GRAND RAPIDS, Mich. (Aug. 5, 2026) — An international team of scientists has developed the most comprehensive compendium to date of cell line and organoid cancer models paired with their original human tumors. The new resource gives researchers powerful tools to better understand tumor biology, predict treatment response and accelerate development of personalized cancer therapies.
Cell line models are cancer cells generally grown in flat layers, while organoids are three-dimensional clusters of cells suspended in gel that mimic the structure and behavior of tumors in the body. These models allow scientists to study the fine details of cancer and evaluate potential treatments in a controlled environment. The closer a model reflects the original tumor’s biological features, the more reliable it is as a research tool.
Many model collections lack important clinical and molecular data that help scientists link tumor characteristics to experimental outcomes. The new compendium closes this gap by including deep genomic, epigenomic and transcriptional data with each model and its matched tumor, creating rich molecular profiles that helps researchers identify and understand the biological mechanisms driving individual cancers.
The project is described in a Nature paper published today titled, “ A compendium of next-generation patient-derived models for diverse cancers .” It is part of a package of three complementary studies from the Human Cancer Models Initiative (HCMI), a multi-institutional collaboration led by the National Cancer Institute of the National Institutes of Health, Cancer Research UK, the Wellcome Sanger Institute and Hubrecht Organoid Technology.
Van Andel Institute’s Peter W. Laird, Ph.D. , and Hui Shen, Ph.D. , are co-authors of the compendium study. Toshinori Hinoue, Ph.D., a bioinformatics scientist in the Laird Lab, is co-first author.
“One of the biggest challenges in cancer research is developing models that retain characteristics of the original tumors and behave in the culture dish like they do in the body. The new compendium addresses this by providing rigorously developed cell line and organoid models that have been deeply analyzed to ensure close fidelity to their source cells,” Laird said. “The take-home message is that these models are excellent representations of their original tumors. We’re excited to share this resource with the scientific community and hope that it will accelerate cancer breakthroughs.”
The compendium comprises 665 models derived from 2,780 donors across 25 cancer types. Of these, 522 models include comprehensive clinical data, 153 are models of rare cancers and 71 are derived from people of non-European ancestry. Researchers can learn more about the compendium and other HCMI resources at cancer.gov/ccg/research/functional-genomics/hcmi .
Laird and Hinoue led the compendium study’s DNA methylation analysis, which assessed how closely the cancer models reflected the methylation state of the original tumors. DNA methylation regulates whether genes are turned “off” or “on.” Disruptions in this crucial epigenetic process are widely recognized as hallmarks of cancer and targets for new treatments.
In addition to data generated by HCMI, the study leveraged data from The Cancer Genome Atlas, a now-concluded, NCI-led effort to molecularly map 33 cancer types across 10,000 tumors. Laird served as a principal investigator and directed DNA methylation analysis for the TCGA consortium . Laird and Shen currently lead epigenetic analysis work for the Genomic Data Analysis Network (GDAN), an NCI team science project that develops tools and resources to help cancer researchers harness data from TCGA and clinical trials. The HCMI DNA methylation analysis conducted by the VAI investigators was supported by GDAN.
In addition to Hinoue, co-first authors of the study include Dina ElHarouni, Ph.D., and Mushriq Al-Jazware, Ph.D., of Broad Institute; Seongmin Choi, M.D., of Memorial Sloan Kettering Cancer Center; Merve Dede, M.D., Ph.D., of University of Texas MD Anderson Cancer Center; Sean A. Misek, Ph.D., of Broad Institute and Dana-Farber Cancer Center; Heeju Noh, Ph.D., of Columbia University Irving Medical Center and the Institute for Systems Biology; and Luca Zanella, Ph.D., of Columbia University Irving Medical Center.
Co-corresponding authors include Mathew J. Garnett, Ph.D., of the Wellcome Sanger Institute; David A. Tuveson, M.D., Ph.D., of Cold Spring Harbor Laboratory Cancer Center; Andrea Califano, Ph.D., of Columbia University; Paul T. Spellman, Ph.D., of University of California, Los Angeles; Keith L. Ligon, M.D., Ph.D., of Broad Institute and Dana-Farber Cancer Institute; Daniela S. Gerhard, Ph.D., of the National Cancer Institute Center for Cancer Genomics; Louis M. Staudt, M.D., Ph.D., of the National Cancer Institute Center for Cancer Research; and Jesse S. Boehm, Ph.D., of Broad Institute and the Koch Institute for Integrative Cancer Research at MIT.
Please see the publication for a full author list.
Research conducted at VAI and reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award no. U24CA264023 (Laird and Shen).
Support received by other co-authors may be found in the publication’s Acknowledgements section.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding organizations.
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Nature
A compendium of next-generation patient-derived models for diverse cancers
5-Aug-2026