Cancers are caused by genomic alterations known as drivers. Hundreds of drivers in coding genes are known but, to date, only a handful of noncoding drivers have been discovered—despite intensive searching1,2. Attention has recently shifted to the role of altered RNA splicing in cancer; driver mutations that lead to transcriptome-wide aberrant splicing have been identified in multiple types of cancer, although these mutations have only been found in protein-coding splicing factors such as splicing factor 3b subunit 1 (SF3B1)3,4,5,6. By contrast, cancer-related alterations in the noncoding component of the spliceosome—a series of small nuclear RNAs (snRNAs)—have barely been studied, owing to the combined challenges of characterizing noncoding cancer drivers and the repetitive nature of snRNA genes1,7,8. Here we report a highly recurrent A>C somatic mutation at the third base of U1 snRNA in several types of tumour. The primary function of U1 snRNA is to recognize the 5′ splice site via base-pairing. This mutation changes the preferential A–U base-pairing between U1 snRNA and the 5′ splice site to C–G base-pairing, and thus creates novel splice junctions and alters the splicing pattern of multiple genes—including known drivers of cancer. Clinically, the A>C mutation is associated with heavy alcohol use in patients with hepatocellular carcinoma, and with the aggressive subtype of chronic lymphocytic leukaemia with unmutated immunoglobulin heavy-chain variable regions. The mutation in U1 snRNA also independently confers an adverse prognosis to patients with chronic lymphocytic leukaemia. Our study demonstrates a noncoding driver in spliceosomal RNAs, reveals a mechanism of aberrant splicing in cancer and may represent a new target for treatment. Our findings also suggest that driver discovery should be extended to a wider range of genomic regions.
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All published computational programs used in this study are indicated in corresponding sections. Scripts used to perform U1 snRNA mutational calling are available at https://github.com/smshuai/U1-snRNA.
PCAWG data are available at ICGC DCC (https://docs.icgc.org/pcawg/data/; donor identifiers in Supplementary Table 1). Additional CLL data (donor identifiers in Supplementary Table 3) are available at ICGC DCC (https://dcc.icgc.org/releases/release_27/Projects/CLLE-ES) and EGA (raw data under accession numbers EGAS00001000374 and EGAS00001001306). Additional HCC data are available at GDC Data Portal (raw and processed data under project code TCGA-LIHC; donor identifiers in Supplementary Table 3). CLL cell line RNA-seq data are available at GSE134197.
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The authors acknowledge the use of pre-embargo whole-genome sequencing alignment data from the PCAWG project, approved by the PCAWG Steering Committee (with L.D.S. recused). This work was supported by the Government of Ontario (S.S. and L.D.S.), the Instituto de Salud Carlos III (project PMP15/00007; to F.N., M.P., J.D., X.S.P., C.L.-O. and E.C.), the ‘la Caixa’ Foundation Grant No HR17-00221 (Health Research 2017 Program; to F.N., M.P., J.D., X.S.P., C.L.-O. and E.C.) and the Ministerio de Economía y Competitividad (MINECO) SAF2013-45836-R (to X.S.P., A.D.-N. and A.G.-F.). A.D.-N. is supported by the Department of Education of the Basque Government (grant number PRE_2017_1_0100). E.C. is supported by ICREA under the ICREA Academia programme. F.N. is supported by a pre-doctoral fellowship of the Ministerio de Economía y Competitividad (MINECO, BES-2016-076372). H.S. is a recipient of a Research Fellowship (Astellas Foundation for Research on Metabolic Disorders).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Peer review information Nature thanks Rotem Karni, Brandon Wainwright and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
a, Computational pipeline used to identify somatic mutations in U1 genes from whole-genome sequencing data. b, Computational pipeline used to infer the U1 g.3A>C status on the basis of splicing patterns. c, Computational pipeline used to infer the U1 g.3A>C status on the basis of expression patterns. d, e, U1 g.3A>C status for 318 CLL (d) and 613 HCC samples (e). Consensus genotypes are used in downstream analysis. One hundred and three undetermined HCC samples are excluded from downstream analysis. Grey indicates data not available.
a, Category of mis-spliced introns. Black boxes indicate exons and lines indicate introns; red lines indicate actual splice junctions. A junction is described as ‘annotated’ if the junction matches any annotated introns. A junction is described as ‘novel annotated’ if both splice sites are annotated but not paired. A cryptic 5′ splice site junction exists if only the 3′ splice site is annotated. A cryptic 3′ splice site junction exists if only the 5′ splice site is annotated. b, Euler plots of mis-spliced intron clusters in CLL and HCC. Increased excision and decreased excision represent intron clusters that have significantly mis-spliced introns with ΔPSI > 0 and ΔPSI < 0, respectively. c, 5′ splice site for introns with increased (n = 239) or decreased excision (n = 294) in HCC with U1 mutation. Top, bar plot shows Benjamini–Hochberg adjusted P values from χ2 tests; red line indicates q = 0.1. d, Category of mis-splicing events in HCC with U1 mutation. e, P value quantile–quantile plots for differential expression analysis. Global gene-expression changes cannot be detected in the randomized dataset. P values are from limma. Biologically independent patient samples are used for CLL (11 cases with U1 mutation versus 254 cases with wild-type U1) and HCC (20 cases with U1 mutation versus 367 cases with wild-type U1). f, g, t-SNE plots showing the accuracy of transcriptome-based inferences. f, t-SNE coordinates are computed from differentially spliced introns, and predictions are made with the splicing-based classifier. g, t-SNE coordinates are computed from differentially expressed genes, and predictions are made with the expression-based classifier.
Extended Data Fig. 3 Premature termination codon-bearing mis-spliced isoforms for MSI2, POLD1 and ABCD3 in CLL with U1 mutation.
a, Coding sequence of MSI2 (ENST00000284073.2), POLD1 (ENST00000440232.2) and ABCD3 (ENST00000370214.4) in CLL. Annotated exons are numbered. Positions 1 to 3 indicate the start codon. The first stop codon in each cryptic exon is labelled as the premature termination codon (PTC). The last exon junction complex (EJC) is also labelled. The distance between premature termination codon and the last exon junction complex is >55 nt in all isoforms with cryptic exons. b, Expression box plots for three of the transcripts that bear premature termination codon, shown in a. P values are from two-sided Wilcoxon rank-sum tests. MUT (n = 6) and WT (n = 61) represent independent CLL samples with or without the g.3A>C mutation, respectively. In the box plot, centre line, box limits, whiskers and points represent the median, 25th and 75th percentiles, 1.5× interquartile range and individual samples, respectively. c, Sashimi plot for mis-splicing of ABCD3. This sashimi plot is the same as those in Fig. 3. d, Splice score of ABCD3 across all CLL samples. The ABCD3 splice score is calculated from RNA-seq data (Methods). e, Using the ABCD3 splice score as a marker for U1 mutation in CLL. The ABCD3 splice score can be used to predict the U1 g.3A>C status with 100% accuracy in CLL.
a, Junctions used in RT–qPCR validations. For MSI2, POLD1 and ABCD3, one annotated junction and one cryptic junction are used. For CD44, one CD44s junction and one CD44v junction are used. Cryptic and CD44v junctions should be over-excised in samples with U1 mutation. b, RT–PCR result for junctions shown in a. MUT (n = 6) and WT (n = 8) represent independent CLL samples with and without the g.3A>C mutation, respectively. GAPDH is used as control. The length of all PCR products is shown on the right. The experiment was conducted once. For gel source data, see Supplementary Fig. 1. c, RT–qPCR result for junctions shown in a. Expression of cryptic and CD44v junctions are significantly higher in CLL samples with U1 mutation, as expected. P values are from two-sided Wilcoxon rank-sum tests. MUT (n = 5) and WT (n = 8) represent biologically independent CLL samples with and without the g.3A>C mutation, respectively.
a, c, Representative upregulated (a) and downregulated (c) gene sets in CLL with U1 mutation (n = 11 independent patients). For a, c, enrichment score (ES), normalized enrichment score (NES) and false-discovery rate (FDR) q are from the permutation test of GSEA. Genes in the x axis are sorted from the most significantly upregulated gene to the most significantly downregulated gene. The dashed line indicates fold change = 1. b, Expression box plots for TERF2 and TERT in the telomere maintenance pathway. q values are from limma. MUT (n = 11) and WT (n = 254) represent biologically independent CLL samples with and without the g.3A>C mutation, respectively. d, e, Expression heat map of ribosomal genes and BCR genes. d, Only the top 10 differentially expressed ribosomal genes are shown. e, Right, bar charts show the log2-transformed fold change and −log10(q value) from limma (n = 11 independent samples of CLL with U1 mutation; n = 254 independent samples of CLL with wild-type U1). f, Expression box plot for TP53. TP53 MUT/DEL (n = 15 independent patients) represents CLL samples with TP53 mutation and/or deletion; U1 MUT (n = 10 independent patients) represents samples with the g.3A>C mutation; WT (n = 273 independent patients) represents the rest of the CLL samples. One sample with both TP53 and g.3A>C mutation is excluded. P values are from two-sided Wilcoxon rank-sum tests. g, Expression box plots for genes in the NF-κB (CHUK and IKBKB), PI3K/AKT (PIK3CA and AKT1) and MAPK/ERK (JUN) pathways. These pathways are downstream effectors of the BCR pathways. q values are from limma. MUT (n = 11) and WT (n = 254) are the same as in b. In all box plots, centre line, box limits, whiskers and points represent median, 25th and 75th percentiles, 1.5× interquartile range and individual samples, respectively.
Extended Data Fig. 6 Exogenous expression of the g.3A>C mutation in cell lines induced transcriptome-wide changes.
a, 5′ RACE confirming the expression of the U1 g.3A>C mutation in three CLL cell lines. CLL cell lines (JVM3, HG3 and MEC1) are infected with lentiviral particles that contain U1 locus with or without the g.3A>C mutation (g.3A>C or WT, respectively). The electropherograms correspond to the sequence of the PCR product (reverse-strand). The arrowheads indicate the location of the 3rd base of U1. The location of the 5′ RACE primer is also indicated. The experiment was conducted once. b, 5′ splice site for introns with increased or decreased excision in CLL cell lines with U1 mutation (n = 3 biological independent cell lines). Top, bar chart shows q values from χ2 tests for base composition difference; red line indicates the q = 0.1 cutoff. c, Category of mis-splicing events in CLL cell lines. Extended Data Figure 2a provides the definition of each category. The number of introns is coloured by the sixth base of 5′ splice site. d, Volcano plot for differential expression analysis of CLL cell lines. q values are from limma. n = 3 biological independent cell lines; each cell line has a form with (MUT) and without (WT) U1 mutation. e–g, Euler plots comparing differentially spliced introns (e) and differentially expressed genes (f, g) between primary CLL from patients and cell lines. e, Over-excised introns with the G6 5′ splice site that are direct targets of the U1 mutation are compared. P values are from one-tailed Fisher’s exact test. The expected number of overlaps is also shown. h, Asymmetric changes in gene expression for cell lines. In all three cell lines, more genes are upregulated (log2-transformed fold change > 1) than downregulated (log2-transformed fold change < −1).
Left, oncoprint shows 36 CLL driver alterations across 313 patients with CLL. Clinical classification (CLL or monoclonal B cell lymphocytosis), gender, IGHV status and U1 g.3A>C status are shown on the top. Right, bar plot shows q values from two-sided Fisher’s exact test that compares each alteration with the U1 g.3A>C status.
Extended Data Fig. 8 Consequence of SF3B1 mutation and its relation to the U1 g.3A>C mutation in CLL.
a, Category of mis-splicing events in CLL with SF3B1 mutation. Unlike the g.3A>C mutation, SF3B1 mutation induces more cryptic 3′ splice site changes. b, c, Sequence motifs of 5′ splice site (b) and 3′ splice site (c) for mis-spliced introns in CLL with SF3B1 mutation (n = 6). In b, c, the bar plots on the top show Benjamini–Hochberg adjusted P values from χ2 tests. Red lines indicate the q = 0.1 cutoff. d, e, Intron retention (d) and exon skipping (e) events in CLL with U1 mutation or SF3B1 mutation. In d, e, pairwise comparisons are made between three groups of U-CLL: with U1 g.3A>C mutation (U1-MUT, n = 6); with SF3B1 mutation (SF3B1-MUT, n = 6); without U1 or SF3B1 mutation (WT; n = 30). The number of events is counted with respect to corresponding controls (ctrl). f, 5′ splice site motifs for intron retention and exon skipping events in samples with U1 mutation, as compared to wild-type samples. The sequence motif for exon skipping events is from 5′ splice site of the red exon in e.
Left, this oncoprint shows 15 major driver alterations across all whole-genome-sequenced samples from two HCC projects: LIHC-US (n = 53) and LIRI-JP (n = 129). Project code, gender, risk factor status (heavy alcohol use, or infection with hepatitis B or C virus) and U1 g.3A>C status are shown on the top. White colour in top annotations indicates that data are not available. Right, bar plot shows q values from Cochran–Mantel–Haenszel χ2 tests that compare each alteration with the U1 mutation. Three types of alterations are used in the plot: simple somatic mutations (SSM), structural variations (SV) and somatic copy-number alterations (SCNA).
a, Kaplan–Meier plot of overall survival for CLL. P value is from the Cox model. b, Independent prognostic value of the U1 g.3A>C mutation in CLL. Multivariate Cox model results for time to first treatment in 301 patients with CLL (number of events = 153). HR, hazard ratio; CI, confidence interval. c, U1 mutation frequency by disease stage in CLL. A and B/C, Binet A and B/C stages. d, Kaplan–Meier plot of overall survival for HCC samples in the LIRI-JP project. MUT indicates samples with the g.3A>C mutation and WT indicates samples without the g.3A>C mutation; 13 MUT and 116 WT samples are used. e, f, Kaplan–Meier plots of OS (e) and progression-free interval (f) for HCC samples in the LIHC-US project. MUT indicates samples with the g.3A>C mutation and WT indicates samples without the g.3A>C mutation; 15 MUT and 335 WT samples are used. For d–f, P values are from two-sided log-rank tests; g, U1 mutation frequency by disease stage in HCC. For LIRI-JP, staging uses the Liver Cancer Study Group of Japan system. For LIHC-US, staging uses the American Joint Committee on Cancer system.
This file contains Supplementary Notes 1-4
Uncropped gel images for Extended Data Fig. 4b
Summary of patient characteristics. This table provides ICGC donor ID, tumour cohort, project code, sex, age, diagnosis (ICD-10) and histology information for patients used in this study
U1 snRNA mutations identified in seven canonical U1 genes. This table provides 277 somatic mutations identified in any of the seven canonical U1 genes for PCAWG patients via WGS. Related to Fig. 1
Consensus U1 g.3A>C status for CLL and HCC. This table provides data availability, WGS-based genotyping, transcriptome-based inference, rhAMP results and consensus genotyping information for each of the CLL (n = 318) and HCC (n = 613) donors. Related to Extended Data Fig. 1
Differentially spliced introns and expressed genes in CLL and HCC. This table provides differentially spliced introns identified by LeafCutter (Methods) and differentially expressed genes identified by limma (Methods) for CLL (11 U1-MUT vs 254 WT; biologically independent patients), HCC (20 U1-MUT vs 367 WT; biologically independent patients) and CLL cell lines (n = 3 biological independent cell lines; each cell line has a MUT and WT form). P is nominal p-values; q is BH-adjusted p-values. Related to Fig. 2, Extended Data Fig. 2 and 6
Gene sets enriched in U1 mutated CLL and HCC. This table provides significant gene sets identified by the Gene Set Enrichment Analysis (GSEA) and g:Profiler for primary CLL, HCC and CLL cell lines. Both nominal p-values and multiple comparisons corrected p-values from GSEA are provided. Related to Extended Data Fig. 5
Primers used in rhAMP and RT-PCR experiments. This table provides all primer sequences used in the experimental validation of CLL
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Shuai, S., Suzuki, H., Diaz-Navarro, A. et al. The U1 spliceosomal RNA is recurrently mutated in multiple cancers. Nature 574, 712–716 (2019) doi:10.1038/s41586-019-1651-z