Comparative analysis of patient-derived organoids and patient-derived xenografts as avatar models for predicting response to anti-cancer therapy

Joan Miguel Romero ^1,2,3, Jamie Magrill ^1,2, Nikita Kalashnikov ¹

¹McGill University, Montreal, Quebec, Canada.
²Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
³Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada.
⁴Université de Montreal, Montreal, Quebec, Canada.
⁵Division of Medical Oncology, The Ohio State University, Columbus, OH, USA.
⁶University of Windsor, Windsor, Ontario, Canada.
⁷Division of Cardiac Surgery, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
⁸Department of Internal Medicine I, Ulm University Hospital, Ulm, Germany.
⁹Institute of Molecular Oncology and Stem Cell Biology, Ulm University Hospital, Ulm, Germany; Division of Interdisciplinary Pancreatology, Department of Internal Medicine I, Ulm University Hospital, Ulm, Germany; Core Facility Organoids, Ulm University, Ulm, Germany.
¹⁰Institute of Molecular Oncology and Stem Cell Biology, Ulm University Hospital, Ulm, Germany.
¹¹University of Texas Southwestern Medical Center, Texas, USA.
¹²Division of Oncology Research, Department of Oncology, Mayo Clinic, Minnesota, USA.
¹³Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo, Japan.
¹⁴Department of Oncology, Western University, London, Ontario, Canada.
¹⁵Department of Otolaryngology-Head & Neck Surgery, Western University, London, Ontario, Canada.
¹⁶Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Fisciano, Italy.
¹⁷Department of Biochemistry and Molecular Biology, Universitat de València, Burjassot, Spain; Centro de Investigación Biomédica en Red (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain; INCLIVA Biomedical Research Institute, Valencia, Spain.
¹⁸Department of Clinical Bio-resource Research and Development, Graduate School of Medicine Kyoto University, Kyoto, Japan.
¹⁹Inserm/University of Strasbourg U1260, Regenerative NanoMedicine, Centre de Recherche en Biomédecine de Strasbourg, Strasbourg, France.
²⁰Department of Urology, University Hospital Strasbourg, Strasbourg, France.
²¹Department of Pathology, Strasbourg University Hospital, Strasbourg, France.
²²Sahlgrenska Center for Cancer Research, University of Gothenburg, Gothenburg, Sweden.
²³Invitrocue Europe AG, Munich, Germany.
²⁴Baylor College of Medicine, Houston, Texas, USA.
²⁵Department of Urology, Baylor College of Medicine, Houston, Texas, USA.
²⁶Department of Head and Neck Surgery, University of Texas, MD Anderson Cancer Center, Texas, USA.
²⁷Institut de Recherche en Cancérologie de Montpellier, Montpellier Université, INSERM, ICM, CNRS, Montpellier, France.
²⁸Department of Medicine, National University of Singapore, Singapore.
²⁹Children's Cancer Centre, Royal Children's Hopital, Melbourne, Australia.
³⁰Departments of Surgery and Medical Biophysics, University of Toronto, Toronto, Canada.
³¹Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada.
³²Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada.

⁰McGill University, Montreal, Quebec, Canada.

Medrxiv : the Preprint Server for Health Sciences +

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August 20, 2025

View abstract on PubMed

Summary

Patient-derived organoids (PDO) and xenografts (PDX) show similar accuracy in predicting cancer treatment response. PDOs offer comparable predictive value to PDX models, with potentially fewer financial and ethical concerns.

Area Of Science

Oncology
Translational Cancer Research
Biomedical Modeling

Background

Patient-derived xenografts (PDX) and organoids (PDO) are crucial for modeling cancer and predicting patient treatment outcomes.
Systematic comparison and validation of their predictive accuracy remain limited.

Purpose Of The Study

To systematically review and compare the predictive accuracy of PDX and PDO models against matched patient treatment responses.
To evaluate the association between model response and patient progression-free survival.

Main Methods

Systematic review and meta-analysis of studies involving solid tumors and matched patient-model treatment data.
Analysis of 411 patient-model pairs (267 PDX, 144 PDO) treated with identical anti-cancer agents.
Bias assessment applied to PDX model data.

Main Results

Overall concordance between patient and model treatment response was 70%, with no significant difference between PDX and PDO.
Comparable sensitivity, specificity, and predictive values were observed for both model types.
Patient progression-free survival was linked to PDO response, and to PDX response in low-bias pairs.

Conclusions

PDOs demonstrate comparable predictive performance to PDX models for patient cancer treatment response.
PDOs may represent a more accessible and ethically favorable alternative to PDX models.
Further research into bias assessment in PDX models is warranted.

Keywords:

PDO PDX avatar organoid xenograft