Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.
理解细胞转化信号模式并控制细胞表型是当前生物学面临的一大挑战。本研究采用细胞状态转换评估与调控（cSTAR）方法，对多株乳腺癌细胞系及正常乳腺组织来源细胞系的单细胞磷酸化蛋白质组扰动数据集进行分析。在区分管腔型、基底型和正常细胞状态后，我们识别了核心调控网络中的信号节点，描绘了因果关系链，并确定了驱动致癌转化及不同乳腺癌亚型间状态转换的关键因子。研究发现，虽然同一乳腺癌亚型内的细胞系具有不同的突变谱和表达谱，但所有管腔型乳腺癌细胞的核心网络结构具有相似性，其中mTOR是主要的致癌驱动因子。相比之下，基底型乳腺癌的核心网络呈现异质性，大致可分为四个主要亚类，各具独特的致癌驱动因子和乳腺癌亚型特征。同样，正常乳腺组织细胞也可分为两个不同亚类。基于实验数据与量化网络拓扑结构，我们建立了机制性cSTAR模型，这些模型可作为数字细胞孪生体，实现在沃丁顿景观中精确调控细胞在不同状态间的转换。这些cSTAR模型为使用小分子抑制剂调控乳腺癌细胞系磷酸化网络提供了策略依据。
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