Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Discovery of regulatory noncoding variants in individual cancer genomes by using cis-X

Abstract

We developed cis-X, a computational method for discovering regulatory noncoding variants in cancer by integrating whole-genome and transcriptome sequencing data from a single cancer sample. cis-X first finds aberrantly cis-activated genes that exhibit allele-specific expression accompanied by an elevated outlier expression. It then searches for causal noncoding variants that may introduce aberrant transcription factor binding motifs or enhancer hijacking by structural variations. Analysis of 13 T-lineage acute lymphoblastic leukemias identified a recurrent intronic variant predicted to cis-activate the TAL1 oncogene, a finding validated in vivo by chromatin immunoprecipitation sequencing of a patient-derived xenograft. Candidate oncogenes include the prolactin receptor PRLR activated by a focal deletion that removes a CTCF-insulated neighborhood boundary. cis-X may be applied to pediatric and adult solid tumors that are aneuploid and heterogeneous. In contrast to existing approaches, which require large sample cohorts, cis-X enables the discovery of regulatory noncoding variants in individual cancer genomes.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: cis-X workflow.
Fig. 2: Candidate cis-regulated genes identified by cis-X in 13 pediatric patients with T-ALL.
Fig. 3: Discovery and validation of a recurrent intronic noncoding mutation activating the TAL1 oncogene.
Fig. 4: A putative oncogene PRLR in T-ALL is identified by cis-X.
Fig. 5: Cis-activation of p16INK4A in melanoma.

Similar content being viewed by others

Data availability

WGS and RNA-seq data for the SCMC cohort analyzed in this study can be accessed from the Genome Sequence Archive for Human under the National Genomics Data Center of China (http://bigd.big.ac.cn/gsa-human), under accession nos. HRA000097 and HRA000096 for WGS and RNA-seq, respectively. The data are publicly available to users following a standard access application process for human genomic and associated phenotypic data. The ChIP–seq data generated in this study can be accessed from the Gene Expression Omnibus under accession nos. GSE113565 and GSE145549, for H3K27Ac and YY1, respectively, with the called peaks (in BED format) available upon request. Whole-exome sequencing and RNA-seq data for the TARGET T-ALL and NBL cohorts have been deposited in the database of Genotypes and Phenotypes (http://www.ncbi.nlm.nih.gov/gap) as part of previous projects under accession nos. phs000464 and phs000467, respectively. The WGS and RNA-seq data for the TCGA melanoma were downloaded from Genomic Data Commons data portal (https://portal.gdc.cancer.gov/legacy-archive/search/f). The complete list of somatic variant calls for the 13 T-ALLs used as the input of the cis-X analysis presented in the manuscript can be accessed from our research laboratory page at http://www.stjuderesearch.org/site/lab/zhang/cis-x. Source data are provided with this paper.

Code availability

The cis-X package, together with detailed instructions and demo data, is available at https://www.stjuderesearch.org/site/lab/zhang/cis-x, https://platform.stjude.cloud/workflows/cis-x and https://github.com/stjude/cis-x. In addition to the source code, we have provided a Dockerfile along with the package to run cis-X in a container via Docker, to minimize the difficulty of running cis-X on different computing platforms.

References

  1. Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    CAS  Google Scholar 

  2. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Khurana, E. et al. Role of non-coding sequence variants in cancer. Nat. Rev. Genet. 17, 93–108 (2016).

    CAS  PubMed  Google Scholar 

  4. Hnisz, D. et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 351, 1454–1458 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Weischenfeldt, J. et al. Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat. Genet. 49, 65–74 (2017).

    CAS  PubMed  Google Scholar 

  6. Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhang, J. et al. Deregulation of DUX4 and ERG in acute lymphoblastic leukemia. Nat. Genet. 48, 1481–1489 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang, X. et al. Identification of focally amplified lineage-specific super-enhancers in human epithelial cancers. Nat. Genet. 48, 176–182 (2016).

    CAS  PubMed  Google Scholar 

  9. Mansour, M. R. et al. Oncogene regulation. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science 346, 1373–1377 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Huang, F. W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Horn, S. et al. TERT promoter mutations in familial and sporadic melanoma. Science 339, 959–961 (2013).

    CAS  PubMed  Google Scholar 

  12. Rheinbay, E. et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature 578, 102–111 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Fredriksson, N. J., Ny, L., Nilsson, J. A. & Larsson, E. Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types. Nat. Genet. 46, 1258–1263 (2014).

    CAS  PubMed  Google Scholar 

  15. Weinhold, N., Jacobsen, A., Schultz, N., Sander, C. & Lee, W. Genome-wide analysis of noncoding regulatory mutations in cancer. Nat. Genet. 46, 1160–1165 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Melton, C., Reuter, J. A., Spacek, D. V. & Snyder, M. Recurrent somatic mutations in regulatory regions of human cancer genomes. Nat. Genet. 47, 710–716 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Kim, K. et al. Chromatin structure-based prediction of recurrent noncoding mutations in cancer. Nat. Genet. 48, 1321–1326 (2016).

    CAS  PubMed  Google Scholar 

  18. Ma, X. et al. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555, 371–376 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Belver, L. & Ferrando, A. The genetics and mechanisms of T cell acute lymphoblastic leukaemia. Nat. Rev. Cancer 16, 494–507 (2016).

    CAS  PubMed  Google Scholar 

  21. Liu, Y. et al. The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia. Nat. Genet. 49, 1211–1218 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Li, Z. et al. APOBEC signature mutation generates an oncogenic enhancer that drives LMO1 expression in T-ALL. Leukemia 31, 2057–2064 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hu, S. et al. Whole-genome noncoding sequence analysis in T-cell acute lymphoblastic leukemia identifies oncogene enhancer mutations. Blood 129, 3264–3268 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Abraham, B. J. et al. Small genomic insertions form enhancers that misregulate oncogenes. Nat. Commun. 8, 14385 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Rahman, S. et al. Activation of the LMO2 oncogene through a somatically acquired neomorphic promoter in T-cell acute lymphoblastic leukemia. Blood 129, 3221–3226 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Mayba, O. et al. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 15, 405 (2014).

    PubMed  PubMed Central  Google Scholar 

  27. Pawlikowska, I. et al. The most informative spacing test effectively discovers biologically relevant outliers or multiple modes in expression. Bioinformatics 30, 1400–1408 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Simonis, M. et al. High-resolution identification of balanced and complex chromosomal rearrangements by 4C technology. Nat. Methods 6, 837–842 (2009).

    CAS  PubMed  Google Scholar 

  29. Weintraub, A. S. et al. YY1 Is a structural regulator of enhancer-promoter loops. Cell 171, 1573–1588.e28 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Ali, S. & Ali, S. Prolactin receptor regulates Stat5 tyrosine phosphorylation and nuclear translocation by two separate pathways. J. Biol. Chem. 273, 7709–7716 (1998).

    CAS  PubMed  Google Scholar 

  31. Goffin, V. Prolactin receptor targeting in breast and prostate cancers: New insights into an old challenge. Pharmacol. Ther. 179, 111–126 (2017).

    CAS  PubMed  Google Scholar 

  32. Pugh, T. J. et al. The genetic landscape of high-risk neuroblastoma. Nat. Genet. 45, 279–284 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Peifer, M. et al. Telomerase activation by genomic rearrangements in high-risk neuroblastoma. Nature 526, 700–704 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Valentijn, L. J. et al. TERT rearrangements are frequent in neuroblastoma and identify aggressive tumors. Nat. Genet. 47, 1411–1414 (2015).

    CAS  PubMed  Google Scholar 

  35. Davis, C. F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhang, Y. et al. High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations. Nat. Commun. 11, 736 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Akbani, R. et al. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

    Google Scholar 

  38. Strub, T. et al. SIRT6 haploinsufficiency induces BRAFV600E melanoma cell resistance to MAPK inhibitors via IGF signalling. Nat. Commun. 9, 3440 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Zhou, B. et al. INO80 governs superenhancer-mediated oncogenic transcription and tumor growth in melanoma. Genes Dev. 30, 1440–1453 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Fontanals-Cirera, B. et al. Harnessing BET inhibitor sensitivity reveals AMIGO2 as a melanoma survival gene. Mol. Cell 68, 731–744.e9 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Kaufman, C. K. et al. A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation. Science 351, aad2197 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. Lin, A. W. & Lowe, S. W. Oncogenic ras activates the ARF-p53 pathway to suppress epithelial cell transformation. Proc. Natl Acad. Sci. USA 98, 5025–5030 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kamijo, T. et al. Tumor suppression at the mouse INK4a locus mediated by the alternative reading frame product p19 ARF. Cell 91, 649–659 (1997).

    CAS  PubMed  Google Scholar 

  44. Zhang, Y. et al. A cis-element within the ARF locus mediates repression of p16 INK4A expression via long-range chromatin interactions. Proc. Natl Acad. Sci. USA 116, 26644–26652 (2019).

    CAS  PubMed Central  Google Scholar 

  45. Zhang, B. & Peng, Z. Defective folding of mutant p16INK4 proteins encoded by tumor-derived alleles. J. Biol. Chem. 271, 28734–28737 (1996).

    CAS  PubMed  Google Scholar 

  46. Walker, G. J., Gabrielli, B. G., Castellano, M. & Hayward, N. K. Functional reassessment of P16 variants using a transfection-based assay. Int. J. Cancer 82, 305–312 (1999).

    CAS  PubMed  Google Scholar 

  47. Yu, M. & Ren, B. The three-dimensional organization of mammalian genomes. Annu. Rev. Cell Dev. Biol. 33, 265–289 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Monk, M. & Holding, C. Human embryonic genes re-expressed in cancer cells. Oncogene 20, 8085–8091 (2001).

    CAS  PubMed  Google Scholar 

  49. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Hidalgo, M. et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Forbes, S. A. et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777–d783 (2017).

    CAS  PubMed  Google Scholar 

  52. Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Kulakovskiy, I. V. et al. HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models. Nucleic Acids Res. 44, D116–D125 (2016).

    CAS  PubMed  Google Scholar 

  54. Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Edmonson, M. N. et al. Bambino: a variant detector and alignment viewer for next-generation sequencing data in the SAM/BAM format. Bioinformatics 27, 865–866 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Chen, X. et al. CONSERTING: integrating copy-number analysis with structural-variation detection. Nat. Methods 12, 527–530 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, J. et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nat. Methods 8, 652–654 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. MacDonald, J. R., Ziman, R., Yuen, R. K., Feuk, L. & Scherer, S. W. The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res. 42, D986–D992 (2014).

    CAS  PubMed  Google Scholar 

  60. Geoffroy, V. et al. AnnotSV: an integrated tool for structural variations annotation. Bioinformatics 34, 3572–3574 (2018).

    CAS  PubMed  Google Scholar 

  61. Parker, M. et al. C11orf95-RELA fusions drive oncogenic NF-κB signalling in ependymoma. Nature 506, 451–455 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Anders, S., Pyl, P. T. & Huber, W. HTSeq: a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  Google Scholar 

  63. Zhang, X.-L. et al. Integrative epigenomic analysis reveals unique epigenetic signatures involved in unipotency of mouse female germline stem cells. Genome Biol. 17, 162 (2016).

    PubMed  PubMed Central  Google Scholar 

  64. Kharchenko, P. V., Tolstorukov, M. Y. & Park, P. J. Design and analysis of ChIP–seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Zhang, Y. et al. Model-based analysis of ChIP–Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  66. Cheng, Y. et al. Principles of regulatory information conservation between mouse and human. Nature 515, 371–375 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was funded in part by National Institutes of Health grant nos. 1R35 CA210064-01 (to A.T.L.) and 1R01 CA216391-01A1 (to J.Z.), and Cancer Center Support Grant no. P30 CA021765 from the National Cancer Institute and the American Lebanese Syrian Associated Charities of St. Jude Children’s Research Hospital. We thank B. Abraham, M. Zimmerman, A. Durbin, D. Wheeler and D. Flasch for critically reviewing the manuscript, and C. Sherr for providing the literature relevant to p16 activation.

Author information

Authors and Affiliations

Authors

Contributions

Yu Liu and J.Z. designed the cis-X software. Yu Liu, M.N.E. and M.M. implemented the software. Yu Liu and J.Z. analyzed the data with help from X.C., M.N.E., K.S., X.M., Yanling Liu and M.C.R. Yu Liu, C.L., S.S. and J.Z. designed the experiments. C.L., S.S., Y.S., J.H., S.W., B.J., B.L. and J.E. performed the experiments. M.M., X.C. and L.T. tested the cis-X software. M.Q., J.J.Y. and S.H. provided independent cohort validation. Yu Liu, A.T.L. and J.Z. wrote the manuscript.

Corresponding authors

Correspondence to Yu Liu or Jinghui Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Transcription imbalance modeling.

Cumulative distribution of transcription imbalance under binomial transcription model (dotted line), beta-binomial model as implemented in MBASED (solid line), balanced transcription model (dashed line) and experimentally observed data (dots). Different RNA-seq coverages (N = 10, 50 and 100) are shown separately.

Extended Data Fig. 2 Simulation analysis of allele-specific expression detection in cis-X.

Each panel represents a simulation of allelic imbalance ranging from 1:1 (no allele-specific expression) to 10000:1 (complete mono-allelic expression). Percentage of simulations identified as allele-specific expression from a group of 2,000 simulations are shown on y-axis, with plots on each panel representing simulation results with different imbalanced transcription ratio between two alleles. The imbalanced ratio of 1:1 represents the false positive rate was showed on the top, while plots in the other lines represent false negative rates of detecting transcription imbalance at various allelic ratio. Coverage for the markers in RNA-seq is shown on the x-axis. Each column, labeled by a distinct color, represents a distinct ploidy group (that is copy number alterations), while shape of each plot represents the number of markers within a gene for assessing allele-specific expression.

Extended Data Fig. 3

Workflow for constructing the gene-specific reference expression matrix.

Extended Data Fig. 4 LMO3 activation in T-ALL.

(a) Allele specific expression of LMO3 in T-ALL SJTALL013797_D1. Eight heterozygous variants are present in LMO3 locus in this tumor, with the B-allele fractions from WGS and RNA-seq plotted on the top of the wiggle plot. (b) Outlier high expression of LMO3 was observed in this sample compared to the NCI TARGET T-ALL cohort (n = 264 samples). (c) Gene expression based clustering of the combined cohort of 13 SCMC T-ALLs and 264 NCI TARGET T-ALLs showed that SJTALL013797_D1 is clustered with other T-ALLs driven by TAL/LMO activation. The same genes from the previous study (Liu et al. Nature Genetics, 2017) were used in clustering the combined cohort. Colors on the top track represent different T-ALL subtypes.

Extended Data Fig. 5 Somatically acquired noncoding mutation activating TAL1 in T-ALL sample SJALL018373.

(a) The heterozygous C to T mutation (indicated by arrow, with mutant allele T shown in red) was only present in the tumor DNA but not in the remission sample from whole genome sequencing data. (b) H3K27Ac profile from ChIP-seq at TAL1 locus. The active enhancer present in the mutation positive PDX sample (as shown in Fig. 3d) was absent from normal T cells (CD3, CD4 and CD8) or from the T-ALL cell line (LOUCY) with no TAL1 expression.

Extended Data Fig. 6 Activating deletion upstream PRLR.

Expression (FPKM on y-axis) of SPEF2 (a) and IL7R (b) in the T-ALLs. The 3 tumors carrying the focal deletions (SJALL043558_D1, PATFYZ, and PATRUN) are labeled. (c) H3K27Ac profiles from ChIP-seq show active enhancer upstream of IL7R in the PDX (derived from patient SJALL018373) and a T-ALL cell line (KOPT-K1) having high IL7R transcription; both samples have the wild-type allele at this locus.

Extended Data Fig. 7 Analysis of pediatric neuroblastoma with cis-X.

(a) Copy number variations identified in the four neuroblastoma cell lines. The blue and red colors represent the deletion and amplifications, respectively, identified in these cell lines. (b) Circos plot showing the cis-activating structural rearrangements identified in NBL cell lines by cis-X. The copy number alterations in each genome are shown in the inner track, with blue lines representing a copy number of 1 and red a copy number of three. The cis-activating structural variants are shown as links in the middle of the plot, with purple links representing inter-chromosome translocations and green for intra-chromosome translocations. The target genes activated by these rearrangements are labeled on the outer track of each plot.

Extended Data Fig. 8 TERT cis-activation by somatic non-coding variants in neuroblastoma.

The analysis was based on 90 NBL primary tumor samples with matching RNA-seq and WGS from TARGET, 42 of which had positive immune cell infiltration signature based on prior analysis (Ma et al, Nature, 2018). (a) Samples with somatic copy number alterations (CNA, marked by red or blue blocks) or/and structural variations (SVs, marked by circles) at TERT locus. All except for one (PARAMT, marked #) were detected by cis-X as cis-activated candidates. Samples marked with * have immune cell infiltration signature. Samples highlighted in gray are used to illustrate allele-specific expression (ASE) below. (b) Examples of ASE detected in neuroblastoma with or without infiltrating immune cells. Variant allele fraction in DNA (by WGS) and RNA (by RNA-seq) of SNPs, depicted as bar graph, demonstrates that ASE analysis is not affected by the presence of immune cell infiltration signature in tumor samples.

Extended Data Fig. 9 TERT expression in melanoma and neuroblastoma.

TERT expression in adult TCGA melanoma (MEL) samples (n = 38), pediatric neuroblastoma (NBL) patient samples from TARGET project (n = 90) and cell lines (n = 4) analyzed in this study. The MEL samples were color-coded by TERT promoter mutation status while the NBL samples were marked by the status of cis-activation, infiltrating immune cells and cell-lines as depicted in figure legend.

Source data

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1 and 2

Reporting Summary

Supplementary Tables

Supplementary Tables 1–9

Source data

Source Data Fig. 5

Statistical Source Data

Source Data Extended Data Fig. 9

Statistical Source Data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Li, C., Shen, S. et al. Discovery of regulatory noncoding variants in individual cancer genomes by using cis-X. Nat Genet 52, 811–818 (2020). https://doi.org/10.1038/s41588-020-0659-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-020-0659-5

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer