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Combined landscape of single-nucleotide variants and copy number alterations in clonal hematopoiesis

Nature Medicine volume 27, pages 1239–1249 (2021)Cite this article

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Abstract

Clonal hematopoiesis (CH) in apparently healthy individuals is implicated in the development of hematological malignancies (HM) and cardiovascular diseases. Previous studies of CH analyzed either single-nucleotide variants and indels (SNVs/indels) or copy number alterations (CNAs), but not both. Here, using a combination of targeted sequencing of 23 CH-related genes and array-based CNA detection of blood-derived DNA, we have delineated the landscape of CH-related SNVs/indels and CNAs in 11,234 individuals without HM from the BioBank Japan cohort, including 672 individuals with subsequent HM development, and studied the effects of these somatic alterations on mortality from HM and cardiovascular disease, as well as on hematological and cardiovascular phenotypes. The total number of both types of CH-related lesions and their clone size positively correlated with blood count abnormalities and mortality from HM. CH-related SNVs/indels and CNAs exhibited statistically significant co-occurrence in the same individuals. In particular, co-occurrence of SNVs/indels and CNAs affecting DNMT3A, TET2, JAK2 and TP53 resulted in biallelic alterations of these genes and was associated with higher HM mortality. Co-occurrence of SNVs/indels and CNAs also modulated risks for cardiovascular mortality. These findings highlight the importance of detecting both SNVs/indels and CNAs in the evaluation of CH.

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Fig. 1: Landscape of SNVs/indels and CNAs in clonal hematopoiesis.
Fig. 2: Co-occurrences of SNVs/indels and CNAs in clonal hematopoiesis.
Fig. 3: Risk factors for CH and effects on blood counts.
Fig. 4: Impact of CH on mortality from HM.
Fig. 5: Impact of CH-related alterations on mortality from HM.
Fig. 6: Effect of SNV/indels and CNAs on cardiovascular mortality.

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Data availability

Tables of somatic SNVs/indels and CNAs detected in this study have been deposited in the Japanese Genome-phenotype Archive under accession code JGAS000293 (https://humandbs.biosciencedbc.jp/en/hum0014-v22). Clinical data used in this study can be provided by the BBJ project upon request (https://biobankjp.org/english/index.html). Source data are provided with this paper.

Code availability

Custom computational codes used to reproduce figures from the paper are available at https://github.com/RSaikiRSaiki/CH_2021.

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Acknowledgements

This work was supported by the Japan Agency for Medical Research and Development (nos. JP15cm0106056h0005, JP19cm0106501h0004, JP16ck0106073h0003 and JP19ck0106250h0003 to S.O.; nos. JP17km0405110h0005 and JP19ck0106470h0001 to H.M.; and no. JP19ck0106353h0003 to Y.N.); the Core Research for Evolutional Science and Technology (no. JP19gm1110011 to S.O.); the Ministry of Education, Culture, Sports, Science and Technology of Japan; the High Performance Computing Infrastructure System Research Project (nos. hp160219, hp170227, hp180198 and hp190158 to S.O. and S. Miyano) (this research used computational resources of the K computer provided by the RIKEN Advanced Institute for Computational Science through the HPCI System Research project); the Japan Society for the Promotion of Science; Scientific Research on Innovative Areas (nos. JP15H05909 to S.O. and S. Miyano and JP15H05912 to S. Miyano) and KAKENHI (nos. JP26221308 and JP19H05656 to S.O., JP16H05338 and JP19H01053 to H.M. and JP15H05707 to S. Miyano); and the Takeda Science Foundation (to S.O., H.M. and T.Y.). S.O. is a recipient of the JSPS Core-to-Core Program A: Advanced Research Networks. DNA samples and subjects’ clinical data were provided by BBJ, the Institute of Medical Science, the University of Tokyo. The supercomputing resource was provided by the Human Genome Center, the Institute of Medical Science, the University of Tokyo. We thank K. Matsuo at the Aichi Cancer Center Research Institute (Nagoya, Japan), who suggested the design of the case-cohort study for estimation of cumulative mortality from, and incidence of, hematological malignancies. We thank the TCGA Consortium and all its members for making publicly available their invaluable data.

Author information

Authors and Affiliations

  1. Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Ryunosuke Saiki, Yasuhito Nannya, Masahiro M. Nakagawa, Yotaro Ochi, Tetsuichi Yoshizato, Hideki Makishima & Seishi Ogawa

  2. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Yukihide Momozawa

  3. Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan

    Masahiro M. Nakagawa & Seishi Ogawa

  4. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Chikashi Terao & Yoichiro Kamatani

  5. Department of Orthopaedic Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Yutaka Kuroda & Shuichi Matsuda

  6. Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan

    Yuichi Shiraishi & Kenichi Chiba

  7. Department of Integrated Data Science, M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan

    Hiroko Tanaka & Satoru Miyano

  8. Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Atsushi Niida

  9. Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Seiya Imoto

  10. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda & Yoichiro Kamatani

  11. Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Takayuki Morisaki & Yoshinori Murakami

  12. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Michiaki Kubo

  13. Department of Medicine, Centre for Haematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden

    Seishi Ogawa

Authors
  1. Ryunosuke Saiki
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Contributions

R.S., H.M. and S.O. designed the study. K.M., Y. Kamatani, T.M. and Y. Murakami provided DNA samples and clinical data. Y. Kuroda and S. Matsuda provided bone marrow samples. C.T. and Y. Kamatani performed copy number analysis. Y. Momozawa and M.K. performed sequencing. M.M.N. performed cell sorting and single-cell analysis. R.S., M.M.N., Y.O., T.Y., Y.S., K.C., H.T., A.N., S.I. and S. Miyano performed bioinformatics analysis. R.S., Y.N., M.M.N., Y.O., T.Y., H.M. and S.O. prepared the manuscript. All authors participated in discussions and interpretation of the data and results.

Corresponding author

Correspondence to Seishi Ogawa.

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The authors declare no competing interests.

Additional information

Peer review information Nature Medicine thanks Daniel Link, Duane Hassane, Todd Druley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Design of case-control and case-cohort study.

a, Design of case-control study (Left). Diagnosis of hematological malignancies (HM) in subjects with or without CH enrolled in the case-control study (Right). b, Design of case-cohort study for death from HM (Left). Diagnosis of HM in subjects with or without CH enrolled in the case-cohort study (Right). AML, acute myeloid leukemia; MDS, myelodysplastic syndromes; MPN, myeloproliferative neoplasms; CML, chronic myeloid leukemia; B-NHL, B-cell non-Hodgkin lymphoma; T-NHL, T-cell non-Hodgkin lymphoma; CLL, chronic lymphoid leukemia; ALL, acute lymphoblastic leukemia; MM, multiple myeloma; PCT, plasma cell tumor.

Extended Data Fig. 2 Landscape of genetic alterations in CH.

a-b, The number of subjects with individual SNVs/indels (a) and CNAs (b). The vertical axis represents the number of subjects with indicated alterations. Unclassifiable CNAs are not included in (b). c, Landscape of SNVs/indels and CNAs in 11,234 subjects. Those without CH-related alterations are omitted. d, The correlations between individual genetic alterations. Combinations seen in 5 or more cases are indicated by asterisks. e-i, VAF of cooccurring SNVs/indels in diagonal plot. Dots above the dashed line fulfill ‘pigeonhole principle’. j, Venn diagram illustrating the overlap between subjects with SNVs/indels and those with CNAs. Frequencies within all subjects in whom SNVs/indels and CNAs were examined (n = 11,234) are indicated. k, Subjects in whom cooccurring SNVs/indels and CNAs were suspected to coexist in the same cells on the basis of ‘pigeonhole principle.’ l, A magnified illustration of microdeletions around TCRA locus (14q11.2). A gray bar represents gene body of TCRA. Blue horizontal bars represent microdeletions. Cooccurring TET2 SNVs are indicated by red dots. Genomic coordinates in hg19 are indicated above. m, Proportions of subjects with different number of cooccurring alterations within those who harbor SNVs/indels in the indicated genes. The proportions of subjects with 1, 2, 3, and ≥4 CNAs are depicted by different colors.

Extended Data Fig. 3 Distribution of CNAs in all chromosomes.

Distributions of CNAs on all chromosomes are illustrated. Loci of known driver genes are indicated by arrows. Each horizontal bar represents one CNA. Cooccurring SNVs/indels are indicated by red dots. Types of CNAs are depicted by different colors as indicated in the annotations.

Extended Data Fig. 4 Chromosomal regions significantly affected by CNAs.

a-c, Chromosomal regions significantly affected by duplications (a), UPDs (b), and deletions (c) in a Japanese cohort (current study) and in a British cohort11. Statistical significance for recurrence of CNAs were evaluated by PART49. Dashed lines indicate thresholds for statistical significance (FDR = 0.25). d-e, Comparison of frequencies of individual CNAs between the current and previous studies8,9,11. Comparisons were performed in those aged 60-75 years. In (d) or (e), CNAs in <5% or ≥5% cell fractions were taken into account, respectively. CNAs significantly enriched in either cohort (FDR < 0.1) were indicated by asterisks in (e).

Extended Data Fig. 5 Analysis of SNVs/indels and CNAs in peripheral blood samples in TCGA cohort.

a, Distribution of the number of genetic alterations in each subject. Subjects with SNVs/indels alone, with CNAs alone, or with both of them are illustrated by different colors. b, Solid lines indicate the prevalence of CH-related SNVs/indels and CNAs, according to age. Colored bands represent the 95% confidence intervals. c, The landscape of CH-related SNVs/indels and CNAs. Each row represents genetic alterations or affected chromosomal arms, and each column represents subjects. Subjects without any alterations are omitted. Types of SNVs/indels and CNAs are depicted by different colors. d, Distributions of CNAs on all chromosomes are illustrated. Loci of cooccurring SNVs/indels are indicated by arrows. Each horizontal bar represents one CNA. Cooccurring SNVs/indels are indicated by red asterisks. Types of CNAs are depicted by different colors.

Extended Data Fig. 6 Interplay between SNVs/indels and CNAs.

a, Number of subjects with SNVs/indels and CNAs involving the same genes/loci. b, Proportion of SNVs/indels associated with CNAs in the same genes/loci. c, Cumulative mortality from hematological malignancies. d, Cumulative mortality from cardiovascular diseases. e, Survival curves for overall survival. f, Profiles of CNAs in subjects with SNVs/indels in TP53. Abnormally high or low blood counts (WBC, Platelet, hemoglobin, and hematocrit) are indicated by red or blue, respectively. Numbers of cooccurring CNAs are indicated on the right side (#CNA), where subjects with ≥3 CNAs were highlighted by purple. Subjects without any CNA are abbreviated. g, Mortality from hematological malignancies in TP53-mutated cases with or without CNAs in 17p. h, Odds ratio for mortality from MDS calculated by multivariate logistic regression in subjects with TP53-involving SNVs/indels. Error bars indicate 95% confidence intervals. We included unclassifiable CNAs involving 17p in 17p alterations (17p alt.) in panel (g-h) because they are most likely to be LOH (UPDs or deletions). TP53-involving SNVs/indels in panel (f-h) included those detected by ddPCR (Supplementary Fig. 3).

Extended Data Fig. 7 Genetic alterations in CH and abnormalities in blood counts.

a, Landscape of SNVs/indels and CNAs in subjects without abnormalities in blood counts (left), in those with any abnormalities in blood counts (middle), and in those with no available blood counts (right). Each row represents a genetic alteration while each column represents a subject. Subjects without any alteration are omitted. Different types of mutations and CNAs are depicted by different colors. b, Enrichment of genetic alterations in subjects with abnormalities in blood counts. Sizes of rectangles indicate significance of enrichment. Colors of rectangles indicate odds ratios. The enrichment of alterations was examined by Fisher exact test. Cytopenia (All), subjects with cytopenia in at least one lineage; Cytopenia (Multi), subjects with cytopenia in ≥2 lineage. WBC, white blood cell; Hb, hemoglobin; Plt, platelet. c, Distribution of blood cell counts in subjects with different CH-related alterations. In all box plots, the median, first and third quartiles (Q1 and Q3) are indicated, and whiskers extend to the furthest value between Q1 – 1.5×the interquartile range (IQR) and Q3 + 1.5×IQR. Numbers of subjects (n) are indicated below the names of alterations. d, Relationships between blood cell counts and VAF of SNVs/indels or cell fractions of CNAs. P values are calculated by two-sided t test in multivariate linear regression models, taking the effect of age and gender into account. Correction for multiple testing is not performed.

Source data

Extended Data Fig. 8 Impact of CH on mortality from HM stratified by number of alterations.

a, Pie chart showing the proportions of difference in mortality from hematological malignancies (HM) between subjects with or without CH (Fig. 4a) which are attributable to each prognostic factor (Online methods). b-c, Cumulative mortality from HM in subjects with different number of SNVs/indels (b), or CNAs (c). d-f, Cumulative mortality from HM in subjects with both SNVs/indels and CNAs or in those with SNVs/indels alone. Subjects with 1 (d), 2 (e), or ≥3 alterations (f) are separately shown. g-i, Cumulative mortality from HM in subjects with both SNVs/indels and CNAs or in those with either of them. Subjects with 2 (g), 3 (h), or 4 alterations (i) are separately shown. Throughout the figure, P values were calculated by two-sided Wald test and not adjusted for multiple comparison.

Extended Data Fig. 9 Association of CH-related SNVs/indels and CNAs with hematological malignancies.

a, Odds ratios for the events (death and/or development) of hematological malignancies in case-control study (Extended Data Fig. 1a). Error bars indicate 95% confidence intervals. b, Design of case-cohort study for development of hematological malignancies. c, Hazard ratios for development of hematological malignancies. Error bars indicate 95% confidence intervals. d-f, Effect of SNVs/indels (d), CNAs (e), and combined SNVs/indels and CNAs (f) on the cumulative incidence of development of hematological malignancies. P values are calculated by two-sided Wald test. n, number of cases with the indicated alterations; SNV + CNA, cooccurrence of both SNVs/indels and CNAs; #SNV, number of SNVs/indels; CF, cell fraction of CNAs; #CNA, number of CNAs.

Extended Data Fig. 10 Combined effect of SNVs/indels and CNAs on overall survival and cardiovascular mortality.

a-c, Effect of SNVs/indels (a), CNAs (b), or combined SNVs/indels and CNAs (c) on overall survivals. In the forest plots, error bars indicate 95% confidence intervals. d-e, Cumulative mortality from cardiovascular diseases stratified by the number of cooccurring SNVs/indels. f-h, Cumulative mortality from cardiovascular diseases in subjects with SNVs/indels (Max VAF > 5%) alone and those with both of SNVs/indels (Max VAF > 5%) and CNAs. Subjects with ≥2 (f), 2 (g), and 3 (h) alterations are separately shown. i, Cumulative mortality from cardiovascular diseases in subjects with different number of CH-related alterations. Throughout the figure, P values were calculated by two-sided Wald test in (a-c, f-i), or two-sided Log-rank test stratified by age and gender in (d-e), and were not corrected for multiple comparison.

Supplementary information

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Supplementary Figs. 1–13 and Tables 1–6.

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Supplementary Data 1

Statistical source data for Supplementary Fig. 10.

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Source Data Fig. 2

Statistical source data for Fig. 2a.

Source Data Fig. 3

Statistical source data for Fig.3 a,b.

Source Data Extended Data Fig. 7

Statistical source data for Extended Data Fig. 7b.

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Saiki, R., Momozawa, Y., Nannya, Y. et al. Combined landscape of single-nucleotide variants and copy number alterations in clonal hematopoiesis. Nat Med 27, 1239–1249 (2021). https://doi.org/10.1038/s41591-021-01411-9

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