Spatial transcriptomic profiling of human retinoblastoma
Bibliographic record
Abstract
Abstract Retinoblastoma (RB) represents one of the most prevalent intraocular cancers in children. Understanding the tumor heterogeneity in RB is important to design better targeted therapies. Here we used spatial transcriptomic to profile human retina and RB tumor to comprehensively dissect the spatial cell-cell communication networks. We found high intratumoral heterogeneity in RB, consisting of 10 transcriptionally distinct subpopulations with varying levels of proliferation capacity. Our results uncovered a complex architecture of the tumor microenvironment that predominantly consisted of cone precursors, as well as glial cells and cancer-associated fibroblasts. We delineated the cell trajectory underlying malignant progression of RB, and identified key signaling pathways driving genetic regulation across RB progression. We also explored the signaling pathways mediating cell-cell communications in RB subpopulations, and mapped the spatial networks of RB subpopulations and region neighbors. Altogether, we constructed the first spatial gene atlas for RB, which allowed us to characterize the transcriptomic landscape in spatially-resolved RB subpopulations, providing novel insights into the complex spatial communications involved in RB progression.
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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 | 0.001 | 0.001 |
| 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".