For nearly a decade, the Cancer Dependency Map, known as DepMap, has helped researchers identify the genetic weaknesses that cancer cells rely on to survive. Now, scientists at the Broad Institute of MIT and Harvard have expanded the resource with dependency data from nearly 150 three-dimensional cancer models grown as organoids or spheroids. Covering 10 cancer types, the new dataset adds a biological dimension largely absent from traditional two-dimensional cancer cell cultures and could reveal therapeutic opportunities hidden by conventional laboratory models.
The study, published August 5, 2026, in Nature, integrates the 3D models into DepMap’s existing collection of more than 1,000 2D cancer models. Researchers found that the two model systems often captured different genetic dependencies, meaning that neither could provide a complete picture of cancer biology on its own. The 3D models also represented cancer subtypes that are difficult or impossible to maintain as conventional cell lines, including certain brain and gastrointestinal tumors. The findings suggest that the next generation of cancer research may depend on using multiple model formats rather than selecting a single “best” system.
Cancer dependencies are genes or molecular pathways that tumor cells require for growth, proliferation, or survival. In principle, disabling such a gene can selectively damage cancer cells while leaving healthy cells less affected. DepMap scientists systematically measure these dependencies by perturbing genes, often with CRISPR-based methods, and then observing whether cancer cells continue to grow. When these results are combined with information about mutations, gene expression, and cellular state, researchers can identify relationships between a tumor’s molecular features and its potential drug vulnerabilities.
Traditional cancer cell lines have been indispensable for this work because they are relatively easy to grow, manipulate, and screen at large scale. However, they are usually adapted to flat plastic surfaces and nutrient-rich laboratory media. During that adaptation, some features of the original tumor can disappear. Organoids and spheroids offer a different environment: cells grow in three dimensions, often within a gel matrix or as suspended clusters, allowing them to establish cell-cell contacts and structural states that more closely resemble those found in patient tumors. Brain tumor models may grow as neurospheres, while many gastrointestinal and pancreatic models are maintained as patient-derived organoids.
The Broad-led team found that 3D models retained mutations and cellular programs observed in human tumors but absent from the available 2D lines. These differences were linked to new genetic dependencies that could not be detected through traditional screening alone. The result is a more diverse dependency landscape, potentially expanding the number of drug targets available for cancers that have been poorly represented in standard laboratory systems. The researchers describe the expanded collection as a step toward a more complete map of the biological requirements that drive different forms of cancer.
One particularly striking example involved glioblastoma, an aggressive and frequently lethal brain cancer. Three-dimensional glioblastoma models lacking the tumor-suppressor gene CDKN2A were substantially more sensitive to suppression of CDK6 than models retaining an intact copy of the gene. CDKN2A normally helps restrain cell-cycle progression, while CDK6 promotes the transition toward DNA replication and cell division. The result indicates that CDKN2A loss may serve as a biomarker for identifying glioblastoma patients who could be more likely to respond to existing CDK6-targeting drugs, although the finding will require validation in additional models and clinical studies.
The researchers also uncovered a vulnerability in pancreatic and other gastrointestinal organoids. Some of these models maintained a gene-expression program previously associated with a clinically important pancreatic cancer subtype. That program was largely lost in conventional cell lines, making it difficult to study using standard approaches. Organoids carrying the transcriptional state depended on several genes involved in WNT signaling, a pathway that regulates cell identity, tissue organization, and stem-cell behavior. The result points to a possible therapeutic opportunity that is specific to a cancer state preserved in 3D culture and may help explain why certain tumors behave differently in patients than in conventional laboratory experiments.
The study further showed that experimental conditions can shape dependency measurements in distinct ways. Genes involved in cell adhesion and cytoskeletal organization were particularly sensitive to whether cells grew in two or three dimensions. By contrast, genes involved in lipid metabolism were influenced more strongly by the composition of the culture medium, regardless of the physical format. Changing either the growth geometry or the nutrients available to cells altered the genes on which they depended. These observations provide a technical warning for cancer researchers: a dependency observed in one culture system may reflect not only tumor genetics but also the artificial conditions used to maintain the cells.
The expanded DepMap also revealed why 2D models remain valuable. Some breast cancer cell lines contained tumor markers that were not found in any of the 3D models examined, showing that organoids do not automatically reproduce every clinically relevant feature. Instead, the two systems offer complementary views of cancer biology. By combining their data, researchers can compare dependencies across genetic backgrounds, tissue architectures, and cellular states, improving the chances of distinguishing broadly useful drug targets from vulnerabilities that emerge only in particular experimental environments. The new resource is now available through the DepMap portal, where scientists can use it to investigate cancer mechanisms, prioritize therapeutic targets, and choose model systems more strategically.
Subject of Research: Cells
Article Title: A dependency map enhanced with next-generation 3D cancer models
News Publication Date: August 5, 2026
Web References: Cancer Dependency Map portal; Nature article
References: 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.
Keywords: Cancer Dependency Map, DepMap, organoids, spheroids, 3D cancer models, cancer dependencies, glioblastoma, CDKN2A, CDK6, WNT signaling, pancreatic cancer, gastrointestinal cancer, precision medicine, drug targets, Broad Institute
