MétaCan
Menu
Back to cohort
Record W3133587423 · doi:10.1186/s13148-021-01037-1

Synergistic effects of type I PRMT and PARP inhibitors against non-small cell lung cancer cells

2021· article· en· W3133587423 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueClinical Epigenetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité LavalUniversité de MontréalMcGill University
FundersCIHR Skin Research Training CentreFonds de Recherche du Québec-Société et Culture
KeywordsLung cancerCombination therapyCancer researchCancerPARP inhibitorMedicineMethyltransferaseTargeted therapyPharmacologyInternal medicineBiologyPoly ADP ribose polymeraseBiochemistryEnzymeDNAMethylation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.292
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it