Maxson Julia E, Abel Melissa L, Wang Jinhua, Deng Xianming, Reckel Sina, Luty Samuel B, Sun Huahang, Gorenstein Julie, Hughes Seamus B, Bottomly Daniel, Wilmot Beth, McWeeney Shannon K, Radich Jerald, Hantschel Oliver, Middleton Richard E, Gray Nathanael S, Druker Brian J, Tyner Jeffrey W
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.
Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon. Division of Hematology and Medical Oncology, Oregon Health and Science University, Portland, Oregon.
Cancer Res. 2016 Jan 1;76(1):127-38. doi: 10.1158/0008-5472.CAN-15-0817. Epub 2015 Dec 17.
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.
目前,有关白血病细胞的基因组信息量远远超过了我们对最终驱动疾病发展和进展的精确遗传事件的整体理解。有效实施个性化医疗将需要工具来区分白血病复杂遗传图谱中的可操作遗传改变。在本研究中,我们进行了激酶抑制剂筛选,以预测急性髓系白血病和慢性粒单核细胞白血病患者原代标本中的功能性基因靶点。对相同患者标本进行深度测序,确定遗传改变,然后使用HitWalker算法将这些改变与功能上重要的靶点整合,以对最有可能解释所观察到的药物敏感性模式的突变基因进行优先级排序。通过这一过程,我们鉴定出具有致癌能力的酪氨酸激酶非受体2(TNK2)点突变。重要的是,使用HitWalker整合功能和基因组数据能够对仅通过基因组分析可能遗漏的罕见致癌突变进行优先级排序。这些突变对多激酶抑制剂达沙替尼敏感,达沙替尼可拮抗TNK2激酶活性,对新型TNK2抑制剂XMD8-87和XMD16-5也敏感,且后者具有更高的靶点特异性。我们还在其他肿瘤类型中鉴定出对XMD8-87和XMD16-5敏感的激活截断突变,例证了这些化合物在依赖TNK2的多种肿瘤类型中的潜在效用。总体而言,我们的研究结果突出了一种更敏感的方法来识别可能很少发生突变或被忽视的可操作基因组病变,并提供了一种对候选基因突变进行优先级排序的新方法。