Systems Pharmacology of Therapeutic and Adverse Responses to ImmuneCheckpoint and Small Molecule Drugs

免疫检查点和小分子药物治疗和不良反应的系统药理学

基本信息

批准号：
10343835
负责人：
PETER Karl SORGER
金额：
$ 192.57万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-08 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10343835
关键词：
Achievement Adverse effects Animal Model Archives Area BRAF gene Back Biological Assay Biopsy CD8-Positive T-Lymphocytes CD8B1 gene Cancer Biology Cancer Center Cancer Patient Cell Communication Cell Line Cell Lineage Cell model Cells Clinical Clinical Trials Code Computer Models Computer software Computing Methodologies Data Data Analyses Data Science Differential Equation Disease Disease Progression Drug Combinations Drug Targeting Drug resistance Ecosystem Education and Outreach Ensure Equilibrium Fostering Funding Generations Genetic Transcription Genotype Glioblastoma Goals Health Image Immune Immune checkpoint inhibitor Immunotherapy Individual Laboratory Study Lead Logic MEKs Machine Learning Malignant - descriptor Malignant Neoplasms Malignant neoplasm of brain Measurement Measures Mediating Medical Metabolic Methods Modeling Mus Mutation Newsletter Non-Malignant Pathway interactions Patients Pharmaceutical Preparations Pharmacology Phenotype Physicians Pilot Projects Population Postdoctoral Fellow Pre-Clinical Model Proteomics Regulatory T-Lymphocyte Resistance Role Sampling Scientist Series Signal Pathway Signal Transduction Skin Software Tools Specimen Students System Systems Biology Teacher Professional Development Technology Testing Therapeutic Therapeutic Effect Therapeutic Studies Therapeutic antibodies Time Tissue Sample Tissues Toxic effect Training Training and Education Translating acquired drug resistance base bench to bedside cancer therapy cancer type career cell type checkpoint therapy clinical research site computing resources data acquisition deep learning effector T cell imaging modality immune checkpoint improved inhibiting antibody inhibitor innovation insight machine learning method meetings melanoma metabolomics mouse model multi-scale modeling multidimensional data multiple omics multiplex assay mutant non-genetic novel novel drug combination novel strategies novel therapeutics outreach pre-clinical precision oncology resistance mechanism response response biomarker small molecule spatiotemporal supervised learning targeted treatment therapy resistant transcriptomics treatment response triple-negative invasive breast carcinoma tumor tumor microenvironment tumor-immune system interactions web site

项目摘要

SUMMARY- OVERALL COMPONENT We will establish a Center for Cancer Systems Pharmacology (CSP Center) that constructs and applies network-level computational models to understand mechanisms of drug response, resistance and toxicity for targeted small molecule drugs and immune checkpoint inhibitors (ICIs). We hypothesize that improved understanding of fundamental cell signaling pathways and interactions between cancer and immune cells will result in greater efficacy while minimizing toxicity. Intrinsic and acquired drug resistance pose the primary challenges to broader application of all cancer therapies. By systematically dissecting how resistance to targeted therapies and ICIs arises, we aim to understand and overcome resistance mechanisms using new drugs or drug combinations, while simultaneously predicting and balancing potential toxicities. These goals will be accomplished by translating findings from the bedside to the bench and then back to the bedside focusing on melanoma, a type of cancer in which both ICIs and targeted drugs are effective, and triple negative breast cancer (TNBC) and brain cancers (GBM) for which ICIs are not approved but where sporadic responses have been observed. We will develop, validate and apply innovative pharmacological concepts and instantiate these in practical form using computational models. Such models will explicitly consider the impact of mutations, phenotypic variability, cell-to-cell interaction and the composition of the tumor microenvironment in mechanisms of action of sequential or simultaneous combinations of targeted drugs and ICIs. Hypothesis generation will focus on deep phenotyping of patient-derived specimens followed by hypothesis testing in pre- clinical settings using complementary multi-omic and computational methods. We will also create and distribute new measurement and software methods to promote systems pharmacology in other areas of cancer biology. Aim 1 will establish an Administrative Core to oversee and coordinate all center activities. Aim 2 will establish a Systems Pharmacology Core to coordinate experimental and computational resources for proteomic, transcriptomic, metabolomic and imaging assays across all three Projects. Aim 3 will establish an Outreach core that promotes training via a website and seminars and ensures curation and distribution of Center data according to FAIR standards. Aim 4 (Project 1) will develop multi-scale computational models of adaptive drug resistance in melanoma that capture and ultimately explain the wide diversity of changes in cell states associated with resistance to RAF/MEK inhibitors. Aim 5 (Project 2) will measure and model the tumor microenvironment before and during treatment, and at the time of drug resistance using a range of innovative, highly-multiplexed assays for malignant and non-malignant cells. Aim 6 (Project 3) will measure and model cell type-specific metabolic, signaling, and transcriptional mechanisms that contribute to the efficacy of ICI combinations, in order to develop improved therapeutic strategies for patients unresponsive to monotherapy.

摘要 - 总体组件我们将建立一个构建和应用的癌症系统药理学中心（CSP中心）网络级计算模型，以了解药物反应，耐药性和毒性的机制靶向小分子药物和免疫检查点抑制剂（ICIS）。我们假设改善了了解基本细胞信号通路以及癌症与免疫细胞之间的相互作用在最小化毒性的同时，导致更大的功效。固有和获得的耐药性构成主要更广泛应用所有癌症疗法的挑战。通过系统地剖析如何抵抗有针对性的疗法和ICIS出现，我们旨在使用新的理解和克服抵抗机制药物或药物组合，同时预测和平衡潜在毒性。这些目标将通过将发现从床边转换为长凳，然后回到床头专注于黑色素瘤，黑色素瘤是一种癌症，其中ICI和靶向药物都是有效的，三重不批准ICI的乳腺癌（TNBC）和脑癌（GBM），而零星已经观察到响应。我们将开发，验证和应用创新的药理学概念，并使用计算模型以实用形式实例化。这样的模型将明确考虑影响突变，表型变异性，细胞间相互作用和肿瘤微环境的组成在靶向药物和ICI的顺序或同时组合的作用机理中。假设产生将重点放在患者衍生标本的深层表型上，然后进行假设检验。使用互补的多词和计算方法的临床环境。我们还将创建和分发在癌症生物学其他领域促进系统药理学的新测量和软件方法。 AIM 1将建立一个管理核心，以监督和协调所有中心活动。 AIM 2将建立系统的药理学核心，以协调蛋白质组学的实验和计算资源，所有三个项目的转录组，代谢组和成像测定法。 AIM 3将建立推广通过网站和研讨会促进培训的核心，并确保中心数据的策划和分配根据公平的标准。 AIM 4（项目1）将开发自适应药物的多尺度计算模型黑色素瘤的耐药性捕获并最终解释了细胞状态变化的广泛多样性与RAF/MEK抑制剂的抗性有关。 AIM 5（项目2）将测量和建模肿瘤在治疗前后，微环境以及使用一系列创新的耐药性时，对恶性细胞和非恶性细胞的高度多形试验。 AIM 6（项目3）将测量和模型细胞类型特异性的代谢，信号传导和转录机制，有助于ICI的功效组合为了制定改善对单一疗法无反应的患者的治疗策略。