ReviewFrontiers in cell and developmental biology2025
Advances in precision oncology using patient-derived organoids and functional biomaterials.
Review in Frontiers in cell and developmental biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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
7 citing papers in PubMed.
- Current Perspectives on 2D and 3D Cell Culture Models in Cancer Research: Molecular Determinants of Tumor Biology and Therapeutic Response.Current issues in molecular biology · 2026Review
- Platelet-derived mitochondrial transfer in cancer metastasis: mechanisms, functional consequences, and translational opportunities.Clinical & experimental metastasis · 2026Review
- [Current status and challenges of artificial intelligence and organoid technologies in precision diagnosis and treatment of gastrointestinal stromal tumors].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Programmed cell death and metastatic evolution in breast cancer: the role of anoikis, necroptosis, and ferroptosis.Apoptosis : an international journal on programmed cell death · 2026Review
- Organoids: generation strategies, applications, and future challenges.Stem cell research & therapy · 2026Review
- Stem Cell-Derived Organoids for Cancer Therapy: Precision Medicine and Drug Selection.International journal of molecular sciences · 2026Review
- Organoid models: applications and research advances in gastric cancer.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite major advances in oncology, cancer therapy continues to face persistent challenges due to intratumoral heterogeneity, drug resistance, and the poor clinical translation of experimental therapeutics. Conventional preclinical models such as 2D cultures and animal systems often fail to accurately recapitulate the tumor microenvironment immune contexture, and patient-specific variability limiting their predictive power. While nanomedicine and advanced drug delivery platforms offer promising solutions, their translational success is hindered by insufficient integration with physiologically relevant tumor models. In this review, we critically examine how patient-derived organoids derived from patient tumors serve as next-generation platforms for modeling cancer heterogeneity, therapeutic response, and biomarker discovery. We further explore how the integration of PDOs with functional biomaterials, extracellular matrix mimetics, and organ-on-chip systems enables dynamic co-culture environments that capture tumor-stroma-immune interactions with high fidelity. By linking the biological underpinnings of resistance, such as genetic mutations, altered signaling, metabolic rewiring, and immune evasion, with smart biomaterial design and drug screening workflows, we propose a unified roadmap for precision oncology. Additionally, we highlight the emergence of PDO biobanks, co-culture innovations, and high-throughput phenotypic screening as essential tools for improving clinical translation. This interdisciplinary synthesis underscores the transformative potential of PDO-based platforms in accelerating personalized cancer therapy.
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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.