Spatial single cell transcriptomic analysis of a novel DICER1 Syndrome GEMM informs the cellular origin and developmental hierarchy of associated sarcomas
Bibliographic record
Abstract
Abstract DICER1 syndrome predisposes children and young adults to tumor development across various organs. Many of these cancers are sarcomas, which uniquely express the RNase IIIb domain-deficient form of DICER1 and exhibit consistent histological and molecular similarities regardless of their anatomical origins. To uncover their cellular origin and developmental hierarchy, we established a lineage-traceable genetically engineered mouse model that allows for precise activation of Dicer1 mutations in Hic1 + mesenchymal stromal cells. This model resulted in the development of renal tumors closely mirroring human DICER1 sarcoma histologically and molecularly. Single-cell transcriptomics coupled with targeted spatial gene expression analysis revealed a Hic1 + progenitor population marked by Pdgfra , Dpt , and Mfap4, corresponding to universal fibroblasts of steady-state kidneys. These fibroblastic progenitors exhibit the capacity to undergo rhabdomyoblastic differentiation or transition to highly proliferative anaplastic sarcoma. Investigation of patient samples identified analogous cell states. This study uncovers a fibroblastic origin for DICER1 sarcoma and provides a faithful model for mechanistic investigation and therapeutic development for tumors within the rhabdomyosarcoma spectrum.
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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".