Nowadays, the heterogeneous characteristics of cancer patients throw a big challenge to precision medicine and targeted therapy. Identifying cancer subtypes shed new light on effective personalized cancer medicine, future therapeutic strategies and minimizing treatment-related costs. Recently, there are many clustering methods have been proposed in categorizing cancer patients. Although these methods obtained a certain achievement in cancer subtype identification, they still fail to fully use the prior known biological information in the model designing process to improve precision and efficiency. It is acknowledged that the driver gene always regulates its downstream genes in the network to perform a certain function. By analyzing the known clinic cancer subtype data, we found some special co-pathways between the driver genes and the downstream genes in the cancer patients of the same subgroup. Hence, we proposed a novel model named DDCMNMF(Driver and Downstream gene Co-Module Assisted Multiple Non-negative Matrix Factorization model) that first for cancer subtypes by identifying co-modules of driver genes and downstream genes. We applied our model on lung and breast cancer datasets and compared it with the other four state-of-the-art models. The final results show that our model could identify the cancer subtypes with high compactness and separateness and achieve a high degree of consistency with the known cancer subtypes. The survival time analysis further proves the significant clinical characteristic of identified cancer subgroups by our model.Availability and implementation: It is available at https://github.com/weiba/DDCMNMF/.

中文翻译：

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通过从驱动突变谱和下游基因表达谱中寻找共模块来识别癌症患者亚组


如今，癌症患者的异质性特征给精准医疗和靶向治疗带来了巨大挑战。识别癌症亚型为有效的个性化癌症医学、未来的治疗策略和最大限度地降低治疗相关成本提供了新的思路。最近，人们提出了许多聚类方法来对癌症患者进行分类。尽管这些方法在癌症亚型识别方面取得了一定的成果，但仍然未能在模型设计过程中充分利用现有已知的生物学信息来提高精度和效率。众所周知，驱动基因总是调节其网络中的下游基因来执行某种功能。通过分析已知的临床癌症亚型数据，我们发现同一亚组癌症患者的驱动基因和下游基因之间存在一些特殊的共通路。因此，我们提出了一种名为 DDCMNMF（驱动程序和下游基因协同模块辅助多重非负矩阵分解模型）的新模型，该模型首先通过识别驱动基因和下游基因的协同模块来针对癌症亚型。我们将我们的模型应用于肺癌和乳腺癌数据集，并将其与其他四种最先进的模型进行比较。最终结果表明，我们的模型可以识别具有高度紧凑性和分离性的癌症亚型，并与已知的癌症亚型实现高度的一致性。生存时间分析进一步证明了我们的模型所识别的癌症亚组的显着临床特征。可用性和实施​​：可在https://github.com/weiba/DDCMNMF/获得。