ArticleNature communications2020
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 2 of them syntheses that pooled it.
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
125 citing papers in PubMed, 2 syntheses or guidelines pooled it, 194 citations in OpenAlex.
- Lung Organoids: Systematic Review of Recent Advancements and its Future Perspectives.Tissue engineering and regenerative medicine · 2024Pooled it
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Transforming Brain Organoids Into Functional Platforms: Convergence of Engineering, Imaging, AI, and Ethics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
- Harnessing human tumor organoids for cancer modeling and precision therapy.Protein & cell · 2026Review
- Artificial intelligence in lung cancer: a bibliometric analysis of emerging trends and research hotspots.Annals of medicine and surgery (2012) · 2026Article
- Organoids, organ-on-a-chip, and microtumors: Biomimetic 3D tumor models advancing drug development and precision medicine.Acta pharmaceutica Sinica. B · 2026Review
- Tumor organoid platform design for drug response modeling: culture architecture, microenvironmental complexity, and AI-assisted readouts.Archives of pharmacal research · 2026Review
- A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.Cell reports methods · 2026Article
- Machine learning-based integration of transcriptome and digital pathology for predicting chemoresistance in muscle-invasive bladder cancer.Experimental & molecular medicine · 2026Article
- The advancements in organoids: Potential and challenges in researching the esophagus and esophageal squamous cell carcinoma.Genes & diseases · 2026Review
- Biomarkers and advances in AML-MRC: from bench to bedside.Annals of hematology · 2026Review
- Harnessing induced pluripotent stem cells and organoids for disease modeling and precision medicine.Stem cell research & therapy · 2026Review
- From organoid culture to manufacturing: technologies for reproducible and scalable organoid production.npj biomedical innovations · 2026Review
- Organoids in Cancer Research and Regenerative Medicine: Current Status, Challenges, and Future Prospects.MedComm · 2026Review
- Tumor organoids for testing metabolic flexibility in precision oncology.Frontiers in oncology · 2026Review
- Patient-derived organoids in functional precision oncology: from experimental models to clinical decision-making.Frontiers in endocrinology · 2026Review
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Advances in oral disease models: a mini-review of developments from 2015 to 2025.Frontiers in dental medicine · 2026Review
- Patient-derived organoids (PDOs) as a promising platform for personalized treatment of colorectal cancer: current applications and future challenges.Cancer biology & therapy · 2025Review
65 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.
Indexed as
Identifiers
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.