Experimental Modeling of Human Breast Cancer in Mice

项目摘要 尽管学术界、政府和制药机构付出了巨大的开支和努力， 在小鼠临床前试验中显示出对人类乳腺癌有希望的药物， 诊所非常需要改善人类乳腺癌的药物反应预测测试， 临床前环境，以确定最佳候选药物。基于我们实验室获得的新见解，作为一个 关键的第一步，我们建议纠正小鼠内分泌环境，以更充分地模仿内分泌 乳腺癌患者的生活环境。 来自两个合作实验室的新颖和独立的见解表明，催乳素作为一种 人乳腺癌中药物敏感性的调节剂。催乳素受体的表达，通常在高水平 在大多数人类乳腺癌中，然而，目前的小鼠模型是不够的，因为小鼠 催乳素阻止人催乳素受体的活化。因此，目前的药物预测测试， 人乳腺癌在选择用于在无催乳素条件下生长的人乳腺癌细胞系上进行 条件我们现在将测试催乳素作为乳腺癌药物敏感性和生物学调节剂的作用。 在这项研究中，表达生理水平的人类催乳素的小鼠模型已经在遗传学上进行了研究。 为更准确地预测人类乳腺癌异种移植物的药物测试而设计。 中心假设是PRL受体信号传导通过其对乳腺细胞存活、生长 和分化，调节乳腺癌细胞对抗肿瘤剂的敏感性。与此相一致，我们 进一步假设小鼠，其中内源性小鼠催乳素已被生理性催乳素取代， 人催乳素水平，将恢复人乳腺癌异种移植物中的催乳素受体信号传导， 为改善乳腺癌临床反应性的预测提供更相关的内分泌环境 癌症治疗剂。最后，我们假设hPRL表达小鼠将使我们能够建立 新的和可移植的乳腺癌细胞系，更接近于原发性人类乳腺癌， 现有的转移来源的肿瘤细胞系。 如果成功，新的小鼠模型将可用于测试大量的人类乳腺癌 候选药物，具有更可靠的临床疗效预测的潜力。此外，成功 移植具有上皮和基质成分的原发性乳腺癌组织将为 新的个性化医疗方法来治疗乳腺癌患者。项目叙述 大多数药物在小鼠身上对人类乳腺癌进行测试时，未能治愈人类乳腺癌。那里 因此，迫切需要提高预测候选乳腺癌药物是否对患者有效的能力。到 为此，我们开发了一种新的小鼠模型，其激素环境更接近于 乳腺癌患者的情况。我们将测试新老鼠代表一种改进的 临床前模型，用于更准确的乳腺癌候选药物预测测试。

Project Summary Despite enormous expenditures and efforts by academic, government, and pharmaceutical institutions, most drugs that show promise against human breast cancer in preclinical testing in mice, fail to cure breast cancer in the clinic. There is a great need for improved drug response-predictive testing of human breast cancer in the preclinical setting to identify the best drug candidates. Based on novel insight gained in our laboratories, as a key first step we propose to correct the mouse endocrine environment to more adequately mimic the endocrine environment of breast cancer patients. Novel and independent insight from the two collaborating laboratories suggest that prolactin acts as a modulator of drug sensitivity in human breast cancer. Prolactin receptors are expressed, often at elevated levels, in a majority of human breast cancers. However, current mouse models are inadequate since mouse prolactin prevents activation of human prolactin receptors. Thus, current predictive testing of drugs against human breast cancer is performed on human breast cancer lines selected for growth under prolactin-free conditions. We will now test the role of prolactin as a modulator of breast cancer drug sensitivity and biology. For this study, a mouse model that expresses physiological levels of human prolactin has been genetically engineered for more accurate predictive testing of drugs on human breast cancer xenotransplants. The central hypothesis is that PRL receptor signaling, through its effects on mammary cell survival, growth, and differentiation, modulates sensitivity of breast cancer cells to anti-tumor agents. Consistent with this, we further hypothesize that mice, in which endogenous mouse prolactin has been replaced with physiological levels of human prolactin, will restore prolactin receptor signaling in human breast cancer xenotransplants and provide a more relevant endocrine environment for improved prediction of clinical responsiveness of breast cancer to therapeutic agents. Finally, we hypothesize that the hPRL expressing mice will allow us to establish new and transplantable breast cancer lines that more closely resemble primary breast human cancer than existing metastasis-derived tumor cell lines. If successful, the new mouse model would be available for testing of a broad number of human breast cancer drug candidates, with the potential for more reliable prediction of clinical efficacy. In addition, successful transplantation of primary breast cancer tissue with epithelial and stromal components will pave the road to a new personalized medicine approach to treatment of breast cancer patients. Project Narrative Most drugs that work when tested on human breast cancer in mice, fail to cure breast cancer in humans. There is a great need to improve the ability to predict whether candidate breast cancer drugs will work in patients. To this end, we have developed a new mouse model with a hormone environment that more closely resembles that of breast cancer patients. We will test the hypothesis that the new mouse represents an improved preclinical model for more accurate predictive testing of breast cancer drug candidates.