Genetic and epigenetic alterations in normal tissueshave differential impacts on cancer risk among tissuesSatoshi Yamashitaa,1, Takayoshi Kishinoa,1, Takamasa Takahashia, Taichi Shimazub, Hadrien Charvatb,Yasuo Kakugawac, Takeshi Nakajimac, Yi-Chia Leed, Naoko Iidaa, Masahiro Maedaa, Naoko Hattoria,Hideyuki Takeshimaa, Reiko Naganoa, Ichiro Odac, Shoichiro Tsuganeb, Ming-Shiang Wud, and Toshikazu Ushijimaa,2

aDivision of Epigenomics, National Cancer Center Research Institute, Tokyo 104-0045, Japan; bEpidemiology and Prevention Group, Center for Public HealthSciences, National Cancer Center, Tokyo 104-0045, Japan; cEndoscopy Division, National Cancer Center Hospital, Tokyo 104-0045, Japan; and dDepartmentof Internal Medicine, College of Medicine, National Taiwan University, Taipei 100, Taiwan

Edited by Peter A. Jones, Van Andel Institute, Grand Rapids, MI, and approved December 12, 2017 (received for review October 3, 2017)

Genetic and epigenetic alterations are both involved in carcinogen-esis, and their low-level accumulation in normal tissues constitutescancer risk. However, their relative importance has never beenexamined, as measurement of low-level mutations has been diffi-cult. Here, we measured low-level accumulations of genetic andepigenetic alterations in normal tissues with low, intermediate, andhigh cancer risk and analyzed their relative effects on cancer risk inthe esophagus and stomach. Accumulation of genetic alterations,estimated as a frequency of rare base substitution mutations, sig-nificantly increased according to cancer risk in esophageal mucosae,but not in gastric mucosae. The mutation patterns reflected theexposure to lifestyle risk factors. In contrast, the accumulation ofepigenetic alterations, measured as DNAmethylation levels of markergenes, significantly increased according to cancer risk in both tis-sues. Patients with cancer (high-risk individuals) were precisely dis-criminated from healthy individuals with exposure to risk factors(intermediate-risk individuals) by a combination of alterations inthe esophagus (odds ratio, 18.2; 95% confidence interval, 3.69–89.9) and by only epigenetic alterations in the stomach (odds ratio,7.67; 95% confidence interval, 2.52–23.3). The relative importanceof epigenetic alterations upon genetic alterations was 1.04 inthe esophagus and 2.31 in the stomach. The differential impactsamong tissues will be critically important for effective cancer pre-vention and precision cancer risk diagnosis.

genetics | epigenetics | mutations | DNA methylation | normal tissues

The vast majority of human cancers develop via the accumu-lation of genetic and epigenetic alterations (1, 2). Both

alterations can be accumulated, affecting both driver andpassenger genes, in normal tissues at low levels long beforecancer develops (3, 4), and this accumulation constitutescancer risk (5). In particular, accumulation of epigenetic al-terations, namely, aberrant DNA methylation, in normal tis-sues of patients with cancer has been well-documented formultiple types of cancer (6–10) and is associated with cancerrisk for some cancers, including gastric and esophageal squa-mous cell cancers (ESCCs) (11, 12). Especially for gastriccancer, a multicenter prospective cohort study established astrong association between accumulation of aberrant DNAmethylation and cancer risk, even in a clinical setting (13, 14).Accumulation of genetic alterations, represented by point mu-

tations (15), also constitutes cancer risk, as demonstrated by manystudies in transgenic animals with a marker gene (16). By using amarker gene, whose point mutations at the 10−5 to 10−4 per genelevel can be accurately measured, associations between carcinogenexposure and accumulation of point mutations and between theaccumulation and cancer risk have been shown (17). In contrast tothese animal studies, in humans, it has been difficult to measureaccumulation levels of point mutations, mainly because of theirvery low frequencies. Taking advantage of clonal patches of cells orclonal expansion of cells, recent studies demonstrated that muta-tions are indeed accumulated in normal human tissues (18, 19).

We also recently developed a method to quantify rare pointmutations in any human DNA sample by analyzing only a smallnumber of DNA molecules (20). Because of its technical simplicity,quantitative assessment of accumulation of point mutations in hu-man normal tissues can be now performed. It is expected that, byanalyzing both genetic and epigenetic alterations in normal tissuesof patients with cancer and healthy individuals (with and withoutexposure to cancer risk factors), we can estimate relative impacts ofgenetic and epigenetic alterations on cancer risk. Clarification oftheir relative importance is expected to have great value for precisecancer risk estimation based on their accumulation and for allo-cation of resources for effective cancer prevention, as etiologies forgenetic and epigenetic alterations are different (21).In the current study, we evaluate the impacts of accumulation

of point mutations and DNA methylation on ESCC and gastriccancer risk and reveal their differential impacts.

ResultsGenetic Alterations in Normal Esophageal and Gastric Mucosae andCancer Risk. First, we measured mutation frequency in normalesophageal and gastric mucosae with three cancer risk levels(samples in the test set, SI Appendix, Table S1; flowchart, SI

Significance

The relative importance of genetic and epigenetic alterations innormal tissues on cancer risk was clearly different betweenesophageal squamous cell and gastric cancers, implying a varietyof differences in various types of cancers. The difference ob-served was well explained by known etiologies: tobacco mu-tagens for esophageal cancer and chronic inflammation forepigenetic alterations in gastric cancer. The study showed that,if epigenetic and genetic alterations in normal tissues are com-bined, reflecting their relative contributions, patients with can-cer can be precisely discriminated, opening up an avenue toprecision cancer risk diagnosis. The study also indicated that foreffective cancer prevention, allocation of resources and effortsagainst genetic and epigenetic alterations should consider theirrelative contributions.

Author contributions: S.Y., T.K., and T.U. designed research; S.Y., T.K., T.T., and R.N.performed research; Y.K., T.N., Y.-C.L., M.M., N.H., H.T., I.O., S.T., and M.-S.W. contributednew reagents/analytic tools; S.Y., T.S., H.C., N.I., and R.N. analyzed data; S.Y. and T.U.wrote the paper; and T.U. supervised the study.

Conflict of interest statement: S.Y. and T.U. made a patent application with Sysmex Corporation.

This article is a PNAS Direct Submission.

Published under the PNAS license.

Data deposition: The data obtained have been deposited in the Gene Expression Omnibus(GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE77991).1S.Y. and T.K. contributed equally to this work.2To whom correspondence should be addressed. Email: tushijim@ncc.go.jp.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1717340115/-/DCSupplemental.

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Appendix, Fig. S1) (20). In esophageal mucosae, mutation fre-quencies in the low-risk (n = 30), intermediate-risk (n = 32), andhigh-risk (n = 31) groups were 1.5 ± 0.2 (mean ± SD), 1.8 ± 0.4,and 2.4 ± 0.6 × 10−5 per base, respectively (Fig. 1A). The fre-quency of genetic alterations clearly and significantly increased ina stepwise manner according to the risk level. In particular, theodds ratio to distinguish patients with ESCC (high-risk group)from healthy individuals exposed to risk factors, namely, alcoholdrinking, betel quid chewing, and cigarette smoking (ABC) (12, 22)(intermediate-risk group), was as high as 18.2 (95% confidenceinterval, 3.7–89.9). The mutation frequency was weakly correlatedwith alcohol drinking (r = 0.362; P = 0.04, SI Appendix, Table S2),but not with smoking or chewing betel quid.In gastric mucosae, surprisingly, a stepwise increase with respect

to risk level was not observed (Fig. 1A). Namely, the mutationfrequencies in the low-risk (n = 32), intermediate-risk (n = 32),and high-risk (n = 32) groups were 0.6 ± 0.3, 0.9 ± 0.4, and 0.7 ±0.4 × 10−5 per base, respectively. Although the difference betweenhealthy individuals exposed to the major risk factor, Helicobacterpylori infection, and those without was significant, the differencebetween healthy individuals with H. pylori infection and patientswith cancer who have had H. pylori infection was not. This sug-gested a larger contribution of epigenetic alterations than that ofgenetic alterations in gastric carcinogenesis.

Characteristics of Mutation Patterns in Normal Tissues. We also an-alyzed mutation patterns in esophageal and gastric mucosae fortheir characteristics and their association with mutation signaturesof specific lifestyle risk factors (Fig. 1B and SI Appendix, Fig. S2).In esophageal mucosae, no significant difference in mutationpatterns was detected among the three risk groups. However,when the analysis was limited to individuals most heavily influ-enced by smoking (individuals in the high-risk group, smokingscore, 4; age, <60 y), the mutation type associated with tobaccosmoking (C:G to A:T transversions) (23, 24) had a significantlyhigher frequency than the low-risk group (P = 0.032; SI Appendix,Fig. S3A and Table S3). In gastric mucosae, importantly, themutation signature of activation-induced cytidine deaminase (RCto RT) was more frequent in individuals with H. pylori infectionthan in those without H. pylori infection (P = 0.006; SI Appendix,Fig. S3B and Table S4), in accordance with a previous report (25).To assess selection bias for protein-altering mutations in normal

tissues, we compared the ratio of nonsynonymous to synonymousmutations (dN/dS). In both esophageal and gastric mucosae, re-gardless of risk groups, nonsynonymous mutations were significantlyless frequent than synonymous mutations (dN/dS = 0.50–0.58; SIAppendix, Fig. S4), suggesting that nonsynonymous mutations arestrongly selected against in normal human tissues. We further ana-lyzed similarity of mutation patterns between normal and cancertissues (SI Appendix, Fig. S5), using mutations reported in esophagealcancers (n = 88) (26) and gastric cancers (n = 100) (27). Cosinesimilarity was very high in the stomach (cosine similarity = 0.97)and high in the esophagus (cosine similarity = 0.88), suggesting thatmutation signatures in normal tissues are reflected in cancer tissues.

Epigenetic Alterations in Normal Esophageal and Gastric Mucosaeand Cancer Risk. We then focused on the impact of epigenetic al-terations on cancer risk (flowchart, SI Appendix, Fig. S1). To as-sess overall epigenome damage accumulated in a tissue, it iscritical to use appropriate marker genes for individual tissues. Foresophageal mucosae, such methylation markers have not beenextensively screened. Therefore, we performed genome-widemethylation analyses of samples in a screening set (n = 27), us-ing two different algorithms (SI Appendix, Fig. S6). One methodinvolved screening for probes with large differences between thetwo groups. The other method involved screening for probes withhigh variance within the high-risk group [i.e., the epigenetic vari-able outliers for risk prediction algorithm (iEVORA) method](10, 28). We obtained a total of eight candidate regions with high-quality quantitative methylation-specific PCR (qMSP) primers(four by each method; SI Appendix, Tables S5 and S6). Methyl-ation levels of the eight regions and one ESCC risk marker pre-viously isolated (HOXA9) (12, 29) were measured by qMSP in thevalidation set (n = 154; SI Appendix, Table S1). Seven markers(TFAP2E, OTX1, OPLAH, CHAD, MARCH11, GALR1, andHOXA9) showed significant differences between the high-risk andintermediate-risk groups and were considered definite risk markersfor ESCC (SI Appendix, Fig. S7).Then we analyzed the methylation levels of the seven markers

in the test set used for the mutation analysis (Fig. 2A and SIAppendix, Fig. S8). The methylation levels of all the sevenmarkers were significantly different between the low- andintermediate-risk groups (due to exposure to lifestyle risk fac-tors) and between the intermediate- and high-risk groups (due tocancer risk). The areas under the curve of the seven markers todistinguish patients with cancer among individuals with exposureto risk factors were 0.61–0.79 (SI Appendix, Fig. S9A). Thus,epigenetic alterations in esophageal mucosae were strongly as-sociated with cancer risk. Age-related methylation was analyzedin the three risk groups. Especially in the low-risk group, theinfluence of exposure to lifestyle risk factors was minimum, butno significant correlations were observed in this group (SI Ap-pendix, Fig. S10).

Fig. 1. Accumulation of somatic mutations in esophageal and gastric mu-cosae. (A) Mutation frequencies in esophageal (n = 93) and gastric (n = 96)mucosae measured by the 100-copy method (20). In esophageal mucosae,the mutation frequency showed a stepwise increase from healthy individualswithout lifestyle risk factors (low risk, n = 30) to healthy individuals withlifestyle risk factor exposures (intermediate risk, n = 32) and patients withcancer (high risk, n = 31). In contrast, in gastric mucosae, the mutationfrequency did not show a stepwise increase with respect to risk level [lowrisk (n = 32), intermediate risk (n = 32), and high risk (n = 32)], but thedifference between the low- and intermediate-risk groups was significant.(B) Mutation patterns in esophageal and gastric mucosae depicted using a96-substitution classification. N shows the number of mutations analyzed ineach panel. The mutation patterns were distinct between the esophagealand gastric mucosae.

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In gastric mucosae, we previously identified methylationmarkers in a genome-wide methylation analysis and demonstratedtheir usefulness as cancer risk markers in a prospective clinicalstudy (13, 14). Therefore, we used three markers established in ourprevious studies (miR-124a-3, EMX1, and NKX6.1) to evaluate theimpact of epigenetic alterations in gastric mucosae. The methyl-ation levels of the three markers increased prominently in astepwise manner according to the risk level (Fig. 2A and SI Ap-pendix, Fig. S11). The areas under the curve of the three markerswere 0.65–0.79 (SI Appendix, Fig. S9B). Epigenetic alteration wasthus also strongly associated with cancer risk in gastric mucosae.Significant correlations among methylation levels of all of the

methylation markers were observed, especially in gastric muco-sae (SI Appendix, Tables S7 and S8). This finding confirmed thatthese markers measured a single component (i.e., the overalllevel of epigenetic alterations in the genome) and indicated thata marker with the highest area under the curve (TFAP2E foresophageal mucosae and miR-124a-3 for gastric mucosae) can beused as a representative for each tissue. In contrast, regardlessof tissue type, a correlation between mutation frequency andmethylation levels for the risk marker genes was not observed inany risk groups, showing that genetic and epigenetic alterationsare independent indicators of cancer risk. Age-related methyl-ation was analyzed, and no significant correlations were observedin the low-risk group (SI Appendix, Fig. S12). The correlation in

the high-risk group was considered to reflect the duration ofH. pylori infection (30).We analyzed the presence of dense methylation, which can si-

lence the transcription of a downstream gene when it is presentin a promoter CpG island (CGI) (31, 32), by deep bisulfite se-quencing. Both in esophageal and gastric mucosae, not only sparsemethylation but also dense methylation increased significantly in astepwise manner according to the risk level (TFAP2E and miR-124a-3; Fig. 2B), and the results by qMSP were confirmed.

Differential Effect of Genetic and Epigenetic Alterations on CancerRisk. Finally, we assessed the relative importance of genetic andepigenetic alterations on cancer risk by logistic regression anal-yses, using the intermediate-risk group (controls) and the high-risk group (cases; flowchart, SI Appendix, Fig. S1). Based on aunivariate analysis, the mutation frequency in esophageal mu-cosae was associated with ESCC risk with a high odds ratio (18.2;95% CI, 3.7–89.9), and the methylation level of a representativemarker (TFAP2E) was also strongly associated (odds ratio, 10.9;95% CI, 3.2–36.9), showing the importance of both genetic andepigenetic alterations (Fig. 3A). In contrast, in gastric mucosae,the mutation frequency was not associated with gastric cancerrisk, whereas the methylation level was closely associated withcancer risk (odds ratio, 6.6; 95% CI, 2.2–19.7). This clearlyshowed the greater importance of epigenetic alterations ongastric cancer risk compared with genetic alterations.We further incorporated the influence of traditional risk fac-

tors (i.e., age and lifestyle risk factors) by a multivariate logisticregression analysis (Fig. 3B). In esophageal mucosae, the addi-tion of mutation frequency to traditional risk factors significantlyimproved the risk prediction. The addition of the TFAP2Emethylation level also significantly improved the risk prediction,showing that combined measurement of genetic and epigeneticalterations was effective for precise cancer risk estimation, withthe relative importance of methylation being 1.0 (Fig. 3C). Incontrast, in gastric mucosae, the addition of the miR-124a-3 meth-ylation level to a traditional risk factor significantly improved riskprediction, but further addition of mutation frequency did not (Fig.3B), with the relative importance of methylation being 2.3 (Fig. 3C).In both esophageal and gastric mucosae, the same relative impor-tance was observed using other methylation markers (SI Appendix,Fig. S13). These results demonstrated that the impacts of geneticand epigenetic alterations on cancer risk are strikingly differentbetween ESCC and gastric cancer, and the impact of epigeneticalterations exceeded that of genetic alterations in gastric cancerrisk (Fig. 3D).

DiscussionOur analyses demonstrated that the impacts of genetic and epi-genetic alterations on cancer risk are strikingly different betweenESCC and gastric cancer, and epigenetic alterations can have agreater impact on cancer risk than genetic alterations for sometypes of cancers. The relative importance of genetic and epige-netic alterations could have great value in cancer prevention, in-cluding the search for carcinogenic factors and the mobilization ofsocial resources. In addition, combining genetic and epigeneticalterations is expected to be useful for precision cancer risk di-agnosis, and this approach is applicable to a broad range of cancertypes. The mutation analysis also supported a long-postulated hy-pothesis that genetic alterations accumulate in normal-appearingtissues after exposure to risk factors and constitute cancer risk,which has been termed field cancerization (33, 34).The difference in impacts of genetic and epigenetic alterations

on cancer risk was in accordance with the effects of epidemio-logically established lifestyle risk factors on ESCCs and gastriccancers (35, 36). As for ESCCs, alcohol drinking, betel chewing,and tobacco smoking have been established in the Taiwanesepopulation (22, 37). Among these, tobacco contains multiple

Fig. 2. Accumulation of aberrant DNA methylation in esophageal andgastric mucosae. (A) DNA methylation levels of definite marker genes weremeasured by qMSP in the same samples as in Fig. 1. In esophageal mucosae,the methylation level of a representative marker (TFAP2E) showed a step-wise increase according to the risk level. In gastric mucosae, the methylationlevel of a representative marker (miR-124a-3) showed a prominent stepwiseincrease. (B) DNA methylation of multiple CpG sites analyzed by deep bi-sulfite sequencing. For the low-, intermediate-, and high-risk groups, sam-ples at the 75th, 50th, and 25th percentiles of the methylation level wereanalyzed. For individual samples, more than 300 molecules were sequencedfor 11 CpG sites (TFAP2E) and 23 sites (miR-124a-3), and the sequences of300 randomly selected molecules are shown. The fraction of densely meth-ylated molecules was in good accordance with the methylation level, andthe presence of dense methylation was observed. This analysis showed aprominent effect of epigenetic alterations on gastric cancer risk.

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mutagens (23), and a high concentration of alcohol can produceacetaldehyde, which is mutagenic (38). At the same time, tobaccohas been reported to be also associated with methylation changes inbuccal tissue (39). In contrast, for gastric cancer, H. pylori infectionis the major cause by far (36). H. pylori infection induces chronicinflammation, which is known to induce aberrant DNAmethylationin gastric mucosae (8, 40, 41) and other tissues (6, 42). These dif-ferences of cancer-causing agents and their carcinogenic mecha-nisms, induction of mutation or methylation, can explain thedifference in the relative importance of genetic and epigenetic al-terations in the two tissues. The difference in the impacts wasfurther in accordance with the fact that, even after extensive se-quencing of gastric cancers, only limited numbers of genetic alter-ations of driver genes with low incidences were identified (43, 44).Mutation signatures that reflect exposure to cancer lifestyle

risk factors were observed in the two normal tissues. In esopha-geal mucosae, the mutation type associated with tobacco smokingand a hallmark signature of ESCC (C to A) (26, 45, 46) wasslightly, but significantly, higher in the high-risk group with asevere smoking history than in the low-risk group. In contrast,the mutation type possibly associated with gastric acid reflux and

a hallmark signature of esophageal adenocarcinoma (T to G)(47, 48) was barely observed. This was thought to be becauseesophageal mucosae analyzed in this study were from individualsin Taiwan, where ESCC is the major histological type by far, andbecause all the noncancerous esophageal mucosa samples wereobtained from patients with ESCC. In gastric mucosae, themutation signature of activation-induced cytidine deaminase wasclearly observed, indicating the prominent effect of H. pyloriinfection on gastric mucosae. In addition, the mutation patternsof normal mucosae and cancers were similar both in the esophagusand in the stomach. Nonsynonymous mutations were significantlyless frequent than synonymous mutations, regardless of the riskgroups, in both esophageal and gastric mucosae. These low dN/dSratios showed that nonsynonymous mutations were selected against.This experimentally supported the neutral theory of molecularevolution (49) for somatic mutations, which has been anotherlong-postulated hypothesis.The methylation markers used here were considered to reflect

overall epigenome damage in individual tissues. To support thisidea, methylation levels of multiple markers in a tissue were highlycorrelated (SI Appendix, Tables S7 and S8), suggesting that themarkers measured a single entity. Also, their levels were clearlyincreased by exposure to lifestyle risk factors, but were not corre-lated with age. In addition, methylation of the marker CGIs didnot appear to lead to any biological consequences. Some of themarker CGIs were located outside promoter regions, methylationof which does not necessarily lead to gene silencing (50). Evenwhen a marker CGI was located in a promoter region, its down-stream gene tended to have very low or no expression in thenormal tissue, including HOXA9 (12, 29). These suggested that themarker CGIs were selected not because of their gene functions butbecause of their correlations with overall epigenome damage.In summary, we demonstrated that epigenetic alterations can

have greater impact on cancer risk than genetic alterations insome tissues. The different impacts of genetic and epigeneticalterations have the potential to be important for effective can-cer prevention and precision cancer risk diagnosis.

Materials and MethodsEsophageal Mucosal Samples. A total of 274 esophageal mucosa samples werecollected endoscopically from adults who underwent cancer screening at theNational Taiwan University Hospital from September 2008 to April 2013. Thestudy was approved by the Research Ethics Committee C National TaiwanUniversity Hospital (approval no. IRB200806039R), and written informedconsent was obtained from all participants. The lifestyle ABC risk factors,whose very strong influence on the risk of ESCCs has been established (12,22), were evaluated for patients based on interviews, as previously described(SI Appendix, Table S9) (12). The 274 samples were classified into three riskgroups based on ABC risk factors and healthy/cancer statuses (SI Appendix,Table S1). The low-risk group was composed of 67 normal esophageal mu-cosae from healthy individuals without a history of ABC. The intermediate-risk group was composed of 96 normal-appearing esophageal mucosae fromhealthy individuals with a history of ABC. The high-risk group was composedof 111 noncancerous esophageal mucosae from ESCC patients with a historyof ABC. The 274 samples were divided into three sample sets (screening set,27 samples for the genome-wide DNA methylation analysis; validation set, 154samples for the gene-specific DNA methylation analysis; and test set,93 samples for the analysis of mutation frequency and methylation levelsof definite risk marker genes). Further information on risk exposure levelsis given in SI Appendix, Tables S10–S12.

Gastric Mucosal Samples. A total of 96 gastric mucosa samples were collectedendoscopically from the antral region of adults who underwent cancerscreening at the Research Center for Cancer Prevention and Screening (51),National Cancer Center, Japan, and who underwent endoscopic submucosaldissection at the National Cancer Center Hospital (13), and were used foranalysis of DNA methylation levels and cancer risk in our previous studies(13, 51). The previous studies and the current study were approved by theNational Cancer Center Ethics Committee (approval nos. 2008-104, 2015-139), and written informed consent was obtained from all participants. Point

Fig. 3. Evaluation of impacts of genetic and epigenetic alterations. (A) Aunivariate analysis and (B) a multivariate logistic regression analysis, re-spectively, in the model of discrimination of patients with cancer (high-riskgroup) among individuals with exposure to lifestyle risk factors (high- andintermediate-risk groups). In the univariate analysis (A), a higher area underthe curve was obtained for genetic alterations in esophageal mucosae andfor epigenetic alterations in gastric mucosae. In the multivariate analysis (B),in esophageal mucosae, the addition of mutation frequency to traditionalrisk factors (age + lifestyle factors, model 1) significantly improved the dis-crimination ability (likelihood ratio test P < 0.05). Further, a significantlylarger c-index was obtained by adding the TFAP2E methylation level to themodel. In gastric mucosae, the addition of a methylation risk marker to thetraditional risk factors (model 1) significantly improved the discriminationability, but further addition of mutation frequency did not. An appropriatecombination of genetic and epigenetic alterations was shown to be criticalfor precision cancer risk diagnosis, taking account of life history. (C) Therelative importance of the methylation level and mutation frequencyquantified by computing the ratio of the standardized coefficients and thegeneralization of this ratio. (D) A schema of the relative importance. Inesophageal mucosae, genetic and epigenetic alterations had equal impactson cancer risk, and, in gastric mucosae, epigenetic alterations had a greaterimpact on cancer risk than genetic alterations.

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mutations were newly analyzed in this study, and DNA methylation resultsand clinical information were obtained from the previous studies (13, 51).

The 96 samples were classified into three groups based on past H. pyloriinfection and healthy/cancer statuses (SI Appendix, Table S1). The low-riskgroup was composed of 32 normal gastric mucosae from healthy individualswithout H. pylori infection. The intermediate-risk group was composed of32 normal-appearing gastric mucosae from healthy individuals with pastH. pylori infection (six or more months after the eradication of H. pylori).The high-risk group was composed of 32 noncancerous gastric mucosae frompatients with gastric cancer with past H. pylori infection.

Both for the esophageal and gastric samples, the distributions of sexes andages were comparable between the intermediate- and high-risk groups. Incontrast, the low-risk group included more females for the esophagealsamples (P < 0.001), and the low-risk group had younger ages comparedwith the other groups for gastric samples (P < 0.001).

Measurement of Point Mutations. Rare base substitution mutations weremeasured by amethod recently established: the 100-copy method (20). Briefly,a sequence library (291 regions of 55 cancer-related genes, covering 48,005base positions) was prepared by multiplex PCR, using Ion AmpliSeq LibraryKits 2.0 (Thermo Fisher Scientific) and 100 copies of genomic DNA as a tem-plate. Libraries from different samples were uniquely barcoded and se-quenced using an Ion PI chip, Ion PI Hi-Q Sequencing 200 Kit, and Ion Protonsequencing system (Thermo Fisher Scientific) with an average sequencingdepth of at least 5,000 reads. A base substitution variant with an allele fre-quency above a cutoff value (0.8%) was counted as a somatic mutation, andinsertions and deletions were disregarded. Among the 48,005 bases ampli-fied in the library, 15,313 base positions showed insufficient amplification(<2,500×), and 17,140 positions had a variant allele frequency ≥0.2% in any ofthe three analyses of the same sample (20). The remaining 15,552 base po-sitions from 201 genomic regions (53 genes) were selected as error-resistantbases and were used to measure rare point mutations. The mutation fre-quency was estimated as the number of somatic mutations at 15,552 basesdivided by 1,555,200 (=15,552 × 100). High reproducibility of the estimationof the frequency of rare mutations was shown in our previous study (20), andsequencing was conducted only once in this study.

Analysis of Mutation Patterns. A mutation pattern was depicted using a six-substitution classification or a 96-substitution classification defined by thesubstitution type and sequence context immediately 5′ and 3′ to a mutatedbase. The similarity of mutation patterns was evaluated using cosine similarity(52). The proportions of specific mutation types were compared betweengroups using Pearson’s χ2 test. Effects of isolated variants were estimated usingVariant Effect Predictor (grch37.ensembl.org/Homo_sapiens/Tools/VEP). A dN/dS value was calculated as the actual ratio of nonsynonymous and synonymousmutations divided by the expected ratio of nonsynonymous and synonymousmutations with the assumption that all the base substitutions are expected tooccur with the same probability. Expected consequences of base substitutionsin the regions analyzed were determined as in our previous study (20).

Genome-Wide DNA Methylation Analysis. A genome-wide DNA methylationanalysis was performed using an Infinium HumanMethylation450 BeadChipArray (Illumina) covering 485,577 CpG sites. To adjust for probe design biases,intra-array normalizationwas conducted using a peak-based correctionmethod,Beta Mixture Quantile dilation (53). The methylation level of a CpG site wasrepresented by a beta value, which ranged from 0 (completely unmethylated)to 1 (completely methylated). Data obtained from the microarray have beendeposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo,accession number GSE77991).

Gene-Specific DNA Methylation Analysis. Genomic DNA was treated with so-dium bisulfite and purified as previously described (29). qMSPwas performed byreal-time PCR, using bisulfite-modified DNA with primers specific to methyl-ated DNA and primers universal to bothmethylated and unmethylated DNA (SIAppendix, Table S13). The methylation level was obtained as the number of

methylated DNA molecules at a target locus divided by the number of all DNAmolecules at a control (the RPPH1 gene) locus and normalized to the samemeasurement obtained using SssI-treated genomic DNA (percentage of thevalue of methylated DNA reference). High reproducibility of the methylationmeasurement was established previously (8), and methylation levels weremeasured once in this analysis. Deep bisulfite sequencing was performed usingIon PGM (Thermo Fisher Scientific) with bisulfite-modified DNA and universalprimers (SI Appendix, Table S13), as previously described (54).

Isolation of Methylation Risk Markers for ESCC. To isolate methylation riskmarkers for ESCC, two algorithms were used to compare methylation levels ofnormal esophagealmucosaebetweenhealthy individuals (low- and intermediate-risk groups) and patients with cancer (high-risk group). First, probes werescreened to identify those with large differences between the low- and in-termediate-risk groups and the high-risk group. Specifically, probes that hadalmost no methylation in normal mucosae (low- and intermediate-risk groups)and peripheral blood (beta value < 0.2) and high methylation in noncancerousmucosae (difference of median beta value > 0.2) were obtained.

Second, probes were screened to identify those with high variance asmarkers for a high-risk group by the iEVORAmethod, using the R script iEVORA.R (10, 28). Probes with a Bartlett’s test false discovery rate of less than 0.001 andan unadjusted P value of less than 0.05 based on a t test were selected.

From the CpG sites selected by the two screening methods, those withinpromoter CGIs or intragenic CGIs were further selected. Cross-reactive/polymorphic probes, as identified by Chen et al. (55), were removed. Whenthree or more consecutive probes at a locus showed such differences, thelocus was considered as a candidate cancer risk marker.

Statistical Analysis. The mutation frequency and methylation levels werecompared between groups, taking their distributions into account usingWelch’s t tests and Mann–Whitney U tests, respectively. The correlations be-tween mutation frequency and methylation levels for risk marker genes,among methylation levels for risk marker genes, and between age andmethylation levels, were assessed by calculating the Pearson’s product-moment correlation coefficient. A receiver operating characteristics analysiswas performed to determine an optimal cutoff value for a mutation frequencyand a methylation level. The ranges of the ratio, such as dN/dS, and fraction ofmutation signature, were assessed by interval estimation by calculating 95%confidence intervals. These statistical analyses were conducted using R version3.2.1 (https://cran.r-project.org/). All statistical analyses were two-sided, and aP-value of less than 0.05 was considered to indicate statistical significance.

To study the effect of amarker on cancer risk, logistic regression analysiswasperformed based on individuals in the intermediate-risk group (controls) andthe high-risk group (cases) in the test set. First, the effect of each methylationmarker (or mutation frequency) was assessed in a univariate model, usingpreviously determined cutoff values to define dichotomous variables corre-sponding to low/high levels of methylation (or mutation frequency). Second, amultivariate analysis including known (or traditional) risk factors (age andlifestyle risk factors), methylation levels, and mutation frequency was con-ducted. Improvement by an additional factor was assessed by the likelihoodratio test, and the performance of a model was assessed by the c-index(equivalent to the area under the curve). The relative importance of geneticand epigenetic alterations was estimated by calculation of the relative im-portance of the methylation levels compared with the mutation frequencybased on the ratio of the corresponding standardized coefficients (56).

ACKNOWLEDGMENTS. We thank Dr. Eriko Okochi-Takada for advice andDr. Chika Kusano, Dr. Yosuke Otake, and Dr. Takuji Gotoda for sample anddata acquisition. Grant support was received from Practical Research forInnovative Cancer Control from Japan Agency for Medical Research andDevelopment, AMED (15ck0106023h0002); the Project for Development ofInnovative Research on Cancer Therapeutics (P-DIRECT) from the Ministry ofEducation, Culture, Sports, Science, and Technology, Japan; the National CancerCenter Research and Development Fund (26-A-15); and the Ministry of Scienceand Technology of the Republic of China (NSC 102-2628-B-002-033-MY3).

1. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: The next generation. Cell 144:

646–674.2. Baylin SB, Jones PA (2016) Epigenetic determinants of cancer. Cold Spring Harb

Perspect Biol 8:a019505.3. Stratton MR, Campbell PJ, Futreal PA (2009) The cancer genome. Nature 458:

719–724.4. Ushijima T, Hattori N (2012) Molecular pathways: Involvement of Helicobacter pylori-

triggered inflammation in the formation of an epigenetic field defect, and its use-

fulness as cancer risk and exposure markers. Clin Cancer Res 18:923–929.

5. Luzzatto L, Pandolfi PP (2015) Causality and chance in the development of cancer. N

Engl J Med 373:84–88.6. Issa JP, Ahuja N, Toyota M, Bronner MP, Brentnall TA (2001) Accelerated age-related

CpG island methylation in ulcerative colitis. Cancer Res 61:3573–3577.7. Cui H, et al. (2003) Loss of IGF2 imprinting: A potential marker of colorectal cancer

risk. Science 299:1753–1755.8. Maekita T, et al. (2006) High levels of aberrant DNA methylation in Helicobacter

pylori-infected gastric mucosae and its possible association with gastric cancer risk.

Clin Cancer Res 12:989–995.

1332 | www.pnas.org/cgi/doi/10.1073/pnas.1717340115 Yamashita et al.

Dow

nloa

ded

by g

uest

on

Dec

embe

r 3,

202

0

9. Yan PS, et al. (2006) Mapping geographic zones of cancer risk with epigenetic bio-markers in normal breast tissue. Clin Cancer Res 12:6626–6636.

10. Teschendorff AE, et al. (2012) Epigenetic variability in cells of normal cytology is as-sociated with the risk of future morphological transformation. Genome Med 4:24.

11. Nakajima T, et al. (2006) Higher methylation levels in gastric mucosae significantlycorrelate with higher risk of gastric cancers. Cancer Epidemiol Biomarkers Prev 15:2317–2321.

12. Lee YC, et al. (2011) Revisit of field cancerization in squamous cell carcinoma of upperaerodigestive tract: Better risk assessment with epigenetic markers. Cancer Prev Res(Phila) 4:1982–1992.

13. Asada K, et al. (2015) Demonstration of the usefulness of epigenetic cancer riskprediction by a multicentre prospective cohort study. Gut 64:388–396.

14. Maeda M, et al. (2016) High impact of methylation accumulation on metachronousgastric cancer: 5-year follow-up of a multicentre prospective cohort study. Gut 66:1721–1723.

15. Vogelstein B, et al. (2013) Cancer genome landscapes. Science 339:1546–1558.16. Nohmi T, Suzuki T, Masumura K (2000) Recent advances in the protocols of transgenic

mouse mutation assays. Mutat Res 455:191–215.17. Suzuki T, Itoh S, Nakajima M, Hachiya N, Hara T (1999) Target organ and time-course

in the mutagenicity of five carcinogens in MutaMouse: A summary report of thesecond collaborative study of the transgenic mouse mutation assay by JEMS/MMS.Mutat Res 444:259–268.

18. Martincorena I, et al. (2015) Tumor evolution. High burden and pervasive positiveselection of somatic mutations in normal human skin. Science 348:880–886.

19. Blokzijl F, et al. (2016) Tissue-specific mutation accumulation in human adult stemcells during life. Nature 538:260–264.

20. Yamashita S, et al. (2017) A novel method to quantify base substitution mutations atthe 10−6 per bp level in DNA samples. Cancer Lett 403:152–158.

21. Ushijima T, Asada K (2010) Aberrant DNA methylation in contrast with mutations.Cancer Sci 101:300–305.

22. Lee CH, et al. (2005) Independent and combined effects of alcohol intake, tobaccosmoking and betel quid chewing on the risk of esophageal cancer in Taiwan. Int JCancer 113:475–482.

23. Pfeifer GP, et al. (2002) Tobacco smoke carcinogens, DNA damage and p53 mutationsin smoking-associated cancers. Oncogene 21:7435–7451.

24. Alexandrov LB, et al.; Australian Pancreatic Cancer Genome Initiative; ICGC BreastCancer Consortium; ICGC MMML-Seq Consortium; ICGC PedBrain (2013) Signatures ofmutational processes in human cancer. Nature 500:415–421.

25. Shimizu T, et al. (2014) Accumulation of somatic mutations in TP53 in gastric epi-thelium with Helicobacter pylori infection. Gastroenterology 147:407–417 e.3.

26. Song Y, et al. (2014) Identification of genomic alterations in oesophageal squamouscell cancer. Nature 509:91–95.

27. Wang K, et al. (2014) Whole-genome sequencing and comprehensive molecularprofiling identify new driver mutations in gastric cancer. Nat Genet 46:573–582.

28. Teschendorff AE, et al. (2016) DNA methylation outliers in normal breast tissueidentify field defects that are enriched in cancer. Nat Commun 7:10478.

29. Oka D, et al. (2009) The presence of aberrant DNA methylation in noncancerousesophageal mucosae in association with smoking history: A target for risk diagnosisand prevention of esophageal cancers. Cancer 115:3412–3426.

30. Takeshima H, et al. (2017) Degree of methylation burden is determined by the ex-posure period to carcinogenic factors. Cancer Sci 108:316–321.

31. Lin JC, et al. (2007) Role of nucleosomal occupancy in the epigenetic silencing of theMLH1 CpG island. Cancer Cell 12:432–444.

32. Jones PA, Baylin SB (2007) The epigenomics of cancer. Cell 128:683–692.

33. Slaughter DP, Southwick HW, Smejkal W (1953) Field cancerization in oral stratifiedsquamous epithelium; clinical implications of multicentric origin. Cancer 6:963–968.

34. Braakhuis BJ, Tabor MP, Kummer JA, Leemans CR, Brakenhoff RH (2003) A geneticexplanation of Slaughter’s concept of field cancerization: Evidence and clinical im-plications. Cancer Res 63:1727–1730.

35. Rustgi AK, El-Serag HB (2014) Esophageal carcinoma. N Engl J Med 371:2499–2509.36. Hunt RH, et al. (2015) The stomach in health and disease. Gut 64:1650–1668.37. Wu MT, et al. (2001) Risk of betel chewing for oesophageal cancer in Taiwan. Br J

Cancer 85:658–660.38. IARC (2010) IARC monographs on the evaluation of the carcinogenic risk of chemicals

to humans. Alcohol Consumption and Ethyl Carbamate (IARC, Lyon, France).39. Teschendorff AE, et al. (2015) Correlation of smoking-associated DNA methylation

changes in buccal cells with DNA methylation changes in epithelial cancer. JAMAOncol 1:476–485.

40. Chan AO, et al. (2003) Promoter methylation of E-cadherin gene in gastric mucosaassociated with Helicobacter pylori infection and in gastric cancer. Gut 52:502–506.

41. Niwa T, et al. (2010) Inflammatory processes triggered by Helicobacter pylori infectioncause aberrant DNA methylation in gastric epithelial cells. Cancer Res 70:1430–1440.

42. Hsieh CJ, et al. (1998) Hypermethylation of the p16INK4a promoter in colectomyspecimens of patients with long-standing and extensive ulcerative colitis. Cancer Res58:3942–3945.

43. Zang ZJ, et al. (2012) Exome sequencing of gastric adenocarcinoma identifies re-current somatic mutations in cell adhesion and chromatin remodeling genes. NatGenet 44:570–574.

44. Cancer Genome Atlas Research Network (2014) Comprehensive molecular charac-terization of gastric adenocarcinoma. Nature 513:202–209.

45. Gao YB, et al. (2014) Genetic landscape of esophageal squamous cell carcinoma. NatGenet 46:1097–1102.

46. Lin DC, et al. (2014) Genomic and molecular characterization of esophageal squamouscell carcinoma. Nat Genet 46:467–473.

47. Dulak AM, et al. (2013) Exome and whole-genome sequencing of esophageal ade-nocarcinoma identifies recurrent driver events and mutational complexity. Nat Genet45:478–486.

48. Secrier M, et al.; Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)Consortium (2016) Mutational signatures in esophageal adenocarcinoma defineetiologically distinct subgroups with therapeutic relevance. Nat Genet 48:1131–1141.

49. Kimura M (1968) Evolutionary rate at the molecular level. Nature 217:624–626.50. Jones PA (2012) Functions of DNA methylation: Islands, start sites, gene bodies and

beyond. Nat Rev Genet 13:484–492.51. Shimazu T, et al. (2015) Association of gastric cancer risk factors with DNA methylation

levels in gastric mucosa of healthy Japanese: A cross-sectional study. Carcinogenesis 36:1291–1298.

52. Alexandrov LB, Nik-Zainal S, Wedge DC, Campbell PJ, Stratton MR (2013) Decipheringsignatures of mutational processes operative in human cancer. Cell Reports 3:246–259.

53. Teschendorff AE, et al. (2013) A beta-mixture quantile normalization method forcorrecting probe design bias in Illumina Infinium 450 k DNA methylation data.Bioinformatics 29:189–196.

54. Yoshida S, et al. (2017) Epigenetic inactivation of FAT4 contributes to gastric fieldcancerization. Gastric Cancer 20:136–145.

55. Chen YA, et al. (2013) Discovery of cross-reactive probes and polymorphic CpGs in theIllumina Infinium HumanMethylation450 microarray. Epigenetics 8:203–209.

56. Silber JH, Rosenbaum PR, Ross RN (1995) Comparing the contributions of groups ofpredictors: Which outcomes vary with hospital rather than patient characteristics?J Am Stat Assoc 90:7–18.

Yamashita et al. PNAS | February 6, 2018 | vol. 115 | no. 6 | 1333

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