A dependency map enhanced with next-generation 3D cancer models

作者：James Neiswender, Samuel Maffa, Lisa Brenan, Dina ElHarouni, Yejie Yun, Isabella Boyle, Kirsty Wienand, Haider Inam, Tate Bertea, Ashley Anderson, Megan Wong, Matias Enriquez, Evan Lenz, Beatriz Villafranca, Nora Shanks, Mary Hager, Nia Lloyd, Hannah Shadmany, Sarah J. Wie, Harry Liang, Konnor Yunghans, Xiaomeng Zhang, LAUREN GOLDEN, Hannah Harris, Serena Day, Philip Montgomery, Samantha Stokes, Ross Giglio, Cynthia Hajal, James R. Whittle, Guadalupe Garcia, Caitlin E. Mills, Mehdi Touat, Kristine Pelton, Hongyu Li, Prem Sai Prabhakar, Sonja Herter, Zoe Hoffmann Kamrat, Dan Gui, Julien Dilly, Chen Khuan Wong, Jimmy A. Guo, Sangita Pal, Yossef Baidi, Ryan Johnston, Daniel D. Brown, Sonam Bhatia, Peter Winter, Srivatsan Raghavan, Rameen Beroukhim, Eva Colas, David L. Spector, Adam J. Bass, Peter K. Sorger, Yu Chen, Sarah J. Hill, Steffi Oesterreich, Adrian V. Lee, Himisha Beltran, Jesse S. Boehm, Yuen-Yi Tseng, David E. Root, William C. Hahn, Andrew J. Aguirre, Catarina D. Campbell, Keith L. Ligon, Joshua M. Dempster, Tsukasa Shibue, Francisca Vázquez · 发表于：Nature · 年份：2026 · DOI：10.1038/s41586-026-10843-7 · 被引用次数：1 · 研究领域：Single-cell and spatial transcriptomics、Melanoma and MAPK Pathways、Cancer Genomics and Diagnostics

Despite advances in precision oncology, effective personalized treatments are still lacking for most patients with cancer1. The Cancer Dependency Map (DepMap) accelerates this field by systematically identifying cancer vulnerabilities in diverse preclinical models. Data from over 1,300 cell lines have led to the discovery of new therapeutic strategies across multiple tumour types2. However, mapping cancer vulnerabilities using traditional cell lines has limitations, including insufficient cancer subtype representation and the impact of culture conditions on perturbation responses. Here we perform 147 genome-scale CRISPR screens and multi-omic characterizations of next-generation (NextGen) cancer models (organoids and spheroids) across 10 cancer types. This strategy enables the expansion of DepMap to cover new genomic and molecular subtypes and to identify new biomarker-associated vulnerabilities. These new models also preserve transcriptional programs that are silenced in traditional cell lines and facilitate the discovery of specific gene dependencies associated with these programs. Comparisons of traditional and NextGen cancer models enable further identification of distinct effects of growth format and culture medium on gene essentiality. The integrated dataset combines data from both model types to offer a valuable, expansive resource for exploring cancer vulnerabilities and is accessible via the DepMap portal. Integration of genome-scale CRISPR screening data from tradit...