Author
Listed:
- Gabriele Picco
(Wellcome Sanger Institute)
- Elisabeth D. Chen
(Wellcome Sanger Institute)
- Luz Garcia Alonso
(European Bioinformatics Institute
Open Targets)
- Fiona M. Behan
(Wellcome Sanger Institute
Open Targets)
- Emanuel Gonçalves
(Wellcome Sanger Institute)
- Graham Bignell
(Wellcome Sanger Institute)
- Angela Matchan
(Wellcome Sanger Institute)
- Beiyuan Fu
(Wellcome Sanger Institute)
- Ruby Banerjee
(Wellcome Sanger Institute)
- Elizabeth Anderson
(Wellcome Sanger Institute)
- Adam Butler
(Wellcome Sanger Institute)
- Cyril H. Benes
(Massachusetts General Hospital)
- Ultan McDermott
(Wellcome Sanger Institute
CRUK Cambridge Institute)
- David Dow
(Open Targets
GlaxoSmithKline
GlaxoSmithKline)
- Francesco Iorio
(Wellcome Sanger Institute
European Bioinformatics Institute
Open Targets)
- Euan Stronach
(Open Targets
GlaxoSmithKline
GlaxoSmithKline)
- Fengtang Yang
(Wellcome Sanger Institute)
- Kosuke Yusa
(Wellcome Sanger Institute)
- Julio Saez-Rodriguez
(European Bioinformatics Institute
Open Targets
Heidelberg University)
- Mathew J. Garnett
(Wellcome Sanger Institute
Open Targets)
Abstract
Many gene fusions are reported in tumours and for most their role remains unknown. As fusions are used for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their function in cancer. To systematically investigate the role of fusions in tumour cell fitness, we utilized RNA-sequencing data from 1011 human cancer cell lines to functionally link 8354 fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness effects. Established clinically-relevant fusions were identified. Overall, detection of functional fusions was rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumour fitness. Therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have clinical implications.
Suggested Citation
Gabriele Picco & Elisabeth D. Chen & Luz Garcia Alonso & Fiona M. Behan & Emanuel Gonçalves & Graham Bignell & Angela Matchan & Beiyuan Fu & Ruby Banerjee & Elizabeth Anderson & Adam Butler & Cyril H., 2019.
"Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR-Cas9 screening,"
Nature Communications, Nature, vol. 10(1), pages 1-12, December.
Handle:
RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09940-1
DOI: 10.1038/s41467-019-09940-1
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Cited by:
- Zhaoxiang Cai & Sofia Apolinário & Ana R. Baião & Clare Pacini & Miguel D. Sousa & Susana Vinga & Roger R. Reddel & Phillip J. Robinson & Mathew J. Garnett & Qing Zhong & Emanuel Gonçalves, 2024.
"Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning,"
Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Nishanth Ulhas Nair & Patricia Greninger & Xiaohu Zhang & Adam A. Friedman & Arnaud Amzallag & Eliane Cortez & Avinash Das Sahu & Joo Sang Lee & Anahita Dastur & Regina K. Egan & Ellen Murchie & Miche, 2023.
"A landscape of response to drug combinations in non-small cell lung cancer,"
Nature Communications, Nature, vol. 14(1), pages 1-19, December.
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