Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens

Hesam Montazeri; Mairene Coto-Llerena; Gaia Bianco; Ehsan Zangene; Stephanie Taha-Mehlitz; Viola Paradiso; Sumana Srivatsa; Antoine de Weck; Guglielmo Roma; Manuela Lanzafame; Martin Bolli; Niko Beerenwinkel; Markus von Flüe; Luigi M Terracciano; Salvatore Piscuoglio; Charlotte K Y Ng

doi:10.1093/nar/gkab627

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens

Nucleic Acids Res. 2021 Sep 7;49(15):8488-8504. doi: 10.1093/nar/gkab627.

Authors

Hesam Montazeri^{1

2}, Mairene Coto-Llerena^{2

3}, Gaia Bianco^{2

3}, Ehsan Zangene¹, Stephanie Taha-Mehlitz³, Viola Paradiso², Sumana Srivatsa^{4

5}, Antoine de Weck⁶, Guglielmo Roma⁶, Manuela Lanzafame², Martin Bolli⁷, Niko Beerenwinkel^{4

5}, Markus von Flüe⁷, Luigi M Terracciano^{8

9}, Salvatore Piscuoglio^{2

3}, Charlotte K Y Ng^{2

10

11}

Affiliations

¹ Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
² Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.
³ Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland.
⁴ Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
⁵ SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
⁶ Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland.
⁷ Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Switzerland.
⁸ Department of Pathology, Humanitas Clinical and Research Center, IRCCS, Milan, Italy.
⁹ Department of Biomedical Sciences, Humanitas University, Milan, Italy.
¹⁰ Department for BioMedical Research, University of Bern, Bern, Switzerland.
¹¹ SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Abstract

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Apoptosis / genetics
Cell Line, Tumor
Cell Transformation, Neoplastic / genetics*
Gene Amplification / genetics
Genes, Neoplasm / genetics*
Genomics*
Humans
Neoplasms / genetics*
Neoplasms / pathology
RNA, Small Interfering / genetics
Signal Transduction / genetics

Substances

RNA, Small Interfering

Grants and funding

609883/ERC_/European Research Council/International