Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.
Biomedical Sciences, Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA.
Sci Rep. 2019 Jan 30;9(1):1005. doi: 10.1038/s41598-018-37835-6.
Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual instances of cancer: we search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. We developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity (95% Confidence Interval (CI) = 89-92%) and 93% specificity (95% CI = 91-94%) on average for seventeen cancer types. We then present an approach based on mutational profile that can be used to distinguish between driver and passenger mutations within these genes. These combinations, with experimental validation, can aid in better diagnosis, provide insights into the etiology of cancer, and provide a rational basis for designing targeted combination therapies.
癌症是已知的结果从一小部分的遗传缺陷。然而，导致绝大多数癌症的突变的具体组合尚未确定。目前的计算方法主要集中在识别驱动基因和突变。虽然这些突变单独可以增加癌症的风险，但如果没有其他突变，它们不会导致癌症。我们提出了一种从根本上不同的方法来确定个体癌症病例的原因：我们寻找具有致癌突变的基因组合（多命中组合），而不是单个驱动基因或突变。我们开发了一种算法，该算法可以识别一组多命中组合，这些组合可以区分肿瘤和正常组织样本，在 17 种癌症类型中，平均具有 91%的敏感性（95%置信区间（CI）= 89-92%）和 93%的特异性（95%CI= 91-94%）。然后，我们提出了一种基于突变谱的方法，可以用于区分这些基因内的驱动突变和乘客突变。这些组合，经过实验验证，可以帮助更好地诊断，深入了解癌症的病因，并为设计靶向联合治疗提供合理的依据。
