麻省理工学院和哈佛大学的布罗德研究所Francisca Vazquez团队的一项最新研究提出了下一代3D癌症模型增强的依赖关系图。相关论文于2026年8月5日发表在《自然》杂志上。

在这里，课题组人员对10种癌症类型的下一代（NextGen）癌症模型（类器官和球状体）进行了147个基因组尺度的CRISPR筛选和多组学表征。这一战略使DepMap能够扩展到新的基因组和分子亚型，并确定新的生物标志物相关脆弱性。这些新模型还保留了传统细胞系中沉默的转录程序，并促进了与这些程序相关的特定基因依赖性的发现。通过对传统癌症模型和NextGen癌症模型的比较，可以进一步确定生长形式和培养基对基因必要性的不同影响。集成的数据集结合了两种模型类型的数据，为探索癌症脆弱性提供了有价值的、广泛的抵抗，并可通过DepMap门户访问。

研究人员表示，尽管精准肿瘤学取得了进步，但大多数癌症患者仍然缺乏有效的个性化治疗。癌症依赖图谱（DepMap）通过系统地识别不同临床前模型中的癌症脆弱性，加速了这一领域的发展。来自1300多个细胞系的数据已经发现了针对多种肿瘤类型的新治疗策略。然而，以传统细胞系为主题绘制癌症脆弱性存在局限性，包括癌症亚型代表性不足以及培养条件对扰动反应的影响。

附：英文原文

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

Author: 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

Issue&Volume: 2026-08-05

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.

DOI: 10.1038/s41586-026-10843-7

Source: https://www.nature.com/articles/s41586-026-10843-7