Evidence map›Paper›PMID 42017177›Full record

ArticleGenes & diseases2026

PAK1 inhibition synergistically enhances the anti-tumor efficacy of PARP inhibitors in ovarian cancers.

Changying Li, Xinyan Li, Ming Gao, Min Deng, Ye-Xiong Li, Zhenkun Lou

Abstract read
In one paragraph

Article in Genes & diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Changying LiState Key Laboratory of Molecular Oncology and Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Xinyan LiState Key Laboratory of Molecular Oncology and Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Ming GaoState Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Min DengState Key Laboratory of Molecular Oncology and Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Ye-Xiong LiState Key Laboratory of Molecular Oncology and Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Zhenkun LouDepartment of Oncology, Mayo Clinic, Rochester, MN 55905, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Poly (ADP-ribose) polymerase inhibitors (PARPi) demonstrate effective treatment outcomes in ovarian cancer patients with BRCA1/2 mutations or homologous recombination (HR) repair deficiencies, leveraging the principle of synthetic lethality. However, PARPi resistance and HR proficiency remain significant challenges in the clinical management of PARPi, necessitating the development of novel strategies for PARPi therapy in ovarian cancer. Our previous research identified PAK1's involvement in replication stress-induced cytotoxicity. Nonetheless, whether PAK1 also affects HR repair and PARPi sensitivity in ovarian cancer remains unresolved. In this research, we found that the expression of PAK1 correlated with an unfavorable prognosis in ovarian cancer. Depletion of PAK1, introduction of a kinase-dead mutation, or treatment with the inhibitor IPA-3 could reduce HR repair efficiency and increase ovarian cancer cell sensitivity to the PARP inhibitor olaparib. The combination of olaparib and IPA-3 synergistically increased olaparib-induced DNA replication stress and double-stranded breaks. Using cell line-derived xenograft, patient-derived organoid, and patient-derived xenograft models, we discovered that IPA-3 potentiated the therapeutic efficacy of olaparib both

Indexed as

HR repairOlaparibOvarian cancerPAK1Synthetic lethality

Identifiers

PMID42017177
PMCPMC13092676

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.