IMMU-36. MULTI-OMIC CHARACTERIZATION OF A SYNGENEIC MOUSE CANCER STEM CELL MODEL OF GBM FOR PRECLINICAL APPLICATIONS
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is resistant to many therapies including immunotherapies. There is an urgent need to explore the role of the microenvironment in resistance to therapy with tailored pre-clinical models. Using multi-omic approaches we describe a syngeneic cancer stem cell mouse model of GBM, with a spontaneous amplification of Igf2. We investigate whether Igf2 influences tumour and microenvironment cells to promote immunosuppression in GBM. Three cell lines were previously established from spontaneous brain tumours in C57Bl/6 Trp53+/-/Nf1+/- mice and maintained under neural stem cell culture conditions. Whole genome sequencing (WGS) was used to determine single nucleotide, copy number, and structural variation, revealing loss of both copies of Trp53 and Nf1 in all three cell lines. However only one cell line, mBT0309, developed tumors when orthotopically allografted. Analysis of genomic alterations and transcriptomics revealed that mBT309 exhibits amplification of the Igf2 loci and overexpression was confirmed at the RNA and protein level. Spatial and single cell transcriptomics showed that Igf2 is overexpressed in mBT0309 allografted tumours and correlated with specific transcriptomic programs. A high-parameter Imaging Mass Cytometry (IMCTM) panel was used for spatial proteomic analysis, to monitor the development of tumours in a time-course experiment. In vitro growth characteristics and stem marker expression in both transcriptomic and spatial IMC data suggest that high levels of Igf2 may regulate GBM stemness features. Analysis of human GBM datasets revealed that Igf2 amplified tumours have reduced CXCL11 expression. These findings suggest that high levels of Igf2 may influence immunosuppression by reducing T-cell recruitment. This syngeneic immunocompetent GBM stem cell model harbouring Igf2 amplification adds to the suite of models to study the GBM microenvironment and its role in immunosuppression and resistance to therapy.
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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) | 0.000 | 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 | 0.001 |
| 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".