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Altered chromosomal topology drives oncogenic programs in SDH-deficient GISTs

Nature volume 575, pages 229–233 (2019)Cite this article

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Abstract

Epigenetic aberrations are widespread in cancer, yet the underlying mechanisms and causality remain poorly understood1,2,3. A subset of gastrointestinal stromal tumours (GISTs) lack canonical kinase mutations but instead have succinate dehydrogenase (SDH) deficiency and global DNA hyper-methylation4,5. Here, we associate this hyper-methylation with changes in genome topology that activate oncogenic programs. To investigate epigenetic alterations systematically, we mapped DNA methylation, CTCF insulators, enhancers, and chromosome topology in KIT-mutant, PDGFRA-mutant and SDH-deficient GISTs. Although these respective subtypes shared similar enhancer landscapes, we identified hundreds of putative insulators where DNA methylation replaced CTCF binding in SDH-deficient GISTs. We focused on a disrupted insulator that normally partitions a core GIST super-enhancer from the FGF4 oncogene. Recurrent loss of this insulator alters locus topology in SDH-deficient GISTs, allowing aberrant physical interaction between enhancer and oncogene. CRISPR-mediated excision of the corresponding CTCF motifs in an SDH-intact GIST model disrupted the boundary between enhancer and oncogene, and strongly upregulated FGF4 expression. We also identified a second recurrent insulator loss event near the KIT oncogene, which is also highly expressed across SDH-deficient GISTs. Finally, we established a patient-derived xenograft (PDX) from an SDH-deficient GIST that faithfully maintains the epigenetics of the parental tumour, including hypermethylation and insulator defects. This PDX model is highly sensitive to FGF receptor (FGFR) inhibition, and more so to combined FGFR and KIT inhibition, validating the functional significance of the underlying epigenetic lesions. Our study reveals how epigenetic alterations can drive oncogenic programs in the absence of canonical kinase mutations, with implications for mechanistic targeting of aberrant pathways in cancers.

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Fig. 1: Insulator dysfunction in SDH-deficient GISTs.
Fig. 2: FGF3–FGF4 locus topology reorganized in SDH-deficient GISTs.
Fig. 3: KIT–PDGFRA locus topology reorganized in SDH-deficient GISTs.
Fig. 4: SDH-deficient GIST PDX trial confirms dependence on FGF signalling.

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Data availability

Sequencing data that support the findings of this study have been deposited in GEO with the accession code GSE107447.

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Acknowledgements

We thank S. Gillespie, M. Miri, F. Najm, P. van Galen and E. Choy for assistance with clinical samples and analysis, H. Gu and A. Gnirke for sequencing assistance, and E. Gaskell for discussions. W.A.F. is supported by an F32 from the National Cancer Institute. Y.D. is supported by the Tosteson Postdoctoral Fellowship. B.E.B. is the Bernard and Mildred Kayden Endowed MGH Research Institute Chair and an American Cancer Society Research Professor. This research was supported by the National Cancer Institute, the NIH Common Fund, the Starr Cancer Consortium, and the Ludwig Center at Harvard.

Author information

Author notes

  1. Yotam Drier

    Present address: The Lautenberg Center for Immunology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University, Jerusalem, Israel

  2. These authors contributed equally: William A. Flavahan, Yotam Drier

Authors and Affiliations

  1. Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    William A. Flavahan, Yotam Drier, Sarah E. Johnstone, Daniel R. Tarjan, Esmat Hegazi, Sarah J. Shareef, Nauman M. Javed & Bradley E. Bernstein

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    William A. Flavahan, Yotam Drier, Sarah E. Johnstone, Daniel R. Tarjan, Esmat Hegazi, Sarah J. Shareef, Nauman M. Javed & Bradley E. Bernstein

  3. Center for Sarcoma and Bone Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Matthew L. Hemming & George D. Demetri

  4. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA

    Matthew L. Hemming & George D. Demetri

  5. Department of Surgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Chandrajit P. Raut

  6. Experimental Therapeutics Core, Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Benjamin K. Eschle & Prafulla C. Gokhale

  7. Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Jason L. Hornick

  8. Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    Ewa T. Sicinska

  9. Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA

    George D. Demetri & Bradley E. Bernstein

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  1. William A. Flavahan
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Contributions

Conception and experimental design: W.A.F., Y.D., S.E.J., M.L.H., E.T.S., G.D.D. and B.E.B. Methodology and data acquisition: W.A.F., Y.D., S.E.J., M.L.H., D.R.T., E.H., S.J.S., N.M.J., C.P.R., B.K.E., P.C.G., J.L.H., E.T.S., G.D.D. and B.E.B. Analysis and interpretation of data: W.A.F., Y.D., M.L.H., S.J.S., N.M.J., G.D.D. and B.E.B. Manuscript writing and revision: W.A.F., Y.D. and B.E.B.

Corresponding authors

Correspondence to Yotam Drier, George D. Demetri or Bradley E. Bernstein.

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Competing interests

B.E.B. is an advisor and equity holder for Fulcrum Therapeutics, 1CellBio, HiFiBio and Arsenal Biosciences, is an advisor for Cell Signaling Technologies, and has equity in Nohla Therapeutics. G.D.D. reports relationships with Novartis, Bayer, Pfizer, EMD-Serono, Sanofi, Ignyta, Roche, Loxo Oncology, AbbVie, Mirati Therapeutics, Epizyme, Daiichi-Sankyo, WIRB Copernicus Group, ZioPharm, Polaris Pharmaceuticals, M.J. Hennessey / OncLive, Adaptimmune, GlaxoSmithKline, Blueprint Medicines, Merrimack Pharmaceuticals, G1 Therapeutics, CARIS Life Sciences, Bessor Pharmaceuticals, ERASCA Pharmaceuticals, CHAMPIONS Oncology, Janssen, PharmaMar; in addition, G.D.D. has a Use patent on imatinib for GIST, licensed to Novartis, with royalties paid to the Dana-Farber Cancer Institute.

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Peer review information Nature thanks Christian Frezza, Michael Heinrich, Michael Rehli and Peter Scacheri for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Epigenomic characterization of GIST.

a, ChIP–seq profiles for H3K27ac were compared for GIST specimens and other gastrointestinal tract tumour specimens (GAC, gastric adenocarcinoma; CRC, colorectal cancer; PDAC, pancreatic ductal adenocarcinoma). Heat map depicts pairwise Pearson correlations between the top 10,000 most variable peaks (yellow indicates high correlation; blue indicates low correlation). The dendrogram (left) was derived by unweighted average distance linkage. Enhancer patterns are relatively consistent across GIST subtypes, compared to other tumour types. b, DNA methylation levels in the vicinity of CTCF sites were profiled genome-wide by hybrid-selection bisulfite sequencing. CTCF sites are binned according to the amount their methylation increased in SDH-deficient GISTs, relative to SDH-intact GISTs (methylation change computed over a 250 bp window centred on the motif). For each bin, bar graphs depict the percentage of sites that lose CTCF binding in SDH-deficient GISTs, per ChIP–seq. Separate plots are shown for CTCF sites for which motifs do or do not contain a CpG. Increased methylation over CTCF sites is associated with more frequent loss of CTCF binding, even when the CTCF motif lacks a CpG. c, Plot depicts correlation between CTCF occupancy and DNA methylation in SDH-deficient GISTs. Red points show Spearman correlations between CTCF ChIP–seq signal and methylation of CpGs at indicated positions relative to the centre of the CTCF motif. Red line reflects correlation to average methylation over 10 bp windows. Randomly permuted data (black) are shown for comparison. Anti-correlation between CTCF occupancy and methylation is evident over a ~250-bp binding footprint. d, Genomic views of a representative 10 Mb region on chromosome 21 depict chromosome topology (HiC, red), CTCF binding (ChIP–seq, orange) and CTCF–CTCF loop interactions (HiChIP, black) for the SDH-intact GIST model, GIST-T1. TADs are visible as triangles of enhanced interaction in HiC data, flanked by boundaries that correspond to loop interactions in HiChIP data. Genes (blue) are also indicated.

Extended Data Fig. 2 Super-enhancers and insulators in GIST.

a, Traces depict H3K27ac ChIP–seq signal for normal stomach muscle (NSM) and GISTs of the indicated subtype over the FGF–ANO1 locus. b, Traces depict H3K27ac ChIP–seq signal for NSM and GISTs of the indicated subtype over the PDGFRA–KIT locus. Genes are indicated in blue, and super-enhancer locations are indicated by green bars. For a, b, traces are representative of 11 KIT-mutant and 6 SDH-deficient tumours with similar results. c, Traces depict CTCF binding over the FGF insulator in normal stomach muscle (NSM) and GIST clinical specimens. d, Plot depicts CTCF ChIP–seq signal over the strongest CTCF peak in the FGF insulator in normal stomach muscle (NSM, n = 4), and KIT mutant (n = 11), PDGFRA mutant (n = 2) and SDH-deficient GISTs (n = 6). e, Traces depict CTCF binding over the KIT insulator in normal stomach muscle (NSM) and GIST clinical specimens. f, Plot depicts CTCF ChIP–seq signal over the strongest CTCF peak in the KIT insulator in normal stomach muscle (NSM, n = 4), and KIT mutant (n = 11), PDGFRA mutant (n = 2) and SDH-deficient GISTs (n = 6). For d and f, horizontal bars reflect mean values and P values indicate significance of CTCF loss in SDH-deficient GIST, as determined by the Walt test (via DEseq254). All n values represent the number of biologically independent clinical specimens.

Extended Data Fig. 3 FGF locus 4C-seq data and insulator deletion.

a, Traces depict 4C-seq data at FGF locus, as in Fig. 2b, except graphed on the same axis to allow for direct comparison. b, c, Bar plots quantify 4C-seq interactions between the super-enhancer viewpoint and FGF4 (b) or FGF3 (c). Expression of these genes in the corresponding SDH-deficient GIST specimens is also shown. d, Traces depict CTCF ChIP–seq signal in GIST-T1 cells infected with CRISPR–Cas9 and either a control sgRNA directed at GFP (black, top) or sgRNAs directed against the two indicated CTCF motifs in the FGF insulator (second row, red).

Extended Data Fig. 4 Allelic imbalance in FGF3 and FGF4 activation.

a, Two heterozygous SNPs in FGF4 (both 3′ UTR) enabled us to evaluate allelic expression in three SDH-deficient GISTs (tumours S6, S1 and S4). Both alleles for each SNP were detected in DNA sequencing data for these tumours, but only one allele was detected in RNA-seq data of tumours S1 and S6, indicative of mono-allelic FGF4 expression. Both alleles are detected in tumour S4, indicating bi-allelic expression of FGF4. b, Heterozygous SNPs in FGF3 exons (both synonymous base substitutions) enabled us to evaluate allelic expression in the SDH-deficient GISTs (tumours S2 and S5). In both cases, DNA sequencing confirmed heterozygosity at the genome level (C/A and T/C, respectively), but RNA-seq data demonstrated mono-allelic FGF3 expression. c, Both alleles of heterozygous SNPs in ANO1 exons were found in the RNA-seq data derived from SDH-deficient GIST samples, confirming bi-allelic expression of ANO1. Similarly, both alleles of heterozygous SNPs were found in the histone H3K27ac ChIP–seq data, confirming the bi-allelic nature of the super-enhancer (not shown). d, One SDH-deficient GIST sample was heterozygous for a SNP (rs386829467) located about 50 bp from the CTCF motif of Peak 2 in the FGF insulator. Allele-agnostic methylation data confirmed 43% methylation of the CTCF peak in this tumour, while essentially no methylation was detected in the SDH-intact tumours (left). Separation of the two alleles using the heterozygous SNP revealed strong allelic bias in the SDH-deficient tumour: one allele was largely unmethylated (~3% methylation), while the other was highly methylated (~75% methylation), consistent with mono-allelic methylation of the CTCF site (right). e, Schematic depicts 4C-seq experimental protocol and primer design for detecting SNPs. DNA elements in close physical proximity are crosslinked and restricted with an enzyme that leaves nucleotide overhangs. These overhangs are then proximity ligated to crosslinked fragments. A second restriction enzyme (with different restriction sites) is then used to circularize the ligated fragments, allowing for inverse PCR. Here we selected restriction enzymes and designed a custom read 2 primer to capture a heterozygous SNP within the super-enhancer. This second read is normally non-informative as contact frequencies are determined through the viewpoint primer (read 1), but in this case enabled us to detect the SNP and assign each ligated fragment to a specific allele. f, The left trace (grey) depicts standard 4C-seq data (allele agnostic), which demonstrates strong interaction between super-enhancer viewpoint and FGF4. However, the SNP covered in the non-viewpoint read enabled us to distinguish interactions involving the minor (top right) or major (bottom right) allele. This revealed that the major allele (purple) is responsible for ~97% of super-enhancer–FGF4 interactions.

Extended Data Fig. 5 KIT locus 4C-seq data and insulator deletion.

a, Traces depict KIT locus 4C-seq data, as in Fig. 3b, except graphed on the same axis to allow for direct comparison. b–d, Bar plots quantify 4C-seq interactions (top, reproduced from Fig. 3b) between the super-enhancer viewpoint and positions within the super-enhancer TAD (b), sequences just beyond the KIT insulator (c), or the KIT gene itself (d). P values indicate significance of difference between SDH-intact and SDH-deficient, by two-sided t-test. e, Traces depict CTCF ChIP–seq signal in GIST-T1 cells infected with Cas9 and either a control sgRNA directed at GFP (black, top) or sgRNAs directed against the two bound CTCF motifs in the KIT insulator (second row, red).

Extended Data Fig. 6 Hypoxia marker induction in GIST-T1 cells.

a, Bar plot depicts levels of the TET product, 5-hydroxymethyl-cytosine (5-hmC), measured by ELISA in GIST-T1 cells infected with CRISPR–Cas9 and either a short guide RNA targeting GFP (sgGFP) or SDHB. Cells were cultured in either control media or media supplemented with 20 μM of dimethylsuccinate (DMS), a membrane-permeable ester of succinate, for 3 days, as indicated. b, c, Plots show relative expression of pseudo-hypoxia-associated genes EPAS1 (also known as HIF2A)67 and IGF1R68 (b), and KITLG (also known as SCF)69 (c) in control GIST-T1 cells (black), SDH-deficient GIST-T1 cells generated by CRISPR–Cas9 knockout of SDHB and cultured with exogenous succinate (red), or GIST-T1 cells treated with the iron chelator DFX to simulate hypoxia (blue). Upregulation of KIT ligand due to pseudo-hypoxia or tumour hypoxia may supplement FGF ligands in promoting RTK signalling in SDH-deficient GIST.

Extended Data Fig. 7 FGF and KIT insulator methylation and expression in GIST subtypes and non-malignant cells.

a, Bar plot depicts methylation of FGF insulator CTCF peak 2 (top) and KIT insulator CTCF peak 2 (bottom) in 34 tissues and primary cells available through ENCODE60. Values are average methylation of CpGs nearest the CTCF motifs, determined by whole genome bisulfite sequencing (WGBS) (KIT insulator, 3 CpGs; FGF insulator, 6 CpGs). Methylation of these sites is also shown for SDH-intact and SDH-deficient GISTs (see Fig. 3e, f), and for flow-sorted CD45−ANO1+KIT+ (ICC enriched) cells from normal stomach muscle (NSM) tissue (n values represent biologically independent specimens). b, Left, table depicts FPKM (fragments per kilobase of transcript per million mapped reads) values of relevant genes in mouse ICCs isolated from jejunum or colon61. Right, dot plot depicts expression of FGF4 in either flow sorted ICCs (green) or GISTs of the indicated subtype: KIT mutant in black, PDGFRA mutant in blue, SDH-deficient in red, and KIT-/PDGFRA wild type in purple62,63,64,65,66. SDH status of the latter group is unknown, but SDH-deficient GIST represent a significant portion of KIT-/PDGFRA wild-type tumours. Data are drawn from the indicated GEO series and publications.

Extended Data Fig. 8 PDX trial of FGFR and KIT combination therapy in SDH-deficient GIST.

a, b, Genomic views depict RNA expression (black), H3K27ac (green) and CTCF occupancy (orange) over the FGF (a) and KIT (b) loci for the S1 primary tumour and PDX. Genes (blue), super-enhancers (green bar and shade) and lost CTCF insulators (orange shade) are indicated. c, Plot depicts tumour volume during treatment and observation periods of experiment, as in Fig. 4g, except with time axis extended until final group reached censor point (one tumour in the group >2,000 mm3). Points represent mean tumour size, error bars represent s.e.m., and shading represents range of tumour sizes for n = 8 biologically independent xenograft-bearing mice per group. For statistics, see Fig. 4g. d, Kaplan–Meier plot depicts survival until clinical endpoint (tumour size >2,000 mm3) for the same PDX trial. Median and range survival are indicated for each group. P values reflect difference in survival between groups as calculated by logrank test.

Supplementary information

Reporting Summary

Supplementary Information Table 1

| Clinical Specimens Analysed Table lists all of the clinical specimens analysed in the manuscript, noting tissue type, source (FFPE vs. fresh frozen), primary site and metastatic status (if known), driver mutations (if known), and experiments performed on the tissue. For FFPE tissue, no genetic testing was performed; instead SDH status was determined by IHC

Supplementary Information Table 2

| Candidate deregulated genes due to insulator loss Table lists results of analysis of genes potentially regulated by insulator loss (see Fig. 1e). Listed are methylation difference at lost insulators and median expression in GIST of various driver mutations

Supplementary Information Table 3

| PDX Tumor Growth Data Table lists measured volumes of Patient Derived Xenograft (PDX) trial (see Fig. 4f, g, Extended Data Fig. 8.) Volumes are listed as mm3 with each column representing serial measurements of a single biologically-independent PDX-bearing mouse; for further details of study, see Methods

Supplementary Information Table 4

| ChIP–seq information Table lists information from ChIPseq runs associated with the work, including sample, antibody, processed data file name(s), instrument, read count, and sequencing strategy (e.g. paired/single end)

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Flavahan, W.A., Drier, Y., Johnstone, S.E. et al. Altered chromosomal topology drives oncogenic programs in SDH-deficient GISTs. Nature 575, 229–233 (2019). https://doi.org/10.1038/s41586-019-1668-3

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  1. DR Gaurav Dixit

    Hi,

    Thank for this article, This article would help people who are seeking help or knowledge as well.

    Dr Gaurav Dixit
    BMT Physician & the Best Hematologist in Delhi
    https://www.drgauravdixit.com/

  2. Arturo Tozzi cns

    UNDERRATED TOPOLOGICAL APPROACHES, APART FROM THE EVERY-DAY NETWORK CONNECTIVITY

    Recent papers provide topological accounts of countless physical and biological phenomena, such as gene topology (Flavahan et al., 2019; Achar et al, 2020), hidden symmetries (Golubitsky and Stewart, 2015), phase transitions (Pettini et al., 2019), unconventional superconductivity (Cao et al., 2018), quantum computing (Collins, 2006), coupled oscillators’synchronization (Arenas et al., 2008), HIV proteins arrangement (Gaiha et al. 2019), receptor dimerization (Mohan et al., 2019). Thanks to its ability to assess systems’ properties in terms of abstract mathematical spaces (Dodson and Parker, 1997), topology is generally used in the context of complex network analysis and graph theory (Kleinberg et al., 2016), looking for, e.g., integrated network topology connectivity ((Fukushima et al. 201), multilevel entropy measures (Maletić and Zhao, 2017), and simplicial complexes (Giusti et al., 2016; Bianconi and Rahmede, 2016) that capture emergent community structures. In the current accounts, the value of topology mostly rests on the possibility to assess modifications in elusive networks subtending systems dynamics, such as transitions from star to mesh arrangements, or from ring to tree.
    Nevertheless, topological changes in biological systems might be used in another, underrated way: by using theorems from algebraic topology. These frequently underrated theorems are useful to assess countless physical and biological dynamics. To provide an example, the Hairy ball theorem (HBT) (that informally states that “given at least some wind on Earth, there must at all times be a cyclone or anticyclone somewhere”) has been used to describe unconventional type-II superconductors (Laver and Forgan, 2010), and surface instabilities of liquid/vapor interfaces (Bormashenko, 2015). We refer the reader to Tozzi et al. (2017) for a survey of the countless applications of the Borsuk-Ulam theorem (“at any moment, there is always a pair of antipodal points on the Earth’s surface with equal temperatures and barometric pressures”), that stands for an unidersal principle underlying countless features from far-flung scientific disciplines. Also the ham sandwich theorem (“given globs of ham, bread, and cheese, placed any way you like, there exists one flat slice of a knife that will bisect each of the ham, bread, and cheese”), the Brouwer fixed-point theorem ( “no matter how you slosh the coffee, some point is always in the same position that it was before the sloshing began”) and the Lusternik–Schnirelmann theorem (“every time you split a sphere in three parts, one of the three must encompass an entire diameter”) display physical and biological counterparts.
    In physical/biological systems, the occurrence of such algebraic theorems leads to a straightforward observation: tiny changes in topological features lead to alterations in macroscopic behavior. To provide an example that demonstrates how small modifications in topology may lead to novel functional biological features, take a perfectly spherical soap bubble. In its spherical surface, due to the Borsuk-Ulam theorem, there must exist at least two antipodal points with identical values of surface tension. When the soap bubble is deformed, the spherical shape gets lost: this leads to the disappearance of antipodal points with the same surface tension.

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    Arturo Tozzi
    Center for Nonlinear Science, Department of Physics, University of North Texas, Denton, Texas, USA
    tozziarturo@libero.it
    Arturo.Tozzi@unt.edu

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