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Publications

Journal Article Ahead of print 7 citations

PubMed: 42557316 DOI: 10.1038/s41586-026-10806-y

A compendium of next-generation patient-derived models for diverse cancers.

Nature

Authors: Dina ElHarouni, Mushriq Al-Jazrawe, Seongmin Choi, Merve Dede, Toshinori Hinoue, Sean A Misek, Heeju Noh, Luca Zanella, Yuen-Yi Tseng, Hayley E Francies, Dennis Plenker, Cindy W Kyi, Julyann Perez-Mayoral, Megan J Stine, Eva Tonsing-Carter, Rachana Agarwal, Jean Claude Zenklusen, James M Clinton, Jennifer M Shelton, Timothy R Chu, William F Hooper, Xavi Loinaz, Paula Keskula, Jordan Tagle, Peyton C Kuhlers, Bahar Tercan, Sylvia F Boj, Alessandro Vasciaveo, Lorenzo Tomassoni, James M Crawford, Shawna Walsh, Claire Sinai, Sonam Bhatia, Priya Sridevi, Hardik Patel, Maria Antonietta Cerone, Kyle Ellrott, Calvin J Kuo, Olivier Elemento, Semir Beyaz, Vincenzo Corbo, David L Spector, Rameen Beroukhim, Martin L Ferguson, Andrew D Cherniack, Peter W Laird, Nicolas Robine, Andrew McPherson, Katherine A Hoadley, Mathew J Garnett … and 7 other authors

Affiliations

  • Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
  • Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Van Andel Institute, Department of Epigenetics, Grand Rapids, MI, USA.
  • Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School Boston, Boston, MA, USA.
  • Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA.
  • Institute for Systems Biology, Seattle, WA, USA.
  • Wellcome Sanger Institute, Cambridge, UK.
  • Cold Spring Harbor Laboratory Cancer Center, Cold Spring Harbor, New York, NY, USA.
  • Center for Cancer Genomics, NCI, Bethesda, MD, USA.
  • Management Services, Calverton, MD, USA.
  • Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
  • American Type Culture Collection, Manassas, VA, USA.
  • New York Genome Center, New York, NY, USA.
  • Krantz Family Center for Cancer Research, Boston, MA, USA.
  • Oregon Health & Science University, Department of Biomedical Engineering, Portland, OR, USA.
  • Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • HUB Organoids, Merck, Utrecht, The Netherlands.
  • Center for Data Science and Artificial Intelligence, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
  • DarwinHealth, New York, NY, USA.
  • Northwell Health, New Hyde Park, NY, USA.
  • Cancer Research Horizons, Therapeutic Innovation, Babraham Research Campus, Cambridge, UK.
  • Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, USA.
  • Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Department of Engineering for Innovation Medicine and ARC-Net Research Centre, University of Verona, Verona, Italy.

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.

MeSH

Neoplasms, Therapeutics