Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Republic of Singapore.
School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Republic of Singapore.
Commun Biol. 2023 Apr 11;6(1):394. doi: 10.1038/s42003-023-04764-8.
Tumors are complex masses composed of malignant and non-malignant cells. Variation in tumor purity (proportion of cancer cells in a sample) can both confound integrative analysis and enable studies of tumor heterogeneity. Here we developed PUREE, which uses a weakly supervised learning approach to infer tumor purity from a tumor gene expression profile. PUREE was trained on gene expression data and genomic consensus purity estimates from 7864 solid tumor samples. PUREE predicted purity with high accuracy across distinct solid tumor types and generalized to tumor samples from unseen tumor types and cohorts. Gene features of PUREE were further validated using single-cell RNA-seq data from distinct tumor types. In a comprehensive benchmark, PUREE outperformed existing transcriptome-based purity estimation approaches. Overall, PUREE is a highly accurate and versatile method for estimating tumor purity and interrogating tumor heterogeneity from bulk tumor gene expression data, which can complement genomics-based approaches or be used in settings where genomic data is unavailable.
肿瘤是由恶性和非恶性细胞组成的复杂肿块。肿瘤纯度（样本中癌细胞的比例）的变化既会混淆综合分析，也能使肿瘤异质性研究成为可能。在这里，我们开发了 PUREE，它使用一种弱监督学习方法从肿瘤基因表达谱中推断肿瘤纯度。PUREE 是在来自 7864 个实体瘤样本的基因表达数据和基因组共识纯度估计值上进行训练的。PUREE 在不同的实体瘤类型中具有高精度的预测纯度，并推广到来自未见肿瘤类型和队列的肿瘤样本。使用来自不同肿瘤类型的单细胞 RNA-seq 数据进一步验证了 PUREE 的基因特征。在全面的基准测试中，PUREE 优于现有的基于转录组的纯度估计方法。总的来说，PUREE 是一种从肿瘤基因表达数据中估计肿瘤纯度和研究肿瘤异质性的高度准确和通用的方法，可以补充基于基因组学的方法，或在没有基因组数据的情况下使用。
