Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, California.
Department of Diabetes Complications & Metabolism, Beckman Research Institute, City of Hope Medical Center, Duarte, California.
Cancer Res. 2020 Aug 1;80(15):3157-3169. doi: 10.1158/0008-5472.CAN-20-0354. Epub 2020 May 15.
Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. SIGNIFICANCE: These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/15/3157/F1.large.jpg.
基因表达的时间动态为与疾病发展和演变相关的细胞和分子变化提供了信息。鉴于高维时间基因组数据的复杂性，需要一个由稳健理论指导的分析框架来解释时间序列变化并预测系统动态。在这里，我们使用急性髓系白血病 (AML) 小鼠模型的时间序列批量 RNA-seq 数据，在代表健康和白血病状态的二维状态空间中对外周血单个核细胞的转录组的时间动态进行建模。状态转换模型确定了准确预测 AML 发展的关键点，并确定了逐步驱动白血病进展的转录组扰动。转录组状态空间的几何形状为基因动力学提供了生物学解释，对齐了在时间上不在小鼠之间同步的基因信号，并允许量化基因和途径对白血病发展的贡献。我们的状态转换模型综合了外周血中多种细胞类型的信息，并确定了从健康到白血病的转变中的关键点，以指导对整个转录组变化的解释，从而预测疾病进展。意义：这些发现将状态转换理论应用于模型的起始和发展急性髓系白血病，确定了准确预测疾病发展时间的转录组扰动。
