Eason Katherine, Nyamundanda Gift, Sadanandam Anguraj
Division of Molecular Pathology, The Institute of Cancer Research (ICR), London, UK.
Centre for Molecular Pathology, Royal Marsden Hospital (RMH), London, UK.
BMC Bioinformatics. 2018 May 25;19(1):182. doi: 10.1186/s12859-018-2204-4.
To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce "polyClustR", a tool that reconciles clusters identified by different methods into subtype "communities" using a hypergeometric test or a measure of relative proportion of common samples.
The polyClustR pipeline was initially tested using a breast cancer dataset to demonstrate how results are compatible with and add to the understanding of this well-characterised cancer. Two uveal melanoma datasets were then utilised to identify and validate novel subtype communities with significant metastasis-free prognostic differences and associations with known chromosomal aberrations.
We demonstrate the value of the polyClustR approach of applying multiple consensus clustering algorithms and systematically reconciling the results in identifying novel subtype communities of two cancer types, which nevertheless are compatible with established understanding of these diseases. An R implementation of the pipeline is available at: https://github.com/syspremed/polyClustR.
为确保癌症患者能够接受最有益的分层治疗，使用聚类方法对高维生物学数据进行分析以定义可靠的分子亚型是当务之急。如果每种方法对相同数据产生不同数量的聚类，就很难找到最优解。在此，我们引入“polyClustR”工具，它使用超几何检验或共同样本相对比例的度量方法，将不同方法识别出的聚类整合为亚型“群落”。
polyClustR流程最初使用乳腺癌数据集进行测试，以展示其结果如何与这种特征明确的癌症相契合，并增进对它的理解。随后利用两个葡萄膜黑色素瘤数据集来识别和验证具有显著无转移预后差异且与已知染色体畸变相关的新型亚型群落。
我们证明了polyClustR方法的价值，即应用多种一致性聚类算法并系统地整合结果，以识别两种癌症类型的新型亚型群落，而这些群落与对这些疾病的既定理解是相符的。该流程的R语言实现可在以下网址获取：https://github.com/syspremed/polyClustR 。
