Chen Shu-Hwa, Yu Bo-Yi, Kuo Wen-Yu, Lin Ya-Bo, Su Sheng-Yao, Chuang Wei-Hsuan, Lu I-Hsuan, Lin Chung-Yen
TMU Research Center of Cancer Translational Medicine, Taipei Medical University, 250 Wu-Xing Street, Taipei, Taiwan.
Research Center for Advanced Science and Technology, the University of Tokyo, 4-6-1 Komaba, Meguro-Ku, Tokyo, 153-8904, Japan.
BMC Cancer. 2025 May 1;25(Suppl 1):733. doi: 10.1186/s12885-025-14089-w.
Accurately resolving the composition of tumor-infiltrating leukocytes is pivotal for advancing cancer immunotherapy strategies. Despite the success of some clinical trials, applying these strategies remains limited due to the challenges in deciphering the immune microenvironment. In this study, we developed a streamlined, two-step workflow to address the complexity of bioinformatics processes involved in analyzing immune cell composition from transcriptomics data. Our dockerized toolkit, DOCexpress_fastqc, integrates the hisat2-stringtie pipeline with customized scripts within Galaxy/Docker environments, facilitating RNA sequencing (RNA-seq) gene expression profiling. The output from DOCexpress_fastqc is seamlessly formatted with mySORT, a web application that employs a deconvolution algorithm to determine the immune content across 21 cell subclasses. We validated mySORT using synthetic pseudo-bulk data derived from single-cell RNA sequencing (scRNA-seq) datasets. Our predictions exhibit strong concordance with the ground-truth immune cell composition, achieving Pearson's correlation coefficients of 0.871 in melanoma patients and 0.775 in head and neck cancer patients. Additionally, mySORT outperforms existing methods like CIBERSORT in accuracy and provides a wide range of data visualization features, such as hierarchical clustering and cell complexity plots. The toolkit and web application are freely available for the research community, providing enhanced resolution for conventional bulk RNA sequencing data and facilitating the analysis of immune microenvironment responses in immunotherapy. The mySORT demo website and Docker image are free at https://mysort.iis.sinica.edu.tw and https://hub.docker.com/r/lsbnb/mysort_2022 .
准确解析肿瘤浸润白细胞的组成对于推进癌症免疫治疗策略至关重要。尽管一些临床试验取得了成功，但由于在解读免疫微环境方面存在挑战，这些策略的应用仍然有限。在本研究中，我们开发了一种简化的两步工作流程，以应对从转录组学数据分析免疫细胞组成所涉及的生物信息学过程的复杂性。我们的容器化工具包DOCexpress_fastqc在Galaxy/Docker环境中将hisat2-stringtie管道与定制脚本集成在一起，便于进行RNA测序（RNA-seq）基因表达谱分析。DOCexpress_fastqc的输出与mySORT无缝格式化，mySORT是一个网络应用程序，它采用反卷积算法来确定21个细胞亚类中的免疫成分。我们使用从单细胞RNA测序（scRNA-seq）数据集衍生的合成伪批量数据验证了mySORT。我们的预测与真实免疫细胞组成表现出很强的一致性，在黑色素瘤患者中皮尔逊相关系数达到0.871，在头颈癌患者中达到0.775。此外，mySORT在准确性方面优于CIBERSORT等现有方法，并提供了广泛的数据可视化功能，如层次聚类和细胞复杂性图。该工具包和网络应用程序可供研究社区免费使用，为传统批量RNA测序数据提供了更高的分辨率，并便于分析免疫治疗中的免疫微环境反应。mySORT演示网站和Docker镜像可在https://mysort.iis.sinica.edu.tw和https://hub.docker.com/r/lsbnb/mysort_2022免费获取。
