文章基本信息

标题：Rapid preliminary purity evaluation of tumor biopsies using deep learning approach
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作者：Fei Fan ; Dan Chen ; Yu Zhao 等
期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2020
卷号：18
页码：1746-1753
DOI：10.1016/j.csbj.2020.06.007
出版社：Computational and Structural Biotechnology Journal
摘要：Tumor biopsy is one of the most widely used materials in cancer diagnoses and molecular studies, where the purity of the biopsies (i.e., proportion of cells that are cancerous) is crucial for both applications. However, conventional approaches for tumor biopsy purity evaluation require experienced pathologists and/or various materials/experiments therefore were time-consuming and error prone. Rapid, easy-to-perform and cost-effective methods are thus still of demand. Recent studies had demonstrated that molecular signatures were informative to this task. Previously, we had developed GeneCT, a deep learning-based cancerous status and tissue-of-origin classifier for pan-tumor/tissue biopsies. In the current work, we applied GeneCT on datasets collected from various groups, where the experimental protocols and cancer types differed from each other. We found that GeneCT showed high accuracies on most datasets; for samples with unexpected results, in-depth investigations suggested that they might suffer from imperfect purity. In silico mixture experiments further showed that GeneCT classification was highly indicative in predicting the purity of the tumor biopsies. Considering that transcriptome profiling is a common and inexpensive experiment in molecular cancer studies, our deep learning-based GeneCT could thus serve as a valuable tool for rapid, preliminary tumor biopsy purity assessment.
关键词：RNA-seq ; Gene expression ; Machine learning ; Cancer