Chromatin, Epigenetics, and RNA Regulation

Nucleome Analysis Reveals Structure–FunctionRelationships for Colon CancerLaura Seaman1, Haiming Chen1, Markus Brown2, Darawalee Wangsa2,Geoff Patterson1, Jordi Camps3,4, Gilbert S. Omenn1,5, Thomas Ried2,and Indika Rajapakse1,6

Abstract

Chromosomal translocations and aneuploidy are hallmarks ofcancer genomes; however, the impact of these aberrations on thenucleome (i.e., nuclear structure and gene expression) is not yetunderstood. Here, the nucleome of the colorectal cancer cell lineHT-29 was analyzed using chromosome conformation capture(Hi-C) to study genome structure, complemented by RNAsequencing (RNA-seq) to determine the consequent changes ingenome function. Importantly, translocations and copy numberchanges were identified at high resolution fromHi-C data and thestructure–function relationships present in normal cells weremaintained in cancer. In addition, a new copy number–basednormalization method for Hi-C data was developed to analyzethe effect of chromosomal aberrations on local chromatin struc-ture. The data demonstrate that at the site of translocations, the

correlation between chromatin organization and gene expressionincreases; thus, chromatin accessibility more directly reflects tran-scription. In addition, the homogeneously staining region ofchromosome band 8q24 of HT-29, which includes the MYConcogene, interacts with various loci throughout the genome andis composed of open chromatin. The methods, described herein,can be applied to the assessment of the nucleome in other celltypes with chromosomal aberrations.

Implications: Findings show that chromosome conformationcapture identifies chromosomal abnormalities at high resolu-tion in cancer cells and that these abnormalities alter therelationship between structure and function. Mol Cancer Res;15(7); 821–30. �2017 AACR.

IntroductionAll cancers have chromosomal aberrations. These aberrations

can be structural (translocations, insertions, deletions, inver-sions) or numerical (aneuploidy; refs. 1, 2). Such aberrationsmay activate tumor-promoting or inactivate tumor-suppressingsignaling pathways (1). However, the interplay between chromo-somal aberrations (structure) and gene expression (function) isnot fully understood (3–6). The development of chromosomeconformation capture techniques provides unprecedentedinsights into spatial chromatin organization and long-range chro-

matin interactions in the interphase nucleus (7). Genome-widechromosome conformation capture (Hi-C) generates matricesthat reflect chromatin interactions by using proximity-basedligation followed by sequence analysis (7). Hi-C data confirmedthat the human genome is partitioned into regions of open andclosed chromatin (7). The first step in identifying these regions isto calculate the correlation matrix of the normalized Hi-C data,which describes the correlation between each pair of genomicregions. To compare the structure measured by the Hi-C matrix[two dimensional (2D)], to DNase I hypersensitivity or geneexpression one-dimensional (1D), Hi-C data are converted to avector using eigendecomposition (Table 1) to extract the firstprincipal component,which identifies the vector that best approx-imates the matrix. Lieberman-Aiden and colleagues showed thatthe sign of the first principal component (positive and negativeregions) divides the genome into two compartments that correlatewith the presence of open or closed chromatin as measured byDNase I hypersensitivity and active or repressed gene expression,respectively (7).

Previous studies of cancer genomes using Hi-C showed long-range interactions betweenknown risk loci for thedevelopment ofcolorectal cancer and regulatory regions (8), demonstrated proto-oncogene activation by disruption of chromosome neighbor-hoods (9), determined changes in interchromosomal interactionfrequency in breast cancer (10), and showed that changes ingenomic copy number subdivide the domain structure of chro-mosomes (11). We have extended this work through a compre-hensive analysis of the colorectal cancer cell line HT-29 to analyzehow chromosomal aberrations affect nuclear structure and geneexpression, that is, the nucleome, by integrating Hi-C and RNAsequencing (RNA-seq) analyses.

1Department of Computational Medicine and Bioinformatics, University ofMichigan, Ann Arbor, Michigan. 2Cancer Genomics, National Cancer Institute,National Institutes of Health, Bethesda, Maryland. 3Gastrointestinal and Pan-creatic Oncology Group, Institut d'Investigacions Biom�ediques August Pi iSunyer (IDIBAPS), Centro de Investigaci�on Biom�edica en Red de EnfermedadesHep�aticas y Digestivas (CIBERehd), Barcelona, Catalonia, Spain. 4Unitat deBiologia Cellular i Gen�etica M�edica, Departament de Biologia Cellular, Fisiologiai Immunologia, Facultat de Medicina, Universitat Aut�onoma de Barcelona,Bellaterra, Catalonia, Spain. 5Departments of Internal Medicine and HumanGenetics, School of Public Health, University of Michigan, Ann Arbor, Michigan.6Department of Mathematics, University of Michigan, Ann Arbor, Michigan.

Note: Supplementary data for this article are available at Molecular CancerResearch Online (http://mcr.aacrjournals.org/).

L. Seaman and H. Chen contributed equally to this article.

Corresponding Author: Indika Rajapakse, University of Michigan, 1600 HuronParkway, NCRC Building 520, Rm. 3392, Ann Arbor, Michigan, 48105. Phone:734-763-0782; Fax: 734-615-6553; E-mail: indikar@umich.edu.

doi: 10.1158/1541-7786.MCR-16-0374

�2017 American Association for Cancer Research.

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Materials and MethodsExperimental protocols

Hi-C, RNA-seq, and FISH data were collected from humanfibroblasts and HT-29 cell lines as described by Chen and collea-gues (12). Cell culture in 2D and three dimensional (3D) growthwas performed as described by Chen and colleagues (13). Extend-ed protocols for RNA-seq, Hi-C, and FISH are mentioned in theSupplementary Methods.

Normalization of Hi-C matricesThe method of Toeplitz normalization used by Chen and

colleagues was adapted to account for uneven genomic copynumber (14). The method, outlined in Supplementary Fig. S1,includes using the total number of reads in each bin of the Hi-Cintrachromosomal region as a measure of the genomic copynumber. A band-pass filter (Butterworth, order 4, 10�6 to reso-lution/ 10�7) was applied to remove the high-frequency noise.Breakpoints were defined as changes in the signal greater than athreshold, and separated by at least 1/8th the chromosome length(Supplementary Table S1). Each matrix was divided into subma-trices based on these breakpoints and independently normalizedas described by Chen and colleagues (14). The submatrices wereput back together to form the final, normalized matrix. TheToeplitz normalization and iterative correction methodsdescribed by Chen and colleagues (14) and Wu and colleagues(15) were implemented for comparison.

The normalized matrix is used to calculate topologically asso-ciated domains (TAD), as described previously by Chen andcolleagues, using the Fiedler vector (14). Briefly, the Fiedler vectoris the second smallest eigenvalue of the normalized Laplacian,L ¼ D�1=2ðD� AÞD�1=2, where A is the adjacency matrix, in thiscase a chromosome's normalizedHi-Cmatrix, andD is the degreematrix (16). The Fiedler vector divides the chromosome into tworegions, onemostly active, the othermostly inactive andwas usedto calculate structure–function correlations. These regions arethen subdivided into TADs by calculating the Fiedler vector ofthe submatrix including one of these regions until the Fiedlernumber of the submatrix was above the threshold of 0.6. Allfigures showing raw matrices are on log2 scale.

Hi-C matrices for translocated chromosomesTo create Hi-C matrices for translocated chromosomes, inter-

chromosomal Hi-C matrices were visualized to identify wheretranslocations occurred, and then the exact location was refinedusing the read-level data (Supplementary Figs. S2–S6). To con-struct Hi-C matrices for the translocated chromosomes, the infor-mation organized according to chromosome number and the

traditional reference chromosomes (hg19) needs to be rear-ranged. To do this, each chromosome is viewed as a matrix thatcan be decomposed into submatrices. On the basis of the locationof a translocation, four submatrices are created as diagramed inthe top of Supplementary Fig. S7. Any two intrachromosomalmatrices, A and B, can be represented as

CA¼ CA 1;1ð Þm�m CA 1;2ð Þm�nCA 2;1ð Þn�m CA 1;1ð Þn�n

� �CB¼ CB 1; 1ð Þr�r CB 1; 2ð Þr�s

CB 2;1ð Þs�r CB 1; 1ð Þs�s

� �

wherem is the location of the translocation (in number of 100-kbbins) and n is the length between the translocation and the end,such that m þ n is the length of chromosome A. Similarly,chromosome B has a translocation at r and total length r þ s.Their interchromosomal space can be written as follows:

CAB ¼ CAB 1; 1ð Þm�r CAB 1;2ð Þm�sCAB 2; 1ð Þn�r CAB 1;1ð Þn�s

� �

From these definitions, the Hi-C matrix for the translocatedchromosome AB can be pulled out:

TAB ¼ CA 1;1ð Þm�m CAB 1;2ð Þm�sCAB 2;1ð Þs�m CB 1; 1ð Þs�s

� �

Note, due to the symmetry of the Hi-C matrix,

CABð1; 2Þm�s ¼ CBAð2; 1Þs�mT . The same notation can be used

on more complex translocations like T3�12, which includes threepieces with breaks at both of the translocations. Matrices werenormalizedwith a forcedbreakpoint at the translocation location.

Gene expression and banding structures were created for eachtranslocated chromosomebypiecing together the relevant parts ofeach chromosome. Neighborhoods were defined as submatricescentered on the translocation of a given size defined by othercriteria (either 300 kb, TAD encompassing, or gene encompass-ing). TAD encompassing were defined as themaximum across thesamples of the smallest size required to cover a TAD boundarywith a single size being chosen for each translocation. Geneencompassing was the minimum size required to include a geneon both sides of the breakpoint. The significance of the change instructure–function correlation was calculated by randomly select-ing 1,000 sets of locations and constructing matrices representingfake translocations. Theobserved change in correlation for the realtranslocations was compared with the average change in correla-tion for the same sized regions of the randomly placed fake-translocations. The von Neumann Entropy of these regions was

calculated asPd

i¼1 lilog2li, where d is the sizes of thematrix andliare the eigenvalues calculated from the submatrix describing theneighborhood of the normalized Hi-C matrix (17).

Table 1. Glossary of terms

Adjacency matrix: square matrix with ðAÞij ¼ wðni; njÞ. If there is an edge between nodes i and j, the entry is the edge's weight, w, otherwise it is 0.

Aneuploidy: an abnormal number of chromosomes, i.e., different from 46 chromosomes for human cells.

Degree matrix: a diagonal matrix ðDÞii ¼Pk

j¼1 ðAÞij , the total number of edges attached to each node.

Eigenvalues and Eigenvectors: a set of numbers associated with linear systems. The decomposition into eigenvalues and eigenvectors is called aseigendecomposition. Eigenvalues are represented by l and eigenvectors by x. Ax ¼ lx with x „ 0.

Entropy: a measure of uncertainty or disorder,P

lilog2li where li are eigenvalues.Fiedler number and Fiedler vector: the Fiedler number is the second smallest eigenvalue of the Laplacian and a measure of the connectivity of a graph. Thecorresponding eigenvector is the Fiedler vector, whose sign can be used to divide a graph into two regions.

Karyotype: the number and appearance of the chromosomes in a cell.

Laplacian: a symmetric matrix, L ¼ D� A, normalized as L ¼ D�1=2ðD� AÞD�1=2 .Topologically associated domain (TAD): a region of a chromosome with increased local contacts and decreased contacts with its neighbors.

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Two-way ANOVATwo-way ANOVA analysis was performed to identify genes

with expression level change between 2D and 3D cultures,between 12-hour and 5-day cultures. Gene Ontology analysiswas performed using Database for Annotation, Visualization andIntegrated Discovery (DAVID; ref. 18) with official gene symbolsand the default background set for human analysis. The statisticaltest comparing samples was performed as described by Chenand colleagues (13).

ResultsInterpretation of Hi-C of aberrant cancer genomes

We analyze the nucleome, that is, structure–function rela-tionships, of the colorectal cancer cell line HT-29 using Hi-C tocharacterize chromatin organization and RNA-seq to under-stand consequent changes to the cellular transcriptome. Hi-C

and RNA-seq datasets were generated for HT-29 cells grown ona flat surface (2D) or as spheroids (3D) for 12 hours or 5 days(indexed as 2D12hr, 2D5day, 3D12hr, and 3D5day). The timepoints and culture conditions were chosen to assess howgrowth conditions and cell density affect genome structure andgene expression. In normal cells, chromosomes are organizedas distinct territories, which appear as diagonal dominantblocks for each chromosome in the genome-wide Hi-C contactmatrix (Supplementary Fig. S8). However, in solid tumors,chromosomal aberrations are common, which is evident fromthe spectral karyotype (SKY) of HT-29 (Fig. 1A). The genome ofHT-29 is near-triploid (mean ¼ 70 chromosomes). Structuralaberrations, such as translocations, deletions and inversions,rearrange the chromosomes. Such aberrations are readily vis-ible on the Hi-C matrix as distinctive L- or X-shaped patterns forunbalanced and balanced translocations, respectively (Fig. 1B).The translocations found by SKY (Fig. 1A), for example, the

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Figure 1.

Chromosomal aberrations in Hi-C data. A, HT-29 karyotype adapted from Knutsen and colleagues (26). The numbers above chromosomes are thepercent of the 21 analyzed cells in which that chromosome was seen. HT-29 averages 70 chromosomes per cell. B, Genome-wide Hi-C matrix for 2D12hrHT-29 cells at 1-Mb resolution. The X pattern marking the t(6;14) translocation and the region of high contacts marking ins(3;12) are identified byblack arrows (see Supplementary Fig. S9 all translocations). The blue arrows identify amplified regions including the HSR on chromosome 8q and theamplification of a small region on chromosome 2 that interacts strongly with the HSR. The uneven copy number between the p and q arms ofchromosome 3 (�2 p arms, �5 q arms) can also be seen by the fact that the first half of chromosome 3 in the Hi-C matrix are a lighter red than the secondhalf. C, The average log2 fold change gene expression (green), change in Hi-C reads (red), and genomic copy number (blue) for each chromosomearm (p-arm first), also in Supplementary Table S2.

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balanced translocation between chromosomes 6 and 14 (blackarrow in Fig. 1B) and the insertion of chromosome 12 materialinto the p arm of chromosome 3 (black arrow), are clearlyvisible. Enlarged representations for each of the translocatedchromosomes are shown in Supplementary Fig. S9. Cytogeneticanalysis detected a homogeneously staining region (HSR) onchromosome 8q that contains the MYC oncogene (6, 19). Theblue arrows in Fig. 1B mark increased interactions between thewhole genome and high copy number regions like the HSR anda smaller amplification on chromosome 2 (Fig. 1B). The smal-ler amplification on chromosome 2 was confirmed by inter-phase FISH analysis. The recapitulation of cytogenetic changesin interphase Hi-C maps is reflected in the contact maps ofchromosome 3, where the short arm (�2 copies) has fewercontacts than the long arm (�5 copies).

We compare genomic copy number with the total number ofHi-C reads for each gene and gene expression patterns based onthe observation that high copy number regions have morecontacts in the genome wide Hi-C matrix. Figure 1C showsthe copy numbers, log2-fold change gene expressions (relativeto fibroblasts), and total Hi-C reads [Fragments per Kilobaseper Million (FPKM)] for each gene averaged for each chromo-some arm (Supplementary Table S2). Genomic copy numberand gene expression exhibit a strong correlation (Pearson r ¼0.65; P � 10�5), consistent with previous work (reviewed inref. 6). The correlation between copy number and the total Hi-Ccontacts is 0.81 (P � 10�9), indicating that the total number ofreads per bin can be used as an approximation for copynumber. Therefore, the numbers of reads are a direct reflectionof the likelihood that chromosome contacts occur in theinterphase nucleus.

A novel copy number–based normalization methodIn cancer cells, the interpretation of Hi-C data is complicated

by the presence of copy number alterations, which can affectread frequencies. Therefore, we developed a novel approach forthe normalization of Hi-C data for cell lines with complexkaryotypes. We validated our new approach by comparing it tohigh-resolution molecular cytogenetic analyses of HT-29 bySKY, FISH, and array-based comparative genomic hybridization(aCGH). For instance, in HT-29, the MYC oncogene is presentin multiple copies in an HSR at the distal end of the q-arm ofchromosome 8 (Fig. 2). Figure 2B shows the total number ofraw Hi-C reads per bin as well as the genomic copy number asmeasured by aCGH. Genomic copy number directly andstrongly influences the total number of Hi-C reads per bin (r¼ 0.77). On the basis of this finding, the total number of readsper bin was used to create a new normalization method inwhich the Hi-C matrix was divided into submatrices with aconstant genomic copy number (blocks in Fig. 2C). The blockswere normalized independently as described by Chen andcolleagues (14), and then combined to form the normalizedmatrix as shown in Fig. 2C. To verify our method, we comparedit to previously published methods using Toeplitz normaliza-tion and iterative correction (14, 15). We found that thecorrelation between structure and function was highest aftercopy number–based normalization (Supplementary Fig. S10,r¼ 0.60, 0.53, and 0.17 for copy number, Toeplitz and iterative,respectively). In addition, the method performed well on all ofthe HT-29 samples (r ¼ 0.59, 0.59, 0.63, and 0.54 for 2D12hr,2D5day, 3D12hr, and 3D5day). We also tested the method on

chromosome 20 from the myelogenous leukemia cell line K562(20, 21) and again found that the correlation between structureand function was highest after copy number–based normali-zation (Supplementary Fig. S11, r ¼ 0.63, 0.59, and 0.20 forcopy number, Toeplitz and iterative, respectively). Similar toWu and Michor (15), our method can be used for any Hi-Cmatrix without requiring copy number information throughother approaches such as aCGH.

Structure and function of the HSRTo further explore the effect of genomic copy number

changes on genome structure, we focused our analysis on theHSR, a highly amplified 27-Mb region on chromosome band8q24 containing MYC and 107 other genes that appears as abright red band in the Hi-C matrix (Fig. 1B). If we assume thetwo normal copies of the region in the HSR provide about thesame number of reads as the two copies of the first third ofchromosome 8, then of the reads coming from the amplifiedregion of chromosome 8, 86% derive from the HSR, while 14%are accounted for by the unamplified copy of the region. TheHSR is visualized on metaphase and interphase cells by FISHwith a genomic probe for MYC in Fig. 3A. We calculatedthe volume of the chromosome 8 territories using 3D-FISH;the chromosomes with the HSR were 2.5 times larger than thenormal copies (Fig. 3A top insert; Supplementary Fig. S12;Supplementary Table S3).

To quantify genome organization, we define the adjacencymatrix (a submatrix of the normalized Hi-C matrix) for a regionof interest. Then we calculate the eigenvalues of the adjacencymatrix to quantify genome organization through approximatingthe entropy (a measure of the distribution of chromatin state) inchromosomal regions. A similar approach has previously beenused to show that, during differentiation, entropy initially increasesbefore a progressive decline as the cell approaches its differentiatedstate (22). Here, we use eigenvalues of the Hi-C matrix to estimatethe entropy of chromosomal regions, which is inversely propor-tional to order. As chromosomal aberrations disrupt the baselinedistribution of the local chromatin state, we hypothesize thatthe entropy near these alterations should increase.

To quantify the structure of the HSR from Hi-C data, wecalculated the entropy of the adjacency matrix that representsthe contacts in the HSR. We define entropy as von Neumann

entropy asPd

i¼1 lilog2li, where d is the size and li are theeigenvalues of the adjacency matrix (17). The degree of entro-py reflects the frequency with which chromatin states changein a given region. Only 6% of randomly selected regions inthe HT-29 genome have lower entropy than the HSR, indicat-ing that the HSR is highly interconnected and ordered. Asthe structural conformation of a DNA region is dictated by thesequence, we expect consistency in conformation across theamplified region (17, 23). Thus, the HSR, which containsmultiple copies of the sequence, has a highly ordered struc-ture. We measured the structure–function relationships bycalculating the correlation between gene expression and chro-matin state, that is, heterochromatin or euchromatin, using theFiedler vector. The sign of the Fiedler vector (positive ornegative values) divides the genome into regions of hetero-chromatin and euchromatin (12). The structure–functioncorrelation of the HSR is greater than in 60% of the rest ofthe genome. In summary, our analysis showed that the chro-mosome containing the HSR is larger than the normal

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chromosome as seen with 3D-FISH (Fig. 3A). The HSR itself ishighly organized, that is, is less entropic, and has a strongstructure–function relationship.

We next explored how the HSR interacts with the rest of thegenome. At 1-Mb resolution, we analyzed genome-wide interac-tions and interactions within the HSR (Fig. 3B). We identified asingle region in chromosome 2 that interacts strongly with theHSR in all of the HT-29 samples (P � 10�9 in all samples,Supplementary Fig. S13). The region includes six genes (STARD7,TMEM127, CIAO1, SNRNP200, ITPRIPL1, LOC285033) andits interactions with the HSR were verified with FISH (Supple-mentary Fig. S14). STARD7 has been previously implicatedin choriocarcinoma, colorectal cancer, breast, and lung cancers(24). This strong interaction between the amplified regions onchromosomes 2 and 8 had not been recognized before.

Hi-C provides high-resolution maps of translocationsIn addition to understanding how numerical aberrations

affect chromatin organization, we explored the consequences

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Figure 2.

Normalization accounting for copy number changes. A, The raw chromosome8 matrix in which the different genomic copy number regions can be clearlyseen by the differences in brightness. The HSR is in the box at the bottom right.B, These changes are measured by the changes in the total reads in eachbin of the Hi-C matrix (black, HT-29 12hr2D), which follow closely the genomiccopy number measured by CGH (medium grey). The normalization locationsof genomic copy number and the location of MYC are shown by dashed lines.The total reads in each bin for chromosome 8 in fibroblasts are shown inlight grey, indicating that the changes in read distribution only occur in samples inwhich the genomic copy number changes are present. C, Each of the blockscreated by the transitions between regions of different copy number inchromosome 8 (black bars) was normalized independently then pieced backtogether to create the normalized matrix.

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Figure 3.

Genome wide HSR interactions. A, A metaphase spread and 2D interphasenucleus (lower inset) withMYC labeled in green allowing visualization of the twonormal copies of the gene as well as multiple copies in the HSR. The 3Dinterphase image (top inset) was used to calculate the volume of thechromosome 8 territories. B, A graph of the total genomic interactions for eachinterchromosomal bin against their interactions with just the HSR for 2D12hr.The red line shows the best-fit line for a region's interactions with the HSR. Thered point is the amplified region on chromosome 2 that interacts more stronglythan any other region in all HT-29 samples (Supplementary Fig. S13).

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Figure 4.

Translocations in Hi-C. A, An unbalanced translocation in which the end of chromosome 15 was added to the end of chromosome 2 shown at 1-Mb resolution.The inset zooms in on the relevant region. B, The translocation, marked with the yellow arrow, was verified by chromosomal painting. Because ofthe small size, this translocation was previously unidentified. Chromosome 2, red; chromosome 15, green. Note that no chromosome 15 containschromosome 2 material, verifying the unbalanced nature of this translocation. C, A seemingly balanced translocation between chromosomes 6 and14 shown at 100-kb resolution. D, The read level Hi-C data for the translocation, showing the breakpoint in chromosome 14 and two breakpoints inchromosome 6 marked by red lines. The hybridization locations of the bacterial artificial chromosome probes are shown. The cyan and yellow probesmark chromosome 14 before and after the translocation. Parts of both the red and green probes on chromosome 6 are in the duplicated region. E,FISH verification of the translocation location, which is different than previously published via SKY. Because of the overlap of the red and greenprobes, both translocated chromosomes contain parts of both probes. On the basis of these results the translocation is not truly balanced because ofthe duplication of a 65-kb segment.

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of chromosomal translocations. As shown in Fig. 1B, transloca-tions generate L or X shaped patterns in the genome wide Hi-C.Hi-C allowed identification of translocations too small to bedetected by molecular cytogenetic techniques, e.g., the unbal-anced translocation between chromosomes 2 and 15 shownin Fig. 4A. We confirmed this aberration using FISH (Fig. 4B). Inaddition, the balanced translocation t(6;14) is clearly visible inthe 100-kb matrix (Fig. 4C). By viewing the translocation in theread-level data, the resolution at which the breakpoint wasidentified increased to 1 kb. Figure 4D shows a single break inchromosome 14 as well as two breaks in chromosome 6. Thetop right shows many reads connecting the portion of chro-mosome 14 proximal to the breakpoint to the portion ofchromosome 6 distal to the breakpoint. The bottom left por-tion shows reads where one of the pairs mapped to the portionof chromosome 6 proximal to the breakpoint, whereas theother mapped to portion of chromosome 14 distal to thebreakpoint. As there is a single horizontal line dividing thelocations of the reads on chromosome 14, there is a singlebreakpoint, as expected for a balanced translocation. However,along chromosome 6 there is a 65-kb region between the twovertical lines that is contained in both translocated chromo-somes thus it interacts with both the distal and proximalportions of chromosome 14. This was confirmed using FISHwith BAC clones that hybridize to the translocation (Fig. 4E).Hence, this seemingly balanced translocation is in factunbalanced.

Translocations increase entropyTo explore the structure and function of translocated chro-

mosomes, we constructed the Hi-C matrices representing thetranslocated chromosomes (Supplementary Fig. S7). Hi-Cmatrices representing the seven translocated chromosomes inHT-29 (Table 2) and the normal chromosomes from whichthey originate were constructed from raw reads (Supplemen-tary Figs. S2–S6). The insertion ins(17;22) was not usedbecause of the presence of at least eight different configura-tions with unknown frequencies and pairings (SupplementaryFig. S15).

After constructing the Hi-C matrices for the translocatedchromosomes, we analyzed the neighborhoods surroundingthe breakpoints. Figure 5 shows the regions surrounding thebreakpoints for the translocation t(6;14). Additional transloca-tions are presented in Supplementary Figs. S16–S22. Each Hi-Cmatrix shows a 6-Mb region centered on the translocationbreakpoint with the natural domain structure of the genome,that is, TADs overlaid. The two plots below the Hi-C matricesshow three different neighborhoods and the gene expression fora region that contains three TADs. Each neighborhood repre-sents a different region surrounding the breakpoint: the smallestpossible neighborhood, a TAD encompassing neighborhoodsized to include a TAD boundary, and a gene encompassingneighborhood sized to encompass one gene on both sides of thetranslocation. The last two neighborhoods vary in size for theanalyzed translocations, with TAD encompassing neighbor-hoods varying from 700 kb to 1.7 Mb. The entropy was calcu-lated for the TAD encompassing neighborhood for each trans-location (Table 2). Unlike in the HSR, the entropy in the regionsurrounding translocations was higher than at the same regionsof the wild-type chromosomes for 5 of the 7 translocations,including t(6;14; avg 1.89 and 2.03 for wild-type and translo-

cated chromosomes, respectively). This suggests that the trans-locations reduce local stability.

To explore whether the results were specific to HT-29, or areflection of more general phenomena in tumors, we analyzedpublicly available Hi-C and RNA-seq data from the myeloge-nous leukemia cell line K562 (20, 21). We constructed Hi-Cmatrices for the six translocated chromosomes in K562 (Sup-plementary Table S4). Supplementary Figures S23–S29 showsthe Hi-C map, neighborhoods, and gene expression for each ofthe translocations and the normal chromosomes from whichthey were derived. The average entropy of the neighborhoodssurrounding the breakpoints on the normal chromosomes inK562 is 1.68, whereas the entropy of the seven translocatedchromosomes averages 1.93, a 15% increase, and each trans-location has higher entropy than either of the normal chromo-somes (Supplementary Table S4). These results suggest a gen-eral pattern in cancer cells.

In addition, we analyzed the structure–function relationship,that is, the correlation between Fiedler vector and gene expres-sion, for TAD-sized neighborhoods surrounding the transloca-tions. For HT-29, the structure–function correlation is slightlygreater in the translocated regions (Table 2, r ¼ 0.36 and 0.34,respectively). The same applies for K562 (Supplementary TableS4, r ¼ 0.43 and 0.32, respectively). Compared with randomlocations, this is a greater increase in correlation than expected(P < 0.09). In conclusion, our results indicate that transloca-tions both increase entropy and the strength of the structure–function relationship.

Table 2. Characterization of HT-29 translocations

Chr Read LocEntropyFib

EntropyHT-29

S-FFib

S-FHT-29

2-15 236760000 2.52 0.452 236760000 2.73 2.65 0.43 0.4315 96682000 3.03 2.39 0.29 0.293-12 p 1.86 0.033 83410000 2.04 1.64 0.00 0.0012 34435000 1.64 1.65 0.26 0.383-12 q 1.84 0.9512 21057000 1.91 1.78 0.08 0.243 89440000 2.13 1.64 0.62 0.945-6 2.26 0.35 54662000 2.59 2.03 0.67 0.586 162295000 2.44 1.96 0.28 0.156-14 1.93 0.266 132825000 2.13 1.78 0.02 0.1514 36508800 1.94 1.87 0.5 0.7114-6 2.04 0.314 36508800 1.94 1.87 0.5 0.716 132890000 2.13 1.78 0.02 0.1519-17 1.79 0.2219 24600000 1.89 1.83 0.45 0.3817 22253300 1.98 1.57 0.03 0.06No Trans Avg 2.18 1.89 0.32 0.34Trans Avg 2.03 0.36

NOTE: The first column indicates which chromosome the translocations are on.Read Loc tells the best estimate of the location of the translocation. Entropy Fiband Entropy HT-29 report the von Neumann Entropy of the TAD-encapsulatingneighborhoods from the 100-kb Hi-C data centered on the translocation infibroblasts and HT-29 (average), respectively. After translocation the entropyincreases on average and for 5 of the 7 translocations. S-F Fib and S-F HT-29show the correlation between the structure (Fiedler vector) and function (RNA-seq) for TAD encompassing neighborhoods for the average of the HT-29samples and the fibroblast sample, respectively.

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Sample differencesWe previously observed differential chromatin interactions

of human fibroblasts cultured in 2D or 3D conditions (13), andnow explore whether such differences can be observed in HT-29, as well as differences between the time points. The percentof intrachromosomal reads that fall along the diagonal is 89%for the 3D5day sample, whereas it is 72% or less for the others.In addition, the 3D5day sample had only 48% of its totalreads as intrachromosomal, whereas for the other samples 55%or more of their reads were intrachromosomal. One explana-tion is that the Hi-C reads are distributed differently due tochanges in the cell cycle. This indicates that the 3D5day sampleis far more diagonally dominated but less intrachromosomalthan the 12-hour samples. This is consistent with previousresults showing the same patterns in fibroblasts grown in 2Dand 3D cultures (13).

Two-way ANOVA (see Materials and Methods) was performedon theRNA-seq data and showed that 287 geneswere significantlydifferentially expressed between 2D and 3D cultures (P � 0.05,Supplementary Table S5). We also explored whether the celldensity influences gene expression. We found 661 genes thatchanged between the 12-hour and 5-day time points (Supple-mentary Table S6), of which 178 also change with growth con-ditions.DAVIDanalysis (18) of these datasets identified anumberof significantly enriched GO terms including cell-cycle processes,cell-cycle phase, cell-cycle checkpoints, regulation of cell cycle,DNA repair, and DNA-dependent DNA replication, suggestingthe changes in expression are mostly related to the cell cycle(Supplementary Tables S7 and S8).

DiscussionWe investigated how genome structure and function are

altered by chromosomal aberrations in cancer cells by analyz-ing Hi-C and RNA-seq data from the colorectal cancer cell lineHT-29. Cells were grown in 2D and 3D conditions for 12 hoursand 5 days. We showed that Hi-C captures chromosomalaberrations, including genomic copy number changes andchromosomal translocations, some of which were previouslyunknown. Next, we mapped the translocations using read levelHi-C data and identified the breakpoints at kilobase resolu-tion. This allowed us to describe a previously unknown unbal-anced translocation, der(2)t(2;15), which was too small to beidentified by SKY or high-resolution aCGH. In addition, werefined the location of the seemingly balanced translocation t(6;14) and showed that it is in fact unbalanced (19). Therefore,in addition to providing information on the 3D organizationand local chromatin stability, we showed that Hi-C can iden-tify translocations with unprecedented resolution. It is remark-able to see the extent to which structural and numericalchromosomal aberrations are recapitulated in the interphasenucleus.

We found that the HSR on chromosome 8q interacts withmany other genomic regions and is highly organized, that is,less entropic. Entropy reflects the frequency with which chro-matin states change in a given region. The HSR has a strongerrelationship between structure and function, that is, geneexpression, than other regions in the genome, indicating thatchromatin accessibility more directly reflects transcription. TheHSR consists of open chromatin, making it conducive fortranscription. We identified a small amplified region in

HT-29 Chr6 HT-29 Chr14

HT-29 t(6;14)HT-29 t(6;14)

Fb Chr 14Fb Chr 6

Translocation’s Bin

TAD boundary

Translocation

Gene expression level

300 kb NeighborhoodNeighborhood containing closest genesNeighborhood containing TAD boundary

Figure 5.

TADs on chromosomes affected by translocations. For each region aroundthe translocation on chromosomes 6, 14 and t(6;14), the Hi-C matrix is shownwith TAD boundaries overlaid. The matrices along the top show a 6-Mbregion with the site of translocation at the center. The line below shows thesite of the translocation as well as three different neighborhoods, a small300-kb neighborhood, the smallest neighborhood that contains a TADboundary, and the neighborhood that contains a gene on each side of thetranslocation. The top plot shows the gene expression. These features areshown for the normal chromosome 6 in the 2D12hr HT-29 sample (A), normalchromosome 14 in the 2D12hr HT-29 sample (B), the translocatedchromosome containing the beginning of chromosome 6 and the end ofchromosome 14 in the 2D12hr HT-29 sample (C), the translocatedchromosome containing the beginning of chromosome 14 and the end ofchromosome 6 in the 2D12hr HT-29 sample as well as the same regions fornormal chromosome 6 in the fibroblast sample (E), and normal chromosome14 in the fibroblast sample (F).

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chromosome 2 that interacts very strongly with the HSR. Thisfinding was confirmed using FISH. The previously unidentifiedregion contains STARD7, which has been previously implicat-ed in cancers (24). One limitation of Hi-C and RNA-seq is thatdifferent alleles of the same region cannot be distinguished.For the HSR analysis, reads from the unamplified copy of theregion cannot be differentiated from those originating fromthe HSR. Of the reads coming from the amplified region ofchromosome 8q, 86% derive from the HSR, whereas 14% areaccounted for by the unamplified copy of the region. Thus, weexpect properties of the HSR to dominate the analysis.

We analyzed local chromatin stability at translocation break-points in HT-29 and K562 neighborhoods and showed thatregions around translocations have increased entropy com-pared with the corresponding regions on the normal chromo-somes. This increase in entropy near breakpoints suggests thattranslocations decrease the local stability of adjacent neighbor-hoods around the translocation. The entropy in fibroblasts washigher than the entropy in HT-29 or K562, which could be areflection of the nonterminal differentiation status of fibro-blasts. We also found that translocations increase the struc-ture–function relationship in the neighborhood flanking thebreakpoint compared with the equivalent regions on normalchromosomes. This might be a reflection of their role intumorigenesis. We analyzed the BCR-ABL translocationbetween chromosomes 9 and 22 present in K562. Like mosttranslocations it showed increased entropy as compared withthe nontranslocated regions. Unlike most of the translocationswe analyzed in HT-29 and K562, the structure function corre-lation for the BCR-ABL translocation is lower than either of thenormal chromosomes it comes from. This might be due to thefact that the BCR-ABL fusion protein exhibits constitutiveactivity and therefore does not require increased expression.We submit that decreasing local stability and increasing thestructure–function relationship is a common phenomenon oftranslocations in cancer cells.

Finally, we characterized the differences between 2D and 3Dcell growth and 12-hour and 5-day time points. We found thatgenes differentially expressed between 2D and 3D growthwere primarily related to cell-cycle regulation and DNArepair. We also found that the 3D5day sample was differentfrom the other HT-29 Hi-C matrices as measured by thecorrelation of interchromosomal reads. The 3D samples hada higher percentage of intrachromosomal reads that fell on thediagonal and lower percentage of all reads that were intra-chromosomal. Change in the distribution of counts in Hi-Cmatrices is consistent with previous results showing the samepatterns in fibroblasts grown in 2D and 3D cultures (13). In

contrast to the 12-hour samples, the 5-day samples werecompletely confluent. The reason the 5 day samples are moreintrachromosomal and less diagonally dominant than the 12-hour sample could be because the cells in the 12-hour sampledid not have enough time to complete nuclear reorganizationinto a 3D growth pattern. Previous results have shown thatmitotic cells lead to purely diagonal matrices as the chromo-somes are organized in tight rods during mitosis (25). Thissuggests some of the differences may be related to cell cycle, assupported by the strong significance of cell cycle and mitosisrelated GO terms.

In summary, our analysis identifies undetected chromosom-al aberrations and provides novel insight into the nucleome ofcancer cells.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: L. Seaman, G.S. Omenn, I. RajapakseDevelopment of methodology: L. Seaman, D. Wangsa, I. RajapakseAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): H. Chen, J. Camps, I. RajapakseAnalysis and interpretation of data (e.g., statistical analysis, biostati-stics, computational analysis): L. Seaman, H. Chen, M. Brown, J. Camps,I. RajapakseWriting, review, and/or revision of the manuscript: L. Seaman, H. Chen,M. Brown, D. Wangsa, G. Patterson, G.S. Omenn, I. RajapakseAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): L. Seaman, H. Chen, I. RajapakseStudy supervision: I. Rajapakse

AcknowledgmentsThe authors would like to thank Daysha Torres and Greg Farnum for

invaluable feedback and Walter Meixner for imaging assistance.

Grant SupportThis study was supported in part by the Intramural Research Program of

the NIH, National Cancer Institute, the University of Michigan RackhamMerit Fellowship program, the DARPA Biochronicity Program, and the NIHES017885 (to G.S. Omenn). J. Camps is supported by the European Commis-sion (COLONGEVA), and received a travel grant from the Instituto de SaludCarlos III and was cofunded by the European Regional Development Fund(ERDF; MV15/00026).

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received October 25, 2016; revised October 25, 2016; accepted February 28,2017; published OnlineFirst March 3, 2017.

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