August 5, 2026 (Cambridge, MA) — For nearly a decade, the Cancer Dependency Map , or DepMap, at the Broad Institute has helped scientists uncover new cancer dependencies — the genetic changes some cancers need to thrive that can potentially be targeted by new cancer drugs.
Now, project scientists have expanded the DepMap resource to include nearly 150 cancer models that grow as 3D cultures, also known as organoids or spheroids, across 10 cancer types. By integrating this data into the DepMap's existing dataset on more than a thousand 2D cancer models, the team has made the DepMap a more comprehensive resource for researchers that promises to reveal more drug targets and disease-driving mechanisms for more cancer types.
In a study published in Nature , the team found new dependencies in the 3D models that weren't seen in the 2D ones, and generated dependency data on cancer subtypes that only grow in 3D, such as certain kinds of brain and gastrointestinal cancer. The findings provide valuable insights to researchers studying these "next-generation" 3D cancer models, and will help scientists choose between traditional and next-generation models for their work.
The study appears alongside two related papers in the same journal by other groups, one led by the National Cancer Institute's Human Cancer Models Initiative and Dana-Farber Cancer Institute, and the other by the Sanger Institute. The companion papers describe the generation and characterization of numerous 3D cancer models, including many that have been incorporated into DepMap.
"These models let us see biology that we couldn't see before," said co-senior author Francisca Vazquez , director of the Cancer Dependency Map. "That is the whole point of DepMap. The more aspects of each tumor you capture, the more of its vulnerabilities you can catch."
CANCER CATALOG
Scientists have long known that some tumors require certain genes, known as dependencies, to thrive. In 2018, researchers at the Broad launched the DepMap Consortium, an academic-industry partnership aimed at uncovering potential new drug targets through systematic screening of cancer models in search of these genetic dependencies. The DepMap has helped scientists find targets for several drug candidates that are now being tested in clinical trials by drug companies.
The DepMap team initially focused on traditional cancer cell models. They recognized that their resource was missing the many next-generation organoid and spheroid models being created by teams around the globe, but the field lacked systematic profiling of dependencies in these 3D models that could be used to identify cancer vulnerabilities. To address this gap, they gathered hundreds of these patient-derived models from research groups and commercial suppliers, and conducted a rigorous systematic characterization of dependency data to understand how they differ from 2D models of the same cancers. Most of the 3D models they examined in the new study are cultured in a small dome of gel on a lab dish, allowing the cells to expand in three dimensions, while the cells from patients with brain tumors grow as neurospheres, or balls of cells suspended in liquid culture medium.
These next-generation models are essential for precision medicine. In their analysis of the models, they found mutations and cell states that had been observed in patient tumors but weren't seen in any of the 2D models. They also identified new genetic dependencies in the 3D models that hadn't been observed in the traditional models, paving the way for a more complete cancer dependency map.
In one striking example, the team found that models of glioblastoma, an aggressive brain cancer, that were missing a tumor-suppressor gene called CDKN2A were far more sensitive to CDK6 suppression than models with an intact copy of the gene. The finding suggests CDKN2A loss could help identify glioblastoma patients most likely to benefit from the existing drugs against CDK6.
Another new finding emerged in pancreatic and other gastrointestinal organoids. The team identified a gene expression program, previously tied to a clinically relevant pancreatic cancer subtype, that persists in 3D organoids but is lost in traditional cell lines. Organoids expressing this program depended on a handful of WNT signaling genes, pointing to a potential therapeutic vulnerability that is specific to this transcriptional state and is only seen in organoid models.
However, some of the 2D models, such as those of breast cancer, carried tumor markers not found in any of the 3D models, demonstrating that both kinds of cancer models are necessary to study the large variety of cancer types. On the whole, Vazquez explained, neither model type is better than the other — they are complementary. "Traditional cell lines and organoid models each reveal dependencies that the other can't and having both makes the map more complete."
Comparing models grown under different conditions, the team mapped these differences to two distinct sources. Genes involved in cell adhesion and the cytoskeleton were sensitive to growth format, whereas genes involved in lipid metabolism were sensitive to the culture medium, regardless of format. Changing either condition modified which genes the cells depended on. These insights could help scientists select the most appropriate experimental methods.
The new DepMap data is available through the project portal and the team invites the scientific community to take advantage of this expanded, rich resource for cancer research. Rather than existing as a stand-alone dataset, this new data is now part of DepMap, moving the resource closer to its long-term goal of a complete map of the dependencies that drive every type of cancer.
Paper cited: Neiswender JV, Maffa S, Brenan L, et al. A dependency map enhanced with next-generation 3D cancer models . Nature. August 5, 2026. DOI: 10.1038/s41586-026-10843-7.
Funding: This work was supported by the Robertson Foundation, 3000 Miles to the Cure Foundation, and the Dependency Map Consortium. Research reported in this story was supported by the National Institutes of Health under grant numbers U01CA250549, R01CA252378, R01CA262462, and R01CA188228.
About the Broad Institute
Broad Institute is an independent, non-profit research organization whose mission is to understand the roots of disease and close the gap between new biological insights and impact for patients.
Founded in 2004 by the visionary Los Angeles philanthropists Eli and Edythe Broad, the Broad Institute exists at the intersection of scientific disciplines, convening scientists and other experts from genomics, cell biology, chemistry, engineering, neuroscience, therapeutics, artificial intelligence/machine learning, computational biology, and public health. The Broad Institute engages thousands of scientists from MIT, Harvard, Harvard's primary teaching hospitals, other academic institutions, and leading corporate partners in the pharmaceutical and biotechnology industries, all of whom share the goal of translating research findings into safe and effective therapeutic interventions for all common and rare diseases.
