Data-driven QSP software for personalized colon cancer treatment

用于个性化结肠癌治疗的数据驱动 QSP 软件

基本信息

批准号：
10227447
负责人：
SUVRA PAL
金额：
$ 15.99万
依托单位：
UNIVERSITY OF MASSACHUSETTS AMHERST
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10227447
关键词：
Adopted Affect Algorithms Alternative Therapies Animals Biological CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cancer Etiology Cell Density Cells Cessation of life Characteristics Chemicals Clinical Clinical Research Colon Carcinoma Colonic Neoplasms Combined Modality Therapy Complex Computer software Data Data Science Data Set Dendritic Cells Differential Equation Epithelial Cells Equation Expression Profiling Fluorouracil Gender Gene Expression Genetic Goals Immune Immune response In Vitro Individual Inflammatory Interferons Interleukin-2 Interleukin-4 Interleukin-6 Killer Cells Laws Least-Squares Analysis Leucovorin Mathematics Measures Methods Modeling Molecular Necrosis Patients Pattern Pharmaceutical Preparations Pharmacologic Substance Pharmacology Pilot Projects Primary Neoplasm Process Proteins Race Radial Running STAT4 gene STAT6 gene Sampling Signal Transduction Source Statistical Methods System T-Cell Activation T-Lymphocyte Techniques Time Treatment outcome Uncertainty United States Variant Woman base biological systems cancer therapy cancer type cell type colon cancer patients colon cancer treatment cytokine density drug action drug testing effective therapy effector T cell efficacy study experimental study in vivo individual patient individualized medicine innovation interest irinotecan macrophage mathematical methods mathematical model men model building mutant novel optimal treatments patient subsets personalized cancer therapy personalized medicine research and development response systems of equations targeted treatment tool treatment strategy tumor tumor growth

项目摘要

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.

抽象的结肠癌是美国男性和女性中与癌症相关死亡的第三大主要原因。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子集可以从特定治疗中受益的患者。由于每个结肠癌都有其独特的特征，获取个性化癌症治疗并找到一种定制治疗策略的方法非常重要每个患者根据每个人的特征，包括种族，性别，遗传因素，免疫响应变化。最近，定量和系统药理学（QSP）通常用于发现，验证，和测试药物。 QSP模型是一个微分方程的系统，该系统模拟了模拟的动态相互作用药物和生物系统。这些数学模型提供了一种集成的“系统级”方法确定药物作用的机制，并找到用单声道改变复杂细胞网络的新方法或联合疗法以获得有效治疗。由于QSP模型是非线性的复杂系统具有许多未知参数的方程式，估计模型参数的值非常困难。 QSP模型的现有参数估计方法通常使用来自各种来源的组装数据比单个策划数据集。这些数据集通常是通过各种生物学实验获得的体内和体内动物研究，因此使QSP模型难以实用。据我们所知，尚未开发用于个性化结肠癌治疗的QSP模型。在这个项目中，我们提出了一种独特的方法来开发数据驱动的QSP软件以提出有效根据原发性肿瘤样品的基因表达数据为每个患者的治疗。由于签名肿瘤的主要特征，例如免疫反应变化，可以在基因表达中发现在原发性肿瘤中，我们使用基因表达数据作为输入。我们将创新框架开发到系统上员工一种数据科学，数学和统计方法的组合，以获得个性化结肠癌治疗。我们采用新颖的逆问题技术来估计模型参数的值，并执行灵敏度分析的统计方法。我们将使用这些技术提出最佳处理每个患者的策略，并预测拟议治疗的效率。该模型也可能暗示某些患者的效率低下，替代疗法。