Data-driven QSP software for personalized colon cancer treatment

摘要 结肠癌是美国男性和女性癌症相关死亡的第三大原因。 一个主要的临床挑战是为每个患者获得有效的治疗策略或至少确定一个子集 可以从特定治疗中贝内的患者。由于每种结肠癌都有其独特的特征， 获得个性化的癌症治疗并找到一种定制治疗策略的方法是非常重要的， 根据每个人的特征，包括种族，性别，遗传因素，免疫反应， 变化. 最近，定量和系统药理学（QSP）已普遍用于发现，验证， 测试药物。QSP模型是一个微分方程系统，它模拟了 药物和生物系统。这些数学模型提供了一种综合的“系统级”方法， 确定药物的作用机制，并找到新的方法来改变复杂的细胞网络与单核细胞 或联合治疗以获得有效的治疗。由于QSP模型是一个复杂的非线性系统， 由于方程中有许多未知参数，估计模型参数的值是极其困难的。 现有的QSP模型参数估计方法通常使用来自不同来源的组合数据， 而不是一个单一的数据集。这些数据集通常通过各种生物实验获得， 体外和体内动物研究，从而使得QSP模型难以用于个性化治疗。 据我们所知，尚未开发出用于个性化结肠癌治疗的QSP模型。 在这个项目中，我们提出了一个独特的方法来开发一个数据驱动的QSP软件，以建议有效的 基于来自原发性肿瘤样品的基因表达数据对每个患者进行治疗。自签署 肿瘤的主要特征，如免疫反应变异，可以在基因表达谱中找到。 在原发性肿瘤中，我们使用基因表达数据作为输入。我们开发了一个创新的框架， 采用数据科学，数学和统计方法的组合，以获得个性化的结肠癌 治疗我们采用新的反问题技术来估计模型的参数值， 统计方法进行敏感性分析。我们将利用这些技术提出一种最佳治疗方案 为每位患者制定治疗策略，并预测治疗方案的有效性。该模型还可能表明， 在某些患者的低效率情况下的替代疗法。

Abstract Colon cancer is the third leading cause of cancer-related deaths in the United States in both men and women. A major clinical challenge is to obtain an effective treatment strategy for each patient or at least identify a subset of patients who could benefit from a particular treatment. Since each colon cancer has its own unique features, it is very important to obtain personalized cancer treatments and find a way to tailor treatment strategies for each patient based on each individual's characteristics, including race, gender, genetic factors, immune response variations. Recently, Quantitative and Systems Pharmacology (QSP) has been commonly used to discover, validate, and test drugs. QSP models are a system of differential equations that model the dynamic interactions between drug(s) and a biological system. These mathematical models provide an integrated “systems level” approach to determining mechanisms of action of drugs and finding new ways to alter complex cellular networks with mono or combination therapy to obtain effective treatments. Since QSP models are a complex system of nonlinear equations with many unknown parameters, estimating the values of the model's parameters is extremely difficult. Existing parameter estimation methods for QSP models often use assembled data from various sources rather than a single curated dataset. These datasets are usually obtained through various biological experiments, in vitro and in vivo animal studies, thus rendering QSP models hard to be practicable for personalized treatments. To the best of our knowledge, no QSP model has been developed for personalized colon cancer treatments. In this project, we propose a unique approach to develop a data-driven QSP software to suggest effective treatment for each patient based on gene expression data from the primary tumor samples. Since signatures of main characteristics of tumors, such as immune response variations, can be found in gene expression profiling of primary tumors, we use gene expression data as input. We develop an innovative framework to systematically employ a combination of data science, mathematical, and statistical methods to obtain personalized colon cancer treatment. We employ novel inverse problem techniques to estimate the values of parameters of the model and statistical methods to perform sensitivity analysis. We will use these techniques to propose an optimal treatment strategy for each patient and predict the efficacy of the proposed treatment. The model might also suggest alternative therapies in case of low efficacy for some patients.