TMOD-06. CLONAL DYNAMICS OF HUMAN GLIOBLASTOMA IN RESPONSE TO CHEMORADIOTHERAPY
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 evolution of hGBM biology in response to chemoradiotherapy will allow for the identification of cellular mechanisms that govern GBM therapy failure. In this study, we coupled cellular DNA barcoding technology with our novel in vitro and in vivo chemoradiotherapy model to profile the clonal evolution of hGBM brain tumour initiating cells (BTIC) through therapy. We report the successful barcoding of primary treatment-naive hGBM BTICs at single cell resolution that were then expanded into clonal populations with 40-60x representation per barcode. We then subjected our barcoded cells to in vitro chemoradiotherapy as well as intracranially engrafted barcoded cells into immune-deficient mice for subsequent delivery of in vivo therapy to identify differential barcode selection in two therapy models. Through this, we determined if the same cellular subpopulation is therapy-resistant in vitro and in vivo and whether a pre-existing or a therapy-driven subpopulation(s) seeds hGBM tumour relapse. Profiling the dynamic nature of heterogeneous hGBM subpopulations through disease progression and treatment may lead to identification of the mode(s) of therapy resistance utilized by hGBM which may drive relapse.
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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".