Genomic studies of cancer cell alterations, such as mutations, copy number variations (CNVs), and translocations, greatly promote our understanding of the genesis and development of cancers. However, the 3D genome architecture of cancers remains less studied due to the complexity of cancer genomes and technical difficulties. To explore the 3D genome structure in clinical lung cancer, we performed Hi-C experiments using paired normal and tumor cells harvested from patients with lung cancer, combining with RNA sequenceing analysis. We demonstrated the feasibility of studying 3D genome of clinical lung cancer samples with a small number of cells (1 × 10⁴), compared the genome architecture between clinical samples and cell lines of lung cancer, and identified conserved and changed spatial chromatin structures between normal and cancer samples. We also showed that Hi-C data can be used to infer CNVs and point mutations in cancer. By integrating those different types of cancer alterations, we showed significant associations between CNVs, 3D genome, and gene expression. We propose that 3D genome mediates the effects of cancer genomic alterations on gene expression through altering regulatory chromatin structures. Our study highlights the importance of analyzing 3D genomes of clinical cancer samples in addition to cancer cell lines and provides an integrative genomic analysis pipeline for future larger-scale studies in lung cancer and other cancers.

对癌细胞改变的基因组研究，如突变、拷贝数变异(CNV) 和易位，极大地促进了我们对癌症发生和发展的理解。然而，由于癌症基因组的复杂性和技术难度，对癌症的3D 基因组结构的研究仍然较少。为了探索临床肺癌中的 3D 基因组结构，我们使用从肺癌患者身上采集的成对的正常细胞和肿瘤细胞结合 RNA 测序分析进行了 Hi-C 实验。我们证明了用少量细胞 (1 × 10 ⁴ ) 研究临床肺癌样本的 3D 基因组的可行性，比较了临床样本之间的基因组结构和肺癌细胞系，并确定了正常和癌症样本之间保守和改变的空间染色质结构。我们还表明，Hi-C 数据可用于推断癌症中的 CNV 和点突变。通过整合这些不同类型的癌症改变，我们展示了 CNV、3D 基因组和基因表达之间的显着关联。我们提出 3D 基因组通过改变调控染色质结构来介导癌症基因组改变对基因表达的影响。我们的研究强调了除癌细胞系外分析临床癌症样本的 3D 基因组的重要性，并为未来肺癌和其他癌症的更大规模研究提供了综合基因组分析管道。