Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Next Generation Medicine Lab, Department of Artificial Intelligence & Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Digital Health & Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Samsung Medical Center, Sungkyunkwan University, Seoul 06351, Republic of Korea.
Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Cell. 2021 Apr 29;184(9):2487-2502.e13. doi: 10.1016/j.cell.2021.03.030. Epub 2021 Apr 14.
Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.
精准肿瘤学取得了重大进展，主要通过针对癌症驱动基因中的可操作突变。为了扩大治疗机会，最近的研究开始探索利用肿瘤转录组来指导患者治疗。在这里，我们介绍 SELECT（通过转录组利用合成致死和拯救介导的精准肿瘤学），这是一种利用遗传相互作用的精准肿瘤学框架，可从肿瘤转录组预测癌症治疗患者的反应。SELECT 在来自 10 种不同癌症类型的 35 个已发表的靶向和免疫治疗临床试验的广泛收集上进行了测试。它可以预测这些临床试验中有 80%的患者的反应，并且在最近的多臂 WINTHER 试验中也是如此。预测特征和代码都可供学术使用，为未来的前瞻性临床研究奠定了基础。
