Pathway Discovery and Target Validation for Outgrowth of Breast Cancer Metastases

项目总结 绝大多数癌症死亡归因于转移，而不是原发肿瘤的生长。在……里面 乳腺癌，转移性复发可能发生在表面上成功的手术后数年至数十年。目前的方法不能 允许对转移性复发风险进行个性化评估，也不能为转移性乳腺癌提供有效的治疗方法 病人。乳腺癌提供了一个独特的研究机会，因为手术和复发之间的长时间间隔提供了 如果有效的抗转移疗法能够被开发出来，那么改善患者预后的可能性。然而，很少有药物 到目前为止，发现工作主要集中在转移过程上。我们应对的挑战是发展和 应用各种方法识别转移的基本机制，然后优先排序和验证基因和蛋白质 潜在的治疗靶点。我们的方法结合了实验方法(Ewald)和计算方法(Bader)的进步 我们已经开发了用于询问转移过程并系统地剖析人类疾病的遗传基础的技术。 在实验上，我们将使用一条依赖于人类乳腺癌组织中的有机物的管道来模拟几个 转移的不同步骤：侵入周围基质，扩散癌细胞群，以及这些癌细胞的生长。 将遥远器官的分子模型聚集在一起。在计算方面，我们开发并应用了强大的方法来连接 数量性状的遗传基础跨越多种复杂的人类疾病。我们现在将应用这些计算 方法剖析乳腺癌转移的分子基础。我们建议的核心观点是已知的 乳腺肿瘤的异质性，虽然与其他方法混淆，但使我们的数量性状基因座方法成为可能。我们会 利用这种异质性，使用计算方法，有可能识别分子之间的差异 原发人类乳腺肿瘤器官类物质，显示转移和非转移细胞行为(目标1)。我们将使用 网络分析技术，将这些作为目标，然后使用哺乳动物基因工程和 小分子扰动首先在有机类系统中验证靶标，然后在接受的小鼠PDX模型中验证靶标 转移性生长(目标2)。最后，我们将把我们新的基于目标的方法与化学和遗传扰动结合起来。 来自CTD2网络和更广泛的药物发现努力(目标3)。通过这种方式，我们可以在现有知识的基础上 加快我们在改善患者预后方面的进展。该计划的成功将为临床提供可操作的靶点 用于预防转移复发或治疗已确诊的乳腺癌转移患者。重要的是，我们的 方法可以为解剖上皮癌的转移提供一个通用的平台。

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.