Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC)

摘要： 肿瘤是由异质细胞群组成的复杂生态系统。了解克隆细胞 肿瘤生态系统内肿瘤和非恶性细胞的组成在 肿瘤的复发、治疗、起始、进展和转移。先前的研究估计免疫细胞 使用从外周血生成的免疫细胞签名在批量肿瘤表达数据中键入内容 单个核细胞。然而，随着单细胞RNA测序方法的出现，我们现在也可以估计 肿瘤相关的非恶性和恶性细胞类型的内容。 在这项建议中，我们描述了一种新的深度网络方法来去卷积实体瘤中不同类型的细胞。 使用单细胞测序数据(ScDEC)。我们还将推断肿瘤相关拷贝数变异(CNV) 使用我们新的多尺度分辨率信号从单细胞RNA测序数据中克隆及其签名 基于处理的算法。我们的方法将不仅估计不同免疫细胞类型的含量和 肿瘤相关的非恶性细胞类型以及不同CNV克隆类型在实体瘤中的含量也不同。 此外，我们将发现细胞类型含量和样本表型(如疾病)之间的新关联。 生存、亚型和结局。我们建议的项目将导致临床护理的重大改进，以指导 各种类型癌症的治疗和预后。

Abstract: Tumors are complex ecosystems composed of heterogeneous cell populations. Understanding the clonal cellular composition of the tumor and the non-malignant cells within the tumor ecosystem provides significant insights in the tumor recurrence, treatment, initiation, progression and metastasis. Previous studies estimated immune cell type content in bulk tumor expression data using immune cell signatures generated from peripheral blood mononuclear cells. However, with the advent of single-cell RNA sequencing methods, we can now also estimate the tumor associated non-malignant and malignant cell type contents. In this proposal, we describe a novel deep net approach for deconvolving different cell types in bulk tumor using single-cell sequencing data (scDEC). We will also infer tumor associated copy number variation (CNV) clones and their signatures from single-cell RNA sequencing data using our novel multiscale resolution signal processing based algorithm. Our approach will estimate not only the content of different immune cell types and tumor associated non-malignant cell types but also the content of different CNV clone types in bulk tumor. Moreover, we will discover new associations between cell type content and sample phenotype such as disease survival, subtype and outcome. Our proposed project will lead to major improvements in clinical care to guide the treatment and prognosis of various types of cancer.