2. Academic Validation
  3. Identification and Characterization of Tyrosine Kinase Nonreceptor 2 Mutations in Leukemia through Integration of Kinase Inhibitor Screening and Genomic Analysis

Identification and Characterization of Tyrosine Kinase Nonreceptor 2 Mutations in Leukemia through Integration of Kinase Inhibitor Screening and Genomic Analysis

  • Cancer Res. 2016 Jan 1;76(1):127-38. doi: 10.1158/0008-5472.CAN-15-0817.
Julia E Maxson 1,  Melissa L Abel 2,  Jinhua Wang 3,  Xianming Deng 3,  Sina Reckel 4,  Samuel B Luty 2,  Huahang Sun 5,  Julie Gorenstein 5,  Seamus B Hughes 6,  Daniel Bottomly 7,  Beth Wilmot 8,  Shannon K McWeeney 8,  Jerald Radich 6,  Oliver Hantschel 4,  Richard E Middleton 5,  Nathanael S Gray 3,  Brian J Druker 9,  Jeffrey W Tyner 10
Affiliations

Affiliations

  • 1 Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon. Division of Hematology and Medical Oncology, Oregon Health and Science University, Portland, Oregon. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
  • 2 Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon. Division of Hematology and Medical Oncology, Oregon Health and Science University, Portland, Oregon.
  • 3 Department of Cancer Biology, Dana Farber Cancer Institute, Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts.
  • 4 Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • 5 Belfer Institute for Applied Cancer Science, Dana Farber Cancer Institute, Boston, Massachusetts.
  • 6 Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
  • 7 Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon. Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon.
  • 8 Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon. Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon. Division of Bioinformatics and Computational Biology, Oregon Health and Science University, Portland, Oregon.
  • 9 Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon. Division of Hematology and Medical Oncology, Oregon Health and Science University, Portland, Oregon. Howard Hughes Medical Institute, Portland, Oregon.
  • 10 Division of Hematology and Medical Oncology, Oregon Health and Science University, Portland, Oregon. Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, Oregon. tynerj@ohsu.edu.
PMID: 26677978 DOI: 10.1158/0008-5472.CAN-15-0817
Abstract

The amount of genomic information about leukemia cells currently far exceeds our overall understanding of the precise genetic events that ultimately drive disease development and progression. Effective implementation of personalized medicine will require tools to distinguish actionable genetic alterations within the complex genetic landscape of leukemia. In this study, we performed kinase inhibitor screens to predict functional gene targets in primary specimens from patients with acute myeloid leukemia and chronic myelomonocytic leukemia. Deep Sequencing of the same patient specimens identified genetic alterations that were then integrated with the functionally important targets using the HitWalker algorithm to prioritize the mutant genes that most likely explain the observed drug sensitivity patterns. Through this process, we identified tyrosine kinase nonreceptor 2 (TNK2) point mutations that exhibited oncogenic capacity. Importantly, the integration of functional and genomic data using HitWalker allowed for prioritization of rare oncogenic mutations that may have been missed through genomic analysis alone. These mutations were sensitive to the multikinase inhibitor dasatinib, which antagonizes TNK2 kinase activity, as well as novel TNK2 inhibitors, XMD8-87 and XMD16-5, with greater target specificity. We also identified activating truncation mutations in Other tumor types that were sensitive to XMD8-87 and XMD16-5, exemplifying the potential utility of these compounds across tumor types dependent on TNK2. Collectively, our findings highlight a more sensitive approach for identifying actionable genomic lesions that may be infrequently mutated or overlooked and provide a new method for the prioritization of candidate genetic mutations.

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