ArticleBMC bioinformatics2020
PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Polymer Drug Conjugate: A Revolution in Drug Delivery.AAPS PharmSciTech · 2026Review
- Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs.Molecular cancer · 2026Review
- Preclinical screening models in anticancer drug development: strengths, limitations, and translational challenges.Frontiers in pharmacology · 2026Review
- Effect of Photosensitiser Chlorin E6 on Cancerous Bone Tumor Cells Using Photodynamic Therapy.Oncology research · 2026Article
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
- Approaches to modeling cancer metastasis: from bench to bedside.Frontiers in oncology · 2025Review
- The roles of patient-derived xenograft models and artificial intelligence toward precision medicine.MedComm · 2024Review
- Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine.Life (Basel, Switzerland) · 2024Review
- Reversion of pathogeniciScience · 2024Article
- Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology.Journal of proteome research · 2024Review
- Precision oncology: a review to assess interpretability in several explainable methods.Briefings in bioinformatics · 2023Review
- Patient-derived xenograft models in cancer therapy: technologies and applications.Signal transduction and targeted therapy · 2023Review
- Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images.Frontiers in medicine · 2023Article
- Implantable Devices for the Treatment of Breast Cancer.Journal of nanotheranostics · 2022Article
- Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021Review
- Bird Eye View of Protein Subcellular Localization Prediction.Life (Basel, Switzerland) · 2020Review
- Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundCancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient's tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients' drug responses based on gene expression and drug activity data from PDX models.
resultsDrug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials.
conclusionOur results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at http://pdxgem.moffitt.org .
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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.