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Somatic mutations and clonal dynamics in healthy and cirrhotic human liver

Abstract

The most common causes of chronic liver disease are excess alcohol intake, viral hepatitis and non-alcoholic fatty liver disease, with the clinical spectrum ranging in severity from hepatic inflammation to cirrhosis, liver failure or hepatocellular carcinoma (HCC). The genome of HCC exhibits diverse mutational signatures, resulting in recurrent mutations across more than 30 cancer genes1,2,3,4,5,6,7. Stem cells from normal livers have a low mutational burden and limited diversity of signatures8, which suggests that the complexity of HCC arises during the progression to chronic liver disease and subsequent malignant transformation. Here, by sequencing whole genomes of 482 microdissections of 100–500 hepatocytes from 5 normal and 9 cirrhotic livers, we show that cirrhotic liver has a higher mutational burden than normal liver. Although rare in normal hepatocytes, structural variants, including chromothripsis, were prominent in cirrhosis. Driver mutations, such as point mutations and structural variants, affected 1–5% of clones. Clonal expansions of millimetres in diameter occurred in cirrhosis, with clones sequestered by the bands of fibrosis that surround regenerative nodules. Some mutational signatures were universal and equally active in both non-malignant hepatocytes and HCCs; some were substantially more active in HCCs than chronic liver disease; and others—arising from exogenous exposures—were present in a subset of patients. The activity of exogenous signatures between adjacent cirrhotic nodules varied by up to tenfold within each patient, as a result of clone-specific and microenvironmental forces. Synchronous HCCs exhibited the same mutational signatures as background cirrhotic liver, but with higher burden. Somatic mutations chronicle the exposures, toxicity, regeneration and clonal structure of liver tissue as it progresses from health to disease.

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Fig. 1: Mutational burden observed in non-cancerous hepatocytes.
Fig. 2: Phylogenetic reconstruction of hepatocyte clones.
Fig. 3: Mutational signatures in normal liver, cirrhotic liver and HCC.
Fig. 4: Links between exogenous factors and mutational signatures.

Data availability

Whole-genome sequencing data across the samples reported in this study have been deposited in the European Genome-Phenome Archive (https://www.ebi.ac.uk/ega/home) in the form of BAM files, with accession number EGAD00001004578. Substitution and indel calls have been deposited on Mendeley Data with the identifier https://doi.org/10.17632/ktx7jp8sch.1 (‘Somatic mutations and clonal dynamics in healthy and cirrhotic human liver’).

Code availability

Single-nucleotide substitutions were called using the CaVEMan algorithm, v.1.11.2 (https://github.com/cancerit/CaVEMan). Small insertions and deletions were called using the Pindel algorithm, v.2.2.2 (https://github.com/genome/pindel). Rearrangements were called using the BRASS (breakpoint via assembly) algorithm v.5.4.1 (https://github.com/cancerit/BRASS). Miscellaneous scripts for downstream analysis are available on Github (https://github.com/sfbrunner/liver-pub-repo). The analysis of mutational signatures was performed using the HDP hierarchical Dirichlet process package v.0.1.5, which is available on Github (https://github.com/nicolaroberts/hdp).

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Acknowledgements

This work was supported by a Wellcome Trust and Cancer Research UK (CRUK) Grand Challenge Award (C98/A24032). P.J.C. is a Wellcome Trust Senior Clinical Fellow (WT088340MA); S.F.B. was supported by the Swiss National Science Foundation (P2SKP3-171753 and P400PB-180790); M.A.S. is supported by a Rubicon fellowship from NWO (019.153LW.038); the Cambridge Human Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre; and M.H. is supported by a CRUK Clinician Scientist Fellowship (C52489/A19924).

Author information

Authors and Affiliations

Authors

Contributions

P.J.C., M.H. and S.F.B. designed the experiments; S.F.B. performed the LCM, data curation and statistical analysis, with L.A.W., M.A.S., F.A. and I.M. providing assistance and advice; M.H., S.J.A. and S.E.D. collated and analysed the clinical and histological data from the patients; N.D.R. developed the hierarchical Dirichlet process for extracting mutational signatures; L.M. and P.E. developed the LCM, DNA extraction and library production protocol used; C.A. and Y.H. assisted with sample preparation, processing and tracking; P.J.C., I.M. and M.R.S. oversaw the analysis of mutational signatures and selection analyses; P.J.C., M.H. and S.F.B. wrote the manuscript, with contributions from all authors.

Corresponding authors

Correspondence to Matthew Hoare or Peter J. Campbell.

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Competing interests

The authors declare no competing interests.

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

Peer review information Nature thanks Jessica Zucman-Rossi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Sensitivity analysis of SNV calls.

a, Overview schematic of the experimental and analytical approach. b, Examples of the VAFs of variants from unrelated (top) and related (bottom) pairs of microdissection samples from four donors (left to right). The x axis represents the VAF of sample 1 from each pair and the y axis represents the VAF of sample 2. Each dot represents one variant. Red, variants called in both samples; yellow, variants called in sample 1; blue, variants called in sample 2. c, Histogram of sensitivities calculated for each sample pair. d, Heat map of modelled sensitivity at different values of VAF and coverage. The overlaid dots represent the sample pairs that were used to fit the model. e, Relationship of VAF, sensitivity and coverage according to the fitted model of sensitivity. The overlaid dots represent the sample pairs that were used to fit the model. f, Comparison of calculated (x axis) and fitted (y axis) sensitivity for each sample pair (n = 34 pairs of samples). The R2 value is the Pearson’s correlation coefficient. g, Proportion of hepatocytes that are multinucleated in the samples analysed here, estimated by counting 500 cells in each H&E-stained section (n = 14 patients). Each point represents the proportion for one patient in the study. The horizontal bars represent the mean for that aetiological group.

Extended Data Fig. 2 Copy-number and structural variants in chronic liver disease.

a, b, Genome-wide copy-number profiles for two samples. Black points represent the read depth of discrete windows along the chromosome, corrected to show overall copy number. Arm-level and whole-chromosome gains and losses are evident. ch, Focal copy-number changes and structural variants. Black points represent the read depth of discrete windows along the chromosome, corrected to show overall copy number. Lines and arcs represent individual structural variants, coloured by the orientation of the joined ends (purple, tail-to-tail inverted; brown, head-to-head inverted; turquoise, tandem-duplication-type orientation; green, deletion-type orientation). Events that affect known HCC genes are marked with labelled arrows (c, e, f).

Extended Data Fig. 3 Events that affect known HCC genes in the cohort.

a, Distribution of somatic point mutations in individual microdissections (x axis) affecting known HCC genes (y axis), coloured by class of mutation according to the key underneath the panel. TERTp, TERT promoter. b, Genomic position of SNVs (top; light-blue strip) and indels (bottom; dark-blue strip) detected in ALB, the gene encoding albumin. c, Relationship of gene expression in liver tissue (x axis) and the proportion of indels as a fraction of all point mutations (y axis). The grey line represents a Poisson regression model with a significant (two-sided likelihood ratio test; P < 10−16) coefficient for gene expression as a predictor for the ratio of indels (n = 5,458 genes included in the model). The grey ribbon represents the 99% confidence interval of the parameter estimates.

Extended Data Fig. 4 Phylogenetic reconstruction of hepatocyte clones in non-cirrhotic liver samples.

Left, heat maps representing the clustering of the variants observed in each microdissection sample (x axis) of the non-cirrhotic livers. Each cluster (y axis) contains mutations for which the VAFs across samples are very similar. The colour scale of the boxes represents the estimated mean VAF for that cluster in that sample. Middle, phylogenetic trees constructed from the clustering information. Solid lines indicate that nesting is in accordance with the pigeonhole principle; dashed lines indicate that nesting is in accordance with the pigeonhole principle, assuming that hepatocytes represent 70% of cells; dotted lines indicate that nesting is only based on clustering (a clone is assigned as nested if its constituent LCMs are a subset of LCMs in the parental clone). For details, see Supplementary Methods. Right, representation of clones according to the physical coordinates of the LCM samples, overlaid onto H&E-stained sections (top). Sections stained with Masson’s trichrome and Oil Red O are also shown (bottom). Locations of immune or inflammatory cell infiltrates are marked with yellow rings. Sample sizes: PD36713, n = 30 microdissections; PD36714, n = 35 microdissections; PD36715, n = 26 microdissections; PD36717, n = 42 microdissections; PD36718, n = 32 microdissections.

Extended Data Fig. 5 Phylogenetic reconstruction of hepatocyte clones in alcohol-related cirrhosis.

Analogous to Extended Data Fig. 4, but for the cirrhotic livers of donors PD37105, PD37107, PD37110 and PD37111. Right, H&E-stained sections (top); Masson’s trichrome-stained sections (bottom left); and macroscopic photographs of the liver, with HCCs indicated by arrows (bottom right). Locations of immune or inflammatory cell infiltrates are marked with yellow rings. Sample sizes: PD37105, n = 31 microdissections; PD37107, n = 41 microdissections; PD37110, n = 22 microdissections; PD37111, n = 39 microdissections.

Extended Data Fig. 6 Phylogenetic reconstruction of hepatocyte clones in non-alcoholic fatty liver disease with cirrhosis.

Analogous to Extended Data Fig. 4, but for the cirrhotic livers of donors PD37113, PD37114, PD37115, PD37116 and PD37118. Right, H&E-stained sections (top); Masson’s trichrome-stained sections (bottom left); and macroscopic photographs of the liver, with HCCs indicated by arrows (bottom right). Locations of immune or inflammatory cell infiltrates are marked with yellow rings. Sample sizes, PD37113, n = 37 microdissections; PD37114, n = 41 microdissections; PD37115, n = 34 microdissections; PD37116, n = 43 microdissections; PD37118, n = 26 microdissections.

Extended Data Fig. 7 Mutation spectra for individual microdissections.

From each donor, we chose five clones to represent the heterogeneity in mutation spectra in the trinucleotide context. The six types of substitution are labelled across the top. Within each panel, the contributions from the trinucleotide context (bases immediately 5′ and 3′ of the mutated base) are shown.

Extended Data Fig. 8 Details of the extraction of mutational signatures.

a, Dot plots showing the concordance for signature attributions between the two signature algorithms (n = 479 microdissections). Mutational signatures on the y axis were extracted using non-negative matrix factorization and those on the x axis were extracted using a Bayesian hierarchical Dirichlet process. The R values are Pearson’s correlation coefficients. b, Signatures extracted by non-negative matrix factorization. The six substitution classes are separated by grey vertical lines, and are presented in the following order: C>A, C>G, C>T, T>A, T>C, T>G. Within each class of mutation, the contributions from the trinucleotide context (bases immediately 5′ and 3′ of the mutated base) are shown. c, Signatures extracted by the Bayesian hierarchical Dirichlet process, as for b. Where a signature matches one from b, it is shown on the same row.

Extended Data Fig. 9 Transcriptional-strand bias in patterns of mutations.

a, Transcriptional-strand bias of T>C mutations at the ATD context before and after the transcription start site (TSS) of highly expressed liver genes. b, Bar plots representing the numbers of C>A variants on the transcribed and non-transcribed strands. Each hepatocyte clone is represented individually (x axis). Note the strand bias in the highly mutated clones of PD37111, in which the tobacco signature is most active; the strand bias indicates that the damaged base is the guanine, as expected for polycyclic aromatic hydrocarbons. c, Bar plots representing the numbers of T>A variants on the transcribed and non-transcribed strands. Each hepatocyte clone is represented individually (x axis). Note the strand bias in the highly mutated clones of PD37107, in which the aristolochic acid signature is most active; the strand bias indicates that the damaged base is the adenine.

Extended Data Fig. 10 Mutations in a B lymphocyte clone in a cirrhotic liver.

a, Illustration of a portion of the B cell receptor (IGH) region on chromosome 14. Shown are the coverage tracks of an LCM sample that does not belong to the lymphocyte lineage (top) and a sample that belongs to the lymphocyte lineage (middle). In the centre of the displayed region there is a drop of copy number in the lymphocyte track, which indicates a structural rearrangement. The bottom track shows the paired-end reads that contribute to a rearrangement event in the lymphocyte sample, colocalized with the drop in copy number. b, Application of the pigeonhole principle: if two clusters of heterozygous mutations in regions of diploid copy number are in different cells, then their median VAFs must sum to ≤0.5 (if they sum to >0.5, equivalent to a combined cellular fraction of >1, then there must be some cells that carry both sets of mutations—hence one cluster would have a subclonal relationship with the other). Cluster 10 is the cluster with the unique VDJ rearrangement of IGH that is shown in a and the large number of mutations attributed to signature 9. Clearly, samples from clusters 2, 11, 55 and so on have VAFs which, when combined with cluster 10, sum to >0.5. Therefore, they must be subclonal to cluster 10, even though they do show signature 9. ch, Representative pairwise decision graphs for clusters of mutations. The median cellular fraction is shown for pairs of clusters across every sample from the patient. Where at least one sample falls above or to the right of the x + y = 1 diagonal line, those two clusters must share a nested clonal–subclonal relationship.

Supplementary information

Supplementary Methods

Bayesian Dirichlet process for clustering VAFs across multiple samples.

Reporting Summary

Supplementary Table 1

Clinical data on subjects included in the study.

Supplementary Table 2

Coverage and QC results from microdissections sampled.

Supplementary Table 3

Copy number changes in non-cancer hepatocytes and HCCs.

Supplementary Table 4

Somatically acquired structural variants identified in non-cancer hepatocytes.

Supplementary Table 5

Elements studied in unbiased driver mutation analysis.

Supplementary Table 6

Top 10 most significant hits per element type from unbiased driver mutation analysis.

Supplementary Table 7

Summary of non-synonymous mutations affecting known HCC genes.

Supplementary Table 8

Results of dN/dS analysis with restricted hypothesis testing on 30 most frequently mutated HCC genes.

Supplementary Table 9

Known regions of significant copy number alteration in HCC, defined by GISTIC.

Supplementary Table 10

Effect size and 95% confidence intervals for differences between cancer and non-cancer tissues in 7 patients with synchronous HCCs.

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Brunner, S.F., Roberts, N.D., Wylie, L.A. et al. Somatic mutations and clonal dynamics in healthy and cirrhotic human liver. Nature 574, 538–542 (2019). https://doi.org/10.1038/s41586-019-1670-9

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