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 切除原发性肿瘤。在此之前，人们对这种潜伏的、微小的骨转移瘤的生物学知之甚少。 它们生长到明显的溶骨性大转移。这表明我们对以下问题的理解存在重大差距： 骨转移靶向尚未完全适应骨微环境的癌细胞， 提供治疗益处并防止明显转移的发生。骨和骨髓包括 几个高度独特的微环境的小生境。背部，单个播散性肿瘤细胞（DTC）驻留 在血管周围的小生境，而增殖，多细胞骨微转移（BELT）被发现在 表现出活跃的成骨特征的成骨龛。通过哪些机制， 不同的小生境发生转移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，以及潜在的治疗靶点， 乳腺癌细胞与骨内不同微环境之间的相互作用。我们将奉行 三个具体目标。首先，我们将解剖血管周围和成骨细胞的时空动力学， 骨微转移模型中的小生境和癌-小生境相互作用。我们将使用高分辨率，全- 骨、多光子显微镜和激光捕获显微切割（LCM），随后进行转录组分析 （LCM-seq）以获得癌细胞和原位小生境细胞之间的相对定位和相互影响。 第二，我们将整合转录组学和成像数据，并开发用于发现 新的机制和疗法，以阻断癌症的生态位的相互作用。已建立和新的算法 将用于揭示微环境分子，自分泌和旁分泌信号通路 介导小生境-肿瘤相互作用。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。我们将系统地了解早期 骨定植并产生可检验的机制和治疗假说。第三，我们将验证 在动物模型中发现机制并预测药物功效。Zhang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实验， 用于验证由Wong组的计算建模生成的计算预测。两 DTC和BTC的转移负荷和频率/分布将作为终点进行检查。本研究将 无偏地描述骨中从DTC到TNM的早期转移进展的分子过程， 在单个到几个单元格的分辨率上进行优化。这种知识是前所未有的，对最终的 转移潜伏期的理解，一个长期的临床挑战。建模工具通过 该研究可能适用于涉及高度时空特异性癌症的其他生物学背景， 生态位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.