Pathway Discovery and Target Validation for Outgrowth of Breast Cancer Metastases

项目摘要 绝大多数癌症死亡是由于转移，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明显成功的手术后数年至数十年。目前的方法不 允许对转移性复发风险进行个体化评估，也不能为转移性乳腺癌提供有效治疗 患者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 如果能够开发出有效的抗转移治疗，则有可能改善患者的预后。然而，很少有药物 迄今为止的发现努力特别集中在转移过程上。我们应对的挑战正在发展， 应用方法来确定转移的基本机制，然后优先考虑和验证基因和蛋白质， 潜在的治疗目标。我们的方法结合了实验（Ewald）和计算（Bader）方法的进步 我们已经开发了询问转移过程和系统地剖析人类疾病的遗传基础。 在实验上，我们将使用一个依赖于来自原发性人类乳腺癌组织的类器官的管道来模拟几个 转移的不同步骤：侵入周围基质，癌细胞簇的扩散，以及这些细胞簇的生长。 群集远距离器官的分子模型。在计算上，我们已经开发并应用了强大的方法来连接 数量性状与其遗传基础在多种复杂的人类疾病。我们现在将这些计算 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，同时与其他方法混淆，使我们的数量性状基因座方法成为可能。我们将 利用这种异质性与计算方法，有可能确定分子之间的差异， 显示转移性与非转移性细胞行为的原发性人乳腺肿瘤类器官（目的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 首先在类器官系统中验证靶标，然后在公认的小鼠PDX模型中验证靶标， 转移性生长（Aim 2）。最后，我们将联合收割机结合我们的新的目标为基础的方法与化学和遗传微扰 来自CTD2网络和更广泛的药物发现工作（目标3）。这样，我们就可以在现有知识的基础上， 加快我们的进展，以改善患者的结果。该计划的成功将提供临床可操作的目标 用于预防转移复发或治疗已确定乳腺癌转移的患者。重要的是我们的 这些方法可以为剖析上皮癌的转移提供一个通用平台。

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.