, our results suggest that CD271 selects for MB progenitor cells. This work highlights a new approach to screening for differentially expressed surface markers across matched samples. We delineated a cell surface fingerprint for BTPC populations from MB molecular variants, however the utility can be seen in normal stem cell biology and across all forms of cancer.
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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 | 0.000 | 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".