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BORIS promotes chromatin regulatory interactions in treatment-resistant cancer cells

Nature volume 572, pages 676–680 (2019)Cite this article

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Abstract

The CCCTC-binding factor (CTCF), which anchors DNA loops that organize the genome into structural domains, has a central role in gene control by facilitating or constraining interactions between genes and their regulatory elements1,2. In cancer cells, the disruption of CTCF binding at specific loci by somatic mutation3,4 or DNA hypermethylation5 results in the loss of loop anchors and consequent activation of oncogenes. By contrast, the germ-cell-specific paralogue of CTCF, BORIS (brother of the regulator of imprinted sites, also known as CTCFL)6, is overexpressed in several cancers7,8,9, but its contributions to the malignant phenotype remain unclear. Here we show that aberrant upregulation of BORIS promotes chromatin interactions in ALK-mutated, MYCN-amplified neuroblastoma10 cells that develop resistance to ALK inhibition. These cells are reprogrammed to a distinct phenotypic state during the acquisition of resistance, a process defined by the initial loss of MYCN expression followed by subsequent overexpression of BORIS and a concomitant switch in cellular dependence from MYCN to BORIS. The resultant BORIS-regulated alterations in chromatin looping lead to the formation of super-enhancers that drive the ectopic expression of a subset of proneural transcription factors that ultimately define the resistance phenotype. These results identify a previously unrecognized role of BORIS—to promote regulatory chromatin interactions that support specific cancer phenotypes.

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Fig. 1: Targeted therapy resistance in neuroblastoma is associated with transcriptional reprogramming and a switch in dependency from amplified MYCN to BORIS.
Fig. 2: BORIS overexpression is associated with its increased chromatin occupancy in resistant cells, whereas CTCF binding is unchanged.
Fig. 3: BORIS promotes new chromatin interactions in resistant cells.
Fig. 4: BORIS-regulated chromatin remodelling supports a phenotypic switch that maintains the resistant state.

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Data availability

The microarray, ChIP–seq, HiChIP and scRNA-seq datasets generated and analysed during the current study are available in the Gene Expression Omnibus (GEO) repository under accession number GSE103084. The authors declare that all other data supporting the findings of this study are available within the paper and its Supplementary Information files.

Code availability

Custom code is available upon reasonable request.

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Acknowledgements

We thank the George, Young and Gray laboratories and J. R. Gilbert for discussions, and C. Li for assistance with the HiChIP experiments. We thank J. Qi for providing JQ1, and D. Sabatini and S. Elledge for sharing plasmids, pLKO.1 GFP shRNA and pInducer20, respectively. We thank A. Ward and C. Clinton at DFCI Pediatric Oncology and J. Chan and the Clark Smith Tumor Bank, Charbonneau Cancer Institute, Calgary, Canada, for the human tumour samples. We thank Applied Pathology Systems, the DFCI Molecular Biology Core, the Whitehead Genome Technology Core, D. Adeegbe and the NYULH Genome Technology Center for technical support. The results shown here are in part based on data generated by the TCGA Research Network: http://cancergenome.nih.gov/, the R2: Genomics Analysis and Visualization Platform: http://r2.amc.nl/ and the Allan Brain Map Data Portal: http://www.brain-map.org/. This work was supported by NIH grants R01CA197336 (R.E.G. and R.A.Y.), R01CA148688 (R.E.G. and N.S.G.) and a Hyundai Hope on Wheels Scholar Grant (R.E.G.). D.N.D. is a recipient of a Young Investigator Grant from the Alex’s Lemonade Stand Foundation/Northwestern Mutual Foundation. D.S.D. is supported by an American Cancer Society fellowship PF-16-146-01-DMC. The NYULH Genome Technology Center is partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

Author information

Author notes

  1. These authors contributed equally: David N. Debruyne, Ruben Dries.

Authors and Affiliations

  1. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    David N. Debruyne, Ruben Dries, Satyaki Sengupta, Davide Seruggia, Yang Gao, Bandana Sharma, Hao Huang, Michael McLane, Stuart H. Orkin & Rani E. George

  2. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    David N. Debruyne, Ruben Dries, Satyaki Sengupta, Yang Gao, Bandana Sharma, Hao Huang, Michael McLane & Rani E. George

  3. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Ruben Dries, Eugenio Marco & Guo-Cheng Yuan

  4. Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA

    Davide Seruggia, Stuart H. Orkin & Rani E. George

  5. Harvard Stem Cell Institute, Harvard Medical School, Boston, MA, USA

    Davide Seruggia & Stuart H. Orkin

  6. Howard Hughes Medical Institute, Boston, MA, USA

    Davide Seruggia & Stuart H. Orkin

  7. Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Lisa Moreau

  8. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Daniel S. Day & Richard A. Young

  9. MIT Department of Biology, Cambridge, MA, USA

    Daniel S. Day & Richard A. Young

  10. Department of Biostatistics, Harvard TC Chan School of Public Health, Boston, MA, USA

    Eugenio Marco & Guo-Cheng Yuan

  11. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Ting Chen

  12. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Nathanael S. Gray

  13. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

    Nathanael S. Gray

  14. Division of Hematology and Medical Oncology, Laura and Isaac Perlmutter Cancer Center, New York University Langone Medical Center, New York, NY, USA

    Kwok-Kin Wong

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  1. David N. Debruyne
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Contributions

D.N.D. and R.E.G. conceived the project and designed the experiments. D.N.D. planned and performed the molecular, cellular and genomic studies. R.D. performed computational analyses with input from D.S.D. and E.M. S.S. contributed to the ChIP–seq and HiChIP experiments. D.S. and S.H.O. contributed to the HiChIP experiment. D.N.D., Y.G. and T.C. performed the mouse experiments. B.S. and M.M. provided technical assistance. H.H. performed the co-immunoprecipitation experiments. L.M. performed the FISH analysis. N.S.G. provided TAE684 and E9. G.-C.Y. supervised the bioinformatics analyses. K.-K.W. enabled the mouse and scRNA-seq studies. D.N.D., R.D., R.A.Y. and R.E.G. interpreted the data. D.N.D., R.D. and R.E.G. wrote the manuscript with input from R.A.Y. R.E.G. supervised the research. All authors edited the manuscript.

Corresponding author

Correspondence to Rani E. George.

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Competing interests

N.S.G. is a founder, SAB member and equity holder of Gatekeeper, Syros Pharmaceuticals, Petra, C4, B2S and Soltego. The Gray laboratory receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Janssen, Kinogen, Voronoi, Her2llc, Deerfield and Sanofi. S.H.O. is a SAB member of Syros. R.A.Y. is a founder and shareholder of Syros, Camp4 Therapeutics, Omega Therapeutics and Dewpoint Therapeutics. R.E.G. is a SAB member of Global Gene Corp.

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Extended data figures and tables

Extended Data Fig. 1 BORIS is expressed in several cancers and associated with high-risk features.

a, b, Relative mRNA expression [log2(FPKM + 1)] of CTCF and BORIS in normal tissues (a) and in various cancer types based on TCGA datasets (b). FPKM, fragments per kilobase of transcript per million mapped reads. Keys to cancer types: ACC, adrenocortical carcinoma; AML, acute myeloid leukaemia; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, diffuse large B-cell lymphoma; ESCA, oesophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; LGG, low-grade glioma; KICH, kidney chromophobe; KIRC, renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukaemia; LIHC, hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; NB, neuroblastoma; OV, serous ovarian cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; RT, rhabdoid tumour; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumour; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma; WT, Wilms tumour. c, Box plots showing the correlation of BORIS expression with risk status, tumour stage (primary versus metastasis/recurrence), presence of cancer stem cells (CD133 positivity) and response to targeted (lapatinib) or cytotoxic (cisplatin) therapy in the tumour types depicted. NSCLC, non-small cell lung cancer. Datasets (Mixed Ewing Sarcoma-Savola-117 and NSCLC-Plamadeala-410) were extracted from the R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl). GSE7181 (glioblastoma); GSE16179 (breast cancer); GSE15372 (ovarian cancer). P values determined by two-sided Wilcoxon rank-sum test. For all panels, sample sizes (n) are depicted in parenthesis and box plots are as defined in Fig. 4.

Extended Data Fig. 2 ALK inhibitor-resistant cells exhibit stable resistance in vivo and no longer rely on ALK signalling.

a, Left, tumour volumes of sensitive and resistant cell xenografts in untreated NU/NU (Crl:NU-Foxn1nu) mice established by subcutaneous injection of 2 × 106 cells into both flanks. Animals were euthanized when tumours reached 1,500–2,000 mm3. Data are mean ± s.e.m., n = 4 per arm. Right, immunoblot analysis of total and phosphorylated ALK in TAE-resistant xenograft tumours (1 and 2) and sensitive and resistant cells in culture. b, Dose–response curves for TAE684 in sensitive and resistant cell lines established from the same tumour xenografts as in a (IC50 values: sensitive, 7.9 nM; resistant, 878.6 nM). Data are mean ± s.d., n = 3 biological replicates. c, Tumour volumes (left) and Kaplan–Meier survival curves (right) of resistant cell xenografts in NU/NU (CrTac:NCr-Foxn1nu) mice treated with TAE684 (10 mg kg−1 by oral gavage once daily) or vehicle control for up to 56 days. Data are mean ± s.e.m., n = 8 per arm. P values determined by Mann–Whitney U test for tumour volumes (P = 0.8404) and by log-rank test for Kaplan–Meier survival analysis (P = 0.8076), both two-sided. d, Dose–response curves for TAE684-sensitive and -resistant cells treated with ceritinib (IC50 values: sensitive, 33.8 nM; resistant, 446.5 nM) or lorlatinib (IC50 values: sensitive, 47.5 nM; resistant, 2,318 nM). Data are mean ± s.d., n = 3 biological replicates. e, Immunoblot analysis of the indicated proteins in sensitive and resistant cells treated with DMSO or 1 μM TAE684 for 6 or 24 h. f, Electropherograms of ALK kinase domain sequencing in sensitive and resistant cells. Arrows show the F1174L mutation characteristic of Kelly cells. HEK293T cells were used as a control for sequencing wild-type ALK. g, Phosphoproteomic analysis of a panel of receptor tyrosine kinases (RTKs) in sensitive and resistant cells. Each RTK is shown in duplicate and the pairs in the corners of each array are positive controls. Numbered RTKs with corresponding names listed on the right represent the highest-phosphorylated proteins. ALK is depicted in red. h, Quantitative reverse transcription PCR (qRT–PCR) and immunoblot analysis of ABCB1 and ABCG2 multidrug transporter expression in sensitive and resistant cells. The qRT–PCR data are means of n = 2 biological replicates. In a (immunoblot), d, f and g, data are representative of two independent experiments (see Supplementary Note 1 for details; for gel source data, see Supplementary Fig. 1).

Source data

Extended Data Fig. 3 Development of resistance is associated with loss of MYCN followed by gradual induction of proneural transcription factors.

a, TAE684 dose–response curves of Kelly neuroblastoma cells during resistance establishment (IC50 values: sensitive, 39.4 nM; intermediate, 618 nM; resistant, 1,739 nM). Data are mean ± s.d., n = 3 biological replicates. Schematic representation of development of resistance is shown above. b, t-SNE plot of scRNA-seq data showing the segregation of sensitive (n = 5,432), intermediate (n = 6,376) and resistant (n = 6,379) cells. c, t-SNE plot depicting unsupervised clusters for the individual subpopulations that underlie the pseudotime analysis. d, Heat map of rescaled gene expression values of the most variable ranked transcription factors in the three cell states. e, qRT–PCR and immunoblot analysis of MYCN expression in TAE684-resistant xenograft tumours (1 and 2) and sensitive and resistant cells in culture. The qRT–PCR data are mean ± s.d., n = 4 biological replicates for sensitive and resistant cells (***P = 1.396 × 10−11; unpaired two-sided t-test) and n = 3 technical replicates for each tumour. f, Fluorescence in situ hybridization of MYCN in sensitive and resistant cells (representative of 20 nuclei per condition). g, ChIP–seq track of H3K27me3 binding at the MYCN locus in sensitive and resistant cells. Signal intensity is given in the top right corner. h, Line plot showing the association between genes ordered by expression (x axis) and changes in absolute gene expression levels (y axis) in sensitive versus resistant cells. Bar plot, total transcriptional yield in sensitive or resistant cells. i, Immunoblot analysis of the indicated proteins in sensitive and resistant cells expressing control (shCtrl) or MYCN (shMYCN-1 and -2) shRNAs. j, Violin plots representing the expression distribution of selected genes in the same cells as in a (centre line, median). k, Bar plot showing the fractions of cells with detectable mRNA levels of the same genes as in d. In e (immunoblot) and f–i, data are representative of two independent experiments (for gel source data, see Supplementary Fig. 1).

Source data

Extended Data Fig. 4 Overexpression of BORIS is seen in resistance models of neuroblastoma and correlates with high-risk disease and a poor outcome.

a, qRT–PCR and immunoblot analysis of BORIS expression in TAE684-resistant Kelly cell xenograft tumours (1 and 2) and sensitive and resistant cells in culture. The qRT–PCR data are mean ± s.d., n = 4 biological replicates for sensitive and resistant cells (**P = 0.0014; unpaired two-sided t-test) and n = 3 technical replicates for each tumour. b, Bisulfite sequencing of the BORIS promoter in sensitive and resistant cells. Black circles represent methylated cytosine residues in a CpG dinucleotide, empty circles are unmethylated cytosines. The B and C TSSs are indicated by arrows. c, Dose–response curves to TAE684 (left) and immunoblot analysis of BORIS expression (right) in TAE684-sensitive and -resistant SK-N-SH neuroblastoma cells (IC50 values: sensitive, 47.9 nM; resistant, 1,739 nM). d, Dose–response curves to the CDK12 inhibitor, E9 (left) and immunoblot analysis of BORIS expression (right) in sensitive and resistant SK-N-BE(2) neuroblastoma cells (IC50 values: sensitive, 9.5 nM; resistant, 638 nM). Data are mean ± s.d., n = 3 biological replicates for c (left) and d (left). e, Immunohistochemical staining of BORIS expression in primary neuroblastoma tumour samples (representative of four independent experiments). Scale bar, 20 μm. f, Box plots showing correlation of BORIS expression with the indicated parameters in a human neuroblastoma dataset (n = 498; Tumour Neuroblastoma-SEQC-498; R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl)). Box plots are as defined in Fig. 4. P values were determined by two-sided Wilcoxon rank-sum test. g, Kaplan–Meier analysis of overall survival based on BORIS expression in the same dataset as in f (n = 498; two-sided log-rank test with Bonferroni correction). In a, c, d (immunoblots) and b, data are representative of two independent experiments. Sample sizes (n) are depicted in parenthesis for f and g (for gel source data, see Supplementary Fig. 1).

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Extended Data Fig. 5 Resistant cells are dependent on BORIS for survival.

a, Dose–response curves to TAE684 in resistant cells expressing control (shCtrl) or BORIS (shBORIS) shRNAs (IC50 values: shCtrl, 537.7 nM; shBORIS, 141.2 nM). Data are mean ± s.d., n = 3 biological replicates. b, Heat map of gene expression values in the same cells as in a (n = 2 biological replicates). Rows are z-scores calculated for each gene in both conditions. c, Immunoblot analysis of the indicated proteins in the same cells as in a. d, e, Immunoblot analysis of the indicated proteins (Cl., cleaved; CC3, cleaved caspase 3) (d), and quantification of trypan blue staining (e) in sensitive and resistant cells expressing control (shCtrl) or BORIS (shBORIS-3 and -4) shRNAs. Data are mean ± s.d., n = 3 biological replicates (*P < 0.05; **P < 0.01; ***P < 0.001; unpaired two-sided t-tests). f–h, Phase-contrast microscopy images (scale bars, 150 μm) (f), growth curves (g) and flow cytometry analyses (h) of propidium iodide (PI) staining in sensitive, intermediate and resistant cells. Data are mean ± s.d., n = 3 biological replicates (***P < 0.0001 for all comparisons; two-way ANOVA). i, qRT–PCR analysis of the expression of the indicated proneural transcription factors in the same sensitive (DMSO) versus MYCNKD and BORISInd (DOX + TAE) cells as in Fig. 1g. Data are mean ± SD, n = 3 biological replicates (*P < 0.05; **P < 0.01; unpaired two-sided t-tests). In c, d, f and h, data are representative of two independent experiments (for gel source data, see Supplementary Fig. 1).

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Extended Data Fig. 6 BORIS colocalizes with CTCF and open chromatin.

a, Bar graphs illustrating the overlap of shared and specific BORIS and CTCF-binding sites in sensitive and resistant cells. Most resistant cell-specific BORIS peaks (red) colocalize with CTCF peaks that are shared between the two cell types. The markedly lower number of BORIS peaks that are unique to sensitive cells (green) or shared between sensitive and resistant cells (grey) typically do not overlap with CTCF peaks that are shared or specific to any cell type (top). Most CTCF peaks are shared (grey) between sensitive and resistant cells and either do not overlap with BORIS peaks, or overlap only with those restricted to resistant cells (bottom). b, Comparison of CTCF and BORIS peaks identified in sensitive and resistant cells. c, Co-immunoprecipitation of BORIS with CTCF in sensitive and resistant cells (representative of two independent experiments). IgG and sample without antibody (Ab) serve as controls. d, Pie charts depicting the percentages of genomic regions bound by BORIS in sensitive (top) and resistant (bottom) cells. Numbers of BORIS-binding peaks in each cell type are given below each pie chart. The regions shown are promoters (TSS ± 2 kb), typical enhancers (H3K27ac), active enhancers (H3K27ac + BRD4), repressed chromatin (H3K27me3), exons, introns, and other (peaks not assigned to any of the previous categories). e, Meta-analysis of average CTCF and BORIS ChIP–seq signals in RPM per bp at enhancer and TSS regions in sensitive and resistant cells. f, Percentage of gene promoters bound by BORIS in sensitive (black) and resistant (red) cells for 10 equal-sized groups ordered based on absolute gene expression levels in resistant cells. Percentage of promoters bound by BORIS in resistant cells that were also originally bound by MYCN in sensitive cells is shown in grey. g, Loess regression analysis of ranked gene expression against BORIS and MYCN occupancies at gene promoters in sensitive and resistant cells. Shaded regions represent 95% confidence intervals. All panels except c depict data from n = 2 biological replicates (for gel source data, see Supplementary Fig. 1).

Extended Data Fig. 7 Regulatory loops in resistant cells are more vulnerable to BORIS depletion.

a, Heat map depicting the Spearman correlation between HiChIP biological replicates of sensitive and resistant cells in genome-wide bins of 5 kb for all merged anchor regions. b, Box plots showing the genomic length distribution (in log2(bp)) for interaction classes that are specific to resistant cells. c, Table depicting HiChIP loop class statistics in resistant cells, including their association with BORIS binding. d, ChIP-seq tracks of the indicated proteins in sensitive and resistant cells at the TCP11L2 locus (representative of two independent experiments), with resistant cell-specific regulatory interactions shown below (HiChIP Res: PET numbers, next to each interaction). Signal intensity is given in the top left corner for each track. e, Heat map depicting the Spearman correlation between HiChIP biological replicates of sensitive, resistant, shCtrl and shBORIS cells in genome-wide bins of 5 kb for all merged anchor regions. f, Bar plots showing the number and fraction of resistant cell-specific loops for all interaction classes that were BORIS negative and positive in resistant cells, and that were lost after BORIS depletion. g, Bar plots showing the odds ratio (two-sided Fisher’s exact test) of losing loops that were previously bound by BORIS for all interaction classes. h, Box plots showing the initial intensities (in normalized read counts) of BORIS and SMC1A binding in the shRNA control cells at the anchors of the resistant cell-specific loops that were significantly lost versus those that were retained in shBORIS cells (two-sided Wilcoxon rank-sum test). i, Box plot showing the difference in SMC1A loss (shBORIS versus shCtrl) on the same anchors as in h. P value determined by two-sided Wilcoxon rank-sum test. All box plots are as defined in Fig. 4. j, Metaplots depicting BORIS, SMC1A and CTCF binding at the anchors of the resistant cell-specific loops that were lost or retained after BORIS depletion. In a–c and e–g, data are from n = 3 biological replicates. In h–j, data are from n = 2 biological replicates.

Extended Data Fig. 8 Redistribution of the super-enhancer landscape with subsequent expression of a BORIS-dependent proneural network in resistant cells.

a, Accumulation of H3K27ac signal at enhancer regions. Typical enhancers (grey) are plotted according to increasing levels of normalized H3K27ac signal (length × density) in sensitive and resistant cells. The highest cut-off based on the inclination point in both sensitive and resistant cells was used to delineate super-enhancers (red). b, Scatter plot showing differential binding of H3K27ac [log2(RPM per bp + 1)] and BRD4 [log2(RPM per bp + 1)] for all detected super-enhancers in both sensitive and resistant cells. Cell-specific super-enhancers were identified based on the combined increase in H3K27ac and BRD4 binding. For each individual histone mark, a 0.75 log2-transformed fold change threshold was applied and a minimum summed 2.5 log2-transformed fold change was used as the final cut-off. c, Bar plot depicting the enrichment (two-sided Fisher’s exact test) and fractions of resistant cell-specific and shared super-enhancers that were located at resistant cell-specific regulatory loop anchors in resistant cells. d, Density plots showing the aggregated accumulation of H3K27ac and H3K27me3 at gene regions, defined as 2 kb upstream of the TSS and 2 kb downstream of the transcription end site (TES). k-means clustering (k = 3) analysis resulted in the separation of genes associated with ‘open’, ‘neutral’ or ‘closed’ chromatin in both sensitive and resistant cells. e, Sankey diagram of the distribution of genes in distinct chromatin states and the switches between sensitive and resistant cells. f, Box plots showing the expression level changes upon BORIS depletion for genes that had a resistant cell-specific and BORIS-positive regulatory interaction and were not associated with a super-enhancer (n = 720), associated with a super-enhancer in both cell types (n = 514) or associated with a super-enhancer seen only in the resistant cells (n = 134) (two-sided Wilcoxon rank-sum test). Box plots are as defined in Fig. 4. g, Heat map of the expression levels of the indicated proneural transcription factor genes during brain development (http://www.brain-map.org/). Gene expression levels are represented as z-scores for different developmental time points (n = 413; pcw, post-conceptional weeks). h, Heat map showing the odds ratios (two-sided Fisher’s exact test) for co-detection of the indicated transcription factors based on the scRNA-seq data in resistant cells (n = 6,379). i, Immunoblot analysis of the indicated proteins in sensitive and resistant cells expressing control (shCtrl) or BORIS (shBORIS-3 and -4) shRNAs. j, k, qRT–PCR analysis of the indicated genes (j) and ChIP–qPCR analysis of BORIS binding at the promoter regions of BORIS and NEUROG2 (k) in sensitive and resistant SK-N-BE(2) neuroblastoma cells. Data are mean ± s.d., n = 3 biological replicates in j and k (*P < 0.05; **P < 0.01; ***P < 0.001; unpaired two-sided t-tests). All other panels except g and h depict data from n = 2 biological replicates (for gel source data, see Supplementary Fig. 1).

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Extended Data Fig. 9 The proneural transcription factor network in resistant cells is sensitive to BRD4 inhibition.

a, Metaplots showing the correlation between BRD4 and BORIS co-occupancies at the promoter regions (± 2 kb) of the 89 top-ranked genes in resistant versus sensitive cells based on the features in Fig. 4b (r, Spearman correlation coefficient). b, Immunoblot analysis of BRD4 and cleaved PARP expression in sensitive and resistant cells expressing control (shCtrl) or BRD4 (shBRD4-A and -B) shRNAs. c, Immunoblot analysis of the indicated proteins in sensitive and resistant cells treated with DMSO, TAE684 (1 µM) or JQ1 (2.5 µM) for 48 h. d, Dose–response curves for sensitive and resistant cells treated with JQ1 or I-BET726 (JQ1 (IC50 values: sensitive, 4,798 nM; resistant, 645 nM); I-BET726 (IC50 values: sensitive, 6,203 nM; resistant, 347 nM)). Data are mean ± s.d., n = 3 biological replicates. e, Box plots comparing the expression of the transcription factors listed in Fig. 4b (n = 13) with that of all genes (n = 18,038) in sensitive versus resistant cells (left), and between DMSO and JQ1-treated resistant cells (right) (P values determined by two-sided Wilcoxon rank-sum test). f, ChIP–seq tracks of the indicated proteins at the SIX1 or SIX4 locus in sensitive, resistant and JQ1-treated resistant cells (2.5 μM for 48 h). Super-enhancers are depicted as coloured rectangles below the tracks. Signal intensity is shown in the top left corner for each track. g, h, Tumour volumes (g) and survival curves (h) in sensitive- and resistant-cell xenografts in NU/NU (Crl:NU-Foxn1nu) mice treated with JQ1 (50 mg kg−1 i.p. once daily) and vehicle control for up to 87 days. Data are mean ± s.e.m., n = 6 per arm. Significance was calculated by Mann–Whitney U test for tumour volumes (sensitive: P = 0.3231; resistant: P = 0.0023) and by log-rank test for Kaplan–Meier survival analysis (sensitive: P = 0.3047; resistant: 0.0348), both two-sided. i, Heat map of gene expression values in sensitive, resistant and JQ1-treated resistant cells. Rows are z-scores calculated for each gene in each condition. j, Number of transcripts in sensitive, JQ1-treated resistant, shBORIS-expressing resistant and resistant cells based on expression array data after spike-in normalization. k, Scatter plot displaying the median-scaled fold-change gene expression values for shBORIS and JQ1-treated resistant cells. The top-ranked transcription factors that show decreased expression levels after both BORIS knockdown and JQ1 treatment are listed in red (bottom left quadrant). The pie chart represents the fraction of all top-ranked transcription factors that are located in the left lower quadrant of the scatter plot. All box plots are as defined in Fig. 4. In b, c and f, data are representative of two independent experiments. In a, e and i–k, data are from n = 2 biological replicates (see Supplementary Note 2 for further details; for gel source data, see Supplementary Fig. 1).

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Extended Data Fig. 10 Aberrantly expressed BORIS binds to regulatory regions and is associated with new super-enhancers in Ewing sarcoma cells.

a, Immunoblot analysis of BORIS expression in TC-32 (pre-chemotherapy), TC-71 and CHLA-10 (relapsed, post-chemotherapy) Ewing sarcoma cells, compared with BORIS expression in resistant (Kelly) neuroblastoma cells. b, Meta-analysis of average BORIS ChIP–seq signals in RPM per bp at all combined BORIS-binding sites for TC-32 and TC-71 cells. c, Meta-analysis of average BORIS, H3K27ac and SMC1A ChIP–seq signals in RPM per bp at TC-71-specific BORIS-binding sites. d, Pie chart depicting the proportions of genomic regions bound by BORIS in TC-71 cells. The regions shown are promoters (TSS ± 2 kb), typical and super-enhancers (H3K27ac), and other (if peaks were not assigned to any of the previous categories). e, Bar plot showing the odds ratios (two-sided Fisher’s exact test) of BORIS localization to regulatory genomic regions in TC-71 cells. All panels are representative of two independent experiments (see Supplementary Note 3 for further details; for gel source data, see Supplementary Fig. 1).

Supplementary information

41586_2019_1472_MOESM1_ESM.pdf

Supplementary Information Supplementary Note 1 - Establishment of stable resistance to ALK inhibitors in neuroblastoma cells. Further details are provided on the establishment and confirmation of stable resistance in both in vitro and in vivo models and analysis of common causes of resistance (pertaining to Extended Data Figures 2-5). Supplementary Note 2 – Sensitivity of resistant cells to BET inhibition. Further details are provided on accumulation of BRD4 at the transcription factors that are overexpressed in resistant cells, treatment of sensitive and resistant cells with various BET inhibitors both in vitro and in vivo, and subsequent analysis of global gene expression and effects on super-enhancers (Extended Data Figure 9). Supplementary Note 3 – Role of BORIS in additional tumour models. This note contains details of generation of resistance to ALK and CDK12 inhibitors in additional neuroblastoma models as well as analysis of Ewing sarcoma cell lines derived from patients at diagnosis and at relapse, the latter with BORIS overexpression (pertaining to Extended Data Figures 4, 8 and 10).

Reporting Summary

41586_2019_1472_MOESM3_ESM.pdf

Supplementary Figure Supplementary Figure 1 - Uncropped immunoblots. Uncropped blots for Fig. 1g and Extended Data Figs. 2a, 2e, 2g (RTK Array), 2h, 3e, 3i, 4a, 4c, 4d, 5c, 5d, 6c, 8i, 9b, 9c and 10a.

Supplementary Table

Supplementary Table – Genes associated with resistant cell-specific phenotypic switch. List of genes associated with features of the phenotypic switch in resistant cells (n = 89).

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Debruyne, D.N., Dries, R., Sengupta, S. et al. BORIS promotes chromatin regulatory interactions in treatment-resistant cancer cells. Nature 572, 676–680 (2019). https://doi.org/10.1038/s41586-019-1472-0

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