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Structural variants drive context-dependent oncogene activation in cancer

Nature volume 612, pages 564–572 (2022)Cite this article

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Abstract

Higher-order chromatin structure is important for the regulation of genes by distal regulatory sequences1,2. 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 expression4,5, 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.

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Fig. 1: TAD fusion events from Hi-C data in cancer samples.
Fig. 2: Interdomain rearrangements in patient tumour samples.
Fig. 3: Engineered rearrangements and MYC gene activation.
Fig. 4: Quantitative models of MYC expression in the context of engineered rearrangements.
Fig. 5: Genome-wide ABC models across cell lines.

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Data availability

All data generated as part of this study is available through the Gene Expression Omnibus (GEO) database with accession number GSE147123.

Code availability

All code used as part of this study is available through GitHub (https://github.com/dixonlab/).

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Acknowledgements

We thank A. Saghatelian and A. Deshpande for contributing cell lines to this study. We thank A. Kim for sharing the mCherry modified version of the pX458 plasmid. We thank T. Popay for helpful comments on the manuscript. This work was supported by the NIH grant DP5OD023071 to J.R.D. and is also supported by the Leona M. and Harry B. Helmsley Charitable Trust grant No. 2017-PG-MED001 to J.R.D. Work in the laboratory of G.M.W. was supported, in part, by the National Institutes of Health/National Cancer Institute (grant no. R35 CA197687) and the Breast Cancer Research Foundation (BCRF). This work was also supported by the Flow Cytometry Core Facility of the Salk Institute and the NGS Core Facility of the Salk Institute with funding from NIH-NCI CCSG (grant no. P30 014195). We thank UC San Diego Biorepository and Tissue technology who shared resources for Biospecimen collection. This work carried out at the UC San Diego Moore’s Cancer Center Comprehensive Biorepository was supported by the National Cancer Institute (grant no. NCI P30CA23100).

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  1. These authors contributed equally: Zhichao Xu, Dong-Sung Lee

Authors and Affiliations

  1. Gene Expression Laboratory; Salk Institute for Biological Studies, La Jolla, CA, USA

    Zhichao Xu, Sahaana Chandran, Victoria T. Le, Rosalind Bump, Jean Yasis, Sofia Dallarda, Samantha Marcotte, Benjamin Clock, Nicholas Haghani, Chae Yun Cho, Geoffrey M. Wahl & Jesse R. Dixon

  2. Department of Life Sciences, University of Seoul, Seoul, South Korea

    Dong-Sung Lee

  3. Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX, USA

    Kadir C. Akdemir & P. Andrew Futreal

  4. Department of Neurosurgery, UT MD Anderson Cancer Center, TX, Houston, USA

    Kadir C. Akdemir

  5. Integrative Biology Laboratory; Salk Institute for Biological Studies, La Jolla, CA, USA

    Selene Tyndale & Graham McVicker

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Contributions

Z.X., D.-S.L. and J.R.D. conceived and designed the study. Z.X., V.T.L., R.B., S.C., J.Y., S.D., S.M., B.C., N.H, C.Y.C, S.T. and J.R.D. conducted the experiments. D.-S.L., Z.X. and J.R.D. led the data analysis. K.C.A. and P.A.F. contributed to the analysis of structural variation in patient tumour samples. G.M.W. and G.M. contributed to and helped supervise the experimental design. Z.X., D.-S.L. and J.R.D. wrote the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Jesse R. Dixon.

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Extended data figures and tables

Extended Data Fig. 1 Identification of rearrangements based on Hi-C data.

a, Pie chart showing all 4,543 rearrangements identified and which cell line or patient tumor sample they are derived from. The order in the pie chart starts with A172 cells and proceeds counter-clockwise. b, Resolution of structural variants calls from Hi-C. Calls are first identified at low resolution and then progressively refined. The resolution reported is the highest resolution with which a given structural variant is identified. c, Chromatin interaction maps from mixed lineage leukemia cell lines with known MLL/KMT2A rearrangements. The maps show the presence of translocations on chromosome 4 in MV4;11 cells (left), chromosome 6 in ML2 cells (middle), and chromosome 9 in MOLM13 cells (right). d, Heat maps showing known disease defining translocations from five Mantle Cell lymphoma cell lines (Rec-1, Mino, Maver, Jeko, Granta). e, Heat maps showing known disease defining translocations in two Chronic Lymphocytic Leukemia cell lines (K562 and KBM7).

Extended Data Fig. 2 Features associated with TAD fusion events.

a, Pie chart showing the fraction of intra-chromosomal vs. inter-chromosomal structural variant predictions. b, The number of observed intra-chromosomal (blue) or inter-chromosomal (red) rearrangements identified in each cell line. c, -log10 (p-values) for the observed frequency of intra-chromosomal rearrangements for each chromosome in each cell line under the null hypothesis that rearrangements are randomly distributed across chromosomes. The dashed line shows the threshold for significance accounting for multiple testing using a Bonferroni correction (p = 2.5 × 10−5). d, Example of high-frequency local rearrangements on chromosome 9 in U343 cells. Below the matrix is an arc plot of predicted rearrangements. e, Example of high-frequency local rearrangements along chromosome 15 in SNU-C1 cells (shown in the upper right-hand half of the matrix) in comparison with data from chromosome 15 in LoVo cells (lower left hand) where no rearrangements are observed. Below the matrix is an arc plot of predicted rearrangements. f, Results of cross validation of the neural network. The violin plots show the distribution of the accuracy and false discovery rate (FDR) across all 82 samples. g, Bar plots showing the percentage of domains containing oncogenes (based on the Cosmic Cancer Gene census) in domains identified as being part of fusion TADs (blue) versus those not identified in fusion TADs (grey). P-value is calculated by Fisher’s exact test. h, Bar plots showing the percentage of domains that contain enhancers for domains that contain TAD fusion events (blue) or do not (gray). The domain/enhancer analysis was performed for each domain in each cell type. P-value is calculated by Fisher’s exact test. i, Violin plots showing the distribution of the frequency of enhancers in domains that show TAD fusion events (blue) versus those that do not (gray). P-value is calculated from the two-sided Wilcoxon Rank Sum test. j, Bar plots showing the percentage of domains that contain super enhancers for domains that contain TAD fusion events (blue) or do not (gray). The domain/super-enhancer analysis was performed for each domain in each cell type. P-value is calculated by Fisher’s exact test. k, Violin plots showing the number of END-seq reads per kb for TADs that contain super enhancers (blue) versus those that do not (gray).

Extended Data Fig. 3 TAD fusion events at the MYC locus.

a, The number of called domains in each of five cell lines (hESC, HCC38, MV411, NCI-H1437, DLD-1) and the number of domains after merging unique boundaries (Merged). b, Quantile-quantile plot for evaluating the false discovery rate for recurrent TAD fusion identification. The observed p-values (Y-axis) are estimated using a Poisson model accounting for the overall frequency of rearrangements and the size of the domain. Randomized p-values are generated from these expected values (x-axis). This randomization analysis was repeated 1000 times to estimate the FDR at different p-value cut-offs. c, Hi-C data over the MYC locus in five cell types used for generating the merged TAD boundary set. The locations of TAD calls are shown in black bars below each heat map. This includes the TAD calls for each cell type as well as the across-cell merged calls (“Union set”). d, Estimated copy number of the MYC gene for samples with a TAD fusion event at the MYC locus versus those that do not. The copy number is estimated from the total number of Hi-C reads over the 100 kb bin surrounding the MYC gene divided by the median read count per 100 kb bin in each cell line. e, Circos plot showing the translocation partner region of each predicted TAD fusion event at the MYC locus. f, Examples of identified TAD fusion events at the MYC locus in two cell lines.

Extended Data Fig. 4 Inter TAD rearrangements at the MYC locus in human patient tumor samples.

a, Bar plot showing the frequency of patient samples containing inter-TAD rearrangements at the MYC locus by tumor type. b, Fraction of PCAWG samples with SVs at the MYC locus based on copy number. Samples are stratified into low copy (<=2), mid-copy (>2 and <=6), and high-copy (>6). c, Violin plots showing MYC expression for PCAWG samples stratified by copy number and the presence or absence of an SV at the MYC locus. P-values are calculated using Kruskal-Wallis test. d, RNA-seq expression of the MYC gene from patient samples with matched structural variant calls for samples with no high-level copy number alterations at the MYC gene (copy <= 6). Samples are separated into those that contain an inter-TAD rearrangement at the MYC locus (blue) and those that do not (black). P-value is from two-sided Wilcoxon Rank Sum test. e, RNA-seq expression of the MYC gene from patient samples with matched structural variant calls that are copy neutral at the MYC gene (copy <= 2). Samples are separated into those that contain an inter-TAD rearrangement at the MYC locus (blue) and those that do not (black). P-value is from two-sided Wilcoxon Rank Sum test. f, Circos plot of all inter-TAD rearrangements at the MYC locus. The Circos plot is zoomed in on cytoband 8q24.21 to show the MYC locus at a higher resolution. The position of TAD calls (black) and genes (green) are marked below the track.

Extended Data Fig. 5 Engineered rearrangements in SK-N-DZ cells.

a, Hi-C heat maps between chromosomes 7 and 8 in SK-N-SH cells (left) and SK-N-DZ cells (right). SK-N-SH cells have an endogenous t(7;8) translocation that creates a TAD fusion event at the locus, while SK-N-DZ cells have no rearrangements at the MYC locus in wild-type cells. b, Schematic for engineering rearrangement strategy. Guide RNAs targeting a locus ~300 kb downstream from the MYC gene and Guide RNAs targeting the partner region are cloned into a vector expressing Cas9. Guides are expressed either as single guides on plasmid with different fluorescent proteins or as dual guides on a plasmid with a single fluorescent protein. Cells are sorted and plated as single cells into 96 well plates. These can then be screened by PCR over the potential breakpoint to identify engineered clones. c, Sanger sequencing of PCR products from different engineered clones. The sequences that align to chromosome 7 are highlighted in green, while the sequences that align to chromosome 8 are highlighted in purple. d, Similar to Fig. 4b, validation of the engineered t(7;8) translocation by chromosome painting. e, MYC expression in cell lines containing endogenous or engineered rearrangements at the MYC locus including the non-rearranged SK-N-DZ parent cell line (purple), engineered clones classified as “Non-activating” (light blue), engineered clones classified as “MYC-activating” (dark red), Neuroblastoma cell lines with endogenous MYC rearrangements (green), and non-Neuroblastoma cell lines with MYC rearrangements (black). f, Scatter plot showing MYC expression (y-axis) and estimated MYC copy number (x-axis). g, Scatter plot showing MYC expression (y-axis) and estimated MYCN copy number (x-axis). h, Scatter plot showing MYC expression (y-axis) and MYCN expression (x-axis). i, FACS plots of mClover2 fluorescence in SK-N-DZ cells with a T2A-mClover2 reporter knocked into the 3′ end of the MYC gene (pink) and in a line derived from this MYC reporter with an engineered translocation between chromosome 1 and 8 (green). j, Heat map of chromosome 1 translocation to chromosome 8 with box showing H3K27ac ChIP-seq data over the partner region. The small inset box on the ChIP-seq track shows the enhancer targeted for deletion. k, FACS showing mClover2 fluorescence levels in the original chromosome 1 and chromosome 8 MYC reporter translocation (red) and in the same line with the targeted enhancer deletion (blue). The gate shows the region classified as “mClover2 low”. An example of the gating strategy for is also shown, including gating for single-cells and mCherry positive cells (FSC – forward scatter, SSC – side scatter, A – area, W – width). l, Percentage of “mClover2 low” cells in the control (red) and deletion (blue) cells. P-value is using Student’s two-sided T-test. m, MYC RPKM of clones with enhancer deletion on wild type allele and MYC-translocated allele. P-value is using two-sided T-test with equal variance.

Extended Data Fig. 6 Models for activation in engineered rearrangements.

a, Example plot showing method for calculating ABC score for MYC with rearranged partner sites. Interaction frequency between the MYC promoter and H3K27ac peaks in the partner region (“contact”) is multiplied by the strength of the H3K27ac signal (“activity”) at each peak across the partner region to obtain a final score for each peak. This signal is then summed across all peaks over the partner region. Of note, this example plot only shows the calculations for the six strongest H3K27ac peaks in the partner region, whereas the actual score is calculated using all H3K27ac peaks. b, Receiver Operating Characteristic (ROC) curve for the TAD delimited ABC model. Shown above the plot is the area under the curve (AUC). c, ROC curve for an ABC model where contacts are measured from genome wide average interaction frequencies. d, Plots showing ABC scores for genes neighboring MYC. Above the plot is the Pearson correlation coefficient for each gene between the genes’ ABC score and expression. e, Heat map of the TAD surrounding MYC as well as the location and relative position of the genes shown in panel D. f, Scatter plot showing ABC scores and summed enhancer activity within 3 Mb for every gene in 30 cancer cell lines. g, Scatter plot showing ABC scores and summed interaction within 3 Mb for every gene in 30 cancer cell lines. h, The number of enhancers per gene linked by the marginal ABC score >= 0.1 for ABC-correlated and non-correlated genes. Gray lines show the paired values for each cell line comparing ABC-correlated and non-correlated genes. P-value is from paired Wilcoxon test. i, Percentage of ABC responsive (blue) and protein-coding genes classified as transcription factors. Protein coding genes are from the Gencode reference annotation. P-value is from Fisher’s Exact test. j, Percentage of ABC responsive (blue) and protein-coding genes classified as oncogenes according to the Cosmic cancer gene census. P-value is from Fisher’s Exact test. k, Normalized interaction frequency as a function of distance for Hi-C interactions at 10 kb resolution in SK-N-DZ cells. Interaction frequency decays exponentially as a function of distance. l, Enhancer activity based on H3K27ac ChIP-seq as quantified by the ROSE super enhancer calling activity for all enhancers in SK-N-DZ cells. Enhancers are displayed ranked according to strength. Super-enhancers show exponentially stronger enhancer activity compared with typical enhancers. m, Enhancer activity required to achieve the equivalent activity-by-contact score for the median enhancer at 20 kb in SK-N-DZ cells as a function of genomic distance. Shown as a dashed line is the minimal enhancer strength categorized as a “super-enhancer” in SK-N-DZ cells by the ROSE algorithm. Due to the exponential decay in interaction frequency. After ~300 kb, the only enhancers capable of producing an ABC score equivalent to the median enhancer at 20 kb are super enhancers.

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Xu, Z., Lee, DS., Chandran, S. et al. Structural variants drive context-dependent oncogene activation in cancer. Nature 612, 564–572 (2022). https://doi.org/10.1038/s41586-022-05504-4

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