Chordoma is a primary bone cancer with no approved therapy1. The identification of therapeutic targets in this disease has been challenging due to the infrequent occurrence of clinically actionable somatic mutations in chordoma tumors2,3. Here we describe the discovery of therapeutically targetable chordoma dependencies via genome-scale CRISPR-Cas9 screening and focused small-molecule sensitivity profiling. These systematic approaches reveal that the developmental transcription factor T (brachyury; TBXT) is the top selectively essential gene in chordoma, and that transcriptional cyclin-dependent kinase (CDK) inhibitors targeting CDK7/12/13 and CDK9 potently suppress chordoma cell proliferation. In other cancer types, transcriptional CDK inhibitors have been observed to downregulate highly expressed, enhancer-associated oncogenic transcription factors4,5. In chordoma, we find that T is associated with a 1.5-Mb region containing ‘super-enhancers’ and is the most highly expressed super-enhancer-associated transcription factor. Notably, transcriptional CDK inhibition leads to preferential and concentration-dependent downregulation of cellular brachyury protein levels in all models tested. In vivo, CDK7/12/13-inhibitor treatment substantially reduces tumor growth. Together, these data demonstrate small-molecule targeting of brachyury transcription factor addiction in chordoma, identify a mechanism of T gene regulation that underlies this therapeutic strategy, and provide a blueprint for applying systematic genetic and chemical screening approaches to discover vulnerabilities in genomically quiet cancers.
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Computational code used for ChIP-seq analysis can be found at github.com/linlabcode/chordoma_code. Code for CRISPR-Cas9 screening analysis is available on GParc (www.gparc.org). Code for small-molecule sensitivity profiling analysis and DNA and RNA-sequencing analysis are available upon reasonable request.
CRISPR-Cas9 screening data for two chordoma cell lines (pertains to Figs. 1, 2, 4 and Extended Data Fig. 1) are available at Figshare (https://doi.org/10.6084/m9.figshare.7302515). CRISPR-Cas9 screening data for all other cancer cell lines (pertains to Figs. 1, 2, 4 and Extended Data Fig. 1) were generated as part of Project Achilles (Broad Institute Project Achilles; https://depmap.org/portal/achilles/). All RNA-sequencing data (pertains to Figs. 1 and 4) are available at Gene Expression Omnibus (GEO) (accession number: GSE121846). Small-molecule sensitivity data generated using non-chordoma cell lines and used for comparative analyses (pertains to Fig. 2) are available at the National Cancer Institute’s CTD2 Data Portal (https://ocg.cancer.gov/programs/ctd2/data-portal) and the CTRP (www.broadinstitute.org/ctrp/). The analysis of new small-molecule primary screening data generated using chordoma cell lines (pertains to Fig. 2) was performed as described previously22, except as noted in the Methods, and the resulting AUC values are provided in Supplementary Table 2. Raw plate-reader data files and accompanying Pipeline Pilot and MATLAB scripts for small-molecule primary screening and low-throughput compound sensitivity analysis (pertains to Fig. 2 and Extended Data Figs. 2a, 3a, 8c and 10a) are available upon reasonable request. Chromatin profiling data (pertains to Figs. 3 and 4 and Extended Data Figs. 4 and 6) are available at GEO (accession number: GSE109794).
Stacchiotti, S., Sommer, J. & Chordoma Global Consensus Group. Building a global consensus approach to chordoma: a position paper from the medical and patient community. Lancet. Oncol. 16, e71–e83 (2015).
Choy, E. et al. Genotyping cancer-associated genes in chordoma identifies mutations in oncogenes and areas of chromosomal loss involving CDKN2A, PTEN, and SMARCB1. PLoS ONE 9, e101283 (2014).
Tarpey, P. S. et al. The driver landscape of sporadic chordoma. Nat. Commun. 8, 890 (2017).
Kwiatkowski, N. et al. Targeting transcription regulation in cancer with a covalent CDK7 inhibitor. Nature 511, 616–620 (2014).
Bradner, J. E., Hnisz, D. & Young, R. A. Transcriptional addiction in cancer. Cell. 168, 629–643 (2017).
McMaster, M. L., Goldstein, A. M., Bromley, C. M., Ishibe, N. & Parry, D. M. Chordoma: incidence and survival patterns in the United States, 1973-1995. Cancer Causes Control 12, 1–11 (2001).
Barry, J. J. et al. The next step: innovative molecular targeted therapies for treatment of intracranial chordoma patients. Neurosurgery 68, 231–240 (2011). discussion 240–231.
Salisbury, J. R. The pathology of the human notochord. J. Pathol. 171, 253–255 (1993).
Vujovic, S. et al. Brachyury, a crucial regulator of notochordal development, is a novel biomarker for chordomas. J. Pathol. 209, 157–165 (2006).
Showell, C., Binder, O. & Conlon, F. L. T-box genes in early embryogenesis. Dev. Dyn. 229, 201–218 (2004).
Yang, X. R. et al. T (brachyury) gene duplication confers major susceptibility to familial chordoma. Nat. Genet. 41, 1176–1178 (2009).
Pillay, N. et al. A common single-nucleotide variant in T is strongly associated with chordoma. Nat. Genet. 44, 1185–1187 (2012).
Presneau, N. et al. Role of the transcription factor T (brachyury) in the pathogenesis of sporadic chordoma: a genetic and functional-based study. J. Pathol. 223, 327–335 (2011).
Hsu, W. et al. Generation of chordoma cell line JHC7 and the identification of Brachyury as a novel molecular target. J. Neurosurg. 115, 760–769 (2011).
Shah, S. R. et al. Brachyury-YAP regulatory axis drives stemness and growth in cancer. Cell Rep. 21, 495–507 (2017).
Palena, C. et al. The human T-box mesodermal transcription factor Brachyury is a candidate target for T-cell-mediated cancer immunotherapy. Clin. Cancer Res. 13, 2471–2478 (2007).
Garraway, L. A. & Sellers, W. R. Lineage dependency and lineage-survival oncogenes in human cancer. Nat. Rev. Cancer 6, 593–602 (2006).
Berg, T. Inhibition of transcription factors with small organic molecules. Curr. Opin. Chem. Biol. 12, 464–471 (2008).
Sharifnia, T., Hong, A. L., Painter, C. A. & Boehm, J. S. Emerging opportunities for target discovery in rare cancers. Cell Chem. Biol. 24, 1075–1091 (2017).
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
Barrionuevo, F., Taketo, M. M., Scherer, G. & Kispert, A. Sox9 is required for notochord maintenance in mice. Dev. Biol. 295, 128–140 (2006).
Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5, 1210–1223 (2015).
Whittaker, S. R., Mallinger, A., Workman, P. & Clarke, P. A. Inhibitors of cyclin-dependent kinases as cancer therapeutics. Pharmacol. Ther. 173, 83–105 (2017).
Zhang, T. et al. Covalent targeting of remote cysteine residues to develop CDK12 and CDK13 inhibitors. Nat. Chem. Biol. 12, 876–884 (2016).
Galli, G. G. et al. YAP drives growth by controlling transcriptional pause release from dynamic enhancers. Mol. Cell 60, 328–337 (2015).
Modjtahedi, H., Cho, B. C., Michel, M. C. & Solca, F. A comprehensive review of the preclinical efficacy profile of the ErbB family blocker afatinib in cancer. Naunyn Schmiedebergs Arch. Pharmacol. 387, 505–521 (2014).
Scheipl, S. et al. EGFR inhibitors identified as a potential treatment for chordoma in a focused compound screen. J. Pathol. 239, 320–334 (2016).
Tripathy, D., Bardia, A. & Sellers, W. R. Ribociclib (LEE011): mechanism of action and clinical impact of this selective cyclin-dependent kinase 4/6 inhibitor in various solid tumors. Clin. Cancer Res. 23, 3251–3262 (2017).
Dale, T. et al. A selective chemical probe for exploring the role of CDK8 and CDK19 in human disease. Nat. Chem. Biol. 11, 973–980 (2015).
Johannessen, L. et al. Small-molecule studies identify CDK8 as a regulator of IL-10 in myeloid cells. Nat. Chem. Biol. 13, 1102–1108 (2017).
Loven, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
Nelson, A. C. et al. An integrated functional genomics approach identifies the regulatory network directed by brachyury (T) in chordoma. J. Pathol. 228, 274–285 (2012).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Lin, C. Y. et al. Active medulloblastoma enhancers reveal subgroup-specific cellular origins. Nature 530, 57–62 (2016).
Schwab, J. H. et al. Chordoma and chondrosarcoma gene profile: implications for immunotherapy. Cancer Immunol. Immunother. 58, 339–349 (2009).
Jager, D. et al. HOXA7, HOXA9, and HOXA10 are differentially expressed in clival and sacral chordomas. Sci. Rep. 7, 2032 (2017).
Shalaby, A. et al. The role of epidermal growth factor receptor in chordoma pathogenesis: a potential therapeutic target. J. Pathol. 223, 336–346 (2011).
Trask, D. K. & Muller, M. T. Stabilization of type I topoisomerase-DNA covalent complexes by actinomycin D. Proc. Natl Acad. Sci. USA 85, 1417–1421 (1988).
Liu, X. et al. Establishment and characterization of a novel chordoma cell line: CH22. J. Orthop. Res. 30, 1666–1673 (2012).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).
Cowley, G. S. et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 1, 140035 (2014).
Aguirre, A. J. et al. Genomic copy number dictates a ene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov. 6, 914–929 (2016).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Dancik, V. et al. Connecting small molecules with similar assay performance profiles leads to new biological hypotheses. J. Biomol. Screen. 19, 771–781 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome. Biol. 15, 550 (2014).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 10 11–10 33 (2013).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome. Biol. 9, R137 (2008).
Brown, J. D. et al. NF-κB directs dynamic super enhancer formation in inflammation and atherogenesis. Mol. Cell 56, 219–231 (2014).
Johannessen, C. M. et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 468, 968–972 (2010).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
The authors thank A. Vrcic, K. Hartland, S. Figueroa-Lazu, C. Biasetti and J. Santos for assistance with small-molecule sensitivity experiments; members of the Broad Institute Genetic Perturbation Platform for sgRNA library construction and technical assistance with DNA processing after pooled screening; the Histology Core Facility at Ohio State University for immunohistochemistry studies; X. Zhang for guidance with RNA sequencing; L. Gechijian and P. Veeraraghavan for technical and analytical assistance; R. Meyers and A. Cherniack for guidance with copy-number analysis and R. Belizaire, Y. Zou, N. Kwiatkowski, J. Dempster, S. Gill and T. Sundberg for helpful discussions. This work was generously supported by the Chordoma Foundation, the Roye family, the Fuchs family and the NCI’s Cancer Target Discovery and Development (CTD2) Network (grant number U01CA176152/U01CA217848 awarded to S.L.S. and U01CA176058 awarded to W.C.H.). In addition to being supported by the Chordoma Foundation, C.Y.L is supported by the Cancer Prevention Research Institute of Texas (RR150093) and by the NIH and NCI (1R01CA215452-01), and is a Pew-Stewart Scholar for Cancer Research (Alexander and Margaret Stewart Trust). The Institute of Cancer Research authors were funded by Cancer Research UK (Program Grant number C2739/A22897). P.W. is a Cancer Research UK Life Fellow. S.L.S. is an investigator at the Howard Hughes Medical Institute. This paper is dedicated to the memory of Todd Fuchs, whose spirit and perseverance were an inspiration for all involved.
T.S. is a consultant for Jnana Therapeutics. N.S.G. is equity holder and scientific advisor for Syros, Gatekeeper, Soltego, C4, Petra, and Aduro companies. Syros has licensed intellectual property from Dana-Farber Cancer Institute covering THZ1. P.A. Clarke, J.B., and P.W. are current employees of The Institute of Cancer Research, which has a Rewards to Inventors scheme and has a commercial interest in the development of inhibitors of the WNT pathway, CDK8/19, and other CDKs, with intellectual property licensed to Merck and Cyclacel Pharmaceuticals. P.W. is a consultant for Astex Pharmaceuticals, CV6 Therapeutics, Nextechinvest, Nuevolution, and Storm Therapeutics and holds equity in Chroma Therapeutics, Nextech, and Storm. D.E.R. receives research funding from members of the Functional Genomics Consortium (Abbvie, Jannsen, Merck, Vir), and is a director of Addgene, Inc. W.C.H. is a consultant for Thermo Fisher, Paraxel, AjuIB, MPM Capital, and KSQ Therapeutics, a founder of KSQ Therapeutics, and receives research support from Deerfield. J.E.B. is now an executive and shareholder of Novartis AG, and has been a founder and shareholder of SHAPE (acquired by Medivir), Acetylon (acquired by Celgene), Tensha (acquired by Roche), Syros, Regency, and C4 Therapeutics. K.K.W. is a founder and equity holder of G1 Therapeutics and he has consulting/sponsored research agreements with AstraZeneca, Janssen, Pfizer, Array, Novartis, Merck, Takeda, Ono, Targimmune, and BMS. C.Y.L. is a consultant for Jnana Therapeutics and is a shareholder of and inventor of intellectual property licensed to Syros Pharmaceuticals. J.D.K. is a founder, executive and shareholder of Jnana Therapeutics. S.L.S. is a member of the Board of Directors of the Genomics Institute of the Novartis Research Foundation (GNF); a shareholder and member of the Board of Directors of Jnana Therapeutics; a shareholder of Forma Therapeutics; a shareholder of and adviser to Decibel Therapeutics; an adviser to Eisai, Inc., the Ono Pharma Foundation, and F-Prime Capital Partners; and a Novartis Faculty Scholar. All other authors declare no competing interests.
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Viability after sgRNA treatment (represented by sgRNA-dependency scores; see Methods) corresponding to each of the primary screening sgRNAs targeting either RPS11 or RPS19 across 127 cancer cell lines (CCLs), including 2 chordoma CCLs (blue circles). For a given sgRNA, the median sgRNA-dependency score across 127 cell lines is indicated (gray line). Lower values indicate greater sgRNA depletion and thus essentiality of the target gene (shaded region).
Extended Data Fig. 2 Sensitivity of chordoma cells to EGFR and/or ERBB2 inhibitors and of non-chordoma cells to CDK9 inhibitors.
a, Validation of primary screening hit compounds targeting EGFR and/or ERBB2. Four chordoma cell lines were treated with indicated concentrations of candidate antiproliferative compounds and assayed for cell viability after 6 d with CellTiter-Glo. Response data are represented by a fitted curve to the mean fractional viability at each concentration relative to vehicle-treated cells; error bars represent the s.e.m. (n = 4 biological samples measured in parallel). b, Dinaciclib and alvocidib have antiproliferative effects across a wide range of cancer cell lines. AUC values corresponding to cell lines in CTRP treated with either dinaciclib or alvocidib. Each point represents a cancer cell line in CTRP treated with the indicated compound. Boxplots depict the inner quartiles (boxes) and median value (horizontal line) with whiskers representing 1.5 × the interquartile range of 445 (dinaciclib-treated) or 440 (alvocidib-treated) cell lines. AUCs were computed as described in the Methods and at https://github.com/remontoire-pac/ctrp-reference/tree/master/auc.
a, Response of chordoma cells to compounds targeting CDK4/6 and CDK8/19 proteins. Four chordoma cell lines were treated with indicated concentrations of compounds and assayed for cell viability after 6 d with CellTiter-Glo. Response data are represented by a fitted curve to the mean fractional viability at each concentration relative to vehicle-treated cells; error bars represent the s.e.m. (n = 4 biological samples measured in parallel). b, Immunoblot analysis of UM-Chor1 cells treated with indicated concentrations of inhibitors or DMSO for 24 h. The experiment was performed twice for CCT251545 (one representative experiment displayed) and once for other compounds. Source Data
a, Enhancers in five chordoma cell lines ranked by H3K27ac signal in each sample. Enhancers proximal (within 100 kb) to the T gene start site are annotated, as described in the figure. Super-enhancers (SEs) were determined by the inflection point of the plot. b, Table showing the ranks of top T-associated enhancers in each chordoma sample.
a, Immunoblot analysis of chordoma and chondrosarcoma cell lines. Chordoma cell lines selectively express high levels of the brachyury protein. The experiment was performed once. b, Expression of T and MAX, as measured by RNA-sequencing, across 935 non-chordoma cancer cell lines derived from diverse tumor types. Data were generated as part of the Broad Institute Cancer Cell Line Encyclopedia (quantified data obtained from: https://ocg.cancer.gov/ctd2-data-project/translational-genomics-research-institute-quantified-cancer-cell-line-encyclopedia). Boxplots depict the inner quartiles (boxes) and median value (horizontal line) with whiskers representing 1.5 × the interquartile range. c, Gene-expression levels of 115 super-enhancer- (SE-) associated transcription factors in five chordoma cell lines (points), ranked by mean expression (horizontal ticks). d, T is amplified in the JHC7 chordoma cell line. Genomic copy-number alterations, inferred from whole-exome sequencing data, in five chordoma cell lines. A region of 2.06 Mb around the T locus on chromosome 6 shows 26-fold amplification in JHC7. This finding is consistent with the 2.6-Mb amplicon inferred from ChIP-seq whole-cell extract. Source Data
a, Enhancers in chordoma tumors ranked by H3K27ac signal in each sample. Super-enhancers (SEs, red) and typical enhancers (black) proximal (within 100 kb) to the T gene start site are annotated. Super-enhancers were determined by the inflection point of the plot. b, Table showing the ranks of top T-associated super-enhancers or typical enhancers in each chordoma sample.
Immunohistochemical staining of patient-derived chordoma tumors for brachyury expression. H&E, hematoxylin and eosin. The experiment was performed once.
Extended Data Fig. 8 THZ1 and actinomycin D reduce expression of T (brachyury) in a concentration- and time-dependent fashion.
a, Immunoblot analysis of UM-Chor1 cells treated with indicated concentrations of THZ1 or DMSO for 12, 24, 36 or 48 h. The experiment was performed once. b, UM-Chor1 cells were treated with indicated concentrations of compound or DMSO for 4, 8 or 12 h and subjected to RT–qPCR. Data are expressed as the log2 fold change of transcript levels relative to vehicle-treated cells, normalized to GAPDH levels, and represent the mean ± s.d. (n = 3 biological samples measured in parallel). Results of statistical analyses of RT–qPCR data, derived from a one-sided Welch’s t-test, are reported in Supplementary Table 9. c, Response of chordoma cells to treatment with actinomycin D. Four chordoma cell lines were treated with indicated concentrations of compound and assayed for cell viability after 6 d with CellTiter-Glo. Response data are represented by a fitted curve to the mean fractional viability at each concentration relative to vehicle-treated cells; error bars represent the s.e.m. (n = 4 biological samples measured in parallel). Source Data
Extended Data Fig. 9 Expression of ATP6V1B2, SAE1, SOX9 and TPX2 is downregulated following sgRNA-mediated T (brachyury) repression.
a, UM-Chor1 cells were transduced with sgRNAs targeting T or a non-targeting sgRNA control and subjected to RT–qPCR. Data are expressed as the fold-change of transcript levels relative to sgRNA control-treated cells, normalized to GAPDH levels and represent the mean (n = 2 biological samples measured in parallel, represented by black points). * P < 0.05; ** P < 0.01; *** P < 0.001; P values were derived from a one-sided Welch’s t-test. Exact P values and effect sizes are reported in Supplementary Table 9. b, Immunoblot analysis of UM-Chor1 cells transduced with sgRNAs targeting T or a non-targeting sgRNA control. SgRNA treatment was performed once and immunoblotting was performed twice (one representative experiment displayed). Source Data
Extended Data Fig. 10 THZ1 treatment can reduce brachyury expression in CH22 cells ex vivo and in vivo.
a, CH22 chordoma cells were treated with indicated concentrations of transcriptional CDK inhibitors and assayed for cell viability after 6 d with CellTiter-Glo. Response data are represented by a fitted curve to the mean fractional viability at each concentration relative to vehicle-treated cells; error bars represent the s.e.m. (n = 4 biological samples measured in parallel). b, Immunoblot analysis of CH22 cells treated with indicated concentrations of inhibitors targeting CDK4/6 (palbociclib), CDK7/12/13 (THZ1) or CDK9 (dinaciclib, NVP-2, alvocidib) or DMSO for 48 h. The experiment was performed once. c, Weight change of mice treated with THZ1 or vehicle for the study depicted in Fig. 4h,i. d, THZ1 can downregulate brachyury expression in vivo. Immunoblot analysis of CH22 xenograft tumors following treatment with indicated doses of THZ1 or vehicle twice daily for 5 d. The experiment was performed once. e, Immunoblot analysis of CH22 xenograft tumors following treatment with THZ1 or vehicle twice daily for 3 d. Top and bottom panels represent two independent studies (bottom panel corresponds to the study depicted in Fig. 4h,i). Source Data
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Sharifnia, T., Wawer, M.J., Chen, T. et al. Small-molecule targeting of brachyury transcription factor addiction in chordoma. Nat Med 25, 292–300 (2019). https://doi.org/10.1038/s41591-018-0312-3
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