Abstract 2611: Cellular origin of DICER1 tumor predisposition syndrome informed by lineage-traceable genetically engineered mouse model
Bibliographic record
Abstract
Abstract Introduction: DICER1 tumor predisposition syndrome is a genetic disorder driven by germline DICER1 pathogenic variants that predisposes pediatric and young adult patients to cancers of various organs. The second, missense mutation in RNase IIIb domain leads to systemic loss of mature 5p-miRNAs. Tumours of various sites are histologically and molecularly similar, suggesting a shared cellular origin and oncogenic mechanisms. To this end, we expanded our first-ever genetically engineered mouse model (GEMM) that recapitulated human Müllerian adenosarcomas, a DICER1 neoplasm, through inducing Dicer1 mutations in a lineage-traceable, tamoxifen inducible, Hypermethylated in Cancer 1 (Hic1)-creERT2-driven mouse strain. Hic1 marks mesenchymal progenitors. This GEMM histologically resembles DICER1-renal neoplasms such as cystic nephroma and anaplastic sarcoma. We leveraged single-cell whole transcriptomics (scRNA-seq) and Xenium spatial transcriptomics platform (379 genes) to uncover cellular origin and hierarchy of Dicer1 sarcomagenesis. Methods: We profiled normal kidney mesenchymal stromal cells (n=3 for scRNA-seq, n=3 for Xenium) and mesenchymal tumor components (n=5 for scRNA-seq, n=25 for Xenium). Trajectory analysis deconvoluted hierarchy of sarcomagenesis. Results: In normal mesenchymal stromal niche, scRNA-seq identified 5 Hic1+ clusters including 2 clusters of universal fibroblasts (UniFibro): Pi16high and Col15a1high, and a cluster of Dpt- Itga8+ fibroblasts. Spatially, Pi16high UniFibro are enriched in renal vascular niche, whereas Col15a1high UniFibro reside in subepithelial basement membrane. ScRNA-seq of tumours revealed a Col15a1high Mfap4high “Progenitor” cluster that is transcriptionally similar to control Col15a1high UniFibro, suggesting the latter represent putative progenitors of the tumors. ScRNA-seq also identified a “ground” population that is transcriptionally similar to control Dpt-Itga8+ kidney fibroblasts, one cluster of Pax7+ satellite cells, a committed Myog+ muscle cluster and a highly proliferative cluster. Pseduotime trajectory analysis showed Col15a1high Mfap4high progenitors can gives rise to a myogenic lineage that progresses via Pax7+ transitional state and holds the potential to progress to highly proliferative sarcoma cells via “Ground” state of cells. Spatially, “Progenitor” cells have low-grade morphology, supporting their role in cancer initiation. Lastly, scRNA-seq of tumors revealed the expansion of Col15a1highMfap4high UniFibro but not Pi16high ones into the “Progenitor” cells. Conclusion: Single cell profiling of renal tumours from Dicer1-GEMM supports Mfap4high Col15a1 highuniversal fibroblasts as cell of origin, and delineates progression underlying DICER1 sarcomagenesis, thereby providing opportunities for therapeutic intervention and mechanistic studies. Citation Format: Joyce Zhang, Felix Kommoss, Branden Lynch, Shary Chen, Janine Senz, Yana Moscovitz, Lesley Ann Hill, Wilder Scott, Jonathan Bush, William Foulkes, Gregg Morin, Michael Underhill, Yemin Wang, David Huntsman. Cellular origin of DICER1 tumor predisposition syndrome informed by lineage-traceable genetically engineered mouse model. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2611.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".