Neiswender, James V, Maffa, Samuel, Brenan, Lisa, ElHarouni, Dina, Yun, Yejie, Boyle, Isabella, Wienand, Kirsty, Inam, Haider, Bertea, Tate, Anderson, Ashley, Wong, Megan, Enriquez, Matias, Lenz, Evan, Villafranca, Beatriz, Shanks, Nora, Hager, Mary, Lloyd, Nia, Shadmany, Hannah, Wie, Sarah J, Liang, Harry, Yunghans, Konnor, Zhang, Xiaomeng, Golden, Lauren, Harris, Hannah, Day, Serena, Montgomery, Philip, Stokes, Samantha, Giglio, Ross M, Hajal, Cynthia, Whittle, James R, Garcia, Guadalupe, Mills, Caitlin E, Touat, Mehdi, Pelton, Kristine, Li, Hongyu, Prabhakar, Prem Sai, Herter, Sonja, Kamrat, Zoe Hoffmann, Gui, Dan, Dilly, Julien, Wong, Chen Khuan, Guo, Jimmy A, Pal, Sangita, Baidi, Yossef, Johnston, Ryan, Brown, Daniel D, Bhatia, Sonam, Winter, Peter S, Raghavan, Srivatsan, Beroukhim, Rameen, Colas, Eva, Spector, David L, Bass, Adam J, Sorger, Peter K, Chen, Yu, Hill, Sarah J, Oesterreich, Steffi, Lee, Adrian V, Beltran, Himisha, Boehm, Jesse S, Tseng, Yuen-Yi, Root, David E, Hahn, William C, Aguirre, Andrew J, Campbell, Catarina D, Ligon, Keith L, Dempster, Joshua M, Shibue, Tsukasa, Vazquez, Francisca (August 2026) A dependency map enhanced with next-generation 3D cancer models. Nature. ISSN 0028-0836
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Abstract
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.
| Item Type: | Paper |
|---|---|
| Subjects: | diseases & disorders > cancer diseases & disorders Investigative techniques and equipment Investigative techniques and equipment > CRISPR-Cas9 |
| CSHL Authors: | |
| Communities: | CSHL labs > Spector lab CSHL Post Doctoral Fellows |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 5 August 2026 |
| Date Deposited: | 07 Aug 2026 14:34 |
| Last Modified: | 07 Aug 2026 14:34 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/42284 |
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