Abstract A008: Identification of EYA2 as a promising biomarker for DICER1-related tumor predisposition
Bibliographic record
Abstract
Abstract Background: DICER1-related tumor predisposition (DRTP), also known as DICER1 syndrome, is a genetic condition associated with an increased risk of developing various tumors, including pleuropulmonary blastoma, thyroid gland neoplasia, ovarian tumors, and cystic nephroma. Currently, the diagnosis and surveillance of DRTP primarily relies on genetic testing for DICER1 mutations. There is a pressing need for surrogate diagnostic biomarkers to improve early detection and monitoring. Objective: To identify and validate EYA2 as a potential biomarker for DICER1-related tumor predisposition. Methods: Murine mesenchymal stromal cell lines were developed to model the consequences of DICER1 mutations, including the DICER1E1705K mutation found in DRTP. Both bulk RNA sequencing (RNAseq) and microRNA sequencing (miRNAseq) were employed to identify differentially expressed mRNAs in these cell lines. The RNA and protein levels of Eya2 were measured by qRT-PCR and Western blot, respectively. De-repression of Eya2 mRNA by miRNA was evaluated by dual luciferase assay to link overexpression with the DICER1 defect of DRTP. Then, immunohistochemistry staining of EYA2 was performed on a panel of DRTP tumors. Results: We found that more than 2000 mRNAs were derepressed in mesenchymal stromal cells expressing DICER1E1705K as compared to DICER1WT. Among these, Eya2 emerged as a top candidate. We observed elevated Eya2 mRNA and protein levels in DICER1E1705K -expressing mesenchymal stromal cells. Utilizing a reporter assay, we also observed de-repression of the 3’ UTR of Eya2 upon expression of DICER1E1705K as compared to DICER1WT. Strong nuclear EYA2 staining was observed in a subset of DRTP tumor samples. Conclusion: Elevated EYA2 expression in tumor tissues from DRTP patients supports its potential use in both diagnostic and surveillance contexts. Further studies are warranted to validate EYA2 as a clinical biomarker and explore its utility in improving early detection and monitoring strategies for individuals at risk of DRTP. Citation Format: Mona Wu, Shengmei Zhou, Felix Kommoss, Yemin Wang, James F Amatruda. Identification of EYA2 as a promising biomarker for DICER1-related tumor predisposition [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A008.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".