A functional genomic approach to actionable gene fusions for precision oncology
Jun Li, Hengyu Lu, Patrick Kwok‐Shing Ng, Angeliki Pantazi, Carman Ka Man Ip, Kang Jin Jeong, Bianca Amador, Richard Tran, Yiu Huen Tsang, Lixing Yang, Xingzhi Song, Turgut Dogruluk, Xiaojia Ren, Angela Hadjipanayis, Christopher A. Bristow, Semin Lee, Melanie H. Kucherlapati, Michael Parfenov, Jiabin Tang, Sahil Seth, Harshad S. Mahadeshwar, Kamalika Mojumdar, Dong Zeng, Jianhua Zhang, Alexei Protopopov, Jonathan G. Seidman, Chad J. Creighton, Yiling Lu, Nidhi Sahni, Kenna Shaw, Funda Meric‐Bernstam, P. Andrew Futreal, Lynda Chin, Kenneth L. Scott, Raju Kucherlapati, Gordon B. Mills, Han Liang
Abstract
Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize ~100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology.