Tumor Biology and Immunology

Comutations in DNA Damage Response PathwaysServe as Potential Biomarkers for ImmuneCheckpoint BlockadeZhijie Wang1, Jing Zhao2, Guoqiang Wang2, Fan Zhang3, Zemin Zhang4,Fan Zhang4, Yuzi Zhang2, Hua Dong5, Xiaochen Zhao2, Jianchun Duan1,Hua Bai1, Yanhua Tian1, Rui Wan1, Miao Han5, Yan Cao5, Lei Xiong2, Li Liu6,Shuhang Wang7, Shangli Cai2, Tony S.K. Mok8, and Jie Wang1

Abstract

Biomarkers such as programmed death receptor 1 ligand(PD-L1) expression, tumor mutational burden (TMB), andhigh microsatellite instability are potentially applicable topredict the efficacy of immune checkpoint blockade (ICB).However, several challenges such as defining the cut-offvalue, test platform uniformity, and low frequencies limittheir broad clinical application. Here we identify comuta-tions in the DNA damage response (DDR) pathways ofhomologous recombination repair and mismatch repair(HRR-MMR) or HRR and base excision repair (HRR-BER;defined as co-mutþ) that are associated with increasedTMB and neoantigen load and increased levels of immunegene expression signatures. In four public clinical cohorts,

co-mutþ patients presented a higher objective responserate and a longer progression-free survival or overall survivalthan co-mut� patients. Overall, identification of DDRcomutations in HRR-MMR or HRR-BER as predictors ofresponse to ICB provides a potentially convenient approachfor future clinical practice.

Significance: Identification of comutations in specificDDR pathways as predictors of superior survival outcomesin response to immune checkpoint blockade providea clinically convenient approach for estimation of tumormutational burden and delivery of ICB therapy. Cancer Res;78(22); 6486–96. �2018 AACR.

IntroductionImmune checkpoint blockades (ICB), including antibodies

to programmed death receptor 1 (PD-1) or its ligand (PD-L1)

and CTL-associated protein 4 (CTLA-4), are now standardtherapies for a range of solid tumors after approval by the FDA(1, 2). However, only a subset of patients benefit from ICBmonotherapy. Biomarkers such as PD-L1 expression (3, 4),tumor mutational burden (TMB; ref. 5), neoantigen load(NAL; ref. 6), tumor-infiltrating lymphocytes (TIL; ref. 7), andimmune-regulatory mRNA expression signatures (8) are poten-tially applicable to the clinical selection of patients for ICBs,but each has limited utility.

Challenges in defining cut-off values, intratumoral hetero-geneity distributions, test platform uniformities, and dynamicchanges have limited the clinical application of PD-L1 expres-sion (9). High TMB and NAL are associated with improvedresponse to ICB treatment (5, 10), but the lack of a validatedcut-off value has limited the use of this method (9). Mismatchrepair deficiency (dMMR) may contribute to genome instabilityand lead to increases in TMB and NAL, which can be indicatedby microsatellite instability-high (MSI-H) status (11), and thus,pembrolizumab was approved by the FDA for dMMR/MSI-Hsolid tumors (1). However, less than 5% of solid tumors aredMMR/MSI-H (12), which is the limiting factor for the clinicalutilization of dMMR/MSI-H.

The DNA damage response (DDR) system comprises eightpathways: mismatch repair (MMR); base excision repair (BER);check point factors; Fanconi anemia; homologous recombina-tion repair (HRR); nucleotide excision repair (NER); nonho-mologous end-joining; and DNA translesion synthesis (13).The DDR system is essential for the preservation of genomicintegrity (14), and thus, mutations in this system may induce a

1State Key Laboratory of Molecular Oncology, Department of Medical Oncology,National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences& Peking Union Medical College, Beijing, P.R. China. 2The Medical Department,3D Medicines Inc., Shanghai, P.R. China. 3Department of Oncology, Chinese PLAGeneral Hospital, Beijing, P.R. China. 4Peking-Tsinghua Center for Life Sciences,Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, P.R.China. 5The Bioinformatics Department, R&D Center of Precision Medicine, 3DMedicines Inc., Shanghai, P.R. China. 6Cancer Center, Union Hospital, TongjiMedical College, Huazhong University of Science and Technology, Wuhan, P.R.China. 7Clinical Trial Center of National Cancer Center, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union MedicalCollege, Beijing, P.R. China. 8Department of Clinical Oncology, State KeyLaboratory of South China, Chinese University of Hong Kong, Shatin, NewTerritories, Hong Kong, P.R. China.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

Z. Wang, J. Zhao, and G. Wang contributed equally to this article.

CorrespondingAuthors: JieWang, StateKeyLaboratoryofMolecularOncology,Department of Medical Oncology, National Cancer Center/Cancer Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College, Pan-jiayuan Nanli No. 17, Chaoyang District, Beijing 100021, P.R. China. Phone: 8610-8778-8029; Fax: 8610-8778-8027; E-mail: zlhuxi@163.com; and Tony S.K. Mok,ChineseUniversity of HongKong, Shatin, NewTerritories, HongKong, P.R. China.Phone: 852-2632-2166; Fax: 852-2648-7097; E-mail: tony@clo.cuhk.edu.hk.

doi: 10.1158/0008-5472.CAN-18-1814

�2018 American Association for Cancer Research.

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hypermutational phenotype. Previous studies have revealedthat gene deficiencies in two DDR pathways of extraordinaryfidelity, MMR (MLH1, MSH2, MSH6, and PMS2) and BER(POLE), resulted in a durable clinical benefit from ICBs (1,15). Patients with melanoma responding to ICBs commonlyharbored mutations in BRCA2, a major gene in the HRRpathway (6). Another study revealed that patients with dele-terious alterations in 34 genes involved in DDR pathwaysexhibited high TMB levels and had improved clinical outcomesof ICB therapy in urothelial cancer (16). However, the numberof DDR genes involved in these studies is limited, and mostother DDR genes are poorly studied. In addition, due to theinteractions among different DDR pathways, the contributionweights of different DDR pathways to TMB should be examinedbut have not been well established to date.

Defects in one DDR pathway lead to a greater dependencyon the remaining DDR pathways, implying higher genomicinstability when multiple deficiencies exist in different DDRpathways (17). Therefore, we hypothesized that mutations inmultiple DDR pathways are associated with higher genomicinstability; as a result, we would expect higher NAL and TMB. Byanalyzing whole-exome sequencing (WES) data from TheCancer Genome Atlas (TCGA) and validating the results usingthe International Cancer Genome Consortium (ICGC) database,we explored the optimized pattern of mutation combinationsacross distinct DDR pathways for TMB and NAL estimation. Weherein report the relationships between comutations in DDRpathways and TMB, NAL, immune-regulatory mRNA expressionsignatures and the clinical benefit of ICBs.

Materials and MethodsData sources

Supplementary Table S1 summarizes the detailed informa-tion regarding the data used in this study. In brief, we obtainedthe WES data of 8,552 solid tumors from TCGA, the mRNAexpression of 8,482 solid tumors across 29 tumor types fromcBioPortal (www.cbioportal.org), and whole-genome sequenc-ing data of 2,638 solid tumors from ICGC (dcc.icgc.org). Theexperimental procedures regarding DNA and RNA extractionfrom tumors, library preparation, sequencing, quality control,and subsequent data processing were published previously byTCGA (18). Neoantigens of 5,935 solid tumors estimated byTCGA data were downloaded from https://tcia.at/neoantigens(19). MSI of 5,930 solid tumors across 16 tumor types wasestimated by Hause and colleagues with TCGA WES data (20).

To further explore the association between DDR pathways andthe clinical benefit of ICBs, we included genomic and clinical datafrom four clinical cohorts treated with ICBs. The first cohortconsisted of 34patientswith non–small-cell lung cancer (NSCLC)treated with anti-PD-1 therapy (Rizvi cohort; ref. 21). The secondand third cohorts consisted of 110 and 64 patients, respectively,with advanced-stage melanoma treated with anti-CTLA-4 therapy(Allen cohort and Snyder cohort, respectively; refs. 10, 22). Thelast cohort comprised 38 patients with melanoma treated withanti-PD-1 therapy (Hugo cohort; ref. 6).

DDR pathway mutationsThe detailed profiles of genes involved in eight DDR path-

ways are listed in Supplementary Table S2 (13). As most DDRgenes have not yet been studied, we defined mutations in DDR

pathways as any nonsilent mutations in gene-coding regionsin the corresponding pathways, including missense, nonsense,insertion, deletion, and splice mutations. Comutations ofHRR-BER or HRR-MMR were defined as "co-mutþ."

TMB and NAL analysisTMBwasdefinedas the totalnonsilent somaticmutationcounts

in coding regions. To explore howmanyDDR pathwaymutationspredictedhigherTMBorNAL,TMBorNALwasclassified intohigh/low based on the top quartile, and a receiver operating character-istic (ROC) curve was applied to determine the cutoff of pathwaymutation counts with the highest Youden index.

mRNA expression profiling analysisThe response to ICBs has been reported to be related to

immune tumor microenvironments, including T-effector,IFNg-associated genes, T-cell receptors, and immune factors(23, 24). The associations between DDR pathway comutationstatus and relevant immune-related genes were analyzed in7,570 patients from TCGA for whom both RNAseq andDNAseq data were available. The immune gene list was mainlybased on a published article that summarized the genesrelated to activated T cells, immune cytolytic activity, andIFNg release (25). Other immune genes were added accordingto two relevant clinical trials (26, 27). A list of 47 immunegenes is provided in Supplementary Table S3. The mRNAexpression from cBioPortal was quantified by RSEM (RNAseqby expectation-maximization; ref. 28). The data were log2-transformed before analysis.

Gene set enrichment analysisGene set enrichment analysis (GSEA) was performed

using the javaGSEA 3.0 Desktop Application (http://software.broadinstitute.org/gsea/index.jsp) to identify whether immune-related gene signatures were associated with DDR pathwaycomutation status. The genes identified to be on the leadingedge of the enrichment profile were subjected to pathwayanalyses. Genes with expression equal to 0 in more than80% of samples were excluded from the GSEA. The normalizedenrichment score (NES) is the primary statistic for examininggene set enrichment results.

Clinical data analysisAcross four ICB-treated cohorts, we analyzed comutation

status, TMB, and PD-L1 expression for association with patientprognosis. The TMB-high group was defined as the top quartile,and the TMB-low group was defined as the bottom threequartiles (29). PD-L1þ was defined as PD-L1þ in � 50% oftumor cells, and PD-L1� was defined as PD-L1þ in <50% oftumor cells (3, 4). We further tested the association of comuta-tion status with survival outcomes stratified by TMB and PD-L1.Measures of patient survival and response were based ondefinitions consistent with how they were evaluated in theabovementioned trials. For the Rizvi cohort and the Allencohort, response was defined by RECIST criteria. For the Hugocohort, responding tumors were defined as exhibiting a com-plete response or a partial response or as stable disease, andnonresponding tumors were defined as exhibiting disease pro-gression by irRECIST. For the Snyder cohort, response datawere not available. We also analyzed the association betweencomutation status and patient prognosis in NSCLC and

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melanoma from TCGA, using propensity-score matching witha ratio of 1:2. Propensity score was estimated by age, sex,pathologic tumor stage, other history of malignancy, and histo-logic classification.

Statistical analysesAll the analyses were performed by R 3.4.2. Correlations

between number of mutated DDR pathways and TMB or NALwere determined by Spearman rank correlation coefficient.Multiple linear regression with a stepwise method was usedto examine the correlations among eight pathways and TMBor NAL. If NAL, TMB, or mRNA were normally distributed,Student t test was used to determine the differences betweentwo groups; otherwise, the Mann–Whitney U test was used.Differences among three or more groups were determinedby the Kruskal–Wallis test with Dunn posttest. Heatmap wasused to depict the mean difference of immune-related genesbetween co-mutþ and co-mut� subgroups by pheatmap pack-age in R. Correlation-based group average hierarchical clus-tering was used for gene clustering and Minkowski distancefor samples clustering. Survival analysis was performedusing Kaplan–Meier curves, with a P value determined by alog-rank test. HR was determined through Cox regression.Proportional hazards assumption was tested before the Coxregression. To explore the modification effects of PD-L1 orTMB on the association between the comutation status andprognosis, stratified analyses were conducted, and interactionswere evaluated with the log likelihood ratio statistic. Fisherexact test or the x2 test was used to identify the associationof comutation status and objective response rate. All report-ed P values were two-tailed, and P < 0.05 was consideredsignificant unless otherwise specified. A false discovery rate(FDR) < 0.05 was considered significant in the pan-cancermRNA analysis and GSEA.

ResultsGene comutations in DDR pathways were favorable surrogatesfor TMB estimation

By analyzing the sequencing data from TCGA and validatingthe findings in ICGC, we identified that both TMB and NALwere significantly higher in patients with mutations in oneor more DDR pathways than in those with wild-type status(Fig. 1A). TMB and NAL increased significantly with the num-ber of mutated DDR pathways (Supplementary Fig. S1A–S1C)with Spearman correlation coefficients of 0.66 (for TMB inTCGA), 0.68 (for TMB in ICGC), and 0.59 (for NAL in TCGA).The TMB levels of the top quartile are commonly consideredTMB-high (TMB-H; ref. 29), which were 128 and 88 counts ofTMB in the TCGA and ICGC databases, regardless of cancertype. However, the median mutation counts for patients withmutations in a single DDR pathway were 52 and 53 in theTCGA and ICGC databases, respectively (SupplementaryFig. S1A and S1B), suggesting that mutation of a single DDRpathway may not account for TMB-H. The ROC curve showedthat mutations covering � 2 DDR pathways demonstrated anoptimal Youden index, with 79.6% sensitivity and 80.9%specificity, compared with other numbers of pathway combi-nations in TMB-H estimation (Fig. 1B). In addition, similarresults were verified in the ICGC TMB data, with 65.7% sen-sitivity and 84.8% specificity (Fig. 1C), and in TCGA NAL data,

with 75.3% sensitivity and 78.8% specificity (Fig. 1D). Theseresults confirmed our hypothesis that comutations in DDRpathways could predict higher TMB and NAL.

Given the potential interactions between different DDRpathways, to identify the most effective patterns of pathwaycombinations, we studied the associations of eight DDR path-ways with TMB or NAL to select DDR pathways that contrib-uted more to TMB or NAL, estimated by multiple linearregression analyses in patients with mutated DDR pathwayslimited to five. Together with the consistency between TMBand NAL data, three pathways, BER, HRR, and MMR, wereselected with satisfactory standardized b coefficients (Fig. 1E).Although we could not fully exclude the functions of othercomutation combinations, more specifically, patients withHRR-BER or HRR-MMR comutations illustrated significantlyor borderline higher TMB and NAL than those with BER-MMRand other comutations (Fig. 1F). Thus, we defined comu-tations in the HRR-BER and HRR-MMR pathways as the"co-mutþ" subgroup.

A total of 740 among 8,552 cases (8.7%) were classifiedas co-mutþ in TCGA dataset. Incidences of co-mutþ variedacross different cancer types, ranging from 0% in pheochro-mocytoma and paraganglioma to 29.2% in bladder urothelialcarcinoma (Fig. 1G). The frequencies of global DDR-relatedgene mutations in each cancer type were similar among theHRR, BER, and MMR pathways (Supplementary Fig. S2). Wethen evaluated whether co-mutþ status was associated with ahigher level of TMB or NAL. The results showed significantlyhigher levels of TMB (Fig. 1H) and NAL (Fig. 1I) in the co-mutþ

subgroups than in the co-mut� subgroups across the cancertypes with indications for ICBs delivery according to the FDA.Similar results were also obtained in other cancer types (Sup-plementary Tables S4 and S5). In addition, co-mutþ wassignificantly associated with MSI-H (P < 0.0001) and predictedMSI-H with 76.1% sensitivity and 98.8% specificity (Fig. 1J). Intumors with high frequency of MSI-H, such as colorectaladenocarcinoma, stomach adenocarcinoma, and uterine cor-pus endometrial carcinoma, most MSI-H samples were co-mutþ, and importantly, co-mutþ covered more cancer typesthan MSI-H (Supplementary Fig. S3). No significant differencein TMB was observed between the co-mutþ/MS stability (MSS)and co-mut�/MSI-H groups (Fig. 1K). These findings suggestedthat co-mutþ was a favorable surrogate for TMB estimationcovering more percentage of patients than MSI-H.

Correlations between co-mutþ and mRNA expression ofimmune-regulatory gene expression signatures

To investigate the correlations between the co-mutþ sub-group and immune-regulatory gene expression signatures, weanalyzed 7,570 samples from TCGA with both RNAseq andWES data. The GSEA revealed prominent enrichment ofmRNA signatures involved in IFNg pathway, inflammatoryresponse, and allograft rejection in the co-mutþ subgroupover a pan-cancer analysis (Fig. 2A). Among the 47 selectedimmune-related genes, the mRNA expression of 31 genes wassignificantly higher in the co-mutþ subgroup than in the co-mut� subgroup (FDR < 0.05) across all cancer types. Themean differences of these 31 genes between the co-mutþ andco-mut� subgroups are shown in the heatmap (Fig. 2B).Specifically, the co-mutþ subgroup demonstrated higher levelsof mRNA expression than did the co-mut� subgroup in the

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Figure 1.

Correlations between comutations of HRR-BER or HRR-MMR and tumor mutational burden, neoantigen load, and MSI-H. A, Comparison of TMB or NALbetween mutated and wild-type DDR. B–D, ROC curves of the number of comutated DDR pathways to predict higher TMB from TCGA (B) and ICGC (C)databases and higher NAL (D). E, Standard b coefficients between eight DDR pathways and TMB or NAL by multiple linear regression. F, Comparison of TMB/NALbetween patients with HRR-BER comutation, HRR-MMR comutation, BER-MMR comutation, and other comutations. G, Frequency of co-mutþ (comutations ofHRR-BER or HRR-MMR) in different tumor types. H, Comparison of TMB between the co-mutþ and co-mut� groups in tumors with approved indication ofimmune therapy. I, Comparison of NAL between the co-mutþ and co-mut� groups in tumors with approved indication for immune therapy. J, The proportions ofpatients with co-mutþ or MSI-H. K, Comparison of tumor mutational burden in different combinations of comutations and MSI status groups. � , P < 0.05, �� , P < 0.01,��� , P < 0.001. CPF, checkpoint factors; FA, Fanconi anemia; NER, nucleotide excision repair; NHEJ, nonhomologous end-joining; TLS, translesion DNA synthesis;BLCA, bladder urothelial carcinoma; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cellcarcinoma; LIHC, liver hepatocellular carcinoma; NSCLC, non–small-cell lung cancer; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma.

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Figure 2.

Correlations between comutations of HRR-BER or HRR-MMR and immune response–related gene mRNA expression. A, Impact of comutation status onimmune response–related pathways. B, Heatmap depicting the mean differences in immune response–related gene mRNA expression between co-mutþ andco-mut� across different tumor types. The x-axis indicates different cancer types and the y-axis indicates gene names. Each square represents the differenceof mean expression level of each indicated immune-related genes between co-mutþ and co-mut� in each cancer types. Red indicates that the gene's meanexpression level was higher in the co-mutþ group than in the co-mut� group. Green indicates that the gene's mean expression level was lower in the co-mutþ

group than in the co-mut� group. C–F, Comparison of the mRNA expression of genes related to immune checkpoints, tumor microenvironment, INFgpathway and T-effector, and T-cell receptor signature between co-mutþ and co-mut� groups in the pan-cancer analysis. ��� , FDR < 0.001. co-mutþ, comutation ofHRR-BER or HRR-MMR; TME, tumor microenvironment; TCR, T-cell receptor.

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following gene clusters: immune checkpoint (PD-L1 andLAG3; Fig. 2C); tumor immune microenvironment (IL1B, IL6,and PTGS2; Fig. 2D); T-effector and IFNg-associated signatures(INFG, CXCL9, CXCL10, CXCL11, EOMES, GZMB, GBP1, andSTAT1; Fig. 2E); and T-cell receptor–related genes (CD8A,CD3G, CD3D, IKZF3, and TIGIT; Fig. 2F). In addition, in14 cancer types, at least one immune-regulatory gene had anexpression level significantly higher in the co-mutþ subgroupthan in the co-mut� subgroup (Supplementary Fig. S4).

Co-mutþ status predicted favorable clinical outcomes ofICB delivery

We further investigated the relationship between co-mutstatus and the clinical outcomes of ICBs. Four publicly avail-able datasets were utilized for this analysis, including oneNSCLC cohort and three melanoma cohorts. As in the abovepan-cancer analyses, co-mutþ status was significantly positivelyassociated with TMB and NAL in these four cohorts (Supple-mentary Fig. S5A–S5C).

The Rizvi cohort (21) included 34 patients with NSCLCtreated with pembrolizumab, among whom 11 patients wereclassified as co-mutþ; their progression-free survival (PFS) wassuperior to that of those without co-mutþ [median, not reached(NR) vs. 4.1 months; HR, 0.21; 95% CI, 0.06–0.71; P ¼0.006, Fig. 3A]. The objective response rates (ORR) in co-mutþ

and co-mut� were 63.6% and 21.7%, respectively (P ¼ 0.02,Fig. 3B).

We performed a pooled analysis on two metastatic melano-ma cohorts [the Allen cohort (10) and the Snyder cohort (22)treated with CTLA-4 antibody, n ¼ 174]. A total of 50 patientswere classified as co-mutþ, and their overall survival (OS) wassignificantly longer (median, 32.4 vs. 10.8 months; HR adjust-ed for cohort, 0.64; 95% CI, 0.42–0.98; P ¼ 0.04; Fig. 3C).However, no difference was observed in tumor response rate(43.8% vs. 29.5%, P ¼ 0.15, Fig. 3D). The Hugo cohort (6)enrolled 38 patients with advanced-stage melanoma who weretreated with anti-PD-1 therapy. Co-mutþ patients demonstrat-ed numerically superior OS (median, NR vs. 27.9 months;HR, 0.52; 95% CI, 0.18–1.44; P ¼ 0.20; Fig. 3E) and ORR(73.3% vs. 43.4%, P ¼ 0.10, Fig. 3F) compared with those ofco-mut� patients. In addition, no significant difference wasfound in survival outcomes between the single DDR path-way mutant and DDR-wild-type groups in these four publiccohorts (Supplementary Fig. S6A–S6C), further supporting thehypothesis that a single mutated DDR pathway was not suffi-cient to predict greater benefit from ICBs.

To verify whether co-mutþ was a predictive or prognosticfactor, we performed survival analyses according to comuta-tion status in TCGA using propensity-score matching. As aresult, no significant difference was found in OS between theco-mutþ and co-mut� subgroups in either the patients withNSCLC or melanoma (Fig. 4A and B), suggesting that co-mutwas a predictor of the clinical benefit of ICBs instead of aprognostic marker.

Relevance of comutation status to PD-L1 expression andTMB as a predictive biomarker

The predictive power of comutation status was further com-pared with PD-1 expression or TMB in the clinical cohorts. TMBcalculated by WES with the top quartile or median as a cut-offvalue failed to consistently predict survival improvement

by ICBs and thus was apparently inferior to comutation statusas a predictive marker (Supplementary Table S6). Furthermore,in TMB-low (defined as less than the top quartile) NSCLCpatients of the Rizvi cohort, both the median PFS and ORRof co-mutþ patients were significantly superior to those of co-mut� patients (median NR vs. 3.8 months, P ¼ 0.04, Fig. 5Aleft; ORR: 100% vs. 18.2%, Fig. 5B left). There was no differencebetween the co-mutþ and co-mut� subgroups in terms ofmedian PFS or ORR in TMB-high patients with NSCLC(Fig. 5A and B, right). No interaction between co-mut andTMB was observed (P ¼ 0.16). In the melanoma cohorts, co-mutþ status also predicted improved OS and ORR in the TMBstratification analysis, although significant P values were notreached (Supplementary Fig. S7A–S7D).

In patients with NSCLC with PD-L1 < 50%, co-mutþ patientshad significantly prolonged PFS (median, 8.3 vs. 2.7 months;HR, 0.26; 95% CI, 0.07–0.93; P ¼ 0.03; Fig. 5C, left) and higherORR (33.3% vs. 7.1%, P ¼ 0.02; Fig. 5D, left) compared withthose without co-mutþ. In patients with NSCLC with PD-L1 �50%, co-mutþ status also predicted a trend of longer PFS(median, NR vs. 14.5; HR, NA; P ¼ 0.08; Fig. 5C, right) andhigher ORR (100% vs. 50%; Fig. 5D, right) than co-mut�. Nosignificant interaction was observed between comutationstatus and PD-L1 expression (P ¼ 0.99).

DiscussionIn this study, we demonstrated that the presence of

comutations in the HRR-MMR and HRR-BER pathways wasassociated with higher TMB, NAL, and immune-regulatorygene expression and is a potential predictive biomarker forICBs. The co-mutþ status identifies a patient population thatmay potentially benefit from ICBs irrespective of PD-L1expression and TMB status.

The DDR system is required to preserve genomic integrityand has multiple functional links with innate immune signal-ing to protect against pathogens such as damaged DNAs (11).Functional mutations in the DDR system reduce genomicstability (17), although it remains unclear which DDR pathwaycontributes more to genomic stability and how their interac-tions may impact clinical outcomes. We found that comuta-tions involving two pathways, HRR-BER and HRR-MMR, arethe key reasons for the elevation of TMB and NAL. The associa-tions between these comutations and increased TMB and NALare consistent with previous reports based on WES data regard-ing genomic and transcriptomic features related to ICBresponse (6). The HRR and BER pathways monitor and repairDNA double-strand breaks (DSB) and single-strand breaks(SSB), respectively (30), whereas the MMR pathway is respon-sible for DNA insertion/deletion corrections (31). We specu-lated that high TMB and NAL were the result of increasing DDRmutation accumulation. Defects in the HRR pathway may leadto an inability to repair the DNA damage caused by BER orMMR deficiency. The underlying mechanism of coordinationbetween HRR-BER and HRR-MMR should be further investi-gated in the future.

Comutation is a potential predictive biomarker for the useof PD-1 blockades in patients with advanced NSCLC. Boththe tumor response rate and median PFS were improved in theco-mutþ subgroup. In melanoma, the median OS was longerin the co-mutþ subgroup, while the difference in ORR was not

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Figure 3.

Patients with co-mutþ showed a favorable clinical benefit from immune checkpoint blockades. A, Kaplan–Meier survival curves of PFS comparing the co-mutþ

and co-mut� groups in patients with NSCLC treated with anti-PD-1 therapy from the Rizvi cohort. B, Comparison of the objective response ratebetween the co-mutþ and co-mut� groups from the Rizvi cohort. C, Kaplan–Meier survival curves of OS comparing co-mutþ with co-mut� groups in patientswith melanoma treated with anti-CTLA4 therapy from the Allen and Snyder cohorts. D, Comparison of the objective response rates between the co-mutþ andco-mut� groups from the Allen cohort. E, Kaplan–Meier survival curves of OS comparing co-mutþ with co-mut� groups in patients with melanomatreated with anti-PD-1 therapy from the Hugo cohort. F, Comparison of the objective response rates between the co-mutþ and co-mut� groups fromthe Hugo cohort.

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significant. Approximately 36.8% (14/38) of the patients inthe Hugo cohort had received MAPK inhibitors, which maymediate cross-resistance to ICBs (6). By comparing co-mutþ

with PD-L1 expression and TMB in clinical NSCLC and mel-anoma cohorts, we found that co-mutþ might be a clinicallypracticable biomarker predictive of response to ICBs and sur-vival. Currently, PD-L1 expression is a standard predictivebiomarker for first-line pembrolizumab (3). To date, the treat-ment for patients with PD-L1 expression < 50% is platinum-based chemotherapy, either alone (32) or in combinationwith pembrolizumab (33). Biomarker selection for the use ofICBs in the population remains debatable. We have demon-strated that co-mutþ patients may benefit from anti-PD-1therapy even if the PD-L1 expression is less than 50%. Simi-larly, in the TMB-low subset, the comutation status may enricha patient population with a higher response rate to ICBs.Comutation is a qualitative biomarker that does not requirea cut-off value. Standard next-generation sequencing (NGS)may be able to determine comutation status accurately. Thismethod would be relatively simple and cost-effective compar-ed with measurement of TMB. Prevalence of co-mutþ status inthe most common cancer types is estimated at 10% to 20%,which is higher than the prevalence of dMMR/MSI-H (12).Furthermore, the gene panel for the determination of comuta-tion status is detectable in plasma cell–free DNA (cfDNA).These features may make comutations in the DDR pathwaysan attractive biomarker for ICBs. Future prospective studiesare warranted.

A recent study suggested that deleterious mutations in34 DDR genes designed in a MSK-IMPACT NGS gene panelwas associated with better clinical outcome of ICBs in urothe-lial cancer (16). However, we extracted the 34 DDR genesincluded in the MSK-IMPACT gene panel and failed to obtainpositive results of these deleterious DDR genes in pre-dicting a clinical benefit in NSCLC or melanoma treated withanti-PD-1 (Supplementary Fig. S8A and S8B). One importantpotential reason was that the TMB and the correspondingNAL caused by a single DDR mutation were not high enoughto lead to significantly improved immunogenicity. Accordingto our results, patients with mutations in a single DDR

pathway did not show a superior response to ICBs comparedwith that of those with wild-type DDR. The median TMBlevels of patients with single mutations in DDR genes were52 (TCGA) and 53 (ICGC), which were very different fromthe previously reported predictive TMB cut-off values (6, 10,21, 22). On the basis of these data, comutations in DDRpathways could better predict ICB efficacy than a singlemutated DDR pathway could.

This study is first limited by the retrospective profiling of theanalysis. However, TCGA is the most reliable public dataset,and validation using the ICGC database supports the reliabil-ity of our results. Moreover, we here did not differentiatewhether the DDR gene mutations were functional. Ourattempts to recruit functional DDR mutations into our co-mutþ pattern was handicapped by the limited informationavailable regarding the functions of different mutations andthe lack of hotspots in DDR gene mutations. In addition, somebenign mutations may also influence protein function (34).Therefore, it is very difficult to clearly differentiate functionalDDR mutations, and thus, missense DDR mutations wereutilized in this study. Finally, we could not fully exclude thefunctions of comutations in other DDR pathways. However,the necessity of at least 2 pathways and the co-mutþ patternprovided optimal accuracy for TMB estimation and efficacyprediction for ICB delivery.

In conclusion, comutations in DDR pathways are associat-ed with an immune phenotypic profile of increased TMB,NAL, and immune response–related mRNA expression. Pre-liminary data from four clinical cohorts strongly suggestedbetter treatment outcomes of ICBs in co-mutþ patients.Comutations of HRR-MMR or HRR-BER are a potential pre-dictive biomarker for ICB therapy that warrants future pro-spective investigation.

Disclosure of Potential Conflicts of Interest.T.S.K. Mok. reports receiving commercial research grants from

AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Clovis Oncology,Merck Sharp & Dohme, Novartis, Pfizer, SFJ Pharmaceuticals, and XCovery;has ownership interest (including stocks and patents) in Sanomicsand Hutchison Chi-Med; is a consultant/advisory board member for

Figure 4.

Association between comutation status and clinical prognosis from TCGA. A, Kaplan–Meier survival curves of overall survival comparing the co-mutþ andco-mut� groups in patients with NSCLC. B, Kaplan–Meier survival curves of overall survival comparing the co-mutþ and co-mut� groups in patientswith skin cutaneous melanoma.

DNA Damage Response Predicts Benefit of Immunotherapy

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Figure 5.

Association between comutation status and clinical prognosis by subgroup analysis in the Rizvi cohort. A, Kaplan–Meier survival curves of PFScomparing the co-mutþ and co-mut� groups stratified by tumor mutational burden (low and high). B, Comparison of the objective response ratesbetween the co-mutþ and co-mut� groups stratified by tumor mutational burden (low or high). C, Kaplan–Meier survival curves of PFS comparing the co-mutþ

and co-mut� groups stratified by PD-L1 expression (<50% or �50%). D, Comparison of the objective response rate between the co-mutþ and co-mut�

groups stratified by PD-L1 expression (<50% or �50%)

Wang et al.

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AstraZeneca, Boehringer Ingelheim, Roche/Genentech, Pfizer, Eli Lilly,Merck Serono, Merck Sharp & Dohme, Novartis, SFJ Pharmaceuticals, ACEABiosciences, Vertex Pharmaceuticals, geneDecode, OncoGenex Technologies,Celgene, Ignyta, Fishawack Facilitate, Cirina, Janssen, Takeda, HutchisonChi-med, OrigiMed, Hengrui Therapeutics, Sanofi-Aventis, Yuhan Corpora-tion, and Amoy Diagnostics. No potential conflicts of interest were disclosedby the other authors.

Authors' ContributionsConception and design: Z. Wang, S. Cai, T.S.K. Mok, J. WangDevelopment of methodology: Z. Wang, S. Cai, T.S.K. Mok, J. WangAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): Z. Wang, Z. Zhang, F. Zhang (Peking University),H. Dong, J. Duan, H. Bai, R. Wan, L. Liu, T.S.K. Mok, J. WangAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis):Z.Wang, J. Zhao,G.Wang, Y. Zhang, X. Zhao, J. Duan,Y. Tian, S. Wang, S. Cai, T.S.K. Mok, J. WangWriting, review, and/or revision of themanuscript: Z.Wang, J. Zhao, G.Wang,F. Zhang (Chinese PLA General Hospital), Y. Zhang, X. Zhao, H. Bai, M. Han, Y.Cao, L. Xiong, S. Wang, S. Cai, T.S.K. Mok, J. WangAdministrative, technical, or material support (i.e., reporting or organiz-ing data, constructing databases): H. Dong, X. Zhao, J. Duan, S. Wang,T.S.K. Mok, J. WangStudy supervision: T.S.K. Mok, J. Wang

Acknowledgments

We thank the financial support from the National Natural SciencesFoundation Key Program, National Key R&D Program of China, NationalHigh Technology Research and Development Program 863, CAMS Inno-vation Fund for Medical Sciences, China National Natural Sciences Foun-dation, Beijing Natural Science Foundation, and Beijing Novel ProgramGrants. This work was supported by grants from the National NaturalSciences Foundation Key Program (81630071 and 81330062 to J Wang),National Key R&D Program of China (2016YFC0902300 to J Wang),National High Technology Research and Development Program 863(SS2015AA020403 to J Wang), CAMS Innovation Fund for MedicalSciences (CIFMS 2016-I2M-3-008 to J Wang), China National NaturalSciences Foundation (81472206 to J Wang and 81871889 to Z.J. Wang),Beijing Natural Science Foundation (7172045 to Z.J. Wang), and BeijingNovel Program Grants (Z141107001814051 to J. Wang).

The costs of publication of this article were defrayed in part by the pay-ment of page charges. This article must therefore be hereby marked advertise-ment in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received June 13, 2018; revised July 25, 2018; accepted August 29, 2018;published first August 31, 2018.

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