Author
Listed:
- Kyuho Han
(Stanford University School of Medicine)
- Sarah E. Pierce
(Stanford University School of Medicine)
- Amy Li
(Stanford University School of Medicine)
- Kaitlyn Spees
(Stanford University School of Medicine)
- Gray R. Anderson
(Stanford University School of Medicine)
- Jose A. Seoane
(Stanford University School of Medicine
Stanford University School of Medicine)
- Yuan-Hung Lo
(Stanford University School of Medicine)
- Michael Dubreuil
(Stanford University School of Medicine
Stanford University School of Medicine)
- Micah Olivas
(Stanford University School of Medicine)
- Roarke A. Kamber
(Stanford University School of Medicine)
- Michael Wainberg
(Stanford University)
- Kaja Kostyrko
(University of California San Francisco)
- Marcus R. Kelly
(Stanford University School of Medicine)
- Maryam Yousefi
(Stanford University School of Medicine)
- Scott W. Simpkins
(Stanford University School of Medicine)
- David Yao
(Stanford University School of Medicine)
- Keonil Lee
(Stanford University School of Medicine)
- Calvin J. Kuo
(Stanford University School of Medicine
Stanford University School of Medicine)
- Peter K. Jackson
(Stanford University School of Medicine
Stanford University School of Medicine)
- Alejandro Sweet-Cordero
(University of California San Francisco)
- Anshul Kundaje
(Stanford University School of Medicine
Stanford University)
- Andrew J. Gentles
(Stanford University School of Medicine)
- Christina Curtis
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
- Monte M. Winslow
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University School of Medicine)
- Michael C. Bassik
(Stanford University School of Medicine
Stanford University School of Medicine
Stanford University)
Abstract
Cancer genomics studies have identified thousands of putative cancer driver genes1. Development of high-throughput and accurate models to define the functions of these genes is a major challenge. Here we devised a scalable cancer-spheroid model and performed genome-wide CRISPR screens in 2D monolayers and 3D lung-cancer spheroids. CRISPR phenotypes in 3D more accurately recapitulated those of in vivo tumours, and genes with differential sensitivities between 2D and 3D conditions were highly enriched for genes that are mutated in lung cancers. These analyses also revealed drivers that are essential for cancer growth in 3D and in vivo, but not in 2D. Notably, we found that carboxypeptidase D is responsible for removal of a C-terminal RKRR motif2 from the α-chain of the insulin-like growth factor 1 receptor that is critical for receptor activity. Carboxypeptidase D expression correlates with patient outcomes in patients with lung cancer, and loss of carboxypeptidase D reduced tumour growth. Our results reveal key differences between 2D and 3D cancer models, and establish a generalizable strategy for performing CRISPR screens in spheroids to reveal cancer vulnerabilities.
Suggested Citation
Kyuho Han & Sarah E. Pierce & Amy Li & Kaitlyn Spees & Gray R. Anderson & Jose A. Seoane & Yuan-Hung Lo & Michael Dubreuil & Micah Olivas & Roarke A. Kamber & Michael Wainberg & Kaja Kostyrko & Marcus, 2020.
"CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities,"
Nature, Nature, vol. 580(7801), pages 136-141, April.
Handle:
RePEc:nat:nature:v:580:y:2020:i:7801:d:10.1038_s41586-020-2099-x
DOI: 10.1038/s41586-020-2099-x
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Citations
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Cited by:
- Dzana Dervovic & Ahmad A. Malik & Edward L. Y. Chen & Masahiro Narimatsu & Nina Adler & Somaieh Afiuni-Zadeh & Dagmar Krenbek & Sebastien Martinez & Ricky Tsai & Jonathan Boucher & Jacob M. Berman & K, 2023.
"In vivo CRISPR screens reveal Serpinb9 and Adam2 as regulators of immune therapy response in lung cancer,"
Nature Communications, Nature, vol. 14(1), pages 1-21, December.
- Qinchang Chen & Guohui Chuai & Haihang Zhang & Jin Tang & Liwen Duan & Huan Guan & Wenhui Li & Wannian Li & Jiaying Wen & Erwei Zuo & Qing Zhang & Qi Liu, 2023.
"Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints,"
Nature Communications, Nature, vol. 14(1), pages 1-17, December.
- Kaja Kostyrko & Marta Román & Alex G. Lee & David R. Simpson & Phuong T. Dinh & Stanley G. Leung & Kieren D. Marini & Marcus R. Kelly & Joshua Broyde & Andrea Califano & Peter K. Jackson & E. Alejandr, 2023.
"UHRF1 is a mediator of KRAS driven oncogenesis in lung adenocarcinoma,"
Nature Communications, Nature, vol. 14(1), pages 1-18, December.
- Peter Eirew & Ciara O’Flanagan & Jerome Ting & Sohrab Salehi & Jazmine Brimhall & Beixi Wang & Justina Biele & Teresa Algara & So Ra Lee & Corey Hoang & Damian Yap & Steven McKinney & Cherie Bates & E, 2022.
"Accurate determination of CRISPR-mediated gene fitness in transplantable tumours,"
Nature Communications, Nature, vol. 13(1), pages 1-19, December.
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