Abstract 1766: Targeting epigenetic regulation in clear cell renal cell carcinoma reveals PRMT1 as a novel target
Bibliographic record
Abstract
Abstract Sporadic Renal Cell Carcinoma (RCC) is dominated by the clear cell subtype (ccRCC) and overwhelmingly associated with a biallelic inactivation of the von Hippel-Lindau (VHL) gene, leading to constitutive activation of the hypoxia response and deleterious alterations to gene expression, metabolism and growth. However, VHL inactivation alone is insufficient to cause cancer. Other key genetic players identified through patient cohort sequencing include frequent inactivating mutations in epigenetic regulatory enzymes. The high frequency of these alterations in ccRCC implicate epigenetic vulnerabilities that may be exploited to develop new therapies.Accordingly, our lab has completed a proliferative screen in patient derived ccRCC models of the Structural Genomics Consortium's (SGC) epiprobe library, a panel of high-quality, small molecule inhibitors directed against an array of epigenetic targets. A particularly favorable inhibition profile was noted for MS023, a probe with potent activity against the type I protein arginine methytransferase family (PRMT1, 3, 4, 6 and 8). PRMTs transfer methyl groups to both nuclear histones and cytoplasmic targets, influencing gene expression, cell signaling, growth and viability. Specific PRMT3, 4 and 6 inhibitors failed to inhibit ccRCC cell growth in our screen, and PRMT8 is not expressed in this cell type, thus PRMT1 is the primary target for growth inhibition of ccRCC by MS023. Upon CRISPR-mediated knockout of PRMT1 followed by a growth competition assay of knock-out vs control cells, all PRMT1-knockout cells dropped out of culture within 4 passages. In ccRCC cell lines with tetracycline-inducible PRMT1 directed shRNAs, cell proliferation was inhibited upon induction with doxycycline both in vitro and in vivo. Additionally, PRMT1 over expression vectors introduced in our ccRCC cell lines, successfully rescued the proliferative phenotype of these cells in the presence of MS023 treatment. Finally, treatment of mice bearing ccRCC xenografts with MS023 led to significant inhibition of tumor growth in vivo. Transcriptomic profiling of our ccRCC models in the presence of MS023 vs control has been performed via RNA Seq and results suggest that this probe is acting as a potent regulator of the cell cycle. We are mapping changes in the H4R3me2a histone mark with MS023 treatment to complement our transcriptomic data and identify direct genetic targets regulated by PRMT1.Evidence continues to mount that dysregulation of PRMT1 is implicated in cancer biology, but to our knowledge, no investigations have been performed in the context of ccRCC. As our nascent understanding of the regulation, function and clinical relevance of arginine methylation continues to expand, this project represents an exciting opportunity to contribute to this body of knowledge, while describing novel therapeutic approaches to ccRCC with the potential for rapid clinical translation. Citation Format: Joseph Paul Walton, Anthony Apostoli, Jalna Meens, Julia Dmytryshyn, Cheryl Arrowsmith, Laurie AIlles. Targeting epigenetic regulation in clear cell renal cell carcinoma reveals PRMT1 as a novel target [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1766.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".