Multi-cellular phenotypic dynamics during the progression of breast tumors. Multi-cellular phenotypic dynamics during the progression of breast tumors

In cancer, improving diagnostics and therapeutic interventions can benefit from understanding the cellular and phenotypic heterogeneity of the tumor microenvironment (TME). In recent years, the TME has been profiled at an unprecedented level of detail by performing single-cell RNA sequencing (scRNAseq) on patient samples. However, from patient samples, studying the temporal dynamics of the TME has been challenging. Characterizing the temporal dynamics of the TME is critical to understand how inter-tumor heterogeneity is organized into a temporally ordered sequence of causes and consequences in cellular events. Here we survey the temporal dynamics of the TME by performing longitudinal scRNAseq on mouse breast tumors at different progression time points. We find multicellular phenotypic dynamics that follow one out of three possible temporal patterns: stable colonization, wave-like, or progressive increase. In particular, IFN-responsive cancer cells, GzmB+ cytotoxic T cells, as well as macrophages of a phagocytic phenotype, progressively increase as tumors progress. These findings establish the single-cell types and phenotypes in a progressing breast tumor, and determine when these players enter and leave the TME. This single-cell dataset could serve to position clinical samples where timing is unknown on a temporal axis of progression, and suggest optimal timing for different therapies. Overall design: Clustering and comparative gene expression analysis on duplicate tumor samples from allograft model originally derived from a MMTV-PyMT GEMM of breast cancer in immunocompetent mice.

在癌症研究领域，深入解析肿瘤微环境（tumor microenvironment, TME）的细胞异质性与表型异质性，可为优化癌症诊断策略与治疗干预手段提供助力。近年来，科研人员通过对患者样本实施单细胞RNA测序（single-cell RNA sequencing, scRNAseq），已以空前的精细程度刻画了TME的特征。然而，依托患者样本研究TME的时间动态始终颇具挑战。阐明TME的时间动态，对于理解肿瘤间异质性如何在细胞事件中形成按时间排布的因果序列至关重要。
本研究通过对不同进展时间节点的小鼠乳腺癌肿瘤开展纵向单细胞RNA测序，系统剖析了TME的时间动态特征。我们发现多细胞表型动态可分为三种潜在时间模式：稳定定植、波浪式变化或渐进式增加。其中，干扰素响应性肿瘤细胞、颗粒酶B（Granzyme B, GzmB）阳性细胞毒性T细胞，以及具有吞噬表型的巨噬细胞，会随肿瘤进展呈现渐进性增多的趋势。
上述研究结果明确了进展性乳腺癌肿瘤中的单细胞类型与表型，并确定了各类细胞类群进入与退出TME的时间节点。本单细胞数据集可用于将时间信息未知的临床样本锚定至肿瘤进展的时间轴上，同时可为不同治疗方案的最优给药时机提供参考。
整体实验设计：对源自免疫健全小鼠体内MMTV-PyMT基因工程小鼠模型（genetically engineered mouse model, GEMM）所构建的乳腺癌同种移植模型的重复肿瘤样本，开展聚类分析与比较基因表达分析。