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

• 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
• 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

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 我们将建立一个癌症系统药理学中心(CSP中心)，建设和应用 用于了解药物反应、耐药性和毒性机制的网络级计算模型 靶向小分子药物和免疫检查点抑制剂(ICIS)。我们假设改进了 了解基本的细胞信号通路以及癌症和免疫细胞之间的相互作用将 产生更大的疗效，同时将毒性降至最低。固有的和获得性的耐药性是主要的 所有癌症疗法的更广泛应用面临挑战。通过系统地剖析抗药性如何 靶向治疗和ICIS的出现，我们的目标是了解和克服耐药性机制，使用新的 药物或药物组合，同时预测和平衡潜在的毒性。 这些目标将通过将调查结果从床边转换到替补席，然后再返回到 床边专注于黑色素瘤，这是一种ICIS和靶向药物都有效的癌症， ICIS未获批准但散发性的阴性乳腺癌(TNBC)和脑癌(GBM) 已经观察到了反应。我们将开发、验证和应用创新的药理概念和 使用计算模型以实际形式实例化这些。这样的模型将明确考虑影响 突变、表型变异、细胞间相互作用和肿瘤微环境的组成 在靶向药物和ICIS的顺序或同时组合的作用机制中。假设 世代将专注于患者来源样本的深层表型鉴定，然后在Pre-Pre-Getty中进行假设检验 使用互补的多组学和计算方法的临床环境。我们还将创建和分发 在癌症生物学的其他领域推广系统药理学的新测量和软件方法。 目标1将建立一个行政核心来监督和协调中心的所有活动。目标2将建立 协调蛋白质组学实验和计算资源的系统药理学核心， 对所有三个项目进行转录、代谢和成像分析。目标3将建立一个外联 通过网站和研讨会促进培训并确保中心数据的管理和分发的核心 按照公平的标准。目标4(项目1)将开发适应药物的多尺度计算模型 黑色素瘤中的耐药性捕获并最终解释了细胞状态的广泛多样性变化 与对RAF/MEK抑制剂的耐药性有关。目标5(项目2)将对肿瘤进行测量和建模 治疗前和治疗期间的微环境，以及在耐药时使用的一系列创新， 恶性和非恶性细胞的高度多元化检测。目标6(项目3)将进行测量和建模 影响ICI疗效的细胞类型特异性代谢、信号和转录机制 联合治疗，以便为对单一治疗无效的患者制定改进的治疗策略。