Spatiotemporal modeling of cancer-niche interactions in breast cancer bone metastasis

摘要 大约20%-40%的乳腺癌患者在手术后数年甚至数十年发生骨转移。 切除原发肿瘤。以前人们对潜伏的、微小的骨转移的生物学知之甚少。 它们不再生长为明显的溶骨性大转移。这代表着我们对 骨转移。靶向尚未完全适应骨骼微环境的癌细胞可能 提供治疗益处，防止显性转移的发生。骨和骨髓包括 几个非常独特的微环境的壁龛。休眠的单个播散性肿瘤细胞(DTC) 在血管周围龛，而增生性多细胞骨微转移(BMMS)被发现在 表现出活跃的成骨作用的成骨生态位。中国经济转型的机制 不同的生态位出现，以改变转移的种子的命运仍然难以捉摸。本项目的总体目标 是研究时空动力学、分子串扰和潜在的治疗靶点 乳腺癌细胞与骨中不同微环境生态位的相互作用。我们将继续追查 三个具体目标。首先，我们将剖析血管周围和成骨的时空动力学。 骨微转移模型中的生态位和肿瘤-生态位的相互作用。我们将使用高分辨率、完整的- 骨、多光子显微镜和激光捕获显微解剖(LCM)以及转录组分析 (LCM-SEQ)，以获得癌细胞和生态位细胞之间的相对定位和相互影响。 其次，我们将整合转录和成像数据，并开发计算模型来发现 阻断癌症-利基相互作用的新机制和新疗法。已建立的和新的算法 将被用来揭示微环境分子、自分泌和旁分泌信号通路 调节壁龛和肿瘤之间的相互作用。将进行药物再利用分析，以确定潜在的治疗方法 已经被用于其他疾病的药物。我们将实现对早期阶段的系统了解 骨定植，并产生可测试的机制和治疗假说。第三，我们将验证 在动物模型中发现了机制并预测了药效。张实验室采用了 并建立了一系列基因工程小鼠模型和骨转移分析，这将是 用于验证由Wong组通过计算建模生成的计算预测。两者都有 转移性负荷和DTC和BMMS的频率/分布将被检查为终点。这项研究将 不偏不倚地描述早期骨转移的分子过程。 单细胞到少数细胞分辨率的BMMS。这种知识是史无前例的，对最终的 了解转移潜伏期，这是一个长期存在的临床挑战。通过开发的建模工具 这项研究可能适用于其他涉及高度时空特异性癌症的生物学背景- 利基互动。计算机辅助的药物再利用可能会导致快速的临床翻译。

ABSTRACT About 20-40% of breast cancer patients develop metastasis to the bone, years to even decades after surgical removal of primary tumors. Little is known about the biology of the latent, microscopic bone metastases before they outgrow to overt osteolytic macrometastases. This represents a significant gap in our understanding of bone metastasis. Targeting cancer cells that have not fully adapted to the bone microenvironment might provide therapeutic benefit and prevent the occurrence of overt metastases. Bone and bone marrow comprise of several highly distinctive microenvironment niches. Dormant, single disseminated tumor cells (DTCs) reside in the perivascular niche, whereas proliferative, multi-cell bone micrometastases (BMMs) are found in the osteogenic niche that exhibits features of active osteogenesis. Mechanisms through which the transition of different niches occurs to switch fates of metastatic seeds remain elusive. The overall objectives of this project are to investigate the spatiotemporal dynamics, the molecular crosstalk, and the therapeutic targets underlying the interaction between breast cancer cells and different microenvironment niches in bone. We will pursue three specific aims. First, we will dissect the spatiotemporal dynamics of the perivascular and osteogenic niches and the cancer-niche interactions in bone micrometastasis models. We will use high-resolution, whole- bone, multi-photon microscopy and laser-captured microdissection (LCM) followed by transcriptome profiling (LCM-seq) to obtain relative localization and mutual impacts between cancer cells and niche cells in situ. Second, we will integrate transcriptomic and imaging data and develop computational models for discovery of new mechanisms and therapies toward blockade of cancer-niche interactions. Established and new algorithms will be used to uncover the microenvironment molecules, and autocrine and paracrine signaling pathways mediating niche-tumor interactions. Drug-repurposing analyses will be carried out to identify potential therapies that have already been used for other diseases. We will achieve a systematic understanding of early-stage bone colonization and generate testable mechanistic and therapeutic hypotheses. Third, we will validate the discovered mechanisms and predicted drug efficacies in animal models. The Zhang laboratory has adopted and established a series of genetically engineered mouse models and bone metastasis assays, which will be utilized to validate computational predictions generated by computational modeling by the Wong group. Both metastatic burden and frequency/distribution of DTCs and BMMs will be examined as endpoints. This study will unbiasedly profile the molecular process of early stage metastasis progression in the bone from DTCs to BMMs at single-to-few cell resolutions. This knowledge is unprecedented and critical for the ultimate understanding of metastasis latency, a long-standing clinical challenge. The modeling tool developed through this study will likely be applicable to other biological contexts involving highly spatiotemporally specific cancer- niche interaction. The computer-aided drug repurposing will likely lead to fast clinical translation.