Multimodal Dimension Reduction and Subtype Classification of Head and Neck Squamous Cell Tumors.
Multimodal Dimension Reduction and Subtype Classification of Head and Neck Squamous Cell Tumors.
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DOI：
10.3389/fonc.2022.892207
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发表时间：
2022
影响因子：
4.7
通讯作者：
Sinha, Satrajit
中科院分区：
文献类型：
--
作者：
Bard, Jonathan E. E.;Nowak, Norma J.;Buck, Michael J.;Sinha, Satrajit
对来自批量测序实验的基因组数据的传统分析旨在将样本队列分组并比较为具有生物学意义的组。为了完成这项任务，已经建立了来自患者的样本的大规模数据库（例如 TCGA 数据库），从而能够查询每个肿瘤的多种数据模式。我们开发了一种计算策略，采用多模态集成与谱聚类和现代降维技术（例如 PHATE）相结合，为癌症亚型分类提供更稳健的方法。使用这种综合方法，我们检查了来自 TCGA 的 514 个头颈鳞状癌 (HNSC) 肿瘤样本的基因表达、DNA 甲基化和微生物组数据模式。我们证明，这些主要为单细胞测序开发的方法可以有效地应用于批量肿瘤测序数据。我们的多模态分析捕捉动态异质性，识别新的和完善的 HNSC 亚型，并沿着明确的细胞轨迹对肿瘤样本进行排序。总的来说，这些结果展示了肿瘤固有的分子复杂性，并为癌症发生和靶向治疗的重要性提供了见解。我们的研究中强调的计算技术提供了一种有机且强大的方法来识别大型且嘈杂的数据集中可能被忽视的粒度模式。
Traditional analysis of genomic data from bulk sequencing experiments seek to group and compare sample cohorts into biologically meaningful groups. To accomplish this task, large scale databases of patient-derived samples, like that of TCGA, have been established, giving the ability to interrogate multiple data modalities per tumor. We have developed a computational strategy employing multimodal integration paired with spectral clustering and modern dimension reduction techniques such as PHATE to provide a more robust method for cancer sub-type classification. Using this integrated approach, we have examined 514 Head and Neck Squamous Carcinoma (HNSC) tumor samples from TCGA across gene-expression, DNA-methylation, and microbiome data modalities. We show that these approaches, primarily developed for single-cell sequencing can be efficiently applied to bulk tumor sequencing data. Our multimodal analysis captures the dynamic heterogeneity, identifies new and refines subtypes of HNSC, and orders tumor samples along well-defined cellular trajectories. Collectively, these results showcase the inherent molecular complexity of tumors and offer insights into carcinogenesis and importance of targeted therapy. Computational techniques as highlighted in our study provide an organic and powerful approach to identify granular patterns in large and noisy datasets that may otherwise be overlooked.
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