Pathway Discovery and Target Validation for Outgrowth of Breast Cancer Metastases

乳腺癌转移的途径发现和靶标验证

• 批准号: 10213664
• 负责人: Joel S. Bader
• 金额: $ 98.25万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10213664
• 关键词: Address; Adjuvant; Area; Biological; Biological Assay; Breast Cancer Genetics; Breast Cancer Patient; Breast Cancer therapy; Breast cancer metastasis; CRISPR/Cas technology; Cancer Biology; Cancer Etiology; Carcinoma; Cells; Cessation of life; Chemicals; Chronic Myeloid Leukemia; Clinical; Complex; Computational Technique; Computing Methodologies; Diagnostic; Disease; Dissection; Distant; Drug Side Effects; Epigenetic Process; Epithelial; Event; Family; Frequencies; Gene Expression; Gene Proteins; Genes; Genetic; Genetic Engineering; Genome; Genomics; Goals; Growth; Heterogeneity; Human; Image Analysis; Immunotherapeutic agent; Knock-out; Knowledge; Logic; Malignant Neoplasms; Mammalian Genetics; Mammary Neoplasms; Maps; Metastatic breast cancer; Metastatic to; Methods; Micrometastasis; Modeling; Molecular; Molecular Target; Mus; Mutation; Neoplasm Metastasis; Network-based; Nonmetastatic; Operative Surgical Procedures; Organ; Organoids; Pathway Analysis; Pathway interactions; Patient-Focused Outcomes; Patients; Phenocopy; Phenotype; Population; Primary Neoplasm; Process; Quantitative Trait Loci; Recurrence; Research; Risk; Sampling; Solid Neoplasm; Specimen; System; Techniques; Technology; Testing; Tissues; Validation; base; cancer cell; cancer initiation; cancer therapy; candidate validation; cell behavior; cell type; chemical genetics; clinically actionable; design; drug discovery; effective therapy; epigenome; experience; fitness; gene function; genetic analysis; genetic approach; human disease; improved; in vivo; innovation; insight; knock-down; malignant breast neoplasm; member; metastatic process; molecular modeling; molecular phenotype; molecular subtypes; mortality; novel; patient derived xenograft model; predictive marker; prevent; programs; real-time images; small hairpin RNA; small molecule; small molecule inhibitor; spectrograph; success; synergism; targeted treatment; therapeutic target; therapy resistant; three dimensional cell culture; trait; transcriptome; tumor; tumor initiation; tumor progression; Address; Adjuvant; Area; Biological; Biological Assay; Breast Cancer Genetics; Breast Cancer Patient; Breast Cancer therapy; Breast cancer metastasis; CRISPR/Cas technology; Cancer Biology; Cancer Etiology; Carcinoma; Cells; Cessation of life; Chemicals; Chronic Myeloid Leukemia; Clinical; Complex; Computational Technique; Computing Methodologies; Diagnostic; Disease; Dissection; Distant; Drug Side Effects; Epigenetic Process; Epithelial; Event; Family; Frequencies; Gene Expression; Gene Proteins; Genes; Genetic; Genetic Engineering; Genome; Genomics; Goals; Growth; Heterogeneity; Human; Image Analysis; Immunotherapeutic agent; Knock-out; Knowledge; Logic; Malignant Neoplasms; Mammalian Genetics; Mammary Neoplasms; Maps; Metastatic breast cancer; Metastatic to; Methods; Micrometastasis; Modeling; Molecular; Molecular Target; Mus; Mutation; Neoplasm Metastasis; Network-based; Nonmetastatic; Operative Surgical Procedures; Organ; Organoids; Pathway Analysis; Pathway interactions; Patient-Focused Outcomes; Patients; Phenocopy; Phenotype; Population; Primary Neoplasm; Process; Quantitative Trait Loci; Recurrence; Research; Risk; Sampling; Solid Neoplasm; Specimen; System; Techniques; Technology; Testing; Tissues; Validation; base; cancer cell; cancer initiation; cancer therapy; candidate validation; cell behavior; cell type; chemical genetics; clinically actionable; design; drug discovery; effective therapy; epigenome; experience; fitness; gene function; genetic analysis; genetic approach; human disease; improved; in vivo; innovation; insight; knock-down; malignant breast neoplasm; member; metastatic process; molecular modeling; molecular phenotype; molecular subtypes; mortality; novel; patient derived xenograft model; predictive marker; prevent; programs; real-time images; small hairpin RNA; small molecule; small molecule inhibitor; spectrograph; success; synergism; targeted treatment; therapeutic target; therapy resistant; three dimensional cell culture; trait; transcriptome; tumor; tumor initiation; tumor progression

PROJECT SUMMARY The overwhelming majority of deaths from cancer are attributable to metastasis, rather than growth of the primary tumor. In breast cancer, metastatic recurrence can occur years to decades after apparently successful surgery. Current methods do not allow individualized assessment of metastatic recurrence risk nor do they offer effective therapies for metastatic breast cancer patients. Breast cancer presents a unique research opportunity because the long interval between surgery and recurrence offers the potential to improve patient outcomes if effective anti-metastatic therapies could be developed. However, few drug discovery efforts to date have focused on the metastatic process specifically. The challenges we address are developing and applying methods to identify the basic mechanisms of metastasis, then prioritizing and validating genes and proteins as potential therapeutic targets. Our approach combines advances in experimental (Ewald) and computational (Bader) methods that we have developed to interrogate the metastatic process and to systematically dissect the genetic basis of human disease. Experimentally, we will use a pipeline that relies on organoids from primary human breast cancer tissue to model several distinct steps of metastasis: invasion into the surrounding matrix, dissemination of cancer cell clusters, and outgrowth of these clusters molecular models of distant organs. Computationally, we have developed and applied powerful methods to connect quantitative traits to their genetic basis across multiple complex human disease. We will now apply these computational methods to dissect the molecular basis of breast cancer metastasis. The central insight of our proposal is that the known heterogeneity of breast tumors, while confounding to other methods, enables our quantitative trait loci approach. We will exploit this heterogeneity with computational methods that have the potential to identify the molecular differences between primary human breast tumor organoids that demonstrate metastatic vs. non-metastatic cell behaviors (Aim 1). We will use network analysis techniques to prioritize these as targets, and then use a combination of mammalian genetic engineering and small molecule perturbations to validate targets first in the organoid system and then in accepted mouse PDX models for metastatic growth (Aim 2). Finally, we will combine our novel target based approaches with chemical and genetic perturbagens from the CTD2 Network and broader drug discovery efforts (Aim 3). In this way, we can build on existing knowledge to accelerate our progress towards improved patient outcomes. Success of this program will provide clinically actionable targets for preventing metastatic recurrence or treating patients with established breast cancer metastases. Importantly, our approaches can provide a general platform for dissecting metastasis across epithelial cancers.

项目摘要 癌症的绝大多数死亡都归因于转移，而不是原发性肿瘤的生长。在 乳腺癌，转移性复发可能发生在显然成功手术后数十年。当前方法没有 允许对转移性复发风险的个性化评估，也不为转移性乳腺癌提供有效的疗法 患者。乳腺癌提出了独特的研究机会，因为手术和复发之间的较长间隔 如果可以开发有效的抗转移疗法，则可以改善患者预后的潜力。但是，很少有药物 迄今为止的发现工作专门针对转移过程。我们所解决的挑战正在发展和 应用方法来识别转移的基本机制，然后将基因和蛋白质的优先级和验证为 潜在的治疗靶标。我们的方法结合了实验（埃瓦尔德）和计算方法（糟糕）方法的进步 我们已经开发出来询问转移过程并系统地剖析人类疾病的遗传基础。 在实验上，我们将使用依赖于原发性人类乳腺癌组织的器官的管道来建模几个 转移的不同步骤：入侵周围的基质，癌细胞簇的传播和这些生长 群体的远处器官分子模型。在计算上，我们开发并应用了强大的方法来连接 在多种复杂人类疾病中，定量性状达到其遗传基础。我们现在将应用这些计算 剖析乳腺癌转移的分子基础的方法。我们提议的核心见解是已知 乳腺肿瘤的异质性在与其他方法混淆的同时，可以实现我们的定量性状基因座方法。我们将 利用具有鉴定分子差异的计算方法利用这种异质性 原发性人类乳腺肿瘤类器官，表现出转移性与非转移性细胞行为（AIM 1）。我们将使用 网络分析技术优先考虑这些目标，然后使用哺乳动物基因工程和 小分子扰动首先在器官系统中验证靶标，然后在接受的小鼠PDX模型中用于 转移增长（AIM 2）。最后，我们将将基于新型目标的方法与化学和遗传性脑外ur相结合 从CTD2网络和更广泛的药物发现工作（AIM 3）。这样，我们可以以现有知识为基础 加速我们在改善患者预后的进展。该计划的成功将提供临床可行的目标 用于预防转移性复发或治疗既定的乳腺癌转移患者。重要的是，我们的 方法可以提供一个通用平台，用于在上皮癌中剖析转移。