Nucleome Analysis Reveals Structure-Function Relationships for Colon Cancer

Perturbations in 3D genome organization can promote acquired drug resistance

Acquired drug resistance is a major problem in the treatment of cancer. hTERT-immortalized, untransformed RPE-1 cells can acquire resistance to Taxol by derepressing the ABCB1 gene, encoding for the multidrug transporter P-gP. Here, we investigate how the ABCB1 gene is derepressed. ABCB1 activation is associated with reduced H3K9 trimethylation, increased H3K27 acetylation, and ABCB1 displacement from the nuclear lamina. While altering DNA methylation and H3K27 methylation had no major impact on ABCB1 expression, nor did it promote resistance, disrupting the nuclear lamina component Lamin B Receptor did promote the acquisition of a Taxol-resistant phenotype in a subset of cells. CRISPRa-mediated gene activation supported the notion that lamina dissociation influences ABCB1 derepression. We propose a model in which nuclear lamina dissociation of a repressed gene allows for its activation, implying that deregulation of the 3D genome topology could play an important role in tumor evolution and the acquisition of drug resistance.

Hi-C analysis of genomic contacts revealed karyotype abnormalities in chicken HD3 cell line

BACKGROUND

Karyotype abnormalities are frequent in immortalized continuous cell lines either transformed or derived from primary tumors. Chromosomal rearrangements can cause dramatic changes in gene expression and affect cellular phenotype and behavior during in vitro culture. Structural variations of chromosomes in many continuous mammalian cell lines are well documented, but chromosome aberrations in cell lines from other vertebrate models often remain understudied. The chicken LSCC-HD3 cell line (HD3), generated from erythroid precursors, was used as an avian model for erythroid differentiation and lineage-specific gene expression. However, karyotype abnormalities in the HD3 cell line were not assessed. In the present study, we applied high-throughput chromosome conformation capture to analyze 3D genome organization and to detect chromosome rearrangements in the HD3 cell line.

RESULTS

We obtained Hi-C maps of genomic interactions for the HD3 cell line and compared A/B compartments and topologically associating domains between HD3 and several other cell types. By analysis of contact patterns in the Hi-C maps of HD3 cells, we identified more than 25 interchromosomal translocations of regions ≥ 200 kb on both micro- and macrochromosomes. We classified most of the observed translocations as unbalanced, leading to the formation of heteromorphic chromosomes. In many cases of microchromosome rearrangements, an entire microchromosome together with other macro- and microchromosomes participated in the emergence of a derivative chromosome, resembling "chromosomal fusions'' between acrocentric microchromosomes. Intrachromosomal inversions, deletions and duplications were also detected in HD3 cells. Several of the identified simple and complex chromosomal rearrangements, such as between GGA2 and GGA1qter; GGA5, GGA4p and GGA7p; GGA4q, GGA6 and GGA19; and duplication of the sex chromosome GGAW, were confirmed by FISH.

CONCLUSIONS

In the erythroid progenitor HD3 cell line, in contrast to mature and immature erythrocytes, the genome is organized into distinct topologically associating domains. The HD3 cell line has a severely rearranged karyotype with most of the chromosomes engaged in translocations and can be used in studies of genome structure-function relationships. Hi-C proved to be a reliable tool for simultaneous assessment of the spatial genome organization and chromosomal aberrations in karyotypes of birds with a large number of microchromosomes.

Structural variants drive context-dependent oncogene activation in cancer

Higher-order chromatin structure is important for the regulation of genes by distal regulatory sequences^1,2. Structural variants (SVs) that alter three-dimensional (3D) genome organization can lead to enhancer-promoter rewiring and human disease, particularly in the context of cancer³. However, only a small minority of SVs are associated with altered gene expression^4,5, and it remains unclear why certain SVs lead to changes in distal gene expression and others do not. To address these questions, we used a combination of genomic profiling and genome engineering to identify sites of recurrent changes in 3D genome structure in cancer and determine the effects of specific rearrangements on oncogene activation. By analysing Hi-C data from 92 cancer cell lines and patient samples, we identified loci affected by recurrent alterations to 3D genome structure, including oncogenes such as MYC, TERT and CCND1. By using CRISPR-Cas9 genome engineering to generate de novo SVs, we show that oncogene activity can be predicted by using 'activity-by-contact' models that consider partner region chromatin contacts and enhancer activity. However, activity-by-contact models are only predictive of specific subsets of genes in the genome, suggesting that different classes of genes engage in distinct modes of regulation by distal regulatory elements. These results indicate that SVs that alter 3D genome organization are widespread in cancer genomes and begin to illustrate predictive rules for the consequences of SVs on oncogene activation.

Chromatin Mechanisms Driving Cancer

The change in cell state from normal to malignant is driven fundamentally by oncogenic mutations in cooperation with epigenetic alterations of chromatin. These alterations in chromatin can be a consequence of environmental stressors or germline and/or somatic mutations that directly alter the structure of chromatin machinery proteins, their levels, or their regulatory function. These changes can result in an inability of the cell to differentiate along a predefined lineage path, or drive a hyperactive, highly proliferative state with addiction to high levels of transcriptional output. We discuss how these genetic alterations hijack the chromatin machinery for the oncogenic process to reveal unique vulnerabilities and novel targets for cancer therapy.

Functional organization of the maternal and paternal human 4D Nucleome

Every human somatic cell inherits a maternal and a paternal genome, which work together to give rise to cellular phenotypes. However, the allele-specific relationship between gene expression and genome structure through the cell cycle is largely unknown. By integrating haplotype-resolved genome-wide chromosome conformation capture, mature and nascent mRNA, and protein binding data from a B lymphoblastoid cell line, we investigate this relationship both globally and locally. We introduce the maternal and paternal 4D Nucleome, enabling detailed analysis of the mechanisms and dynamics of genome structure and gene function for diploid organisms. Our analyses find significant coordination between allelic expression biases and local genome conformation, and notably absent expression bias in universally essential cell cycle and glycolysis genes. We propose a model in which coordinated biallelic expression reflects prioritized preservation of essential gene sets.

Oncogenic extrachromosomal DNA functions as mobile enhancers to globally amplify chromosomal transcription

Extrachromosomal, circular DNA (ecDNA) is emerging as a prevalent yet less characterized oncogenic alteration in cancer genomes. We leverage ChIA-PET and ChIA-Drop chromatin interaction assays to characterize genome-wide ecDNA-mediated chromatin contacts that impact transcriptional programs in cancers. ecDNAs in glioblastoma patient-derived neurosphere and prostate cancer cell cultures are marked by widespread intra-ecDNA and genome-wide chromosomal interactions. ecDNA-chromatin contact foci are characterized by broad and high-level H3K27ac signals converging predominantly on chromosomal genes of increased expression levels. Prostate cancer cells harboring synthetic ecDNA circles composed of characterized enhancers result in the genome-wide activation of chromosomal gene transcription. Deciphering the chromosomal targets of ecDNAs at single-molecule resolution reveals an association with actively expressed oncogenes spatially clustered within ecDNA-directed interaction networks. Our results suggest that ecDNA can function as mobile transcriptional enhancers to promote tumor progression and manifest a potential synthetic aneuploidy mechanism of transcription control in cancer.

Epithelial Cells in 2D and 3D Cultures Exhibit Large Differences in Higher-order Genomic Interactions

Recent studies have characterized the genomic structures of many eukaryotic cells, often focusing on their relation to gene expression. However, these studies have largely investigated cells grown in 2D cultures, although the transcriptomes of 3D-cultured cells are generally closer to their in vivo phenotypes. To examine the effects of spatial constraints on chromosome conformation, we investigated the genomic architecture of mouse hepatocytes grown in 2D and 3D cultures using in situ Hi-C. Our results reveal significant differences in higher-order genomic interactions, notably in compartment identity and strength as well as in topologically associating domain (TAD)–TAD interactions, but only minor differences are found at the TAD level. Our RNA-seq analysis reveals an up-regulated expression of genes involved in physiological hepatocyte functions in the 3D-cultured cells. These genes are associated with a subset of structural changes, suggesting that differences in genomic structure are critically important for transcriptional regulation. However, there are also many structural differences that are not directly associated with changes in gene expression, whose cause remains to be determined. Overall, our results indicate that growth in 3D significantly alters higher-order genomic interactions, which may be consequential for a subset of genes that are important for the physiological functioning of the cell.

TCF7L2 silencing results in altered gene expression patterns accompanied by local genomic reorganization

Canonical Wnt signaling is crucial for intestinal homeostasis as TCF4, the major Wnt signaling effector in the intestines, is required for stem cell maintenance. The capability of TCF4 to maintain the stem cell phenotype is contingent upon β-catenin, a potent transcriptional activator, which interacts with histone acetyltransferases and chromatin remodeling complexes. We used RNAi to explore the influence of TCF4 on chromatin structure (Hi-C) and gene expression (RNA sequencing) across a 72-hour time series in colon cancer. We found that TCF4 reduction results in a disproportionate up-regulation of gene expression, including a powerful induction of SOX2. Integration of RNA sequencing and Hi-C data revealed a TAD boundary loss, which occurred concomitantly with the over-expression of a cluster of CEACAM genes on chromosome 19. We identified EMT and E2F as the 2 most deregulated pathways upon TCF4 depletion and LUM, TMPO, and AURKA as highly influential genes in these networks using measures of centrality. Results from gene expression, chromatin structure, and centrality analyses were integrated to generate a list of candidate transcription factors crucial for colon cancer cell homeostasis. The top ranked factor was c-JUN, an oncoprotein known to interact with TCF4 and β-catenin, confirming the usefulness of this approach.

Bioinformatics for The Prognostic Value and Function of Cubilin (CUBN) in Colorectal Cancer

Background

Cubilin (CUBN) gene was reported to be a novel risk variant for colorectal cancer (CRC). Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed CRC to identify CUBN that can predict patient outcome and function by using bioinformatics.

Material/Methods

The association in expression, clinicopathological parameters, and survival were analyzed by using Oncomine, UNCLA, and GEPIA, while CUBN alterations and related functional networks were identified using cBioPortal. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) of CUBN in CRC were explored by using LinkOmics. Gene set enrichment analysis (GSEA) examined target networks of kinases, miRNAs, and transcription factors.

Results

We found that CUBN was overexpressed in CRC. Patients who were in advanced TNM stage tended to express higher CUBN mRNA levels, while those who received radiotherapy tended to express relatively lower CUBN mRNA levels. Higher expression of CUBN was found to be associated with shorter overall survival (OS) and disease-free survival (DFS). Moreover, functional networks analysis suggested that CUBN can regulate mismatch repair, terpenoid backbone biosynthesis, base excision repair, and proteasome via vitamin digestion and absorption pathway to influence CRC occurrence.

Conclusions

These findings suggested that CUBN could serve as a prognostic and therapeutic biomarker of CRC in the future.

Structural variation in the sequencing era

Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.

Disordered chromatin packing regulates phenotypic plasticity

Three-dimensional supranucleosomal chromatin packing plays a profound role in modulating gene expression by regulating transcription reactions through mechanisms such as gene accessibility, binding affinities, and molecular diffusion. Here, we use a computational model that integrates disordered chromatin packing (CP) with local macromolecular crowding (MC) to study how physical factors, including chromatin density, the scaling of chromatin packing, and the size of chromatin packing domains, influence gene expression. We computationally and experimentally identify a major role of these physical factors, specifically chromatin packing scaling, in regulating phenotypic plasticity, determining responsiveness to external stressors by influencing both intercellular transcriptional malleability and heterogeneity. Applying CPMC model predictions to transcriptional data from cancer patients, we identify an inverse relationship between patient survival and phenotypic plasticity of tumor cells.

Biomaterial Scaffolds Recruit an Aggressive Population of Metastatic Tumor Cells In Vivo

For most cancers, metastasis is the point at which clinical treatment shifts from curative intent to extending survival. Biomaterial implants acting as a synthetic premetastatic niche recruit metastatic cancer cells and provide a survival advantage, and their use as a diagnostic platform requires assessing their relevance to disease progression. Here, we showed that scaffold-captured tumor cells (SCAF) were 30 times more metastatic to the lung than primary tumor (PT) cells, similar to cells derived from lung micrometastases (LUNG). SCAF cells were more aggressive in vitro, demonstrated higher levels of migration, invasion, and mammosphere formation, and had a greater proportion of cancer stem cells than PT. SCAF cells were highly enriched for gene expression signatures associated with metastasis and had associated genomic structural changes, including globally enhanced entropy. Collectively, our findings demonstrate that SCAF cells are distinct from PT and more closely resemble LUNG, indicating that tumor cells retrieved from scaffolds are reflective of cells at metastatic sites. SIGNIFICANCE: These findings suggest that metastatic tumor cells captured by a biomaterial scaffold may serve as a diagnostic for molecular staging of metastasis.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/8/2042/F1.large.jpg.

Single Chromosome Aneuploidy Induces Genome-Wide Perturbation of Nuclear Organization and Gene Expression

Chromosomal aneuploidy is a defining feature of carcinomas and results in tumor-entity specific genomic imbalances. For instance, most sporadic colorectal carcinomas carry extra copies of chromosome 7, an aneuploidy that emerges already in premalignant adenomas, and is maintained throughout tumor progression and in derived cell lines. A comprehensive understanding on how chromosomal aneuploidy affects nuclear organization and gene expression, i.e., the nucleome, remains elusive. We now analyzed a cell line established from healthy colon mucosa with a normal karyotype (46,XY) and its isogenic derived cell line that acquired an extra copy of chromosome 7 as its sole anomaly (47,XY,+7). We studied structure/function relationships consequent to aneuploidization using genome-wide chromosome conformation capture (Hi-C), RNA sequencing and protein profiling. The gain of chromosome 7 resulted in an increase of transcript levels of resident genes as well as genome-wide gene and protein expression changes. The Hi-C analysis showed that the extra copy of chromosome 7 is reflected in more interchromosomal contacts between the triploid chromosomes. Chromatin organization changes are observed genome-wide, as determined by changes in A/B compartmentalization and topologically associating domain (TAD) boundaries. Most notably, chromosome 4 shows a profound loss of chromatin organization, and chromosome 14 contains a large A/B compartment switch region, concurrent with resident gene expression changes. No changes to the nuclear position of the additional chromosome 7 territory were observed when measuring distances of chromosome painting probes by interphase FISH. Genome and protein data showed enrichment in signaling pathways crucial for malignant transformation, such as the HGF/MET-axis. We conclude that a specific chromosomal aneuploidy has profound impact on nuclear structure and function, both locally and genome-wide. Our study provides a benchmark for the analysis of cancer nucleomes with complex karyotypes.

The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions

Here, we introduce the 3D Genome Browser, http://3dgenome.org , which allows users to conveniently explore both their own and over 300 publicly available chromatin interaction data of different types. We design a new binary data format for Hi-C data that reduces the file size by at least a magnitude and allows users to visualize chromatin interactions over millions of base pairs within seconds. Our browser provides multiple methods linking distal cis-regulatory elements with their potential target genes. Users can seamlessly integrate thousands of other omics data to gain a comprehensive view of both regulatory landscape and 3D genome structure.

Visual Pattern-Driven Exploration of Big Data

Pattern extraction algorithms are enabling insights into the ever-growing amount of today's datasets by translating reoccurring data properties into compact representations. Yet, a practical problem arises: With increasing data volumes and complexity also the number of patterns increases, leaving the analyst with a vast result space. Current algorithmic and especially visualization approaches often fail to answer central overview questions essential for a comprehensive understanding of pattern distributions and support, their quality, and relevance to the analysis task. To address these challenges, we contribute a visual analytics pipeline targeted on the pattern-driven exploration of result spaces in a semi-automatic fashion. Specifically, we combine image feature analysis and unsupervised learning to partition the pattern space into interpretable, coherent chunks, which should be given priority in a subsequent in-depth analysis. In our analysis scenarios, no ground-truth is given. Thus, we employ and evaluate novel quality metrics derived from the distance distributions of our image feature vectors and the derived cluster model to guide the feature selection process. We visualize our results interactively, allowing the user to drill down from overview to detail into the pattern space and demonstrate our techniques in two case studies on Earth observation and biomedical genomic data.

Integrative detection and analysis of structural variation in cancer genomes

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

HiGlass: web-based visual exploration and analysis of genome interaction maps

We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.

Modeling three-dimensional genomic organization in evolution and pathogenesis

The regulation of gene expression is mediated via the complex three-dimensional (3D) conformation of the genetic material and its interactions with various intracellular factors. Various experimental and computational approaches have been developed in recent years for understating the relation between the 3D conformation of the genome and the phenotypes of cells in normal condition and diseases. In this review, we will discuss novel approaches for analyzing and modeling the 3D genomic conformation, focusing on deciphering disease-causing mutations that affect gene expression. We conclude that as this is a very challenging mission, an important direction should involve the comparative analysis of various 3D models from various organisms or cells.

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

This paper presents an interactive visualization interface-HiPiler-for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like "snippets". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

Using single-cell multiple omics approaches to resolve tumor heterogeneity

It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.

Identification of copy number variations and translocations in cancer cells from Hi-C data

MOTIVATION

Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities [e.g. copy number variations (CNVs) and translocations] altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g. Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing.

RESULTS

In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions (HiCnv), (ii) to call inter-chromosomal translocations and their boundaries (HiCtrans) from Hi-C experiments and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities (AveSim) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing data among chromosomes with CNV events for a breast cancer cell line (r = 0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes and homogeneously staining regions. Considering the inherent limitations of existing techniques for karyotyping (i.e. missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100-300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that our framework will pave the way towards comprehensive mapping of genomic rearrangements in primary cells from cancer patients using Hi-C.

AVAILABILITY AND IMPLEMENTATION

CNV calling: https://github.com/ay-lab/HiCnv, Translocation calling: https://github.com/ay-lab/HiCtrans and Hi-C simulation: https://github.com/ay-lab/AveSim.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.