Accounting for tumor purity improves cancer subtype classification from DNA methylation data

作者：Zhang, Weiwei; Feng, Hao; Wu, Hao^*; Zheng, Xiaoqi^*

来源：Bioinformatics, 2017, 33(17): 2651-2657.

DOI：10.1093/bioinformatics/btx303

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Motivation: Tumor sample classification has long been an important task in cancer research. Classifying tumors into different subtypes greatly benefits therapeutic development and facilitates application of precision medicine on patients. In practice, solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. Thus, the data obtained from these samples are mixed signals. The 'tumor purity', or the percentage of cancer cells in cancer tissue sample, will bias the clustering results if not properly accounted for.
Results: In this article, we developed a model-based clustering method and an R function which uses DNA methylation microarray data to infer tumor subtypes with the consideration of tumor purity. Simulation studies and the analyses of The Cancer Genome Atlas data demonstrate improved results compared with existing methods.

• 出版日期2017-9-1
• 单位上海师范大学