Comparative analysis of patient-derived organoids and patient-derived xenografts as avatar models for predicting response to anti-cancer therapy
Year: 2026
Type: article
Study designs: Systematic Review, Meta-Analysis
Abstract: Patient-derived xenografts (PDX) and patient-derived organoids (PDO) are widely used to model cancer and predict treatment response in matched patients. However, their predictive accuracy has not been systematically studied nor compared. We conducted a systematic review and meta-analysis of studies using PDX or PDO from solid tumors treated with identical anti-cancer agents as the matched patient, identifying 411 patient-model pairs (267 PDX, 144 PDO). Overall concordance in treatment response between patients and matched models was 70%, with no significant differences between PDX and PDO. Sensitivity, specificity, and positive and negative predictive value were also comparable. Patients whose matched PDO responded to therapy had prolonged progression-free survival. For PDX, this association held only when analyses were restricted to patient-model pairs with low risk of bias after applying a bias assessment metric. Together, these findings suggest that in some contexts, PDO perform similarly to PDX in predicting matched-patient response while potentially offering lower financial and ethical burdens. Given that both platforms have distinct strengths and weaknesses, they continue to serve complementary roles in translational cancer research. Additional prospective studies will be required before definitive recommendations can be made.
Source: Cancer Treatment Reviews
Authors: Joan Miguel Romero, Jamie Magrill, Nikita Kalashnikov, Yi Zhen Luo, Owen J. Chen, Sandrine Busque, Rong Ma, Aline Atallah, Anna-Maria Lazaratos, Daniel Mendelson, Liam Wilson, Shriya Deshmukh, Tarek Taifour, Mark Sorin, Isabella Arthur, Hellen Kuasne, Jeremy Y. Levett, Yifan Wang, Thomas Seufferlein, Alexander Kleger, Johann Gout, Alica K. Michels, James Brugarolas, Zachry S. Poshusta, Tara L. Hogenson, Martin E. Fernandez-Zapico, Akinobu Hamada, Shigehiro Yagishita, Kushtrim Kryeziu, Ragnhild A. Lothe, Anthony Nichols, John W. Barrett, Federica Papaccio, Josefa Castillo, Masahiro Inoue, Thierry Massfelder, Hervé Lang, Veronique Lindner, Jonas Nilsson, Zahra Dantes, Hanna Seppanen, Gabrielle A. Wells, Sung Han Kim, Michael M. Ittmann, Hugo Villanueva, Seth P. Lerner, Andrew G. Sikora, Charles Theillet, Daniel Q. Huang, Dong-Anh Khuong-Quang, Jonathan C. Yeung, Morag Park, Peter M. Siegel, Ian R. Watson, George Zogopoulos, April A.N. Rose, Matthew Dankner
Institutions: Institut du cancer Rosalind & Morris Goodman, Research Institute of the McGill University Health Centre, McGill University, Université de Montréal, The Ohio State University, University of Windsor, University Hospital Ulm, Universität Ulm, The University of Texas Southwestern Medical Center, Mayo Clinic, National Cancer Center Research Institute, Western University, Oslo University Hospital, University of Salerno, Universitat de València, Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red, INCLIVA Health Research Institute, Centro de Investigación Biomédica en Red de Cáncer, Kyoto University, Inserm, Regenerative NanoMedicine, Université de Strasbourg, Hôpitaux Universitaires de Strasbourg, University of Gothenburg, Baylor College of Medicine, University of Helsinki, Helsinki University Hospital, The University of Texas MD Anderson Cancer Center, Centre National de la Recherche Scientifique, Institut de Recherche en Cancérologie de Montpellier, National University of Singapore, Royal Children's Hospital, University of Toronto, Jewish General Hospital, Lady Davis Institute for Medical Research
Language: English
Topic: Cancer Cells and Metastasis
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,910
OpenAlex ID: W4413128233