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


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.

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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.

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 (, 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 ( 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 ( 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 Source data are provided with this paper.

Code availability

The cis-X package, together with detailed instructions and demo data, is available at, and 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.


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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




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.

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The authors declare no competing interests.

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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.

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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).

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