Here, we explore the potential of single-cell genomic analysis in blood for early detection of cancer; we consider a method that screens the presence of recurrent patterns of copy number (CN) alterations using sparse single-cell sequencing. We argue for feasibility, based on in silico analysis of existing single-cell data and cancer CN profiles. Sampling procedures from existing diploid single cells can render data for a cell with any given profile. Sampling from multiple published tumor profiles can interrogate cancer clonality via an algorithm that tests the multiplicity of close pairwise similarities among single-cell cancer genomes. The majority of common solid cancers would be detectable in this manner. As any early detection method must be verifiable and actionable, we describe how further analysis of suspect cells can aid in determining risk and anatomic origin. Future affordability rests on currently available procedures for tumor cell enrichment and inexpensive methods for single-cell analysis.

在这里，我们探索血液中单细胞基因组分析在癌症早期检测中的潜力。我们考虑一种使用稀疏单细胞测序筛选拷贝数（CN）重复出现模式的方法。我们基于计算机技术论证可行性分析现有的单细胞数据和癌症CN档案。现有二倍体单细胞的采样程序可以为具有任何给定配置文件的细胞提供数据。通过测试单细胞癌症基因组中成对紧密相似性的算法，从多个已发表的肿瘤概况中取样可以询问癌症的克隆性。以这种方式可以检测到大多数常见的实体癌。由于任何早期检测方法都必须是可验证且可操作的，因此我们描述了对可疑细胞的进一步分析如何有助于确定风险和解剖学起源。未来的负担能力取决于目前可用的肿瘤细胞富集程序和用于单细胞分析的廉价方法。