Phenotype

Pathology

Biology

Gene

Cancer

Spheroid

In vitro

Lung cancer

Computational biology

CRISPR

Cancer research

Genetics

Medicine

Cancer cell

Nature

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&nbsp;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. CRISPR screens in a 3D spheroid cancer model system more accurately recapitulate cancer phenotypes than existing 2D models and were used to identify carboxypeptidase D, acting via the IGF1R, as a 3D-specific driver of cancer growth.

CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities