ArticleNature communications2015
Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 169 papers, 1 of them a synthesis 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.
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Who cites it
169 citing papers in PubMed, 1 synthesis or guideline pooled it, 306 citations in OpenAlex.
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- Immune microenvironment modulation unmasks therapeutic benefit of radiotherapy and checkpoint inhibition.Journal for immunotherapy of cancer · 2019Trial
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- Nanoparticle-Based Biomaterials in Cancer Research: From Mechanistic Insights to Therapeutic Innovation.International journal of molecular sciences · 2026Review
- Repurposed cAMP-Modulating Agents Enhance 5-Fluorouracil Response Through Membrane-Dependent Mechanisms.Membranes · 2026Review
- Clinical Utility of an Ex Vivo Functional Test in Personalized Cancer Treatment.Journal of personalized medicine · 2026Article
- Integrated Experimental-Computational Framework for Drug Transport Quantification in 3DMicromachines · 2026Article
- A live tumor fragment platform to assess immunotherapy response in core needle biopsies while addressing challenges of tumor heterogeneity.Journal of translational medicine · 2026Observational
- Fluid-Derived Organoids from Pleural Effusion and Ascites: Emerging Models for Drug Resistance and Personalized Oncology.Journal of Cancer · 2026Review
- Zebrafish Xenograft Model for Predicting Cisplatin Efficacy in Muscle-Invasive Bladder Cancer.Cancer science · 2025Article
- Patient-derived organotypic tumor spheroids, tumoroids, and organoids: advancing immunotherapy using state-of-the-art 3D tumor model systems.Lab on a chip · 2025Review
- Personalized drug stratification using endoscopic samples to assess ex vivo gastric cancer tissue susceptibility to chemotherapy and immune checkpoint inhibitors.Clinical and experimental medicine · 2025Article
- IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.Journal of advanced research · 2025Article
- Personalising glioblastoma medicine: explant organoid applications, challenges and future perspectives.Acta neuropathologica communications · 2025Review
- Case Report: Personalized, functional drug sensitivity-guided chemotherapy achieves long-term disease-free survival in canine pulmonary adenocarcinoma.Frontiers in veterinary science · 2025Article
- Cancer organoids 2.0: modelling the complexity of the tumour immune microenvironment.Nature reviews. Cancer · 2024Review
- Patient-derived tumor organoids: a new avenue for preclinical research and precision medicine in oncology.Experimental & molecular medicine · 2024Review
- The OVAREX study: Establishment of ex vivo ovarian cancer models to validate innovative therapies and to identify predictive biomarkers.BMC cancer · 2024Observational
- An optimised patient-derived explant platform for breast cancer reflects clinical responses to chemotherapy and antibody-directed therapy.Scientific reports · 2024Article
- Rebuilding the microenvironment of primary tumors in humans: a focus on stroma.Experimental & molecular medicine · 2024Review
109 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
23 authors at 9 institutions in 2 countries.
Funding
Abstract
Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.
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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.