Abstract A026 Cellular and zebrafish models for DICER1 related tumour predisposition (DRTP)
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) are short non-coding RNA molecules that downregulate messenger RNA (mRNA) expression—critical for transcriptome modulation. MiRNAs are divided into two populations: 5 prime (5p miRNAs), and 3 prime (3p miRNAs). Cleavage of pre-miRNAs via the RNAse IIIa and IIIb domains of the endonuclease DICER1 to produces 3p and 5p miRNAs respectively. Mature miRNAs are loaded into the AGO2-containing RNA-Induced Silencing Complex (RISC) where they regulate protein translation by recognizing seed sequences in the UTRs or target mRNAs. Children bearing pathogenic variants in DICER1 have a highly elevated risk of cancers in many different organs– recognized as DICER1 related tumour predisposition (DRTP) or DICER1 syndrome. These tumours/lesions have one inactivated copy of DICER1 and one “hotspot” mutated copy of DICER1. All “hotspot” DICER1 mutations impair 5p miRNA production. Neither cellular nor zebrafish models of DRTP exist but are critical for the development of diagnostic, surveillance, and therapeutic tools. DICER1 DNA sequencing has become the diagnostic standard but diagnostic and surveillance biomarkers may improve patient care via faster return of results and earlier relapse detection. I developed murine mesenchymal stromal cell lines that allow comparison of the consequences having a: full 5p and 3p miRNA repertoire (DICER1WT) vs having neither 5p nor 3p miRNAs (empty vector) vs having only 3p miRNAs (DICER1D1320A) vs having only 5p miRNAs (DICER1E1705K; the situation in DRTP). Using immunoprecipitation of mRNA:miRNA chimeras associated with AGO2 (miR-eCLIP), I identified >147 candidate biomarkers (chimera missing in DICER1E1705K vs DICER1WT). Candidate RNA and protein levels were measured (RNAseq and Western blot respectively). Presence of protein and mRNA for candidates in extracellular vesicles was also performed. I optimized immunohistochemistry (IHC) conditions for certain candidate biomarkers in human formalin-fixed paraffin embedded positive controls. A pilot study (3 healthy controls vs. 3 DRTP patients) of blood extracellular vesicle contents was conducted to assess surveillance biomarkers. I am developing a zebrafish model (integration of human mutant DICER1 (DICER1E1705K) into dicer1+/- tp53-/- animals + mosaic Cas9-mediated knockout of endogenous dicer1) to better understand cell(s) of origin and processes involved in disease initiation in a developmental context. A zebrafish that transmits the human transgene DICER1E1705K has been created and does produce tumours on certain genetic backgrounds. These novel data pave the way to identify diagnostic and surveillance biomarkers as well as identifying cell(s) of origin for DRTP. Citation Format: Mona K. Wu, Paolo Neviani, James F. Amatruda. Cellular and zebrafish models for DICER1 related tumour predisposition (DRTP) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A026.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".