Abstract 135: Resolving the tumorigenesis continuum of DICER1 syndrome with novel lineage-trackable genetically engineered mouse model
Bibliographic record
Abstract
Abstract Background: DICER1 syndrome is a rare cancer predisposition syndrome associated with germline DICER1 mutations, which develops pulmonary or extra-pulmonary manifestations mostly in pediatric patients. Unlike classic tumor suppressors, the 2nd hit in DICER1 gene is a missense mutation that renders the RNase IIIb domain defective; therefore, this mutant form of DICER1 is the only protein expressed in these cancer cells, leading to a biased 5p-miRNA production deficiency. Many DICER1 cancers are sarcomas; we therefore postulate the cell of origin is mesenchymal. We recently developed a RNase IIIb-mutant mouse strain and embryonic activation of this mutant along with silencing of the other Dicer1 allele in Mullerian mesenchymal progenitor cells led to the development of tumors resembling sarcomas in human patients. To expand the model and enable lineage tracking, we developed a tamoxifen inducible, tdTomato-trackable, Hypermethylated in Cancer 1 (HIC1)-creERT2 driven transgenic mouse strain. Hic1 marks mesenchymal progenitors. With this strain, we created a model that histologically recapitulates the 3 renal tumors in DICER1 syndrome: cystic nephromas, Wilms tumors, and anaplastic sarcomas. Objective: to identify oncogenic events underlying Dicer1 mutation-driven murine kidney tumor development with single cell RNA-sequencing (scRNA-seq). Method: To build the tumorigenesis continuum, we harvested kidneys from Dicer1+/fl-D1693N and Dicer1fl/fl-D1693N mice at 1, 3, 6, months post tamoxifen injection, sorted out tdTomato+/CD45-/Epcam- cells, and performed scRNA-seq. Five endpoint tumors were included for analyses. Single cell transcriptomic profiles were integrated and clustered using Seurat to identify cell populations that emerge and expand along the tumor development continuum. Differential gene expression, pathway analysis were conducted to identify genes/oncogenic pathways that are activated along the course of tumor development. Cell differentiation trajectory analysis will be performed using Monocle. Results: Integration of 5 tumors revealed diverse cell populations: epithelial, mesenchymal, endothelial cells, lymphocytes, and macrophages. We further clustered the mesenchymal population and identified sub-populations such as highly proliferative cells, muscle satellite cells, and terminally differentiated muscle cells. These mesenchymal cells show high expression of blastema markers (Ncam1, Sox11) - a histological component in Wilms tumors. Temporal trajectory of tumorigenesis with time point samples is currently being analyzed. Conclusion: With scRNA-seq, we begin to unravel heterogeneity of these murine tumors and will identify critical oncogenic events driving the development of DICER1 syndrome-associated cancer, enabling us to utilize this model to develop therapeutic approaches to improve patient management. Citation Format: Joyce Zhang, Shary Chen, Yana Moscovitz, Branden Lynch, Maxwell Douglas, Janine Senz, Wilder Scott, Michael Underhill, Yemin Wang, David Huntsman. Resolving the tumorigenesis continuum of DICER1 syndrome with novel lineage-trackable genetically engineered mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 135.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".