Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Genome Med. 2020 Feb 28;12(1):24. doi: 10.1186/s13073-020-0720-0.
Patient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment.
We developed a computational deconvolution method, DeClust, that stratifies patients into subtypes based on cancer cell-intrinsic signals identified by distinguishing cancer-type-specific signals from non-cancer signals in bulk tumor transcriptomic data. DeClust differs from most existing methods by directly incorporating molecular subtyping of solid tumors into the deconvolution process and outputting molecular subtype-specific tumor reference profiles for the cohort rather than individual tumor profiles. In addition, DeClust does not require reference expression profiles or signature matrices as inputs and estimates cancer-type-specific microenvironment signals from bulk tumor transcriptomic data.
DeClust was evaluated on both simulated data and 13 solid tumor datasets from The Cancer Genome Atlas (TCGA). DeClust performed among the best, relative to existing methods, for estimation of cellular composition. Compared to molecular subtypes reported by TCGA or other similar approaches, the subtypes generated by DeClust had higher correlations with cancer-intrinsic genomic alterations (e.g., somatic mutations and copy number variations) and lower correlations with tumor purity. While DeClust-identified subtypes were not more significantly associated with survival in general, DeClust identified a poor prognosis subtype of clear cell renal cancer, papillary renal cancer, and lung adenocarcinoma, all of which were characterized by CDKN2A deletions. As a reference profile-free deconvolution method, the tumor-type-specific stromal profiles and cancer cell-intrinsic subtypes generated by DeClust were supported by single-cell RNA sequencing data.
DeClust is a useful tool for cancer cell-intrinsic molecular subtyping of solid tumors. DeClust subtypes, together with the tumor-type-specific stromal profiles generated by this pan-cancer study, may lead to mechanistic and clinical insights across multiple tumor types.
基于分子亚型的患者分层是癌症精准医学的重要策略。从全肿瘤组织样本的转录组数据中推导出具有临床意义的癌症分子亚型是一项艰巨的任务，尤其是考虑到肿瘤微环境中与癌细胞交织在一起的各种非癌细胞成分。
我们开发了一种计算去卷积方法 DeClust，该方法基于从批量肿瘤转录组数据中区分癌症特异性信号和非癌症信号来识别肿瘤内固有信号，从而将患者分层为不同的亚型。DeClust 与大多数现有方法的不同之处在于，它直接将实体瘤的分子亚型纳入去卷积过程，并为队列输出分子亚型特异性肿瘤参考图谱，而不是单个肿瘤图谱。此外，DeClust 不需要参考表达图谱或特征矩阵作为输入，并从批量肿瘤转录组数据中估计癌症特异性微环境信号。
DeClust 在模拟数据和来自癌症基因组图谱 (TCGA) 的 13 个实体瘤数据集上进行了评估。与其他现有方法相比，DeClust 在细胞成分估计方面表现最佳。与 TCGA 或其他类似方法报告的分子亚型相比，DeClust 生成的亚型与癌症内在的基因组改变（例如体细胞突变和拷贝数变异）具有更高的相关性，与肿瘤纯度的相关性更低。虽然 DeClust 识别的亚型通常与生存无关，但它识别出了透明细胞肾细胞癌、乳头状肾细胞癌和肺腺癌的预后不良亚型，这些亚型均具有 CDKN2A 缺失的特征。作为一种无参考图谱的去卷积方法，DeClust 生成的肿瘤类型特异性基质图谱和肿瘤内固有亚型得到了单细胞 RNA 测序数据的支持。
DeClust 是一种用于实体瘤肿瘤内固有分子亚型分析的有用工具。DeClust 亚型，以及本泛癌研究生成的肿瘤类型特异性基质图谱，可能会为多种肿瘤类型带来机制和临床方面的见解。
