A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

生物医学领域的一个重大挑战是开发将大量分散的数据集整合到综合框架中以用于生成最佳临床决策的方法。单细胞分析的最新技术进步允许对细胞和群体进行高维分子表征，但迄今为止，很少有数学模型尝试将单细胞尺度的测量结果与其他类型的纵向数据整合起来。在这里，我们提出了一个框架，可以将机器学习模型的静态输出付诸行动，并利用这些作为治疗反应动态模型中状态变量的测量。我们将此框架应用于乳腺癌细胞，将单细胞转录组数据与纵向大量细胞群（大量时间过程）数据整合起来。我们证明，明确包含源自单细胞 RNA 测序数据 (scRNA-seq) 的表型组成估计，可以提高新治疗预测的准确性，其一致性相关系数 (CCC) 为 0.92，而预测精度为当仅拟合纵向散装细胞群数据时，CCC = 0.64。据我们所知，这是第一项将单细胞克隆解析转录组数据集与大量时间过程数据明确整合以共同校准耐药动力学数学模型的工作。我们预计这种方法将成为证明将多种数据类型纳入数学模型以根据数据开发优化治疗方案的可行性的第一步。