MODL-50. CLONAL EXPANSION OF SPONTANEOUS AND INJECTED TUMORS IN SYNGENEIC MOUSE MODELS OF GLIOBLASTOMA
Bibliographic record
Abstract
Abstract INTRODUCTION Glioblastoma (GBM) tumors are highly heterogenous and plastic. To better understand how tumors respond to treatment, it is important to use immunocompetent mouse models. We've created various mouse GBM models with common genetic mutations found in different subtypes of GBM, including classical, proneural, mesenchymal, and BRAF-driven. METHODS Using a combination of CRISPR and PiggyBac Transposon constructs, we injected plasmid mixtures into the ventricles of the mouse brain during the post-natal period. Electroporation was then used to enable the plasmids to enter nearby neural stem cells, which led to the spontaneous formation of tumors (spontaneous model). We collected tumor cells from these models and injected them into the adult mouse brain to create the injected mouse GBM model. Here we use spatial and single cell transcriptome profiling approaches to characterize these models at endpoint. Spatial transcriptome data was collected using the Visium 10X platform and tumor spots were deconvoluted from non-malignant spots using consensus non-negative matrix factorization. Copy number alterations (CNAs) were inferred using non-malignant spots and single-cell clusters as a reference. RESULTS We found that copy number states are largely homogenous within each sample, supporting an early population expansion in vivo. CNAs found in spontaneous tumors are also found in the matched injected tumors, which in turn harbour additional CNAs, suggesting that further selection occurs in vitro. Some of the CNAs include relevant cancer genes, suggesting the observed clonal expansions are potentially driven by additional somatic alterations that cooperate with the engineered driver mutations. Studying these models using orthogonal methods will allow us to discover alterations that synergize with the original drivers and delineate principles of GBM initiation and progression in an immune-competent environment.
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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 | 0.002 | 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) | 0.004 | 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".