McGranahan Nicholas, Favero Francesco, de Bruin Elza C, Birkbak Nicolai Juul, Szallasi Zoltan, Swanton Charles
Cancer Research UK London Research Institute, London WC2A 3LY, UK. Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London WC1E 6BT, UK.
Cancer System Biology, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby 2800, Denmark.
Sci Transl Med. 2015 Apr 15;7(283):283ra54. doi: 10.1126/scitranslmed.aaa1408.
Deciphering whether actionable driver mutations are found in all or a subset of tumor cells will likely be required to improve drug development and precision medicine strategies. We analyzed nine cancer types to determine the subclonal frequencies of driver events, to time mutational processes during cancer evolution, and to identify drivers of subclonal expansions. Although mutations in known driver genes typically occurred early in cancer evolution, we also identified later subclonal "actionable" mutations, including BRAF (V600E), IDH1 (R132H), PIK3CA (E545K), EGFR (L858R), and KRAS (G12D), which may compromise the efficacy of targeted therapy approaches. More than 20% of IDH1 mutations in glioblastomas, and 15% of mutations in genes in the PI3K (phosphatidylinositol 3-kinase)-AKT-mTOR (mammalian target of rapamycin) signaling axis across all tumor types were subclonal. Mutations in the RAS-MEK (mitogen-activated protein kinase kinase) signaling axis were less likely to be subclonal than mutations in genes associated with PI3K-AKT-mTOR signaling. Analysis of late mutations revealed a link between APOBEC-mediated mutagenesis and the acquisition of subclonal driver mutations and uncovered putative cancer genes involved in subclonal expansions, including CTNNA2 and ATXN1. Our results provide a pan-cancer census of driver events within the context of intratumor heterogeneity and reveal patterns of tumor evolution across cancers. The frequent presence of subclonal driver mutations suggests the need to stratify targeted therapy response according to the proportion of tumor cells in which the driver is identified.
为了改进药物研发和精准医疗策略,可能需要确定在所有肿瘤细胞还是部分肿瘤细胞中发现了可靶向治疗的驱动基因突变。我们分析了九种癌症类型,以确定驱动事件的亚克隆频率,确定癌症进化过程中的突变时间,并识别亚克隆扩增的驱动因素。虽然已知驱动基因的突变通常发生在癌症进化的早期,但我们也发现了后期的亚克隆“可靶向治疗”突变,包括BRAF(V600E)、IDH1(R132H)、PIK3CA(E545K)、EGFR(L858R)和KRAS(G12D),这些突变可能会影响靶向治疗方法的疗效。胶质母细胞瘤中超过20%的IDH1突变,以及所有肿瘤类型中PI3K(磷脂酰肌醇3激酶)-AKT-mTOR(雷帕霉素哺乳动物靶蛋白)信号轴上15%的基因突变是亚克隆性的。与PI3K-AKT-mTOR信号相关基因的突变相比,RAS-MEK(丝裂原活化蛋白激酶激酶)信号轴上的突变更不容易是亚克隆性的。对晚期突变的分析揭示了APOBEC介导的诱变与亚克隆驱动基因突变的获得之间的联系,并发现了参与亚克隆扩增的推定癌症基因,包括CTNNA2和ATXN1。我们的结果提供了肿瘤内异质性背景下驱动事件的泛癌普查,并揭示了不同癌症的肿瘤进化模式。亚克隆驱动基因突变的频繁出现表明,需要根据鉴定出驱动基因的肿瘤细胞比例对靶向治疗反应进行分层。