Cancer Is Associated with Alterations in the Three-Dimensional Organization of the Genome

Chromosome conformation capture technologies as tools to detect structural variations and their repercussion in chromatin 3D configuration

3D genome organization regulates gene expression in different physiological and pathological contexts. Characterization of chromatin structure at different scales has provided information about how the genome organizes in the nuclear space, from chromosome territories, compartments of euchromatin and heterochromatin, topologically associated domains to punctual chromatin loops between genomic regulatory elements and gene promoters. In recent years, chromosome conformation capture technologies have also been used to characterize structural variations (SVs) de novo in pathological conditions. The study of SVs in cancer, has brought information about transcriptional misregulation that relates directly to the incidence and prognosis of the disease. For example, gene fusions have been discovered arising from chromosomal translocations that upregulate oncogenes expression, and other types of SVs have been described that alter large genomic regions encompassing many genes. However, studying SVs in 2D cannot capture all their regulatory implications in the genome. Recently, several bioinformatic tools have been developed to identify and classify SVs from chromosome conformation capture data and clarify how they impact chromatin structure in 3D, resulting in transcriptional misregulation. Here, we review recent literature concerning bioinformatic tools to characterize SVs from chromosome conformation capture technologies and exemplify their vast potential to rebuild the 3D landscape of genomes in cancer. The study of SVs from the 3D perspective can produce essential information about drivers, molecular targets, and disease evolution.

Chromatin Organization and Transcriptional Programming of Breast Cancer Cell Identity

The advent of sequencing technologies for assessing chromosome conformations has provided a wealth of information on the organization of the 3-dimensional genome and its role in cancer progression. It is now known that changes in chromatin folding and accessibility can promote aberrant activation or repression of transcriptional programs that can drive tumorigenesis and progression in diverse cancers. This includes breast cancer, which comprises several distinct subtypes defined by their unique transcriptomes that dictate treatment response and patient outcomes. Of these, basal-like breast cancer is an aggressive subtype controlled by a pluripotency-enforcing transcriptome. Meanwhile, the more differentiated luminal subtype of breast cancer is driven by an estrogen receptor-dominated transcriptome that underlies its responsiveness to antihormone therapies and conveys improved patient outcomes. Despite the clear differences in molecular signatures, the genesis of each subtype from normal mammary epithelial cells remains unclear. Recent technical advances have revealed key distinctions in chromatin folding and organization between subtypes that could underlie their transcriptomic and, hence, phenotypic differences. These studies also suggest that proteins controlling particular chromatin states may be useful targets for treating aggressive disease. In this review, we explore the current state of understanding of chromatin architecture in breast cancer subtypes and its potential role in defining their phenotypic characteristics.

Three-Dimensional Genome Organization in Breast and Gynecological Cancers: How Chromatin Folding Influences Tumorigenic Transcriptional Programs

A growing body of research on the transcriptome and cancer genome has demonstrated that many gynecological tumor-specific gene mutations are located in cis-regulatory elements. Through chromosomal looping, cis-regulatory elements interact which each other to control gene expression by bringing distant regulatory elements, such as enhancers and insulators, into close proximity with promoters. It is well known that chromatin connections may be disrupted in cancer cells, promoting transcriptional dysregulation and the expression of abnormal tumor suppressor genes and oncogenes. In this review, we examine the roles of alterations in 3D chromatin interactions. This includes changes in CTCF protein function, cancer-risk single nucleotide polymorphisms, viral integration, and hormonal response as part of the mechanisms that lead to the acquisition of enhancers or super-enhancers. The translocation of existing enhancers, as well as enhancer loss or acquisition of insulator elements that interact with gene promoters, is also revised. Remarkably, similar processes that modify 3D chromatin contacts in gene promoters may also influence the expression of non-coding RNAs, such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), which have emerged as key regulators of gene expression in a variety of cancers, including gynecological malignancies.

Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics

Cancer reflects the dysregulation of the underlying gene network, which is strongly related to the 3D genome organization. Numerous efforts have been spent on experimental characterizations of the structural alterations in cancer genomes. However, there is still a lack of genomic structural-level understanding of the temporal dynamics for cancer initiation and progression. Here, we use a landscape-switching model to investigate the chromosome structural transition during the cancerization and reversion processes. We find that the chromosome undergoes a non-monotonic structural shape-changing pathway with initial expansion followed by compaction during both of these processes. Furthermore, our analysis reveals that the chromosome with a more expanding structure than those at both the normal and cancer cell during cancerization exhibits a sparse contact pattern, which shows significant structural similarity to the one at the embryonic stem cell in many aspects, including the trend of contact probability declining with the genomic distance, the global structural shape geometry and the spatial distribution of loci on the chromosome. In light of the intimate structure-function relationship at the chromosomal level, we further describe the cell state transition processes by the chromosome structural changes, suggesting an elevated cell stemness during the formation of the cancer cells. We show that cell cancerization and reversion are highly irreversible processes in terms of the chromosome structural transition pathways, spatial repositioning of chromosomal loci and hysteresis loop of contact evolution analysis. Our model draws a molecular-scale picture of cell cancerization from the chromosome structural perspective. The process contains initial reprogramming towards the stem cell followed by the differentiation towards the cancer cell, accompanied by an initial increase and subsequent decrease of the cell stemness.

Profiling of 3D Genome Organization in Nasopharyngeal Cancer Needle Biopsy Patient Samples by a Modified Hi-C Approach

Nasopharyngeal cancer (NPC), a cancer derived from epithelial cells in the nasopharynx, is a cancer common in China, Southeast Asia, and Africa. The three-dimensional (3D) genome organization of nasopharyngeal cancer is poorly understood. A major challenge in understanding the 3D genome organization of cancer samples is the lack of a method for the characterization of chromatin interactions in solid cancer needle biopsy samples. Here, we developed Biop-C, a modified in situ Hi-C method using solid cancer needle biopsy samples. We applied Biop-C to characterize three nasopharyngeal cancer solid cancer needle biopsy patient samples. We identified topologically associated domains (TADs), chromatin interaction loops, and frequently interacting regions (FIREs) at key oncogenes in nasopharyngeal cancer from the Biop-C heatmaps. We observed that the genomic features are shared at some important oncogenes, but the patients also display extensive heterogeneity at certain genomic loci. On analyzing the super enhancer landscape in nasopharyngeal cancer cell lines, we found that the super enhancers are associated with FIREs and can be linked to distal genes via chromatin loops in NPC. Taken together, our results demonstrate the utility of our Biop-C method in investigating 3D genome organization in solid cancers.

Genomes for Kids: The scope of pathogenic mutations in pediatric cancer revealed by comprehensive DNA and RNA sequencing

Genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. Here, we used a three-platform sequencing approach, including whole genome (WGS), exome, and RNA sequencing, to examine tumor and germline genomes from 309 prospectively identified children with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type. Eighty-six percent of patients harbored diagnostic (53%), prognostic (57%), therapeutically-relevant (25%), and/or cancer predisposing (18%) variants. Inclusion of WGS enabled detection of activating gene fusions and enhancer hijacks (36% and 8% of tumors, respectively), small intragenic deletions (15% of tumors) and mutational signatures revealing of pathogenic variant effects. Evaluation of paired tumor-normal data revealed relevance to tumor development for 55% of pathogenic germline variants. This study demonstrates the power of a three-platform approach that incorporates WGS to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers.

PredTAD: A machine learning framework that models 3D chromatin organization alterations leading to oncogene dysregulation in breast cancer cell lines

Topologically associating domains, or TADs, play important roles in genome organization and gene regulation; however, they are often altered in diseases. High-throughput chromatin conformation capturing assays, such as Hi-C, can capture domains of increased interactions, and TADs and boundaries can be identified using well-established analytical tools. However, generating Hi-C data is expensive. In our study, we addressed the relationship between multi-omics data and higher-order chromatin structures using a newly developed machine-learning model called PredTAD. Our tool uses already-available and cost-effective datatypes such as transcription factor and histone modification ChIPseq data. Specifically, PredTAD utilizes both epigenetic and genetic features as well as neighboring information to classify the entire human genome as boundary or non-boundary regions. Our tool can predict boundary changes between normal and breast cancer genomes. Among the most important features for predicting boundary alterations were CTCF, subunits of cohesin (RAD21 and SMC3), and chromosome number, suggesting their roles in conserved and dynamic boundaries formation. Upon further analysis, we observed that genes near altered TAD boundaries were found to be involved in several important breast cancer signaling pathways such as Ras, Jak-STAT, and estrogen signaling pathways. We also discovered a TAD boundary alteration that contributes to RET oncogene overexpression. PredTAD can also successfully predict TAD boundary changes in other conditions and diseases. In conclusion, our newly developed machine learning tool allowed for a more complete understanding of the dynamic 3D chromatin structures involved in signaling pathway activation, altered gene expression, and disease state in breast cancer cells.

TAD boundary and strength prediction by integrating sequence and epigenetic profile information

Topologically associated domains (TADs) are one of the important higher order chromatin structures with various sizes in the eukaryotic genomes. TAD boundaries, as the flanking regions between adjacent domains, can restrict the interactions of regulatory elements, including enhancers and promoters, and are generally dynamic and variable in different cells. However, the influence of sequence and epigenetic profile-based features in the identification of TAD boundaries is largely unknown. In this work, we proposed a method called pTADS (prediction of TAD boundary and strength), to predict TAD boundaries and boundary strength across multiple cell lines with DNA sequence and epigenetic profile information. The performance was assessed in seven cell lines and three TAD calling methods. The results demonstrate that the TAD boundary can be well predicted by the selected shared features across multiple cell lines. Especially, the model can be transferable to predict the TAD boundary from one cell line to other cell lines. The boundary strength can be characterized by boundary score with good performance. The predicted TAD boundary and TAD boundary strength are further confirmed by three Hi-C contact matrix-based methods across multiple cell lines. The codes and datasets are available at https://github.com/chrom3DEpi/pTADS.

Functional Interrogation of Enhancer Connectome Prioritizes Candidate Target Genes at Ovarian Cancer Susceptibility Loci

Identifying causal regulatory variants and their target genes from the majority of non-coding disease-associated genetic loci is the main challenge in post-Genome-Wide Association Studies (GWAS) functional studies. Although chromosome conformation capture (3C) and its derivative technologies have been successfully applied to nominate putative causal genes for non-coding variants, many GWAS target genes have not been identified yet. This study generated a high-resolution contact map from epithelial ovarian cancer (EOC) cells with two H3K27ac-HiChIP libraries and analyzed the underlying gene networks for 15 risk loci identified from the largest EOC GWAS. By combinatory analysis of 4,021 fine-mapped credible variants of EOC GWAS and high-resolution contact map, we obtained 162 target genes that mainly enriched in cancer related pathways. Compared with GTEx eQTL genes in ovarian tissue and annotated proximal genes, 132 HiChIP targets were first identified for EOC causal variants. More than half of the credible variants (CVs) involved interactions that were over 185 kb in distance, indicating that long-range transcriptional regulation is an important mechanism for the function of GWAS variants in EOC. We also found that many HiChIP gene targets showed significantly differential expressions between normal ovarian and EOC tumor samples. We validated one of these targets by manipulating the rs9303542 located region with CRISPR-Cas9 deletion and dCas9-VP64 activation experiments and found altered expression of HOXB7 and HOXB8 at 17q21.32. This study presents a systematic analysis to identify putative target genes for causal variants of EOC, providing an in-depth investigation of the mechanisms of non-coding regulatory variants in the etiology and pathogenesis of ovarian cancer.

Independent Transposon Exaptation Is a Widespread Mechanism of Redundant Enhancer Evolution in the Mammalian Genome

Many regulatory networks appear to involve partially redundant enhancers. Traditionally, such enhancers have been hypothesized to originate mainly by sequence duplication. An alternative model postulates that they arise independently, through convergent evolution. This mechanism appears to be counterintuitive to natural selection: Redundant sequences are expected to either diverge and acquire new functions or accumulate mutations and become nonfunctional. Nevertheless, we show that at least 31% of the redundant enhancer pairs in the human genome (and 17% in the mouse genome) indeed originated in this manner. Specifically, for virtually all transposon-derived redundant enhancer pairs, both enhancer partners have evolved independently, from the exaptation of two different transposons. In addition to conferring robustness to the system, redundant enhancers could provide an evolutionary advantage by fine-tuning gene expression. Consistent with this hypothesis, we observed that the target genes of redundant enhancers exhibit higher expression levels and tissue specificity as compared with other genes. Finally, we found that although enhancer redundancy appears to be an intrinsic property of certain mammalian regulatory networks, the corresponding enhancers are largely species-specific. In other words, the redundancy in these networks is most likely a result of convergent evolution.