TMOD-23. DYNAMIC PATTERNS OF GLIOBLASTOMA CLONAL EVOLUTION IN RESPONSE TO CHEMORADIOTHERAPY
Bibliographic record
Abstract
Despite aggressive multimodal therapy, glioblastoma (GBM) remains incurable and inevitably relapses. Recent data have implicated intratumoral heterogeneity as the driver of therapy resistance and tumour relapse in GBM. Models that capture the evolution of GBM biology in response to standard-of-care (SoC) 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 patient-derived xenograft SoC model (combined temozolomide and radiation treatment) to profile the clonal evolution of GBM stem cells (GSCs) through therapy. We report the successful barcoding of patient-derived primary, treatment-naive GSCs at a single cell resolution that were expanded into clonal populations, intracranially engrafted in immune-deficient mice, and treated with SoC therapy. We performed MRI imaging to identify spatial recurrence patterns of GSCs through the in vivo chemoradiotherapy model. We then interrogated the temporal fate of clonal barcoded GSC populations through SoC therapy model to identify differential barcode selection in response to treatment. Through this, we determined dynamics patterns of a pre-existing or a therapy-driven GSC subpopulation(s) seeding GBM tumour relapse. Profiling the dynamic nature of heterogeneous GBM subpopulations through disease progression and SoC treatment may lead to the identification of the modes of therapy resistance utilized by GBM to drive disease 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".