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A pan-tissue survey of mosaic chromosomal alterations in 948 individuals

Nature Genetics volume 55, pages 1901–1911 (2023)Cite this article

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Abstract

Genetic mutations accumulate in an organism’s body throughout its lifetime. While somatic single-nucleotide variants have been well characterized in the human body, the patterns and consequences of large chromosomal alterations in normal tissues remain largely unknown. Here, we present a pan-tissue survey of mosaic chromosomal alterations (mCAs) in 948 healthy individuals from the Genotype-Tissue Expression project, augmenting RNA-based allelic imbalance estimation with haplotype phasing. We found that approximately a quarter of the individuals carry a clonally-expanded mCA in at least one tissue, with incidence strongly correlated with age. The prevalence and genome-wide patterns of mCAs vary considerably across tissue types, suggesting tissue-specific mutagenic exposure and selection pressures. The mCA landscapes in normal adrenal and pituitary glands resemble those in tumors arising from these tissues, whereas the same is not true for the esophagus and skin. Together, our findings show a widespread age-dependent emergence of mCAs across normal human tissues with intricate connections to tumorigenesis.

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Fig. 1: Detection of mCAs from the GTEx dataset.
Fig. 2: Examples of mCAs detected in different tissues.
Fig. 3: Age-related acquisition and tissue-specific prevalence of mCAs.
Fig. 4: Genomic distributions of mosaic chromosomal alterations across tissues.
Fig. 5: Risk factors of mCAs and associations with morphological features.
Fig. 6: Mosaic CNLoHs impact gene expression and splicing patterns via QTLs.
Fig. 7: Single-cell dissection of mCAs in the adrenal gland.

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Data availability

GTEx v8 mCA calls are provided as source data for Fig. 1. Genome-wide mCA recurrence levels are provided as source data for Fig. 4. Access to GTEx v8 data (including additional WGS and genotyping data in the v9 release) can be requested through dbGaP (phs000424.v8.p2; phs000424.v9.p2). Processed copy number segments for cSCC, BCC and normal blood can be obtained from the original publications12,74,75. Copy number profiles, WGS and RNA-seq data for TCGA samples can be downloaded from the GDC Data Portal (https://portal.gdc.cancer.gov) with appropriate access permission from dbGaP (phs000178.v11.p8). Pituitary sequencing data from Bi et al. can be obtained from the European Genome-phenome Archive (EGA; EGAS00001001714). Somatic SNV calls from Yizhak et al.2 with original GTEx sample IDs can be obtained through dbGaP (phs000424). Processed gene expression data of the DO6 adrenal gland scRNA-seq sample are available on Zenodo (https://doi.org/10.5281/zenodo.8336489)77. Access to the raw sequencing data for this donor sample can be requested through EGA (EGAD00001011288), subject to approval by the data access committee and under the condition that the data will not be propagated further. Access to the source human adrenal tissue materials is restricted due to privacy agreements with the research participants. The list of differentially expressed genes between mutant and normal adrenal cells is provided as Source data for Fig. 7. Source data are provided with this paper.

Code availability

Custom scripts and analysis notebooks for reproducing results in the paper are available on Zenodo (https://doi.org/10.5281/zenodo.8310299)78. The HaHMMR method for mCA detection from bulk RNA-seq data is available on GitHub (https://github.com/kharchenkolab/hahmmr)79.

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Acknowledgements

P.V.K., I.A. and T.G. were in part supported by ERC Synergy grant no. 85629 (KILL-OR-DIFFERENTIATE) from the European Research Council. P.V.K. serves on the scientific advisory board of Celsius Therapeutics and Biomage. P.J.P. was supported by NIH grant R01HG012573. The work of I.A. was additionally supported by the Knut and Alice Wallenberg Foundation, the Swedish Research Council, the Bertil Hallsten Foundation, the Paradifference Foundation and an Austrian Science Fund Project Grant. The work of A.S.T. was funded by the Paradifference Foundation. R.O. was funded by the Vienna Science and Technology Fund (WWTF; “Genomics-based immunologic risk stratification”, #10.47379/LS20081). P.-R.L. was supported by NIH grant DP2 ES030554, a Burroughs Wellcome Fund Career Award at the Scientific Interfaces and the Next Generation Fund at the Broad Institute of MIT and Harvard. V.L. is supported by the Swedish Research Council (2020-00583). M.E.K. was supported by the Novo Nordisk Foundation (postdoctoral fellowship in Endocrinology and Metabolism at International Elite Environments, NNF17OC0026874) and Stiftelsen Riksbankens Jubileumsfond (Erik Rönnbergs fund stipend). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The GTEx project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. We thank K. Ardlie for sharing data on cancer incidental findings in GTEx; J. Mitchel, D. Glodzik and V. Viswanadham for helpful discussions; and S. Ehmsen for creating graphical illustrations.

Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Teng Gao, Viktor Ljungström, Peter J. Park & Peter V. Kharchenko

  2. Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria

    Maria Eleni Kastriti & Igor Adameyko

  3. Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden

    Maria Eleni Kastriti & Igor Adameyko

  4. Department of Nephrology, Internal Medicine III, Medical University of Vienna, Vienna, Austria

    Andreas Heinzel & Rainer Oberbauer

  5. Department of Pathology and Laboratory Medicine, Tufts Medical Center, Boston, MA, USA

    Arthur S. Tischler

  6. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Po-Ru Loh

  7. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Po-Ru Loh

  8. San Diego Institute of Science, Altos Labs, San Diego, CA, USA

    Peter V. Kharchenko

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Contributions

T.G. implemented computational methods and carried out data analysis. P.V.K. and P.J.P. supervised the study and helped T.G. interpret the results. T.G. and P.V.K. drafted the manuscript with input from P.J.P. and the other authors. R.O., A.H., M.E.K. and I.A. carried out screening and processing of adult adrenal glands. M.E.K. and I.A. carried out scRNA-seq and histology characterization on the adult adrenal samples. A.S.T. interpreted histology data. V.L. advised on a comparison with cancer data. P.-R.L. advised on phasing methods and associated tests. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Peter J. Park or Peter V. Kharchenko.

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Competing interests

P.V.K. serves on the scientific advisory board of Celsius Therapeutics and Biomage. P.V.K. is an employee of Altos Labs, Inc. The rest of the authors declare no competing interests.

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Nature Genetics thanks Stephen J. Chanock, Jamie Blundell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Performance evaluation of mCA detection using HaHMMR.

a, Detection sensitivity with respect to clonal fraction. b, False positive rate (1-specificity) as a function of Q value cutoff. The center (marked by black line) and shaded area of the error band respectively represent the observed false positive rates and 95% confidence intervals from the binomial distribution. Red bar indicates the range of Q value cutoffs at which a false positive rate of 6.1 × 10−5 is achieved. Red triangle marks the Q value cutoff chosen for the study and the expected false positive rate. c, Estimated false discovery rate (FDR) as a function of clonal fraction and event prevalence among samples.

Extended Data Fig. 2 Assignment of mCA types.

a, Expression\(\,{\log }_{2}\) fold-change (logFC) and haplotype imbalance of detected events. Each dot represents a detected event. Dashed lines indicate the expected logFC and haplotype imbalance for different mutant cell fractions and alteration types. b, Genome-wide distribution of detected mCAs. Each line represents a detected mCA, colored by alteration type. Multi-tissue chr10 alterations from one individual are not shown.

Extended Data Fig. 3 Genomic distribution of mCAs for each individual tissue.

Each line represents a detected mCA, colored by alteration type. Multi-tissue chr10 alterations from one individual are not shown.

Extended Data Fig. 4 Age-related acquisition of mCAs in different tissue types.

The centers (marked by solid dots) and whiskers (vertical lines) of the error bars respectively represent the observed fractions of samples with detectable mCA and 95% confidence intervals from the binomial distribution. For each group, the number of mCA-positive cases and the total number of biologically independent samples are indicated in brackets. Multi-tissue chr10 alterations from one individual are excluded.

Extended Data Fig. 5 Point mutation burden and mCAs in normal tissues.

SNV burden (VAF ≥ 5%) in samples with and without detectable mCAs. Black curves and shaded areas represent the estimated probability density. Each dot represents a distinct sample. The total numbers of biologically independent samples are marked in brackets. Only tissue types with more than 3 samples with mCAs are included. Q values (FDR-corrected P values) from two-sided Wilcoxon tests are shown on top of each panel.

Extended Data Fig. 6 Examples of overlapping mCAs detected in different tissues of the same individual.

In the subjects shown in a and b, a mirrored pattern of haplotype imbalance is observed in the tissue pair. In the subjects shown in c and d, a concordant pattern of haplotype imbalance is observed in the tissue pair, possibly reflecting the same event that arose during development or blood infiltration. In all sets of figures, the left panels show allele profiles of the affected chromosomes. Alleles in altered regions are colored by the inferred haplotypes. Gray vertical dashed lines denote centromere positions. The middle panels show the inferred total copy number and haplotype proportion for pairs of events. The centers (marked by diamonds) of the error bars represent the maximum likelihood estimates of total copy numbers (y-axis) and haplotype proportions (x-axis). The whiskers of the error bars represent 95% confidence intervals derived from the model likelihood. The right panels show schematics of possible chromosomal alterations.

Extended Data Fig. 7 Mosaic chromosomal alteration types and genome-wide burden per tissue.

a, Number of events by alteration type in each tissue. The number on top of each bar denotes the total number of events. b, Fraction of genome altered in samples with detectable mCAs from each tissue type. Each dot represents a distinct sample with detectable mCAs. The total numbers of biologically independent samples are indicated in brackets.

Extended Data Fig. 8 Comparison of cancer and normal chromosomal alteration landscapes.

a, Chromosomal alteration frequencies by genomic positions in the normal pituitary and pituitary adenomas (PA). b, Chromosomal alteration frequencies by genomic positions in the normal esophagus, esophageal squamous cell carcinoma (ESCC), and esophageal adenocarcinoma (EAC). c, Chromosomal alteration frequencies by genomic positions in the normal skin, skin cutaneous melanoma (SKCM), cutaneous squamous cell carcinoma (cSCC), and skin basal cell carcinoma (BCC). For a-c, the numbers of biologically independent samples are indicated in brackets. Event frequencies are plotted by 5 Mb bins.

Extended Data Fig. 9 Associations of different drink types and drink frequencies with mCAs in the esophagus mucosa.

In both a and b, the centers (marked by diamonds) and whiskers of the error bars respectively represent the estimated odds ratio (OR) and 95% confidence interval from multivariate logistic regression (Methods). Unadjusted two-sided P values from the multivariate logistic regression model are shown on the right. The numbers of biologically independent samples examined are n = 535 for drink type and n = 510 for drink frequency.

Extended Data Fig. 10 Histological characterization of adult adrenal glands with and without mCAs.

a, Hematoxylin-eosin (H&E) stain of adrenal section of an age- and gender-matched donor (DO3, female, age 59 years) and b, that of the donor with genome aberration (DO6). In DO6, diffuse cortical hyperplasia is evident by the thickened cortical layers. Star denotes extracapsular cortical nubbin. Black arrows indicate cortical extrusions. c, RNAscope® in situ hybridization for cortical markers HSD3B2 and CYP11B2. A zoomed-in view of the tissue section is shown for the region highlighted in a (black rectangle). d, Left panel, immunofluorescent staining of adrenal gland section of DO6 against cortical marker CYP11B1 and smooth muscle actin (SMA) as a marker of blood vessels. Right panel, immunofluorescent staining against chromaffin cell marker chromogranin A (CHGA) and SMA. ZG, zona glomerulosa. ZF, zona fasciculate. ZR, zona reticularis. v, vessel. Extensive extracapsular cortical extrusions and nubbin are observed in the cortex. In contrast, the organization of the medulla remains normal. Each experiment in a-d was performed once.

Supplementary information

Supplementary Information

Supplementary Figs. 1–14, Tables 1–3 and Methods.

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Source data

Source Data Fig. 1

GTEx v8 mCA calls.

Source Data Fig. 4

Recurrence of mCAs across the genome.

Source Data Fig. 7

Differentially expressed genes between mutant and normal cells in adrenal gland scRNA-seq.

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Gao, T., Kastriti, M.E., Ljungström, V. et al. A pan-tissue survey of mosaic chromosomal alterations in 948 individuals. Nat Genet 55, 1901–1911 (2023). https://doi.org/10.1038/s41588-023-01537-1

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