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Abstract：
Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. Decoding the interconnections among different biological axes of plasticity is crucial to understand the molecular origins of phenotypic heterogeneity. Here, we use multi-modal transcriptomic data-bulk, single-cell, and spatial transcriptomics-from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity-two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. Mathematical modeling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and identify interventions to restrict it.
摘要：
肿瘤内表型异质性促进肿瘤复发和治疗抗性，仍然是一个尚未解决的临床挑战。解码不同生物可塑性轴之间的相互联系对于理解表型异质性的分子起源至关重要。这里,我们使用多模式转录组数据批量，单细胞,和空间转录组学-来自乳腺癌细胞系和原发性肿瘤样本，确定上皮-间质转化（EMT）和腔-基底可塑性之间的关联-这两个导致异质性的关键过程。我们表明管腔内乳腺癌与上皮细胞状态密切相关，但基底乳腺癌与杂合上皮/间质表型和较高的表型异质性相关。代表腔-基底轴和上皮-间充质轴之间串扰的核心基础基因调控网络的数学建模阐明了从转录组数据观察到的关联的机制基础。我们基于系统的方法将多模态数据分析与基于机制的建模相结合，提供了一个预测框架来表征肿瘤内异质性并识别干预措施以限制它。
