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 (2021). Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens. Nucleic acids research, 49(15), pp. 8488-8504. Oxford University Press 10.1093/nar/gkab627
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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.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) |
UniBE Contributor: |
Ng, Kiu Yan Charlotte |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
0305-1048 |
Publisher: |
Oxford University Press |
Language: |
English |
Submitter: |
Marla Rittiner |
Date Deposited: |
24 Jan 2022 07:34 |
Last Modified: |
27 Jun 2023 12:52 |
Publisher DOI: |
10.1093/nar/gkab627 |
PubMed ID: |
34313788 |
BORIS DOI: |
10.48350/163430 |
URI: |
https://boris.unibe.ch/id/eprint/163430 |
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