The Hi-C method is widely used to study the functional roles of the three-dimensional (3D) architecture of genomes. Here, we integrate Hi-C, whole-genome sequencing (WGS) and RNA-seq to study the 3D genome architecture of multiple myeloma (MM) and how it associates with genomic variation and gene expression. Our results show that Hi-C interaction matrices are biased by copy number variations (CNVs) and can be used to detect CNVs. Also, combining Hi-C and WGS data can improve the detection of translocations. We find that CNV breakpoints significantly overlap with topologically associating domain (TAD) boundaries. Compared to normal B cells, the numbers of TADs increases by 25% in MM, the average size of TADs is smaller, and about 20% of genomic regions switch their chromatin A/B compartment types. In summary, we report a 3D genome interaction map of aneuploid MM cells and reveal the relationship among CNVs, translocations, 3D genome reorganization, and gene expression regulation.

The chromosomes in eukaryotic cells are highly folded and organized to form dynamic three-dimensional (3D) structures. In recent years, many technologies including chromosome conformation capture (3C) and 3C-based technologies (Hi-C, ChIA-PET) have been developed to investigate the 3D structure of chromosomes. These technologies are enabling research on how gene regulatory events are affected by the 3D genome structure, which is increasingly implicated in the regulation of gene expression and cellular functions. Importantly, many diseases are associated with genetic variations, most of which are located in non-coding regions. However, it is difficult to determine the mechanisms by which these variations lead to diseases. With 3D genome technologies, we can now better determine the consequences of non-coding genome alterations via their impact on chromatin interactions and structures in cancer and other diseases. In this review, we introduce the various 3D genome technologies, with a focus on their application to cancer and disease research, as well as future developments to extend their utility.

ObjectiveChallenges remain in current practices of colorectal cancer (CRC) screening, such as low compliance, low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data.MethodsThe study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees (CART) algorithm. Receiver operating characteristic (ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening.ResultsAfter data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers (age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve (AUC) of the CART model was 0.88 [95% confidence interval (95% CI), 0.87−0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2% (95% CI, 58.1%−66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value (1.6%) than fecal immunochemical test (0.3%).ConclusionsAs an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare.

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.