TMIC-13. INTEGRATING SNRNASEQ AND SPATIAL TRANSCRIPTOME (ST) SHOWS DIVERSE TRANSCRIPTOMIC LANDSCAPE FOR TUMOR MICROENVIRONMENT (TME) IN INTRACRANIAL GERMINOMAS
Bibliographic record
Abstract
Abstract The incidence of intracranial germ cell tumors is 5-8 times higher in East Asia and histologically, germinoma is well known to show intense lymphocytic infiltrates. In spite of germinoma’s well known immune infiltration, there have only been a few studies on its tumor micro-environment (TME). There has been no study using the recent and comprehensive techniques of single nucleus sequencing or spatial transcriptomics to study its TME. In this study, we studied snRNAseq (Chromium 10X) of FFPE tissues of 24 intracranial germinomas, and spatial transcriptomes (STs) (Visium Spatial) from fresh tissues from ten matched cases. For the TME, in contrast to common belief, macrophages and fibroblasts were equally represented as T- and NK-cells as immune cells. Clinical stage correlated with a lesser proportion of fibroblasts and T- and NK-cells. There was no correlation with proportion of tumor cells with either focal vs bifocal disease or clinical stage. Common gene modules (non-negative matrix factorization) were epithelial-mesenchymal transition (complete and partial), cell cycle and immune cells. Cell interaction analysis showed strong interaction between germ cells and macrophages involving CXCL, MIF and MK pathways. By ligand-receptor analysis, MIF combined with CD74 might promote tumor development. For ten cases, we studied STs with fresh tissues and integrated the results with snRNSeq, deconvoluting the spots for ST using snRNAseq cell types as reference (SPOTight) and achieved integrated single cell expression atlases of the STs (Seurat package). STs revealed a similar CNV plot pattern as those derived from snRNAseqs. Multimodal Intersection Analysis integrated significant genes based on snRNAseq and different regions’ significant genes based on STs. There was correlation between the expression of the four gene modules for tumor and immune cells for cell neighborhood. In conclusion, we showed in details the heterogenous expression landscape of TME of germinomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".