TMOD-02. Developing models of therapy resistance for the identification of treatment-refractory cell population(s) in human glioblastoma
Bibliographic record
Abstract
Despite aggressive multimodal therapy, human glioblastoma (hGBM), a highly malignant grade IV astrocytic tumour, remains incurable and inevitably relapses. Recent data has implicated intratumoral heterogeneity as the driver of therapy resistance and tumour relapse in hGBM. Thus models that capture the evolving hGBM biology in response to chemoradiotherapy will allow for the identification of cellular pathways that govern GBM therapy failure. In this study, we have developed a novel model to profile the clonal evolution of treatment naïve brain tumour initiating cell (BTIC) enriched hGBMs through chemoradiotherapy using: stem cell assays, BTIC marker expression and transcriptome analysis, immunohistochemistry, and cellular DNA barcoding technology. We report that treatment of hGBM BTICs leads to increased self-renewal capacity and higher transcript expression of stem cell genes Bmi1 and Sox2. Based on global transcriptome analysis of the in vitro treated hGBM, we also identify a hyper-aggressive form of glioma. Using our therapy-adapted hGBM-mouse xenograft model, we discover that despite tumour regression and increased mouse survival post-therapy, tumour relapse remains inevitable. The treatment-refractory cells again have increased self-renewal capacity and higher expression of Bmi1 and Sox2. Furthermore, by combining cellular DNA barcoding technology, which barcodes hGBM at single cell resolution, with our novel in vitro and in vivo therapy models, we are able to determine whether a pre-existing or a therapy driven subpopulation(s) seeds hGBM tumour relapse. Preliminary data shows a chemoradiotherapy driven selection of subclonal barcoded GBM populations, suggesting a divergent clonal evolution pattern in hGBM recurrence. Profiling the dynamic nature of heterogeneous hGBM subpopulations through disease progression and treatment may lead to the identification of novel therapeutic targets for the treatment of recurrent hGBM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".