Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data
Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data
复制标题
差异变异分析可利用单细胞RNA测序数据检测肿瘤异质性
DOI：
10.1158/0008-5472.can-18-3882
复制
发表时间：
2019-10-01
期刊：
影响因子：
11.2
通讯作者：
Fertig, Elana J.
中科院分区：
文献类型：
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作者：
Davis-Marcisak, Emily F.;Sherman, Thomas D.;Fertig, Elana J.
肿瘤异质性给癌症治疗带来了复杂的挑战，并且是治疗反应、疾病复发和患者存活的关键组成部分。单细胞RNA测序（scRNA-seq）技术揭示了肿瘤内和肿瘤间异质性的普遍性。计算技术对于量化不同细胞类型、肿瘤亚型和患者之间这些谱的变化差异以充分表征肿瘤内和肿瘤间的分子异质性是必不可少的。在这项研究中，我们调整了我们的通路失调算法，表达变异分析（伊娃），以进行scRNA-seq基因组表达差异变异的多变量统计分析。伊娃对检测模拟数据中具有真差异异质性的通路具有较高的灵敏度和特异性。将伊娃应用于几个公共领域scRNA-seq肿瘤数据集，以量化癌症基因组学中几个关键应用（如免疫原性、转移和癌症亚型）中的肿瘤异质性状况。乳腺肿瘤中造血细胞群的免疫途径异质性对应于每个个体T细胞库中存在的多样性。来自头颈部鳞状细胞癌（HNSCC）原发肿瘤的细胞比来自转移瘤的细胞具有更大的异质性，这与克隆生长模型一致。此外，在HNSCC基底原发性肿瘤中，通路失调存在显著差异。在基底原发性肿瘤中，在肿瘤微环境中存在高比例成纤维细胞的个体中免疫失调增加。这些结果证明了伊娃在从scRNA-seq数据中量化肿瘤间和肿瘤内异质性而不依赖于低维可视化的广泛效用。显著性：本研究提出了一种用于评估单细胞RNA-seq数据中通路或基因集内基因表达异质性的稳健统计算法
Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization.Significance: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data