ReviewBiomarker research2026
Clinical application of PARP inhibitors and emerging strategies to overcome resistance: a pan-cancer perspective.
Review in Biomarker research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Molecular landscape and clinical translation of DNA damage response alterations in solid tumors: A pan-cancer perspective on precision oncology.Neoplasia (New York, N.Y.) · 2026Review
- Homologous recombination repair and breast cancer gene testing in prostate cancer: expert perspectives and practical guidance on best practice from sample acquisition to biomarker-informed clinical decision-making.Frontiers in oncology · 2026Article
- From dual inhibition to precision selectivity: the molecular rationale and clinical evolution of next-generation PARP1-selective inhibitors in solid tumors.Frontiers in oncology · 2026Review
- PARP inhibitors across different malignancies: clinical applications, resistance mechanisms, and strategies to enhance efficacy through combination therapies.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Poly (ADP-ribose) polymerase (PARP) inhibitors have emerged as a paradigm-shifting therapeutic strategy in oncology, demonstrating significant synthetic lethality in tumors characterized by homologous recombination deficiency. Due to their substantial efficacy, PARP inhibitors (PARPi) have received approval for the treatment of four malignancies: ovarian cancer, breast cancer, prostate cancer, and pancreatic cancer. However, the specific clinical indications and optimal utilization settings vary among these types of cancers. Identifying novel biomarkers is thus essential for accurately predicting patients’ responses to PARPi, therefore enhancing effective patient stratification. Notably, the emergence of acquired resistance to PARPi presents a considerable challenge to their practical implementation in clinical practice. Both preclinical and clinical studies have elucidated numerous molecular alterations contributing to this resistance, offering valuable insights into potential strategies for overcoming it. This review comprehensively summarizes landmark clinical trials involving both PARPi monotherapy and various combination strategies, and outlines future research directions. We compare existing predictive tools for resistance to PARPi, aiming to refine future clinical applications and identify critical gaps requiring further investigation. This review also presents new insights into primary and acquired resistance by updating mechanisms of PARPi action and summarizing the biological processes involved in PARPi resistance. Additionally, we discuss potential strategies designed to overcome these mechanisms.
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What OpenQuestion holds
Registered trials
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.