School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, Shaanxi, China.
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab164.
Different subtypes of the same cancer often show distinct genomic signatures and require targeted treatments. The differences at the cellular and molecular levels of tumor microenvironment in different cancer subtypes have significant effects on tumor pathogenesis and prognostic outcomes. Although there have been significant researches on the prognostic association of tumor infiltrating lymphocytes in selected histological subtypes, few investigations have systemically reported the prognostic impacts of immune cells in molecular subtypes, as quantified by machine learning approaches on multi-omics datasets. This paper describes a new computational framework, ProTICS, to quantify the differences in the proportion of immune cells in tumor microenvironment and estimate their prognostic effects in different subtypes. First, we stratified patients into molecular subtypes based on gene expression and methylation profiles by applying nonnegative tensor factorization technique. Then we quantified the proportion of cell types in each specimen using an mRNA-based deconvolution method. For tumors in each subtype, we estimated the prognostic effects of immune cell types by applying Cox proportional hazard regression. At the molecular level, we also predicted the prognosis of signature genes for each subtype. Finally, we benchmarked the performance of ProTICS on three TCGA datasets and another independent METABRIC dataset. ProTICS successfully stratified tumors into different molecular subtypes manifested by distinct overall survival. Furthermore, the different immune cell types showed distinct prognostic patterns with respect to molecular subtypes. This study provides new insights into the prognostic association between immune cells and molecular subtypes, showing the utility of immune cells as potential prognostic markers. Availability: R code is available at https://github.com/liu-shuhui/ProTICS.
不同类型的癌症通常表现出明显不同的基因组特征，需要针对性的治疗。肿瘤微环境在不同癌症亚型的细胞和分子水平上的差异，对肿瘤的发病机制和预后结果有重要影响。虽然已经有大量研究关注选定组织学亚型中肿瘤浸润淋巴细胞的预后相关性，但很少有研究系统地报道过机器学习方法在多组学数据集中对分子亚型进行量化时，免疫细胞对预后的影响。本文描述了一种新的计算框架 ProTICS，用于量化肿瘤微环境中免疫细胞比例的差异，并估计其在不同亚型中的预后效应。首先，我们应用非负张量分解技术，根据基因表达和甲基化谱将患者分为分子亚型。然后，我们使用基于 mRNA 的去卷积方法来量化每个标本中细胞类型的比例。对于每个亚型的肿瘤，我们应用 Cox 比例风险回归来估计免疫细胞类型的预后效应。在分子水平上，我们还预测了每个亚型特征基因的预后。最后，我们在三个 TCGA 数据集和另一个独立的 METABRIC 数据集上对 ProTICS 的性能进行了基准测试。ProTICS 成功地将肿瘤分为不同的分子亚型，表现出明显不同的总生存率。此外，不同的免疫细胞类型在分子亚型方面表现出不同的预后模式。这项研究提供了免疫细胞与分子亚型之间预后相关性的新见解，表明免疫细胞作为潜在的预后标志物的效用。可获取性：R 代码可在 https://github.com/liu-shuhui/ProTICS 上获取。
