Peer-reviewedCC BYNature
A compendium of next-generation patient-derived models for diverse cancers
35 affiliations
- 1Broad Institute of MIT and Harvard, Cambridge, MA, USA
- 2Department of Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- 3Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- 4Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 5The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 6Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- 7Van Andel Institute, Department of Epigenetics, Grand Rapids, MI, USA
- 8Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School Boston, Boston, MA, USA
- 9Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA
- 10Institute for Systems Biology, Seattle, WA, USA
- 11Wellcome Sanger Institute, Cambridge, UK
- 12Cold Spring Harbor Laboratory Cancer Center, Cold Spring Harbor, New York, NY, USA
- 13Center for Cancer Genomics, NCI, Bethesda, MD, USA
- 14Management Services, Calverton, MD, USA
- 15Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- 16American Type Culture Collection, Manassas, VA, USA
- 17New York Genome Center, New York, NY, USA
- 18Krantz Family Center for Cancer Research, Boston, MA, USA
- 19Oregon Health & Science University, Department of Biomedical Engineering, Portland, OR, USA
- 20Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- 21HUB Organoids, Merck, Utrecht, The Netherlands
- 22Center for Data Science and Artificial Intelligence, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- 23DarwinHealth, New York, NY, USA
- 24Northwell Health, New Hyde Park, NY, USA
- 25Cancer Research Horizons, Therapeutic Innovation, Babraham Research Campus, Cambridge, UK
- 26SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- 27Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- 28Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- 29Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- 30University of Verona, Verona, Italy
- 31Case Western Reserve University, Cleveland, OH, USA
- 32Department of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- 33Department of Computer Science, Princeton University, Princeton, NJ, USA
- 34Milken Institute, Washington, DC, USA
- 35Buck Institute for Research on Aging, Novato, CA, USA
Abstract
The development of new therapeutics and the validation of pathogenetic cancer mechanisms require representative laboratory models 1,2 . However, existing collections represent only a fraction of the diversity observed in human cancer 2–4 . Recent technologies have enabled efficient in vitro model derivation (for example, tumour organoids) 5 . However, whether these maintain essential properties of patient tumours during long-term expansion has not been systematically investigated. Here we present results of a large-scale international programme—the Human Cancer Models Initiative—which involved the generation of a resource of 665 next-generation models from 2,780 donors with 25 cancer types and integrated tumour–model whole genome, exome, methylome and transcriptome analyses. The resource provides 522 models with comprehensive clinical data, 153 models of rare cancers and 71 models from participants with non-European ancestry. Analyses of 421 matched tumour–model pairs reveal high genetic (97.8%) and epigenetic (95%) concordance and define correlates of model discordance. Single-nucleus RNA sequencing of tumour–model pairs reveals subsets of models in which culture conditions significantly influence cell states. Finally, we characterize model preservation of extrachromosomal DNA and post-treatment mutational signatures to provide opportunities to study therapeutic resistance. This model repository is being made available to the community—including multimodal molecular profiling, clinical information and integrative software tools—thus providing a valuable resource for preclinical investigation of cancer pathogenesis and treatment response.
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BibTeX
@article{elharouni2026compendium,
title = {{A compendium of next-generation patient-derived models for diverse cancers}},
author = {Dina ElHarouni and Mushriq Al‐Jazrawe and Seongmin Choi and Merve Dede and Toshinori Hinoue and Sean A. Misek and Heeju Noh and Luca Zanella and Yuen-Yi Tseng and Hayley E. Francies and Dennis Plenker and Cindy W. Kyi and Julyann Pérez‐Mayoral and Megan J. Stine and Eva Y. Tonsing-Carter and Rachana Agarwal and Jean Claude Zenklusen and James M. Clinton and Jennifer Marie Shelton and Timothy R. Chu and William F. Hooper and Xavi Loinaz and Paula Keskula and Jordan Lee and Peyton C. Kuhlers and Bahar Tercan and Sylvia F. Boj and Alessandro Vasciaveo and Lorenzo Tomassoni and James M. Crawford and Shawna Walsh and Claire E. Sinai and Sonam Bhatia and Priya Sridevi and Hardik Patel and Maria Antonietta Cerone and The HCMI Network and Mubarak Akadri and Andrew J. Aguirre and Rehan Akbani and Majd Al Assaad and Wael Al Zoughbi and Alyaa Al‐Ibraheemi and Sahar Alkhairy and Nasser Altorki and Silvia Andreani and Joshua Araya and Gayatri Arun and Adel Atari and Stefanie Avril and Toby M. Baker and Metin Balaban and Michael Barnes and Caitlyn W. Barrett and Adam Joel Bass and Alexandra Beck and Pascal Belleau and Christopher C. Benz and Bhavneet Bhinder and Shriram G. Bhosle},
journal = {Nature},
year = {2026},
month = aug,
doi = {10.1038/s41586-026-10806-y},
url = {https://doi.org/10.1038/s41586-026-10806-y},
note = {CC BY},
}
RIS
TY - JOUR
TI - A compendium of next-generation patient-derived models for diverse cancers
AU - Dina ElHarouni
AU - Mushriq Al‐Jazrawe
AU - Seongmin Choi
AU - Merve Dede
AU - Toshinori Hinoue
AU - Sean A. Misek
AU - Heeju Noh
AU - Luca Zanella
AU - Yuen-Yi Tseng
AU - Hayley E. Francies
AU - Dennis Plenker
AU - Cindy W. Kyi
AU - Julyann Pérez‐Mayoral
AU - Megan J. Stine
AU - Eva Y. Tonsing-Carter
AU - Rachana Agarwal
AU - Jean Claude Zenklusen
AU - James M. Clinton
AU - Jennifer Marie Shelton
AU - Timothy R. Chu
AU - William F. Hooper
AU - Xavi Loinaz
AU - Paula Keskula
AU - Jordan Lee
AU - Peyton C. Kuhlers
AU - Bahar Tercan
AU - Sylvia F. Boj
AU - Alessandro Vasciaveo
AU - Lorenzo Tomassoni
AU - James M. Crawford
AU - Shawna Walsh
AU - Claire E. Sinai
AU - Sonam Bhatia
AU - Priya Sridevi
AU - Hardik Patel
AU - Maria Antonietta Cerone
AU - The HCMI Network
AU - Mubarak Akadri
AU - Andrew J. Aguirre
AU - Rehan Akbani
AU - Majd Al Assaad
AU - Wael Al Zoughbi
AU - Alyaa Al‐Ibraheemi
AU - Sahar Alkhairy
AU - Nasser Altorki
AU - Silvia Andreani
AU - Joshua Araya
AU - Gayatri Arun
AU - Adel Atari
AU - Stefanie Avril
AU - Toby M. Baker
AU - Metin Balaban
AU - Michael Barnes
AU - Caitlyn W. Barrett
AU - Adam Joel Bass
AU - Alexandra Beck
AU - Pascal Belleau
AU - Christopher C. Benz
AU - Bhavneet Bhinder
AU - Shriram G. Bhosle
PY - 2026
DA - 2026/08/05
JO - Nature
DO - 10.1038/s41586-026-10806-y
UR - https://doi.org/10.1038/s41586-026-10806-y
AB - The development of new therapeutics and the validation of pathogenetic cancer mechanisms require representative laboratory models 1,2 . However, existing collections represent only a fraction of the diversity observed in human cancer 2–4 . Recent technologies have enabled efficient in vitro model derivation (for example, tumour organoids) 5 . However, whether these maintain essential properties of patient tumours during long-term expansion has not been systematically investigated. Here we present results of a large-scale international programme—the Human Cancer Models Initiative—which involved the generation of a resource of 665 next-generation models from 2,780 donors with 25 cancer types and integrated tumour–model whole genome, exome, methylome and transcriptome analyses. The resource provides 522 models with comprehensive clinical data, 153 models of rare cancers and 71 models from participants with non-European ancestry. Analyses of 421 matched tumour–model pairs reveal high genetic (97.8%) and epigenetic (95%) concordance and define correlates of model discordance. Single-nucleus RNA sequencing of tumour–model pairs reveals subsets of models in which culture conditions significantly influence cell states. Finally, we characterize model preservation of extrachromosomal DNA and post-treatment mutational signatures to provide opportunities to study therapeutic resistance. This model repository is being made available to the community—including multimodal molecular profiling, clinical information and integrative software tools—thus providing a valuable resource for preclinical investigation of cancer pathogenesis and treatment response.
SN - 0028-0836
SN - 1476-4687
ER -