Peer-reviewedCC BY-NC-NDNature
A dependency map enhanced with next-generation 3D cancer models
18 affiliations
- 1Broad Institute of MIT and Harvard, Cambridge, MA, USA
- 2Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
- 3Group of Biomedical Research in Gynecology, Vall d'Hebron Institute of Research (VHIR), Instituto de Investigación Sanitaria Hospital Universitari Vall d'Hebron (IIS IR-HUVH), Universitat Autònoma de Barcelona, CIBERONC, Barcelona, Spain
- 4Department of Bioengineering, Northeastern University, Boston, MA, USA
- 5Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA
- 6Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- 7Personalised Oncology Division, Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- 8Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- 9Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- 10Neuro-oncology Department, Pitié-Salpétrière Hospital, Assistance Publique-Hôpitaux de Paris, Sorbonne Université, Paris, France
- 11Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- 12Harvard Medical School, Boston, MA, USA
- 13Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 14Institute for Precision Medicine, University of Pittsburgh and UPMC, Pittsburgh, PA, USA
- 15Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- 16Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 17UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- 18Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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. Integration of genome-scale CRISPR screening data from traditional cell lines and next-generation cancer models expands the representation of tumour subtypes and genomic alterations in The Cancer Dependency Map.
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BibTeX
@article{neiswender2026dependency,
title = {{A dependency map enhanced with next-generation 3D cancer models}},
author = {James V. Neiswender and Samuel Maffa and Lisa Brenan and Dina ElHarouni and Yejie Yun and Isabella Boyle and Kirsty Wienand and Haider Inam and Tate Bertea and Ashley Anderson and Megan Wong and Matias Enriquez and Evan Lenz and Beatriz Villafranca-Magdalena and Nora Shanks and Mary Hager and Nia Lloyd and Hannah Shadmany and Sarah J. Wie and Harry Liang and Konnor Yunghans and Xiaomeng Zhang and Lauren Golden and Hannah Harris and Serena Day and Philip G. Montgomery and Samantha Stokes and Ross M. Giglio and Cynthia Hajal and James Richard Whittle and Guadalupe Garcia and Caitlin E. Mills and Mehdi Touat and Kristine Pelton and Hongyu Li and Prem Sai Prabhakar and Sonja Herter and Zoe Hoffmann Kamrat and Dan Y. Gui and Julien Dilly and Chen Khuan Wong and Jimmy A. Guo and Sangita Pal and Yossef Baidi and Ryan Johnston and Daniel D. Brown and Sonam Bhatia and Peter S. Winter and Srivatsan Raghavan and Rameen Beroukhim and Eva Colás and David L. Spector and Adam Joel Bass and Peter Karl Sorger and Yu Chen and Sarah J. Hill and Steffi Oesterreich and Adrian V. Lee and Himisha Beltran and Jesse S. Boehm},
journal = {Nature},
year = {2026},
month = aug,
doi = {10.1038/s41586-026-10843-7},
url = {https://doi.org/10.1038/s41586-026-10843-7},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A dependency map enhanced with next-generation 3D cancer models
AU - James V. Neiswender
AU - Samuel Maffa
AU - Lisa Brenan
AU - Dina ElHarouni
AU - Yejie Yun
AU - Isabella Boyle
AU - Kirsty Wienand
AU - Haider Inam
AU - Tate Bertea
AU - Ashley Anderson
AU - Megan Wong
AU - Matias Enriquez
AU - Evan Lenz
AU - Beatriz Villafranca-Magdalena
AU - Nora Shanks
AU - Mary Hager
AU - Nia Lloyd
AU - Hannah Shadmany
AU - Sarah J. Wie
AU - Harry Liang
AU - Konnor Yunghans
AU - Xiaomeng Zhang
AU - Lauren Golden
AU - Hannah Harris
AU - Serena Day
AU - Philip G. Montgomery
AU - Samantha Stokes
AU - Ross M. Giglio
AU - Cynthia Hajal
AU - James Richard Whittle
AU - Guadalupe Garcia
AU - Caitlin E. Mills
AU - Mehdi Touat
AU - Kristine Pelton
AU - Hongyu Li
AU - Prem Sai Prabhakar
AU - Sonja Herter
AU - Zoe Hoffmann Kamrat
AU - Dan Y. Gui
AU - Julien Dilly
AU - Chen Khuan Wong
AU - Jimmy A. Guo
AU - Sangita Pal
AU - Yossef Baidi
AU - Ryan Johnston
AU - Daniel D. Brown
AU - Sonam Bhatia
AU - Peter S. Winter
AU - Srivatsan Raghavan
AU - Rameen Beroukhim
AU - Eva Colás
AU - David L. Spector
AU - Adam Joel Bass
AU - Peter Karl Sorger
AU - Yu Chen
AU - Sarah J. Hill
AU - Steffi Oesterreich
AU - Adrian V. Lee
AU - Himisha Beltran
AU - Jesse S. Boehm
PY - 2026
DA - 2026/08/05
JO - Nature
DO - 10.1038/s41586-026-10843-7
UR - https://doi.org/10.1038/s41586-026-10843-7
AB - 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 traditional cell lines and next-generation cancer models expands the representation of tumour subtypes and genomic alterations in The Cancer Dependency Map.
SN - 0028-0836
SN - 1476-4687
ER -