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

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 powe...

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Bibliographic Details
Published in:Nucleic acids research 2021-09, Vol.49 (15), p.8488-8504
Main Authors: Montazeri, Hesam, Coto-Llerena, Mairene, Bianco, Gaia, Zangene, Ehsan, Taha-Mehlitz, Stephanie, Paradiso, Viola, Srivatsa, Sumana, de Weck, Antoine, Roma, Guglielmo, Lanzafame, Manuela, Bolli, Martin, Beerenwinkel, Niko, von Flüe, Markus, Terracciano, Luigi M, Piscuoglio, Salvatore, Ng, Charlotte K Y
Format: Article
Language:English
Subjects:
Apoptosis - genetics
Cell Line, Tumor
Cell Transformation, Neoplastic - genetics
Computational Biology
Gene Amplification - genetics
Genes, Neoplasm - genetics
Genomics
Humans
Neoplasms - genetics
Neoplasms - pathology
RNA, Small Interfering - genetics
Signal Transduction - genetics
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Summary: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.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkab627