Abstract
Clonal hematopoiesis (CH) results from somatic genomic alterations that drive clonal expansion of blood cells. Somatic gene mutations associated with hematologic malignancies detected in hematopoietic cells of healthy individuals, referred to as CH of indeterminate potential (CHIP), have been associated with myeloid malignancies, while mosaic chromosomal alterations (mCAs) have been associated with lymphoid malignancies. Here, we analyzed CHIP in 55,383 individuals and autosomal mCAs in 420,969 individuals with no history of hematologic malignancies in the UK Biobank and Mass General Brigham Biobank. We distinguished myeloid and lymphoid somatic gene mutations, as well as myeloid and lymphoid mCAs, and found both to be associated with risk of lineage-specific hematologic malignancies. Further, we performed an integrated analysis of somatic alterations with peripheral blood count parameters to stratify the risk of incident myeloid and lymphoid malignancies. These genetic alterations can be readily detected in clinical sequencing panels and used with blood count parameters to identify individuals at high risk of developing hematologic malignancies.
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Data availability
The source data are available to the approved researchers through the UKB and MGBB. The data generated in this study, including somatic variants and chromosomal alterations, are available as Supplementary information, which will be submitted to the respective biobanks to enable linking with individual-level data and sharing with other approved researchers. Usage of these data will be covered by the data use agreements with the respective biobanks, and no additional restrictions apply. Individual-level MGBB data are available from https://personalizedmedicine.partners.org/Biobank/Default.aspx, but restrictions apply to the availability of these data, which were used under institutional review board approval for the current study and so are not publicly available. Individual-level UKB data are available for approved researchers from https://www.ukbiobank.ac.uk. The present article includes all other data generated or analyzed during this study. Additional databases used in this study include: gnomAD (https://gnomad.broadinstitute.org), cBioPortal for cancer genomics (https://www.cbioportal.org) and the Atlas of Genetics and Cytogenetics in Oncology and Haematology (http://atlasgeneticsoncology.org).
Code availability
The workflow utilized to identify somatic variants from alignment bam files is available in WDL format at GitHub (https://github.com/gatk-workflows/gatk4-somatic-snvs-indels). Custom codes were used to process and analyze the data and generate figures, which are available upon request from the authors or at GitHub (https://github.com/abhisheknrl/myeloid_lymphoid_CH).
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Acknowledgements
This work was supported by the NIH (nos. R01HL082945, P01CA108631, P50CA206963 and R35CA253125), the Howard Hughes Medical Institute and the Fondation Leducq to B.L.E. A.N. was supported by funds from the Knut and Alice Wallenberg Foundation (no. KAW2017.0436). P.N. is supported by grants from the National Heart, Lung, and Blood Institute (nos. R01HL142711, R01HL148050, R01HL151283 and R01HL148565), Fondation Leducq (no. TNE-18CVD04) and Massachusetts General Hospital (Hassenfeld Research Scholar). M.C.H. is supported by the National Heart, Lung, and Blood Institute (no. T32HL094301-07). G.K.G. is supported by the Damon Runyon Cancer Research Foundation. M.A. received research support for this work from the Deutsche Forschungsgemeinschaft (no. AG252/1-1). K.P. is supported by the National Heart, Lung, and Blood Institute (no. T32HL007208-43).
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Contributions
A.N. and B.L.E. designed and conceived the project. A.N., M.A.M. and A.S. performed statistical analyses and interpreted the data. A.N., A.G.B., M.M.U., C.J.G., G.K.G. and S.M.Z. generated somatic mutation calls. M.C.H. and P.N. provided ascertainment for coronary artery disease. A.N., A.S., M.T., M.A., W.J.W. and K.P. analyzed data in MGBB. A.N. and B.L.E. drafted the manuscript. All authors made substantial contributions to data analysis and/or interpretation, drafted and/or revised the manuscript and approved the final version for publication.
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Competing interests
B.L.E. has received research funding from Celgene, Deerfield and Novartis and consulting fees from GRAIL. He serves on the scientific advisory boards for Skyhawk Therapeutics, Exo Therapeutics and Neomorph Therapeutics, none of which are directly related to the content of this paper. P.N. reports grant support from Amgen, Apple, AstraZeneca, Boston Scientific and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Genentech and Novartis and spousal employment at Vertex, all unrelated to the present work. G.K.G. reports affiliation to Moderna Therapeutics, which is unrelated to the present work. M.A. received consulting fees from German Accelerator Life Sciences and is a cofounder of iuvando Health and holds equity, all unrelated to the present work. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Expanded CH clones confer higher risk of malignancies.
a-b, M-CHIP and L-CHIP are associated with incident myeloid and lymphoid malignancies, respectively. Hazards associated with L-CHIP for developing myeloid malignancies could not be computed due to the small number of events, and are indicated as NA. c,d, The mCAs increase the risk of myeloid and lymphoid malignancies, respectively. The M-mCA and L-mCA are associated with the highest risk of malignancies in the respective lineages. In all cases, expanded CH clones had higher risk of developing malignancies. (a-d) Data are presented as hazard ratios and 95% confidence intervals, computed using Cox proportional hazards models adjusting for age, sex, smoking, genetic ethnic ancestry, and genetic principal components 1–5. HR, hazard ratio; CI, confidence interval; VAF, variant allele fraction; CF, cell fraction; U-mCA, unclassified mCA.
,
Extended Data Fig. 2 Association between the types of mCA and hematologic malignancies.
All three categories of mCAs, i.e., copy loss, copy gain, and copy neutral loss of heterozygosity, are associated with the risk of a) myeloid malignancies and b) lymphoid malignancies. (a-b) Data are presented as hazard ratios and 95% confidence intervals, computed by Cox proportional hazards models adjusting for age, sex, smoking, genetic ethnic ancestry, and genetic principal components 1–5. LOH, loss of heterozygosity; del, copy loss (deletion); gain, copy gain; HR, hazard ratio; CI, confidence interval.
Extended Data Fig. 3 Association between CH and sub-types of myeloid and lymphoid malignancies.
Data are presented as hazard ratio and 95% confidence intervals, computed by Cox proportional hazards model adjusting for age, sex, smoking, genetic ethnic ancestry, and genetic principal components 1–5. Groups with less than <4 events were excluded from the Cox model and are indicated as NA. MDS, myelodysplastic syndrome; AML, acute myeloid leukemia; MPN, myeloproliferative neoplasms; CLL, chronic lymphocytic leukemia; SLL, small lymphocytic lymphoma; MM, multiple myeloma; MGUS, monoclonal gammopathy of undetermined significance; NHL, non-Hodgkin’s lymphoma; DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; WM, Waldenstrom’s macroglobulinaemia; HL, Hodgkin’s lymphoma; U-mCA, unclassified mCAs.
Extended Data Fig. 4 Myeloid and lymphoid CHIP and mCAs identified in the MGBB cohort.
a) Top 25 myeloid and lymphoid driver genes mutated in MGBB cohort. b) Prevalence of M-CHIP and L-CHIP increase with age. c) Prevalence of M-mCA, L-mCA, and U-mCA increase with age. (b-c) Data is fit with the general additive model using cubic regression splines and the shaded bands indicate the estimated 95% confidence interval. d-e) M-mCA and L-mCA increase risk of myeloid and lymphoid malignancies, respectively. The incidence curves are un-adjusted for covariates. Data are presented as hazard ratio and 95% confidence intervals, computed by Cox proportional hazards model adjusting for age, sex, and genetic principal components 1–5. Incident multiple myeloma (MM) and monoclonal gammopathy of undetermined significance (MGUS) cases were excluded since those were only weakly associated with L-mCA in the UKB cohort. Groups with <2 events were excluded in the Cox model. M-CHIP, CHIP with myeloid driver, L-CHIP, CHIP with lymphoid driver; M-mCA, mCA with myeloid driver; L-mCA, mCA with lymphoid driver; A-mCA, mCA with ambiguous driver; U-mCA, unclassified mCAs; HR, hazard ratio; CI, confidence interval.
Extended Data Fig. 5 Co-occurrence of CHIP and mCAs.
a) Number of individuals with M-CHIP, L-CHIP, and mCAs. b) Genomic loci harboring CHIP variants and overlapping mCAs, leading to bi-allelic genetic alterations. Copy-neutral loss of heterozygosity at JAK2, TET2, and DNMT3A account for most of the overlapping CHIP and mCA alterations. c-d) Correlation between variant allele fraction of CHIP variants and cell fraction of mCAs. Overlapping CHIP and mCAs are marked by larger dots.
Extended Data Fig. 6 Co-occurrence of CHIP and mCA amplify risk of malignancies.
a) Risk of myeloid malignancies due to M-CHIP and M-mCA/A-mCA. b) Risk of lymphoid malignancies due to L-CHIP and L-mCA/A-mCA. (a-b) Since A-mCA were associated with risk of both myeloid and lymphoid malignancies, these are combined with M-mCA in the analysis of myeloid malignancies and with L-mCA in the analysis of lymphoid malignancies. Data are presented as hazard ratio and 95% confidence intervals, computed using Cox proportional hazards models adjusting for age, sex, smoking, genetic ethnic ancestry and genetic principal components 1–5. HR, hazard ratio; CI, confidence interval.
Extended Data Fig. 7 Multiple CH events bring higher risk of developing malignancies.
a-b) Multiple CH amplify risk of myeloid and lymphoid malignancies irrespective of the type of alterations. The analysis was performed among individuals with both WES and SNP-array data. Dashed curves indicate groups with multiple CH events. c-d) Multiple mCAs amplify risk of myeloid and lymphoid malignancies. (a-d) Since A-mCA were associated with risk of both myeloid and lymphoid malignancies, these are combined with M-mCA in the analysis of myeloid malignancies and with L-mCA in the analysis of lymphoid malignancies. Data are presented as hazard ratios and 95% confidence intervals, computed using Cox proportional hazards models adjusting for age, sex, smoking, genetic ethnic ancestry and genetic principal components 1–5. Individuals with >1 M-mCA/A-mCA and >1 L-CHIP were excluded in the multivariable Cox proportional hazard model due to the small sample size and <3 events. HR, hazard ratio; CI, confidence interval.
Extended Data Fig. 8 Enrichment of blood cell indices among CH cases.
a, M-CHIP b, M-mCA c, L-CHIP d, L-mCA e, A-mCA f, unclassified mCAs (U-mCA). The associations between CH and normalized blood cell indices included in complete blood count and differentials were tested using linear regression. The associations were tested separately in males and females and adjusted for age, ever smoking status, genetic ethnic ancestry, and genetic principal components. The dashed horizontal line indicates the significance threshold (adjusted p-value = 0.05). PCT, platelet crit; PLT, platelet count; ANC, absolute neutrophil count; Neuts%, neutrophil percentage; RDW, red blood cell distribution width; Monos, monocyte count; Monos%, monocyte percentage; Baso, basophil count; WBC, white blood cell count; Eos%, eosinophil percentage; ALC, absolute lymphocyte count; Lymphs%, lymphocyte percentage.
Extended Data Fig. 9 Abnormal CBC parameters increase risk of myeloid and lymphoid malignancies.
Both high and low myeloid cell parameters (neutrophil count, platelet count, or red blood cell count) are associated with increased risk of myeloid malignancies, and high lymphocyte count is associated with increased risk of CLL/SLL in a, the WES cohort and b, the SNP-array cohort. (a-b) Data are presented as hazard ratio and 95% confidence intervals, computed using Cox proportional hazards models adjusting for age, sex, smoking, genetic ethnic ancestry and genetic principal components 1–5. Groups with small sample size and <3 events were excluded in the multivariable Cox proportional hazard model. CBC, complete blood count; High myeloid; high myeloid cell parameters, Low myeloid, low myeloid cell parameters; CLL, chronic lymphocytic leukemia, SLL, small lymphocytic lymphoma; HR, hazard ratio; CI, confidence interval.
Extended Data Fig. 10 Risk of mortality and coronary artery disease among CH cases.
a, M-CHIP and all classes of mCAs were associated with increased risk of all-cause-mortality, but L-CHIP did not increase the risk of mortality. b, Only M-CHIP, A-mCA and U-mCA were associated with mortality unrelated to hematologic malignancies. c, M-CHIP is associated with increased risk of coronary artery disease. No other CH categories were associated with risk of coronary artery disease. (a-c) Data are presented as hazard ratio and 95% confidence intervals, computed using Cox proportional hazards models adjusting for age, sex, smoking, genetic ethnic ancestry and genetic principal components 1–5. HR, hazard ratio; CI, confidence interval; VAF, variant allele fraction; CF, cell fraction.
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Niroula, A., Sekar, A., Murakami, M.A. et al. Distinction of lymphoid and myeloid clonal hematopoiesis. Nat Med 27, 1921–1927 (2021). https://doi.org/10.1038/s41591-021-01521-4
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DOI: https://doi.org/10.1038/s41591-021-01521-4
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