MB-14 * DISCOVERING THE TREATMENT REFRACTORY BTIC POPULATION IN GROUP 3 MEDULLOBLASTOMA
Bibliographic record
Abstract
Medulloblastoma (MB) is the most common malignant pediatric brain tumour, and is categorized into four molecular subgroups: WNT, SHH, Group 3 and Group 4 of which Group 3 tumours are associated with increased chances of metastasis and poor patient outcome. Using a cell-surface marker, CD133, a subpopulation of cells with stem cell properties, termed brain tumour initiating cells (BTICs) was identified, and was further shown to drive medulloblastoma tumorigenesis in vitro and in vivo. Previous in silico analyses revealed enriched expression of many stem cell self-renewal regulatory genes in Group 3 MBs. In this work, we aim to identify and characterize the treatment-refractory BTIC population in Group 3 MBs by developing a mouse- adapted therapy treatment model that mimics the clinical treatment of MB. A mouse-adapted MB therapy model was developed using our human-mouse BTIC xenograft, in which MB cells tagged with GFP were intracranially transplanted into the frontal lobes of NOD-SCID mice. After tumour engraftment, mice assigned to receive treatment of craniospinal radiation, followed by intraperitoneal injections of chemotherapeutic drugs Vincristine, Cisplatin and Cyclophosphamide. Following enrichment of tumour cells from cultured brains, in vitro stem cell assays for self-renewal, flow characterization, and candidate gene expression profiling by NanoString were performed. Spinal cords were also harvested from mice in order to elucidate spinal dissemination of human MB cells. We found that the cells cultured from xenografts of treatment group showed an increase in self-renewal and despite evident tumour regression post-therapy, treated tumours showed an increase in expression of CD133, Sox2 and Bmi1 compared to untreated tumours. Profiling genomic changes in “treatment-responsive” tumors against those that fail therapy will generate a differential profile of the refractory BTIC, which may guide future therapeutic approaches targeting this cell, and will serve as a model for targeting such CSCs in other TIC-driven solid tumours.
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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 | 0.000 | 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) | 0.001 | 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 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".