Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA.
Department of Biological Chemistry, Center for Epigenetics and Metabolism, Chao Family Comprehensive Cancer Center, University of California, Irvine, CA, USA.
Science. 2019 Jun 28;364(6447). doi: 10.1126/science.aaw2872.
Cancer drivers require statistical modeling to distinguish them from passenger events, which accumulate during tumorigenesis but provide no fitness advantage to cancer cells. The discovery of driver genes and mutations relies on the assumption that exact positional recurrence is unlikely by chance; thus, the precise sharing of mutations across patients identifies drivers. Examining the mutation landscape in cancer genomes, we found that many recurrent cancer mutations previously designated as drivers are likely passengers. Our integrated bioinformatic and biochemical analyses revealed that these passenger hotspot mutations arise from the preference of APOBEC3A, a cytidine deaminase, for DNA stem-loops. Conversely, recurrent APOBEC-signature mutations not in stem-loops are enriched in well-characterized driver genes and may predict new drivers. This demonstrates that mesoscale genomic features need to be integrated into computational models aimed at identifying mutations linked to diseases.
癌症驱动因素需要通过统计建模来区分，这些驱动因素与在肿瘤发生过程中积累但对癌细胞没有任何优势的乘客事件区分开来。驱动基因和突变的发现依赖于这样一种假设，即确切的位置重复不太可能是偶然的；因此，患者之间精确共享的突变可以识别出驱动因素。在研究癌症基因组中的突变景观时，我们发现，以前被指定为驱动因素的许多复发性癌症突变很可能是乘客突变。我们的综合生物信息学和生化分析表明，这些乘客热点突变是由胞嘧啶脱氨酶 APOBEC3A 对 DNA 茎环的偏好产生的。相反，茎环中未出现的复发性 APOBEC 特征突变富集在特征明确的驱动基因中，并且可能预测新的驱动因素。这表明需要将中尺度基因组特征整合到旨在识别与疾病相关的突变的计算模型中。
