Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Cancer Res. 2020 Feb 1;80(3):591-601. doi: 10.1158/0008-5472.CAN-19-1940. Epub 2019 Nov 1.
Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79-1.49) and 1.54 (1.01-2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients. SIGNIFICANCE: A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.
超过 50%的结直肠癌患者在短暂的治疗反应后会产生耐药性。从进化的角度理解肿瘤耐药性，有助于更好地预测治疗结果。本研究的目的是开发一种计算框架，以分析肿瘤的纵向测量值，并重现转移性结直肠癌 (mCRC) 患者的个体进化动态。建立了一个随机模型框架来描述诊断前、治疗期间和治疗后肿瘤进化的全貌。该进化模型使用基于 599 名 mCRC 患者肝转移病灶的纵向测量值的非线性混合效应 (NLME) 方法进行了优化。NLME 模型的确定性限制被应用于为每个患者优化随机模型。Cox 比例风险模型与最小绝对收缩和选择算子 (LASSO) 算法相结合，用于预测患者的无进展生存期 (PFS) 和总生存期 (OS)。随机进化模型很好地描述了肿瘤大小的纵向特征。为每个患者优化的进化参数表明了显著的个体间变异性。诊断时耐药亚克隆的数量被发现是生存的显著预测因子，具有三个或更多耐药亚克隆的患者的风险比及其 95%CI 分别为 1.09（0.79-1.49）和 1.54（1.01-2.34）。结合几个患者特征，进化参数强烈预测了患者的 PFS 和 OS。成功开发了一个随机计算框架来重现 mCRC 患者的个体进化动态，可用于预测患者的临床生存结果。意义：数据分析框架描述了 mCRC 患者的个体进化动态，并可推广用于预测患者的生存结果。
