Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA.
Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37235, USA.
Cell Syst. 2022 Sep 21;13(9):690-710.e17. doi: 10.1016/j.cels.2022.07.006. Epub 2022 Aug 17.
Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.
小细胞肺癌 (SCLC) 肿瘤由细胞状态的异质混合物组成,分为神经内分泌 (NE) 和非神经内分泌 (non-NE) 转录亚型。可塑性驱动的 NE 到非 NE 状态转变可能是对治疗的适应性和极差的生存率的基础。在这里,我们应用典型分析通过多任务进化理论重新构建 SCLC 表型异质性来模拟可塑性。细胞系和肿瘤转录组学数据很好地拟合在一个五维凸多面体中,其顶点优化类似于肺 NE 细胞的任务,这是 SCLC 的正常对应物。这些任务得到了知识和实验数据的支持,包括增殖、滑行、代谢、分泌和损伤修复,反映了癌症的特征。SCLC 亚型,无论是在群体还是单细胞水平,都可以通过批量或单细胞转录组学分别在典型空间中定位,并通过与典型顶点特征的距离来表征为任务专家或多任务通才。在典型空间中,将单细胞可塑性建模为沿基础状态流形的马尔可夫过程表明,任务权衡可能会因微环境扰动或治疗而驱动细胞可塑性。抑制表型转变和可塑性可能为 SCLC 急需的转化进展提供新的目标。本文的透明同行评审过程记录包含在补充信息中。