Single-cell analysis of tumor-microenvironment interactions in follicular lymphoma

项目摘要 滤泡性淋巴瘤（FL）是淋巴系统的一种缓慢生长的癌症，每三个中就有一个受到影响。 千人。虽然现有的一线疗法在抑制FL方面是有效的，但它仍然是一种无法治愈的疾病。 中位生存期为8-10年。五分之一的患者对标准药物产生耐药性 在头两年内治疗。了解促进耐药性的机制并及早发现 分子预后指标仍然是高度优先的。 FL肿瘤具有复杂的组织结构，除了转化的B细胞外还含有多种其他， 非恶性细胞类型。已经证明，这种肿瘤微环境的组成 显示出显著的预后相关性，然而， 这种联系仍然不清楚。肿瘤微环境的某些部分可能反映了抗肿瘤作用。 炎症反应，而其他人可以促进肿瘤生长。研究肿瘤微环境 相互作用一直是具有挑战性的，因为在广泛的细胞类型中， 现有的实验技术中存在多种细胞亚群，如T细胞或巨噬细胞亚群， 往往会产生相反的效果。 为了更好地了解肿瘤微环境的相互作用，我们将使用基于液滴的单细胞 转录组学进行FL肿瘤的无偏表征。检查患者的样本 不同的治疗反应，以及化疗前后的系列样品，我们将定量细胞 类型组成并捕获每个识别的细胞类型内的不同细胞状态。使用如此详细的 我们将确定FL肿瘤细胞之间的共变异的主要模式， 他们的微环境。然后，我们将使用更传统的批量转录组学和免疫组织化学 筛选较大组可用FL肿瘤样本的测定，以评估衍生的 分子特征与患者的进展和对治疗的反应。通过这种方式，工作的目的是 提供FL肿瘤微环境的详细分子描述，并确定 破坏有害的肿瘤微环境相互作用。

Project Summary Follicular Lymphoma (FL) is a slow-growing cancer of the lymphatic system, which affects one in every three thousand individuals. While existing frontline therapies are effective at suppressing FL, it remains an incurable disease with a median survival of 8-10 years. A fifth of the patients develop resistance to the standard treatment within the first two years. Understanding the mechanisms facilitating resistance and identifying early molecular prognostic indicators remains a high priority. FL tumors have a complex organization, and in addition to the transformed B cells contain a variety of other, non-malignant cell types. It has been demonstrated that the composition of such tumor microenvironment shows significant prognostic association, however the mechanisms and cellular interactions contributing to such association remain unclear. Some portions of the tumor microenvironment may reflect antitumor inflammatory response, while others can facilitate tumor growth. Investigating tumor-microenvironment interactions has been challenging because within the broad cell types that could be readily identified using existing experimental techniques there exist multiple subsets of cells, such as T cells or macrophage subtypes, that can often exert opposing effect. To gain better understanding of the tumor-microenvironment interactions, we will use droplet-based single-cell transcriptomics to perform unbiased characterization of the FL tumors. Examining samples from patients with distinct treatment responses, as well as serial samples before and after chemotherapy, we will quantify cell type composition and capture distinct cell states within each of the identified cell types. Using such detailed descriptions we will identify principal modes of co-variation between FL tumor cells and different aspects of their microenvironment. We will then use more traditional bulk transcriptomic and immunohistochemistry assays to screen larger panels of available FL tumor samples to evaluate the association of the derived molecular signatures with the patient’s progression and response to treatment. In this way the work will aim to provide a detailed molecular description of FL tumor microenvironment and identify potential avenues for disrupting detrimental tumor-microenvironment interactions.