Synergistic effects of type I PRMT and PARP inhibitors against non-small cell lung cancer cells
Bibliographic record
Abstract
BACKGROUND: Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related death and represents a major health burden worldwide. Current therapies for NSCLC include chemotherapy, immunotherapy, and targeted molecular agents such as tyrosine kinase inhibitors and epigenetic drugs such as DNA methyltransferase inhibitors. However, survival rates remain low for patients with NSCLC, especially those with metastatic disease. A major cause for therapeutic failure is drug resistance, highlighting the need for novel therapies and combination strategies. Given that epigenetic modulators such as protein arginine methyltransferases (PRMTs) are frequently overexpressed in cancers, PRMT inhibitors are a promising class of cancer therapeutics. We screened a library of epigenetic and anticancer drugs to identify compounds that would synergize with MS023, a type I PRMT inhibitor, in decreasing the viability of methylthioadenosine phosphorylase (MTAP)-negative NSCLC cells. RESULTS: Among 181 compounds, we identified PARP inhibitors (PARPi) as having a strong synergistic interaction with type I PRMT inhibition. The combination of MS023 and the PARP inhibitor BMN-673 (Talazoparib) demonstrated strong synergistic interaction at low nanomolar concentrations in MTAP-negative NSCLC cell lines A549, SK-LU-1 and HCC4006. The re-introduction of MTAP decreased the sensitivity of the combination therapy in A549. The combination therapy resulted in elevated γ-H2AX foci indicating increased DNA damage causing decreased cell viability. Lastly, the combination therapy was effective in PARPi resistant ovarian cancer cells, suggesting that type I PRMT inhibitors could mitigate PARPi resistance, thus potentially having an important clinical impact for cancer treatment. CONCLUSIONS: These findings identify a novel cancer drug combination therapy, which is more potent than the separate single-agent therapies. Thus, combining PARP inhibitors and type I PRMT inhibitors represents a new therapeutic opportunity for MTAP-negative NSCLC and certain cancer cells resistant to PARP 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) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".