RET fusions observed in lung and colorectal cancers are sensitive to ponatinib
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Joseph M. Gozgit1, Tzu-Hsiu Chen1, Youngchul Song1, Scott Wardwell1, Frank Wang1, Jie Cai2, Henry Li2, Henrik Edgren3, Victor M. Rivera1 and Justin Pritchard1,4
1ARIAD Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA
2Crown Bioscience, Shanghai, P.R. China
3Medisapiens Ltd., Helsinki, Finland
4Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, USA
Victor M. Rivera, email: email@example.com
Justin Pritchard, email: firstname.lastname@example.org
Keywords: RET; colorectal cancer; non-small cell lung cancer; ponatinib; tyrosine kinase inhibitor
Received: November 28, 2017 Accepted: April 21, 2018 Published: July 03, 2018
Genomic studies are revolutionizing clinical oncology, but bridging the lab and the bedside requires the ability to efficiently interrogate rare genetic lesions in unexpected pathological settings using preclinical models. Oncogenes can exhibit intrinsic drug resistance to targeted therapy in different cells of origin, adding complexity to clinical interpretations of genomic findings. Here, we capitalize on the flexibility of engineered cell systems to rapidly profile known multi-kinase inhibitors that harbor rearranged during transfection (RET) kinase activity across multiple RET fusions. Identifying ponatinib as the most potent RET inhibitor tested, we used ponatinib to gauge therapeutic responsiveness in RET fusion-positive patient-derived xenograft (PDX) models. Using a genomics guided outlier approach, we identified 4 RET fusion PDX models with 3 different fusion partners (KIF5B, CCDC6, and NCOA4) in both non-small cell lung cancer and colorectal cancer. By comparing ponatinib activity in RET fusion-positive and RET fusion-negative PDX models alongside a standard of care chemotherapeutic agent, we show that RET fusions in colorectal tumors are therapeutically responsive to RET inhibition. Finally, we suggest that coupling engineered cell systems and genomics guided PDX model selection provides a rapid workflow to triage rare genomics findings.
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