Systematic Functional Annotation of Somatic Mutations in Cancer

Publication date: 12 March 2018
Source:Cancer Cell, Volume 33, Issue 3
Author(s): Patrick Kwok-Shing Ng, Jun Li, Kang Jin Jeong, Shan Shao, Hu Chen, Yiu Huen Tsang, Sohini Sengupta, Zixing Wang, Venkata Hemanjani Bhavana, Richard Tran, Stephanie Soewito, Darlan Conterno Minussi, Daniela Moreno, Kathleen Kong, Turgut Dogruluk, Hengyu Lu, Jianjiong Gao, Collin Tokheim, Daniel Cui Zhou, Amber M. Johnson, Jia Zeng, Carman Ka Man Ip, Zhenlin Ju, Matthew Wester, Shuangxing Yu, Yongsheng Li, Christopher P. Vellano, Nikolaus Schultz, Rachel Karchin, Li Ding, Yiling Lu, Lydia Wai Ting Cheung, Ken Chen, Kenna R. Shaw, Funda Meric-Bernstam, Kenneth L. Scott, Song Yi, Nidhi Sahni, Han Liang, Gordon B. Mills
The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

Graphical abstract

Teaser

Ng et al. develop a moderate-throughput functional genomic platform and use it to annotate >1,000 cancer variants of unknown significance. The approach is sufficiently sensitive to identify weak drivers, potentially doubling the number of driver mutations characterized in clinically actionable genes.


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