Inflammatory cytokines promote clonal hematopoiesis with specific mutations in ulcerative colitis patients
2019; Elsevier BV; Volume: 80; Linguagem: Inglês
10.1016/j.exphem.2019.11.008
ISSN1873-2399
AutoresChristine Zhang, Darren Nix, Martin Gregory, Matthew A. Ciorba, Elizabeth L. Ostrander, Rodney D. Newberry, David H. Spencer, Grant A. Challen,
Tópico(s)Neutrophil, Myeloperoxidase and Oxidative Mechanisms
Resumo•Clonal hematopoiesis in ulcerative colitis patients is associated with an increased incidence of DNMT3A and PPM1D mutations.•Elevated serum interferon γ in ulcerative colitis patients is associated with DNMT3A mutant clones.•The incidence of clonal hematopoiesis is slightly elevated in ulcerative colitis patients. Epidemiological sequencing studies have revealed that somatic mutations characteristic of myeloid neoplasms can be detected in the blood of asymptomatic individuals decades prior to presentation of any clinical symptoms. This premalignant condition is known as clonal hematopoiesis of indeterminate potential (CHIP). Despite the fact these mutant clones become readily detectable in the blood of elderly individuals (∼10% of people over the age of 65), the overall rate of disease progression remains relatively low. Thus, in addition to genetic mutations, there are likely environmental factors that contribute to clonal evolution in people with CHIP. One environmental stress that increases with age is inflammation. Although chronic inflammation is detrimental to the long-term function of normal hematopoietic stem cells, several recent studies in animal models have indicated hematopoietic stem cells with CHIP mutations may be resistant to these deleterious effects. However, direct evidence indicating a correlation between increased inflammation and accelerated CHIP in humans is currently lacking. In this study, we sequenced the peripheral blood cells of a cohort of patients with ulcerative colitis, an autoimmune disease characterized by increased levels of pro-inflammatory cytokines. This analysis revealed that the inflammatory environment of ulcerative colitis promoted CHIP with a distinct mutational spectrum, notably positive selection of clones with DNMT3A and PPM1D mutations. We also show a specific association between elevated levels of serum interferon gamma and DNMT3A mutations. These data add to our understanding of how cell extrinsic factors select for clones with specific mutations to promote clonal hematopoiesis. Epidemiological sequencing studies have revealed that somatic mutations characteristic of myeloid neoplasms can be detected in the blood of asymptomatic individuals decades prior to presentation of any clinical symptoms. This premalignant condition is known as clonal hematopoiesis of indeterminate potential (CHIP). Despite the fact these mutant clones become readily detectable in the blood of elderly individuals (∼10% of people over the age of 65), the overall rate of disease progression remains relatively low. Thus, in addition to genetic mutations, there are likely environmental factors that contribute to clonal evolution in people with CHIP. One environmental stress that increases with age is inflammation. Although chronic inflammation is detrimental to the long-term function of normal hematopoietic stem cells, several recent studies in animal models have indicated hematopoietic stem cells with CHIP mutations may be resistant to these deleterious effects. However, direct evidence indicating a correlation between increased inflammation and accelerated CHIP in humans is currently lacking. In this study, we sequenced the peripheral blood cells of a cohort of patients with ulcerative colitis, an autoimmune disease characterized by increased levels of pro-inflammatory cytokines. This analysis revealed that the inflammatory environment of ulcerative colitis promoted CHIP with a distinct mutational spectrum, notably positive selection of clones with DNMT3A and PPM1D mutations. We also show a specific association between elevated levels of serum interferon gamma and DNMT3A mutations. These data add to our understanding of how cell extrinsic factors select for clones with specific mutations to promote clonal hematopoiesis. Clonal hematopoiesis of indeterminate potential (CHIP) is a pre-malignant condition associated with expansion of blood cell populations driven by age-dependent acquisition of somatic mutations in hematopoietic stem cells (HSCs) [1Jaiswal S Fontanillas P Flannick J et al.Age-related clonal hematopoiesis associated with adverse outcomes.N Engl J Med. 2014; 371: 2488-2498Google Scholar, 2Genovese G Kahler AK Handsaker RE et al.Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.N Engl J Med. 2014; 371: 2477-2487Google Scholar, 3Xie M Lu C Wang J et al.Age-related mutations associatedwith clonal hematopoietic expansion and malignancies.Nat Med. 2014; 20: 1472-1478Google Scholar, 4Zink F Stacey SN Norddahl GL et al.Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly.Blood. 2017; 130: 742-752Google Scholar, 5Abelson S Collord G Ng SWK et al.Prediction of acute myeloid leukaemia risk in healthy individuals.Nature. 2018; 559: 400-404Google Scholar, 6Arends CM Galan-Sousa J Hoyer K et al.Hematopoietic lineage distribution and evolutionary dynamics of clonal hematopoiesis.Leukemia. 2018; 32: 1908-1919Google Scholar, 7Buscarlet M Provost S Zada YF et al.DNMT3A and TET2 dominate clonal hematopoiesis and demonstrate benign phenotypes and different genetic predispositions.Blood. 2017; 130: 753-762Google Scholar]. Although CHIP mutations are typically present at a low variant allele fraction (VAF), they are cancer drivers and frequently involve genes mutated in myeloid malignancies [1Jaiswal S Fontanillas P Flannick J et al.Age-related clonal hematopoiesis associated with adverse outcomes.N Engl J Med. 2014; 371: 2488-2498Google Scholar, 2Genovese G Kahler AK Handsaker RE et al.Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.N Engl J Med. 2014; 371: 2477-2487Google Scholar, 3Xie M Lu C Wang J et al.Age-related mutations associatedwith clonal hematopoietic expansion and malignancies.Nat Med. 2014; 20: 1472-1478Google Scholar,8Young AL Challen GA Birmann BM Druley TE Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults.Nat Commun. 2016; 7: 12484Google Scholar]. Despite the overall prevalence of CHIP, few of these individuals progress to hematopoietic malignancy [1Jaiswal S Fontanillas P Flannick J et al.Age-related clonal hematopoiesis associated with adverse outcomes.N Engl J Med. 2014; 371: 2488-2498Google Scholar,2Genovese G Kahler AK Handsaker RE et al.Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.N Engl J Med. 2014; 371: 2477-2487Google Scholar,5Abelson S Collord G Ng SWK et al.Prediction of acute myeloid leukaemia risk in healthy individuals.Nature. 2018; 559: 400-404Google Scholar,7Buscarlet M Provost S Zada YF et al.DNMT3A and TET2 dominate clonal hematopoiesis and demonstrate benign phenotypes and different genetic predispositions.Blood. 2017; 130: 753-762Google Scholar,8Young AL Challen GA Birmann BM Druley TE Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults.Nat Commun. 2016; 7: 12484Google Scholar]. In addition to somatic mutations, environmental factors likely contribute to clonal evolution of CHIP. Accumulating evidence suggests inflammation might serve as a selective pressure that contributes to malignant progression of CHIP clones. Individuals with autoimmune diseases [9Anderson LA Pfeiffer RM Landgren O Gadalla S Berndt SI Engels EA Risks of myeloid malignancies in patients with autoimmune conditions.Br J Cancer. 2009; 100: 822-828Google Scholar] or a history of chronic infection [10Kristinsson SY Bjorkholm M Hultcrantz M Derolf AR Landgren O Goldin LR Chronic immune stimulation might act as a trigger for the development of acute myeloid leukemia or myelodysplastic syndromes.J Clin Oncol. 2011; 29: 2897-2903Google Scholar] have increased lifetime risk of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). TET2 is a negative regulator of the inflammatory response in myeloid cells [11Cull AH Snetsinger B Buckstein R Wells RA Rauh MJ Tet2 restrains inflammatory gene expression in macrophages.Exp Hematol. 2017; 55 (e13): 56-70Google Scholar], and TET2-mutant CHIP is associated with overproduction of promotes mutant HSC expansion [12Cai Z Kotzin JJ Ramdas B et al.Inhibition of inflammatory signaling in Tet2 mutant preleukemic cells mitigates stress-induced abnormalities and clonal hematopoiesis.Cell Stem Cell. 2018; 23 (e835): 833-849Google Scholar] and accelerates atherosclerosis [13Jaiswal S Natarajan P Silver AJ et al.Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease.N Engl J Med. 2017; 377: 111-121Google Scholar]. As chronic inflammation compromises HSC function, inflammation could shape hematopoiesis by selecting for HSCs with mutations that increase stress tolerance. However, direct evidence of this is lacking. A recent study suggested that patients with rheumatoid arthritis, a chronic inflammatory autoimmune disease, had no increased incidence of CHIP [14Savola P Lundgren S Keranen MAI et al.Clonal hematopoiesis in patients with rheumatoid arthritis.Blood Cancer J. 2018; 8: 69Google Scholar]. But as this patient cohort was relatively small, we sequenced 187 patients with ulcerative colitis (UC), an inflammatory bowel disease characterized by infiltration of T cells in the colon and overproduction of pro-inflammatory cytokines such as tumor necrosis factor α (TNFα) and interferon (IFNγ) to evaluate the role of inflammation in CHIP. Peripheral blood mononuclear cell DNA from ulcerative colitis patients (>50 years old) without prior history of hematologic disease was obtained from the Digestive Diseases Research Core Center (DDRCC) under Washington University Institutional Review Board Protocol 20111078. Samples were sequenced using a panel targeting 40 genes and hotspots recurrently mutated in CHIP, MDS, and AML (Washington University MyeloSeq; Supplementary Table E1, online only, available at www.exphem.org). HaloplexHS (Agilent) amplicon sequencing provides coverage of each target with multiple, distinct amplicons that incorporate unique molecular indexes (UMIs) into sequencing reads for error correction. Libraries were generated using 500 ng DNA, then sequenced on the NovaSeq (Illumina) platform to obtain 16 million reads per sample (10,000 × raw sequence coverage). Sequence data were aligned to build 37 human references with BWA-MEM, creating “read families” from multiple reads with the same UMI sequence, which were used as input for variant identification using VarScan 2, Platypus, and Pindel. Variant filtering was performed on the union of identified variants from these callers using only read families with at least three supporting reads, a VAF >0.5%, at least two supporting HaloplexHS amplicons, more than eight error-corrected reads that support the alternative allele, and at least three supporting read families on each strand. Variants with a population allele frequency >0.1% in the ExAC database or that possessed a VAF >35% were excluded as likely germline variants. The frequency of mutated genes in ulcerative colitis patients was compared with the aggregate incidence across indicated control cohorts. Serum TNFα and IFNγ levels were examined using a cytokine-specific ProQuantum Immunoassay kit (Invitrogen, USA). Patients groups were matched for age and sex. Comparisons of CHIP prevalence in this study with that in published data sets were performed using a χ2 test for each age range and gene independently, followed by Bonferroni correction for multiple hypothesis testing. One-way analysis of variance (ANOVA) with Bonferroni multiple test correction was used for analysis of serum cytokine levels. Generalized linear models for multivariate analysis were established using R (version 3.5.3). We sequenced 40 CHIP-associated genes in 187 UC patients (Table 1) to identify genetic variants in their blood compartment. As germline DNA was unavailable, variants with VAFs >35% that were identified in the ExAC database were excluded as likely germline variants. To compare these data with the incidence of CHIP in the general population, we used the following definition of CHIP (CHIP-classical or CHIP-c) as outlined by a prior study with similar limitations [15Coombs CC Zehir A Devlin SM et al.Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes.Cell Stem Cell. 2017; 21 (e374): 374-382Google Scholar]. CHIP-c is defined here as follows:•VAF >2%•Variant reported in COSMIC in “hematopoietic and lymphoid” category OR○Damaging variants (frameshift and stop_gain mutations) in DNMT3A gene within exons 7–23○Any damaging variants in the genes ASXL1, TET2, PPM1D, and TP53○CALR exon 9 indels.Table 1Demographic information of UC patient cohortParameterCH−n = 145CH+n = 42Patients without information availableNo. (%)Males (% of patients)72.727.30 (0)Females (% of patients)83.017.00 (0)Age (y)63.1 ± 0.68 (50–84)66.6 ± 1.18 (55–81)0 (0)Time from diagnosis to blood draw (y)11.9 ± 1.1 (0.087–48.4)12.8 ± 1.67 (0.70–39.1)47 (25.1)Colectomy (% of patients)55.952.069 (36.9)Flare at time of blood collection (% of patients)64.761.359 (31.6)Blood countsWBC (× 1000/mL)9.6 ± 0.47 (3.7–27.3)9.1 ± 1.1 (3.7–34.5)60 (32.1)Neutrophils (× 1000/mL)7.7 ± 0.49 (1.4–25.3)7.3 ± 1.1 (2.2–32.1)66 (35.3)Lymphocytes (× 1000/mL)1.2 ± 0.070 (0.10–3.4)1.3 ± 0.12 (0.30–3.0)66 (35.3)Hemoglobin B (g/dL)11.6 ± 0.25 (6.6–16.0)11.6 ± 0.38 (8.2–15.8)61 (32.6)Hematocrit (%)34.5 ± 0.69 (19.0–47.1)36.8 ± 2.2 (23.0–44.6)61 (32.6)MCV (fL)90.6 ± 0.78 (70.8–107.7)89.8 ± 2.7 (33.4–113.4)62 (33.2)Platelets (× 1000/mL)301.1 ± 12.9 (97.0–706.0)308.1 ± 24.2 (98.0–580.0)61 (32.6)TreatmentsCurrent anti-TNF (% of patients)27.923.175 (40.1)Previous anti-TNF (% of patients)24.431.277 (41.2)Current immuno-modulator (% of patients)27.923.376 (40.6)Previous immuno-modulator (% of patients)29.423.777 (41.2)Current steroids (% of patients)30.319.675 (40.1)Previous steroids (% of patients)29.023.876 (40.6)Values are expressed as mean ± SEM (range) unless otherwise indicated. Open table in a new tab Supplementary Table E1Amplicons covered by MyeloSeq (Washington University, St. Louis, MO) custom gene panelGeneCategoryTarget(s)Frequency in AML*Frequency obtained from TCGA.Frequency in MDS⁎⁎Frequency averaged from Haferlach et al. (Leukemia, 2014), Papaemmanuil et al. (Blood, 2013), and Walter et al. (Leukemia, 2013).BRAFActivated signalingV600E0.0%0.6%FLT3Activated signalingTKD and ITD28.0%1.3%JAK2Activated signalingV617, exon 120.5%3.3%KITActivated signalingexons 2, 8-13, 174.0%1.4%KRASActivated signalingG12, G13, Q614.0%2.1%MPLActivated signalingexon 100.0%2.3%NF1Activated signalingwhole gene1.5%4.8%NRASActivated signalingG12, G13, Q618.0%4.1%PTPN11Activated signalingexons 3, 13, 144.0%1.2%ASXL1Chromatin modifierswhole gene2.5%14.4%EZH2Chromatin modifierswhole gene1.0%7.2%SUZ12Chromatin modifierswhole gene1.4%0.0%CSF3RChromatin modifierswhole gene0.5%RAD21Cohesinwhole gene2.0%1.2%SMC1ACohesinwhole gene0.5%1.5%SMC3Cohesinwhole gene1.0%1.5%STAG2Cohesinwhole gene3.0%6.5%DNMT3ADNA methylationwhole gene22.0%11.6%IDH1DNA methylationR13210.0%2.1%IDH2DNA methylationR140, R17210.0%3.7%TET2DNA methylationwhole gene9.0%23.9%CALROther genesexon 91.0%0.0%CBLOther genesexons 8, 91.0%4.2%NPM1Other genesexon 1127.0%2.5%PIGAOther geneswhole gene0.0%0.6%PPM1DOther genesexon 60.0%0.1%CUX1Other geneswhole geneSF3B1Spliceosomewhole gene0.5%21.5%SRSF2Spliceosomeexon 10.5%11.0%U2AF1Spliceosomeexons 2, 64.0%8.2%ZRSR2Spliceosomewhole gene0.0%5.0%BCORTranscriptional regulatorwhole gene1.0%5.2%BCORL1Transcriptional regulatorwhole gene0.5%4.2%CEBPATranscriptional regulatorwhole gene6.0%0.7%ETV6Transcriptional regulatorwhole gene1.0%2.5%GATA2Transcriptional regulatorwhole gene0.0%1.6%RUNX1Transcriptional regulatorwhole gene12.0%8.4%PHF6Tumor suppressorswhole gene2.0%2.6%TP53Tumor suppressorswhole gene8.0%10.1%WT1Tumor suppressorswhole gene6.0%1.2% Frequency obtained from TCGA. Frequency averaged from Haferlach et al. (Leukemia, 2014), Papaemmanuil et al. (Blood, 2013), and Walter et al. (Leukemia, 2013). Open table in a new tab Values are expressed as mean ± SEM (range) unless otherwise indicated. Because of the sequencing depth and error correction, the limit of detection of 0.5% VAF facilitated a more complete view of clonal hematopoiesis in these patients. Such variants were classified as CHIP-high sensitivity (CHIP-hs) mutations by satisfying the following criteria; VAF >0.5% and variant reported in COSMIC in “hematopoietic and lymphoid” category >2 times. CHIP-c was identified in 12.8% of patients in this cohort, with 22.5% of patients being positive for CHIP-hs. Twenty-six CHIP-c variants were identified in 24 patients (Figure 1A), with an additional 31 CHIP-hs variants harbored in 18 patients (Figure 1B; Supplementary Table E2, online only, available at www.exphem.org). C>T transitions were the most prevalent single-nucleotide variants, consistent with an age-dependent mutational signature (Figure 1A, B). The most recurrently mutated gene in UC patients with CHIP-c (Figure 1C) and CHIP-hs (Figure 1D) was DNMT3A, followed by PPM1D. As a comparable control cohort was not available for this study, to gain a sense of the prevalence of CHIP in UC patients compared with the general population, we compared our data with epidemiological sequencing cohorts using the quantitative definition of CHIP outlined above across all data sets. The frequency of CHIP-c in UC patients trended higher after the sixth decade, although sample size restrictions in older individuals in the UC cohort limit this comparison (Figure 1E). There was an age-dependent increase in CHIP-hs in UC patients without sex bias (Figure 1F). The frequency of DNMT3A+ and PPM1D+ CHIP was higher in UC patients (Figure 1G) compared with the aggregate incidence across the other studies [1Jaiswal S Fontanillas P Flannick J et al.Age-related clonal hematopoiesis associated with adverse outcomes.N Engl J Med. 2014; 371: 2488-2498Google Scholar, 2Genovese G Kahler AK Handsaker RE et al.Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.N Engl J Med. 2014; 371: 2477-2487Google Scholar, 3Xie M Lu C Wang J et al.Age-related mutations associatedwith clonal hematopoietic expansion and malignancies.Nat Med. 2014; 20: 1472-1478Google Scholar, 4Zink F Stacey SN Norddahl GL et al.Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly.Blood. 2017; 130: 742-752Google Scholar, 5Abelson S Collord G Ng SWK et al.Prediction of acute myeloid leukaemia risk in healthy individuals.Nature. 2018; 559: 400-404Google Scholar, 6Arends CM Galan-Sousa J Hoyer K et al.Hematopoietic lineage distribution and evolutionary dynamics of clonal hematopoiesis.Leukemia. 2018; 32: 1908-1919Google Scholar, 7Buscarlet M Provost S Zada YF et al.DNMT3A and TET2 dominate clonal hematopoiesis and demonstrate benign phenotypes and different genetic predispositions.Blood. 2017; 130: 753-762Google Scholar]. TET2 is typically the second most mutated gene (after DNMT3A) in CHIP sequencing studies of healthy individuals [1Jaiswal S Fontanillas P Flannick J et al.Age-related clonal hematopoiesis associated with adverse outcomes.N Engl J Med. 2014; 371: 2488-2498Google Scholar,2Genovese G Kahler AK Handsaker RE et al.Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.N Engl J Med. 2014; 371: 2477-2487Google Scholar,7Buscarlet M Provost S Zada YF et al.DNMT3A and TET2 dominate clonal hematopoiesis and demonstrate benign phenotypes and different genetic predispositions.Blood. 2017; 130: 753-762Google Scholar,8Young AL Challen GA Birmann BM Druley TE Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults.Nat Commun. 2016; 7: 12484Google Scholar], but was less frequent in UC patients (Figure 1C, D). This was intriguing because certain inflammatory cytokines (e.g., IL-6) enhance the competitive fitness of Tet2-mutant HSCs [12Cai Z Kotzin JJ Ramdas B et al.Inhibition of inflammatory signaling in Tet2 mutant preleukemic cells mitigates stress-induced abnormalities and clonal hematopoiesis.Cell Stem Cell. 2018; 23 (e835): 833-849Google Scholar]. It is possible the inflammatory milieu of UC is not conducive for the clonal expansion of TET2-mutant clones. The relative incidence of TET2+ CHIP was lower in UC because certain mutations were positively selected, notably PPM1D (Figure 1G). PPM1D-mutant clones undergo strong positive selections in response to stress such as chemotherapy. The stress of chronic inflammation in UC patients may provide another environment that promotes growth of PPM1D-mutant clones.Supplementary Table E2Annotation of variants identified from MyeloSeq analysis of 187 patients with UCSAMPLE IDGenderAgeCHROMPOSREFALTVAFSYMBOLConsequenceIMPACTBIOTYPECDS_positionProtein_positionAmino_acidsCodonsExisting_variation1012-1-112706Female57225463541GC1.53%DNMT3Amissense_variantMODERATEprotein_coding2141714S/CtCc/tGcrs367909007&COSM442677&COSM5580894&COSM5580895&COSM870111079-2-022108Male664106190906TG6.89%TET2splice_donor_variant&NMD_transcript_variantHIGHnonsense_mediated_decay225467497GA4.65%DNMT3Astop_gainedHIGHprotein_coding1579527Q/*Cag/TagCOSM5095526&COSM5095527109-101609-1Female701913054627AATTGTC15.68%CALRframeshift_variantHIGHprotein_coding1154-1155385K/NCXaag/aaTTGTCgrs7654765091103-1-071509Male681758740749CT0.50%PPM1Dstop_gainedHIGHprotein_coding1654552R/*Cga/Tgars779070661&COSM9822261163-1-062813Male514106155939TAACTCTGT0.62%TET2frameshift_variant&NMD_transcript_variantHIGHnonsense_mediated_decay841-847281-283NSE/XAACTCTGag/ag1382-1-070705Male80225467073CT0.99%DNMT3Astop_gainedHIGHprotein_coding1802601W/*tGg/tAgrs9413253741422-1-042408Female67225457252TC3.20%DNMT3Amissense_variantMODERATEprotein_coding2635879N/DAac/GacCOSM1583135&COSM58787431637-1-100407Male78225470498GA1.94%DNMT3Amissense_variantMODERATEprotein_coding976326R/CCgc/Tgcrs747448117&COSM4169721&COSM43836001645-2-110607Male75225468121CT1.72%DNMT3Asplice_donor_variantHIGHprotein_codingrs766110518&COSM4775128&COSM4775129177-2-092806Male68225471016GA0.97%DNMT3Astop_gainedHIGHprotein_coding745249Q/*Cag/Tagrs7597474761785-2-050806Male6295073770GT5.60%JAK2missense_variantMODERATEprotein_coding1849617V/FGtc/Ttcrs77375493&CM123094&COSM12600&COSM291171792-1-051611Male731758740603CA4.94%PPM1Dstop_gainedHIGHprotein_coding1508503S/*tCa/tAars3756184231811-1-062807Female70225470560CT3.23%DNMT3Astop_gainedHIGHprotein_coding914305W/*tGg/tAgrs765341003&COSM11696367148523591GA1.87%EZH2stop_gainedHIGHprotein_coding862288R/*Cga/TgaCOSM1000721&COSM4384289225470581CT1.60%DNMT3Amissense_variantMODERATEprotein_coding893298G/EgGg/gAgCOSM5878868&COSM5878869225466766CT1.06%DNMT3Asplice_donor_variantHIGHprotein_coding1846-1-121707Male64225457242CT19.20%DNMT3Amissense_variantMODERATEprotein_coding2645882R/HcGc/cAcrs147001633&COSM1583129&COSM3356083&COSM442676&COSM52944&COSM997402028-1-101513Male70225466849GTG2.27%DNMT3Aframeshift_variant&splice_region_variantHIGHprotein_coding1853618D/XgAc/gc2207-2-082307Male68225469918AC2.17%DNMT3Asplice_donor_variantHIGHprotein_codingCOSM4766077225469919CT1.81%DNMT3Asplice_donor_variantHIGHprotein_codingrs747220514&COSM5878851&COSM58788522225-3-083106Female67225463241AC2.80%DNMT3Amissense_variantMODERATEprotein_coding2252751F/CtTc/tGcrs7658133042716-1-032008Male71225458595AG1.38%DNMT3Amissense_variantMODERATEprotein_coding2578860W/RTgg/Cggrs373014701&COSM231568&COSM43835242862-1-092310Male74225458648TC4.50%DNMT3Amissense_variantMODERATEprotein_coding2525842Q/RcAg/cGgrs7711743921758740749CT0.95%PPM1Dstop_gainedHIGHprotein_coding1654552R/*Cga/Tgars779070661&COSM9822263096-4-091406Female711758740689ACA4.50%PPM1Dframeshift_variantHIGHprotein_coding1595532T/XaCa/aa3179-1-032107Female70225469646CT0.61%DNMT3Asplice_acceptor_variantHIGHprotein_coding3205-1-092208Male682031022786GCCATGCCAGGCCTTG24.79%ASXL1frameshift_variantHIGHprotein_coding2257-2270753-757PCQAL/XCCATGCCAGGCCTTg/g2031024021CCT1.89%ASXL1frameshift_variantHIGHprotein_coding3491-34921164S/SXtct/tcTt3240-2-102606Female55225470516GA2.61%DNMT3Astop_gainedHIGHprotein_coding958320R/*Cga/Tgars778270132&COSM1318922&COSM1337213352-1-100107Male624106164764GA2.70%TET2missense_variant&NMD_transcript_variantMODERATEnonsense_mediated_decay35411181V/IGtt/AttCOSM871241758740749CT2.04%PPM1Dstop_gainedHIGHprotein_coding1654552R/*Cga/Tgars779070661&COSM9822263602-1-012606Female53225469946GT1.86%DNMT3Amissense_variantMODERATEprotein_coding1096366R/SCgc/AgcCOSM59449763876-1-102212Male7010112361545TC2.90%SMC3missense_variantMODERATEprotein_coding2795932L/PcTa/cCars758156711&COSM5487671225457176GA0.97%DNMT3Amissense_variantMODERATEprotein_coding2711904P/LcCg/cTgrs149095705&COSM1741211&COSM870073880-2-031308Male751758740749CT0.92%PPM1Dstop_gainedHIGHprotein_coding1654552R/*Cga/Tgars779070661&COSM9822263925-1-072506Female84225463235CAGAC1.28%DNMT3Ainframe_deletionMODERATEprotein_coding2255-2257752-753FW/WtTCTgg/tggrs749132507&COSM133723&COSM53517313943-2-072706Female704106157827CT1.05%TET2stop_gained&NMD_transcript_variantHIGHnonsense_mediated_decay2728910Q/*Caa/TaaCOSM4383868&COSM4383869225468898TCT0.87%DNMT3Aframeshift_variantHIGHprotein_coding1464488R/XcgG/cg401-2-100807Female69225469624TCT2.77%DNMT3Aframeshift_variantHIGHprotein_coding1143381G/XggG/ggrs745939351225468186CT0.88%DNMT3Amissense_variantMODERATEprotein_coding1490497C/YtGt/tAtrs779323387&COSM1318925&COSM1318926404-012207-2Male812031021176CAGC12.09%ASXL1frameshift_variantHIGHprotein_coding1161-1162387-388SV/SXtcAGtg/tctg4564-1-032014Female74225467032GA2.16%DNMT3Astop_gainedHIGHprotein_coding1843615Q/*Cag/Tagrs768548187&COSM1318929&COSM1318930225459804CT0.75%DNMT3Asplice_donor_variantHIGHprotein_codingrs762213449&COSM4766078482-1-092210Male64225457181GAG2.51%DNMT3Aframeshift_variantHIGHprotein_coding2705902F/XtTc/tc225463248GA1.91%DNMT3Amissense_variantMODERATEprotein_coding2245749R/CCgc/Tgcrs754613602&COSM219133&COSM35802524907-4527Male55225463602GC0.74%DNMT3Asplice_region_variant&intron_variantLOWprotein_coding528-1-061413Female60225457236GT3.94%DNMT3Amissense_variantMODERATEprotein_coding2651884A/EgCg/gAgCOSM231547559-2-061306Male71225470498GA0.61%DNMT3Amissense_variantMODERATEprotein_coding976326R/CCgc/Tgcrs747448117&COSM4169721&COSM4383600588-1-101906Female601758740532TAT2.21%PPM1Dframeshift_variantHIGHprotein_coding1438480K/XAaa/aars749097315677-1-011714Male691758740800ACCTCACAGA2.10%PPM1Dframeshift_variantHIGHprotein_coding1706-1713569-571TSQ/XaCCTCACAG/aX133551267CA1.84%PHF6stop_gainedHIGHprotein_coding903301Y/*taC/taACOSM144570&COSM3558102&COSM3558103&COSM5945628706-1-080306Male66225463247CT9.62%DNMT3Amissense_variantMODERATEprotein_coding2246749R/HcGc/cAcrs34843713&COSM221577747-1-013112Male56225467134AC1.97%DNMT3Amissense_variantMODERATEprotein_coding1741581W/GTgg/GggCOSM1583082&COSM231554&COSM231558858-2-070507Male62225464439GA1.44%DNMT3Astop_gainedHIGHprotein_coding2074692Q/*Cag/Tag916-1-052406Male70225462054CTC0.61%DNMT3Aframeshift_variantHIGHprotein_coding2352784E/XgaA/ga225462006TC0.51%DNMT3Amissense_variantMODERATEprotein_coding2401801M/VAtg/Gtgrs753567076&COSM5944905 Open table in a new tab The incidence of CHIP-hs in UC patients was not related to colectomy, a disease flare at time of blood collection (diarrhea, blood in stool, abdominal pain), current therapy, or treatment history (Figure 2A). CHIP-hs+ patients in this cohort were associated with increased age (Figure 2B), but not time since UC diagnosis (Figure 2C). No difference in blood counts was noted between CHIP-hs+ and CHIP– individuals (Figure 2D). The VAF of most variants was <5%; however, there were several cases where clones dominated the blood, particularly for ASXL1 mutations and DNMT3AR882H (Figure 2E). The distribution of CHIP-hs+ mutations in UC patients was not clustered with age or sex for any given gene (Figure 2F). The mutational spectrum of DNMT3A in UC patients reflected the typical pattern for this gene in CHIP, with mutations clustered in functional domains without enrichment for the R882 hotspot (Figure 2G). PP1MD mutations were concentrated in the C-terminus (Figure 2G), mirroring those in therapy-related myeloid neoplasms [16Kahn JD Miller PG Silver AJ et al.PPM1D-truncating mutations confer resistance to chemotherapy and sensitivity to PPM1D inhibition in hematopoietic cells.Blood. 2018; 132: 1095-1105Google Scholar,17Hsu JI Dayaram T Tovy A et al.PPM1D mutations drive clonal hematopoiesis in response to cytotoxic chemotherapy.Cell Stem Cell. 2018; 23 (e706): 700-713Google Scholar]. We sought to determine if inflammatory signals selected for specific clones in UC patients. UC patients were stratified into three groups matched for age and sex: CHIP− (n = 21), CHIP-hs+ without DNMT3A mutations (CH+DNMT3A–, n = 17), and CHIP-hs+ with DNMT3A mutations (CH+DNMT3A+, n = 19). We focused on TNFα and IFNγ because these cytokines have described roles in the symptoms of UC, and treatment can involve targeting these molecules. Serum TNFα levels did not differ among the three patient groups (Figure 2H). However, CH+DNMT3A+ patients were associated with significantly higher levels of serum IFNγ (Figure 2H). Average clone size did not contribute to the observed variances (Figure 2H), although DNMT3A VAF was a significant contributor to increased IFNγ levels in multivariate analysis using a generalized linear model (p = 0.026). These data imply that increased IFNγ may select for clones with DNMT3A mutations. Focused mechanistic studies will be required to show this relationship is causative and not correlative. Cumulatively, these data indicate that UC patients may harbor slightly higher levels of CHIP than the general population and that the inflammatory environment of UC potentially selects for the growth of HSC clones with specific mutations. The Washington University Digestive Diseases Research Cores Center (DDRCC) BioBank Core is supported by National Institutes of Health (NIH) Grant P30 DK052574 . This work was supported by the Washington University McDonnell Genome Institute and the NIH (Grant R01 DK102428 to GAC). ELO was supported by NIH Grant F31DK114951 . Philanthropic support comes from the Lawrence C. Pakula IBD Research Innovations Fund (MAC). GAC is a scholar of the Leukemia and Lymphoma Society. The authors declare no competing interests. GAC was responsible for the project conceptualization and experimental design. Experiments were performed by CRCZ, ELO, and GAC. Critical reagents were provided by DN and RDN; CRCZ, MG, MAC, ELO, DHS, and GAC performed the data analysis. CRCZ prepared the original draft of the manuscript and GAC reviewed and edited it. GAC was responsible for project administration and funding acquisition.
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