美国芝加哥大学Matthew Stephens和北卡罗来纳大学教堂山分校Yusha Liu团队，利用广义二元协方差分解分析单细胞RNA-seq数据，发现肿瘤转录的异质性。2025年1月2日，国际学术期刊《自然—遗传学》发表了这一成果。

据悉，利用单细胞RNA测序分析肿瘤有可能发现，与癌症进展相关的重复转录变异模式，并为治疗提供相关的依据。然而，肿瘤间的强烈异质性可能会掩盖肿瘤间共有的微妙模式。

研究人员引入了一种统计方法-广义二元协方差分解（GBCD），来解决肿瘤异质性导致的分析难题。研究表明，广义二元协方差分解能将转录异质性分解为可解释的部分——包括患者特异性、数据集特异性和与疾病亚型有关的共有成分——而且在肿瘤间存在巨大异质性的情况下，它能产生比现有方法更可解释的结果。

利用GBCD分析胰腺导管腺癌数据时，能对现有的肿瘤亚型特征进行细化，并发现了一种与肿瘤分期以及亚型无关的不良生存预示基因表达程序。该基因表达程序富含参与应激反应的基因，揭示了综合应激反应在胰腺导管腺癌中的作用。

附：英文原文

Title: Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition

Author: Liu, Yusha, Carbonetto, Peter, Willwerscheid, Jason, Oakes, Scott A., Macleod, Kay F., Stephens, Matthew

Issue&Volume: 2025-01-02

Abstract: Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong intertumor heterogeneity can obscure more subtle patterns that are shared across tumors. Here we introduce a statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can decompose transcriptional heterogeneity into interpretable components—including patient-specific, dataset-specific and shared components relevant to disease subtypes—and that, in the presence of strong intertumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma.

DOI: 10.1038/s41588-024-01997-z

Source: https://www.nature.com/articles/s41588-024-01997-z