Abstract P096: Using CRISPR-Cas9 screens to identify microRNA involved in aggressive prostate cancer phenotypes
Bibliographic record
Abstract
Abstract Background: Prostate cancer (PCa) is the most common cancer diagnosis in Canadian men. For many, the tumour can remain “dormant” negating a need for treatment, but if the tumour progresses and becomes metastatic, prognosis is poor. Despite the introduction of new treatments, metastatic PCa remains a lethal and incurable disease. This is invariably due to the onset of castration-resistance in advanced cases producing a more aggressive tumour with increased metastatic potential. We hypothesize that microRNA can promote these aggressive PCa phenotypes due to their ability to regulate diverse gene networks simultaneously. Methods: We have generated miRKOv2, the second version of our microRNA CRISPR Knockout library using the latest on- and off-target scoring algorithms and other microRNA-specific features for CRISPR-Cas9 guide RNA (gRNA) design. miRKOv2 will be used in three separate screens to identify microRNA that are essential for PCa growth, microRNA that are involved in PCa metastasis, and microRNA that can confer castration-resistance, respectively. Results: Next generation sequencing of the plasmid miRKOv2 library showed a median gRNA coverage of ~1100X. In silico comparisons to other microRNA libraries demonstrate that miRKOv2 is a superior sgRNA library. DU145 cells stably expressing Cas9 are highly active and proof of principle growth assays demonstrate that essential gene knockout and subsequent decrease in cell viability is detectable. Generation of a Cas9+ PCa cell line panel and screens are ongoing. Conclusion: miRKOv2 is an improved microRNA-focused CRISPR library with high gRNA coverage. Proof of principle assays demonstrate that knockout of essential genes results in a reduction in cell viability, indicating that a CRISPR dropout screen is appropriate for our in vitro model. This project aims to identify microRNA dependencies in metastatic and castration-resistant PCa that can be leveraged into new biomarkers and therapies. Citation Format: Jonathan Tak-Sum Chow, Daniel K. C. Lee, Martino Marco Gabra, Norman Fu, Keyue Chen, Leonardo Salmena. Using CRISPR-Cas9 screens to identify microRNA involved in aggressive prostate cancer phenotypes [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P096.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".