Epithelial-to-mesenchymal transition (EMT) is the most commonly cited mechanism for cancer metastasis, but it is difficult to distinguish from profiles of normal stromal cells in the tumour microenvironment. In this study we use published single cell RNA-seq data to directly compare mesenchymal signatures from cancer and stromal cells. Informed by these comparisons, we developed a computational framework to decouple these two sources of mesenchymal expression profiles using bulk RNA-seq datasets. This deconvolution offers the opportunity to characterise EMT across hundreds of tumours and examine its association with metastasis and other clinical features. With this approach, we find three distinct patterns of EMT, associated with squamous, gynaecological and gastrointestinal cancer types. Surprisingly, in most cancer types, EMT patterns are not associated with increased chance of metastasis, suggesting that other steps in the metastatic cascade may represent the main bottleneck. This work provides a comprehensive evaluation of EMT profiles and their functional significance across hundreds of tumours while circumventing the confounding effect of stromal cells.

上皮间质转化（EMT）是最常被引用的癌症转移机制，但很难将其与肿瘤微环境中正常基质细胞的特征区分开来。在这项研究中，我们使用已发表的单细胞 RNA-seq 数据来直接比较癌症和基质细胞的间充质特征。根据这些比较，我们开发了一个计算框架，使用批量 RNA-seq 数据集将这两个来源的间充质表达谱解耦。这种反卷积提供了表征数百种肿瘤的 EMT 并检查其与转移和其他临床特征的关联的机会。通过这种方法，我们发现了与鳞状癌、妇科癌和胃肠道癌类型相关的三种不同的 EMT 模式。令人惊讶的是，在大多数癌症类型中，EMT 模式与转移机会增加无关，这表明转移级联中的其他步骤可能是主要瓶颈。这项工作对数百种肿瘤中的 EMT 谱及其功能意义进行了全面评估，同时避免了基质细胞的混杂效应。