RM-06 * IN VITRO CLONAL EVOLUTION OF GLIOBLASTOMA (GBM) BRAIN TUMOUR INITIATING CELLS (BTIC) TO MODEL TUMOUR RECURRENCE
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common and highly aggressive primary adult brain tumour. Despite multimodal therapy, patients on average experience relapse at 9 months and median survival rarely extends beyond 15 months. Targeting the cells that drive GBM formation as well as its inevitable and rapid recurrence has remained a major challenge, likely due to intra-tumoral heterogeneity. At the genetic level, this heterogeneity has prompted a molecular classification of GBM based on differential transcriptome profiling by TCGA. At the cellular level, this heterogeneity may be explained by the existence of multiple subpopulations of cancer cells that have acquired stem cell properties, termed brain tumour initiating cells (BTICs). We postulate that different BTIC subpopulations are capable of first initiating the tumor, and later evading therapy to seed the tumor relapse or recurrence, as they undergo clonal evolution over time in response to various environmental cues including chemotherapy and radiotherapy. In this study, we developed a novel in vitro BTIC model to profile the clonal evolution of treatment naïve GBM BTICs through therapy (temozolomide and radiation treatment) based on transcriptome analysis, stem cell assays and BTIC protein marker expression (CD133, CD15, Sox2 and Bmi1). The expression profile of in vitro treated GBM was compared to recurrent GBM patient samples to determine if our model recapitulated clonal BTIC evolution as seen in patients. Profiling the dynamic nature of BTICs and their evolution over the course of treatment and tumour progression may offer novel therapeutic targets for the treatment of primary and recurrent GBM.
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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 | 0.001 | 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".