Synthetic lethal targeting of RNF20 through PARP1 silencing and inhibition
Bibliographic record
Abstract
PURPOSE: The identification of novel therapeutic targets that exploit the aberrant genetics driving oncogenesis is critical to better combat cancer. RNF20 is somatically altered in numerous cancers, and its diminished expression drives genome instability, a driving factor of oncogenesis. Accordingly, we sought to determine whether PARP1 silencing and inhibition could preferentially kill RNF20-deficient cells using a synthetic lethal strategy. METHODS: RNF20 and PARP1 were silenced using RNAi-based approaches. Direct synthetic lethal tests were performed by silencing RNF20 with and without PARP1 and the impact on cell numbers was evaluated using semi-quantitative imaging microscopy. Next, Olaparib and BMN673 (PARP1 inhibitors) were evaluated for their ability to induce preferential killing in RNF20 silenced cells, while real-time cell analyses were used to distinguish cell cytotoxicity from cell cycle arrest. Finally, quantitative imaging microscopy was employed to evaluate marks associated with DNA double-strand breaks (γ-H2AX) and apoptosis (cleaved Caspase-3). RESULTS: We found that PARP1 silencing resulted in a decrease in number of RNF20 silenced cells relative to controls. We further found that Olaparib and BMN673 treatments also resulted in fewer RNF20 silenced cells relative to controls. Finally, we found by quantitative imaging microscopy that RNF20 silenced cells treated with BMN673 exhibited significant increases in γ-H2AX and cleaved Caspase-3, suggesting that these treatments induce DNA double-strand breaks that are not adequately repaired within RNF20-silenced cells. CONCLUSIONS: Collectively, our data indicate that RNF20 and PARP1 are synthetic lethal interactors, suggesting that cancers with diminished RNF20 expression and/or function may be susceptible to PARP1 inhibitors.
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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 | 0.000 |
| 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) | 0.003 | 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".