Immune Profiling Among Colorectal Cancer Subtypes Using Dependent Mixture Models

• 成果类型: Article
• 署名作者: Duan, Yunshan; Guo, Shuai; Wang, Wenyi; Mueller, Peter
• 署名单位: University of Texas System; University of Texas Austin; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2427936
• 发表日期: 2025
• 页码: 671-684
• 关键词: cell; inference; receptor; number

摘要

Comparison of transcriptomic data across different conditions is of interest in many biomedical studies. In this article, we consider comparative immune cell profiling for early-onset (EO) versus late-onset (LO) colorectal cancer (CRC). EOCRC, diagnosed between ages 18-45, is a rising public health concern that needs to be urgently addressed. However, its etiology remains poorly understood. We work toward filling this gap by identifying homogeneous T cell sub-populations that show significantly distinct characteristics across the two tumor types, and identifying others that are shared between EOCRC and LOCRC. We develop dependent finite mixture models where immune subtypes enriched under a specific condition are characterized by terms in the mixture model with common atoms but distinct weights across conditions, whereas common subtypes are characterized by sharing both atoms and relative weights. The proposed model facilitates the desired comparison across conditions by introducing highly structured multi-layer Dirichlet priors. We illustrate inference with simulation studies and data examples. Results identify EO- and LO-enriched T cells subtypes whose biomarkers are found to be linked to mechanisms of tumor progression, and potentially motivate insights into treatment of CRC. Code implementing the proposed method is available at: https://github.com/YunshanDYS/SASCcode. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.