Co-mutations in DNA damage response pathways serve as potential biomarkers for immune checkpoint blockade

Biomarkers such as programmed death receptor 1 ligand (PD-L1) expression, tumor mutational burden (TMB), and high microsatellite instability are potentially applicable to predict the efficacy of immune checkpoint blockade (ICB). However, several challenges such as defining the cut-off value, test platform uniformity, and low frequencies limit their broad clinical application. Here we identify co-mutations in the DNA damage response (DDR) pathways of homologous recombination repair and mismatch repair (HRR-MMR) or HRR and base excision repair (HRR-BER) (defined as co-mut+) that are associated with increased TMB and neoantigen load and increased levels of immune gene expression signatures. In four public clinical cohorts, co-mut+ patients presented a higher objective response rate and a longer progression-free survival or overall survival than co-mut- patients. Overall, identification of DDR co-mutations in HRR-MMR or HRR-BER as predictors of response to ICB provides a potentially convenient approach for future clinical practice.


Abbreviations list
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Author Manuscript Published OnlineFirst on August 31, 2018; DOI: 10.1158/0008-5472. CAN-18-1814 Abstract Biomarkers such as programmed death receptor 1 ligand (PD-L1) expression, tumor mutational burden (TMB), and high microsatellite instability are potentially applicable to predict the efficacy of immune checkpoint blockade (ICB). However, several challenges such as defining the cut-off value, test platform uniformity, and low frequencies limit their broad clinical application. Here we identify co-mutations in the DNA damage response (DDR) pathways of homologous recombination repair and mismatch repair (HRR-MMR) or HRR and base excision repair (HRR-BER) (defined as co-mut+) that are associated with increased TMB and neoantigen load and increased levels of immune gene expression signatures. In four public clinical cohorts, co-mut+ patients presented a higher objective response rate and a longer progression-free survival or overall survival than co-mut-patients. Overall, identification of DDR comutations in HRR-MMR or HRR-BER as predictors of response to ICB provides a potentially convenient approach for future clinical practice.

Significance
Identification of co-mutations in specific DDR pathways as predictors of superior survival outcomes in response to immune checkpoint blockade provide a clinically convenient approach for estimation of tumor mutational burden and delivery of ICB therapy.
Challenges in defining cut-off values, intratumoral heterogeneity distributions, test platform uniformities and dynamic changes have limited the clinical application of PD-L1 expression (9). High TMB and NAL are associated with improved response to ICB treatment (5,10), but the lack of a validated cut-off value has limited the use of this method (9). Mismatch repair deficiency (dMMR) may contribute to genome instability and lead to increases in TMB and NAL, which can be indicated by microsatellite instability-high (MSI-H) status (11), and thus, pembrolizumab was approved by the FDA for dMMR/MSI-H solid tumors (1). However, less than 5% of solid tumors are dMMR/MSI-H (12), which is the limiting factor for the clinical utilization of dMMR/MSI-H.
The DNA damage response (DDR) system comprises eight pathways: mismatch repair (MMR); base excision repair (BER); check point factors (CPF); Fanconi anemia (FA); homologous recombination repair (HRR); nucleotide excision repair (NER); nonhomologous end-joining (NHEJ); and DNA translesion synthesis (TLS) (13). The DDR system is essential for the preservation of genomic integrity (14), and thus,  (1,15). Melanoma patients responding to ICBs commonly harbored mutations in BRCA2, a major gene in the HRR pathway (6). Another study revealed that patients with deleterious alterations in 34 genes involved in DDR pathways exhibited high TMB levels and had improved clinical outcomes of ICB therapy in urothelial cancer (16). However, the number of DDR genes involved in these studies is limited, and most other DDR genes are poorly studied. Additionally, due to the interactions among different DDR pathways, the contribution weights of different DDR pathways to TMB should be examined but have not been well established to date.
Defects in one DDR pathway lead to a greater dependency on the remaining DDR pathways, implying higher genomic instability when multiple deficiencies exist in different DDR pathways (17). Therefore, we hypothesized that mutations in multiple DDR pathways are associated with higher genomic instability; as a result, we would expect higher NAL and TMB. By analyzing whole exome sequencing (WES) data from The Cancer Genome Atlas (TCGA) and validating the results using the International Cancer Genome Consortium (ICGC) database, we explored the optimized pattern of mutation combinations across distinct DDR pathways for TMB and NAL estimation.
We herein report the relationships between co-mutations in DDR pathways and TMB, NAL, immune-regulatory mRNA expression signatures and the clinical benefit of ICBs. Research.
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To further explore the association between DDR pathways and the clinical benefit of ICBs, we included genomic and clinical data from four clinical cohorts treated with ICBs. The first cohort consisted of 34 nonsmall cell lung cancer (NSCLC) patients treated with anti-PD-1 therapy (Rizvi cohort) (21). The second and 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) (10,22). The last cohort comprised 38 patients with melanoma treated with anti-PD-1 therapy (Hugo cohort) (6).

DDR pathway mutations
The detailed profiles of genes involved in eight DDR pathways are listed in Supplementary Table S2 (13). As most DDR genes have not yet been studied, we defined mutations in DDR pathways as any nonsilent mutations in gene coding regions Research.
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Tumor mutational burden and neoantigen load analysis
TMB was defined as the total nonsilent somatic mutation counts in coding regions.
To explore how many DDR pathway mutations predicted higher TMB or NAL, TMB or NAL was classified into high/low based on the top quartile, and a receiver operating characteristic (ROC) curve was applied to determine the cut-off of pathway mutation counts with the highest Youden index.

mRNA expression profiling analysis
The response to ICBs has been reported to be related to immune tumor microenvironments, including T-effector, IFN-γ-associated genes, T-cell receptors and immune factors (23,24). The associations between DDR pathway co-mutation status and relevant immune-related genes were analyzed in 7570 patients from TCGA for whom both RNAseq and DNAseq data were available. The immune gene list was mainly based on a published article that summarized the genes related to activated T cells, immune cytolytic activity and IFN-γ release (25). Other immune genes were added according to two relevant clinical trials (26,27). A list of 47 immune genes is provided in Supplementary Table S3. The mRNA expression from cBioPortal was quantified by RSEM (RNAseq by expectation-maximization) (28). The data were log2transformed before analysis.
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Author Manuscript Published OnlineFirst on August 31, 2018; DOI: 10.1158/0008-5472.CAN-  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 pathway co-mutation status. The genes identified to be on the leading edge of the enrichment profile were subjected to pathway analyses. Genes with expression equal to 0 in more than 80% of samples were excluded from the GSEA analysis. The normalized enrichment score (NES) is the primary statistic for examining gene set enrichment results.

Clinical data analysis
Across four ICB-treated cohorts, we analyzed co-mut status, TMB, and PD-L1 expression for association with patient prognosis. The TMB-high group was defined as the top quartile, and the TMB-low group was defined as the bottom three quartiles (29).
PD-L1+ was defined as PD-L1 positive in ≥ 50% of tumor cells, and PD-L1-was defined as PD-L1 positive in < 50% of tumor cells (3,4). We further tested the association of co-mut status with survival outcomes stratified by TMB and PD-L1.
Measures of patient survival and response were based on definitions consistent with how they were evaluated in the abovementioned trials. For the Rizvi cohort and the Allen cohort, response was defined by RECIST criteria. For the Hugo cohort, responding tumors were defined as exhibiting a complete response or a partial response or as stable disease, and nonresponding tumors were defined as exhibiting disease progression by irRECIST. For the Snyder cohort, response data were not available. We also analyzed the association between co-mut status and patient prognosis in NSCLC and melanoma from TCGA, using propensity-score matching with a ratio of 1:2.

Statistical analyses
All the analyses were performed by R 3.4.2. Correlations between number of mutated DDR pathways and TMB or NAL were determined by Spearman's rank correlation coefficient. Multiple linear regression with a stepwise method was used to examine the correlations among eight pathways and TMB or NAL. If NAL, TMB or mRNA were normally distributed, Student's t-test was used to determine the differences between two groups; otherwise, the Mann-Whitney U test was used. Differences among three or more groups were determined by the Kruskal-Wallis test with Dunn's posttest.
Heatmap was used to depict the mean difference of immune-related genes between co-mut+ and co-mut-subgroups by pheatmap package in R. Correlation based group average hierarchical clustering was used for gene clustering and Minkowski distance for samples clustering. Survival analysis was performed using Kaplan-Meier curves, with a P value determined by a log-rank test. Hazard ratio (HR) was determined through Cox regression. Proportional hazards assumption was tested before the Cox regression.
To explore the modification effects of PD-L1 or TMB on the association between comut status and prognosis, stratified analyses were conducted, and interactions were evaluated with the log likelihood ratio statistic. Fisher's exact test or the chi-square test was used to identify the association of co-mut status and objective response rate. All reported P values were two-tailed, and P < 0.05 was considered significant unless otherwise specified. A false discovery rate (FDR) < 0.05 was considered significant in the pan-cancer mRNA analysis and GSEA. Research.
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Author Manuscript Published OnlineFirst on August 31, 2018; DOI: 10.1158/0008-5472.CAN-  Results Gene co-mutations in DDR pathways were favorable surrogates for TMB estimation By analyzing the sequencing data from TCGA and validating the findings in ICGC, we identified that both TMB and NAL were significantly higher in patients with mutations in one or more DDR pathways than in those with wild-type status (Fig. 1A).  Fig. S1A, 1B), suggesting that mutation of a single DDR pathway may not account for TMB-H. The ROC curve showed that mutations covering ≥ 2 DDR pathways demonstrated an optimal Youden index, with 79.6% sensitivity and 80.9% specificity, compared with other numbers of pathway combinations in TMB-H estimation (Fig. 1B). In addition, similar results were verified in the ICGC TMB data, with 65.7% sensitivity and 84.8% specificity (Fig. 1C), and in TCGA NAL data, with 75.3% sensitivity and 78.8% specificity (Fig. 1D). These results confirmed our hypothesis that co-mutations in DDR pathways could predict higher TMB and NAL.
Given the potential interactions between different DDR pathways, to identify the most effective patterns of pathway combinations, we studied the associations of eight DDR pathways with TMB or NAL to select DDR pathways that contributed more to TMB or NAL, estimated by multiple linear regression analyses in patients with mutated Research.
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Author Manuscript Published OnlineFirst on August 31, 2018; DOI: 10.1158/0008-5472.CAN-  DDR pathways limited to five. Together with the consistency between TMB and NAL data, three pathways, BER, HRR, and MMR, were selected with satisfactory standardized beta coefficients (Fig. 1E). Although we could not fully exclude the functions of other co-mutation combinations, more specifically, patients with HRR-BER or HRR-MMR co-mutations illustrated significantly or borderline higher TMB and NAL than those with BER-MMR and other co-mutations (Fig. 1F). Thus, we defined co-mutations in the HRR-BER and HRR-MMR pathways as the "co-mut+" subgroup.
A total of 740 among 8552 cases (8.7%) were classified as co-mut+ in TCGA dataset. Incidences of co-mut+ varied across different cancer types, ranging from 0% in pheochromocytoma and paraganglioma (PCPG) to 29.2% in bladder urothelial carcinoma (BLCA) (Fig. 1G). The frequencies of global DDR-related gene mutations in each cancer type were similar among the HRR, BER and MMR pathways (Supplementary Fig. S2). We then evaluated whether co-mut+ status was associated with a higher level of TMB or NAL. The results showed significantly higher levels of TMB (Fig. 1H) and NAL (Fig. 1I) in the co-mut+ subgroups than in the co-mutsubgroups across the cancer types with indications for ICBs delivery according to the FDA. Similar results were also obtained in other cancer types (Supplementary Table   S4 and Table S5). In addition, co-mut+ was significantly associated with MSI-H (P < 0.0001) and predicted MSI-H with 76.1% sensitivity and 98.8% specificity (Fig. 1J).
In tumors with high frequency of MSI-H, such as colorectal adenocarcinoma, stomach adenocarcinoma and uterine corpus endometrial carcinoma, most MSI-H samples were co-mut+, and importantly, co-mut+ covered more cancer types than MSI-H (Supplementary Fig. S3). No significant difference in TMB was observed between the co-mut+/MS stability (MSS) and co-mut-/MSI-H groups (Fig. 1K). These findings Research.
on July 10, 2020. © 2018 American Association for Cancer cancerres.aacrjournals.org Downloaded from suggested that co-mut+ was a favorable surrogate for TMB estimation covering more percentage of patients than MSI-H.

Correlations between co-mut+ and mRNA expression of immune-regulatory gene expression signatures
To investigate the correlations between the co-mut+ subgroup and immuneregulatory gene expression signatures, we analyzed 7570 samples from TCGA with both RNAseq and WES data. The GSEA revealed prominent enrichment of mRNA signatures involved in IFNγ pathway, inflammatory response and allograft rejection in the co-mut+ subgroup over a pan-cancer analysis ( Fig. 2A). Among the 47 selected immune-related genes, the mRNA expression of 31 genes was significantly higher in the co-mut+ subgroup than in the co-mut-subgroup (FDR < 0.05) across all cancer types. The mean differences of these 31 genes between the co-mut+ and co-mutsubgroups are shown in the heatmap (Fig. 2B). Specifically, the co-mut+ subgroup demonstrated higher levels of mRNA expression than did the co-mut-subgroup in the  Fig. 2F). Additionally, in 14 cancer types, at least one immuneregulatory gene had an expression level significantly higher in the co-mut+ subgroup than in the co-mut-subgroup (Supplementary Fig. S4).

Co-mut+ status predicted favorable clinical outcomes of ICB delivery
We further investigated the relationship between co-mut status and the clinical Research.
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The Rizvi cohort
We performed a pooled analysis on two metastatic melanoma cohorts [the Allen cohort (10) and the Snyder cohort (22) Fig. 3F) compared with those of co-mut-patients. Additionally, no significant difference was found in survival outcomes between the single-DDR-pathway mutant and DDR-wild-type groups in these four public cohorts (Supplementary Fig. S6A-C), further supporting the hypothesis that a single mutated DDR pathway was not sufficient to predict greater benefit from ICBs.
To verify whether co-mut+ was a predictive or prognostic factor, we performed Research.
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As a result, no significant difference was found in OS between the co-mut+ and comut-subgroups in either the NSCLC patients or melanoma patients (Fig. 4A, B), suggesting that co-mut was a predictor of the clinical benefit of ICBs instead of a prognostic marker.

Relevance of co-mutation status to PD-L1 expression and TMB as a predictive biomarker
The predictive power of co-mut status was further compared with PD-1 expression or TMB in the clinical cohorts. TMB calculated by WES with the top quartile or median as a cut-off value failed to consistently predict survival improvement by ICBs and thus was apparently inferior to co-mut status as a predictive marker (Supplementary Table S6). Furthermore, in TMB-low (defined as less than the top quartile) NSCLC patients of the Rizvi cohort, both the median PFS and ORR of co-mut+ patients were significantly superior to those of co-mut-patients  Fig. 5B left). There was no difference between the co-mut+ and co-mut-subgroups in terms of median PFS or ORR in TMB-high NSCLC patients (Fig. 5A right, Fig. 5B right).
No interaction between co-mut and TMB was observed (P = 0.16). In the melanoma cohorts, co-mut+ status also predicted improved OS and ORR in the TMB stratification analysis, though significant P values were not reached ( Supplementary   Fig. S7A-D).
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Discussion
In this study, we demonstrated that the presence of co-mutations in the HRR-MMR and HRR-BER pathways was associated with higher TMB, NAL and immuneregulatory gene expression and is a potential predictive biomarker for ICBs. The co-mut+ status identifies a patient population that may potentially benefit from ICBs irrespective of PD-L1 expression and TMB status.
The DDR system is required to preserve genomic integrity and has multiple functional links with innate immune signaling to protect against pathogens such as damaged DNAs (11). Functional mutations in the DDR system reduce genomic stability (17), although it remains unclear which DDR pathway contributes more to genomic stability and how their interactions may impact clinical outcomes. We found that co-mutations involving two pathways, HRR-BER and HRR-MMR, are the key reasons for the elevation of TMB and NAL. The associations between these comutations and increased TMB and NAL are consistent with previous reports based on WES data regarding genomic and transcriptomic features related to ICB response (6).
The HRR and BER pathways monitor and repair DNA double-strand breaks (DSBs) and single-strand breaks (SSBs), respectively (31), while the MMR pathway is responsible for DNA insertion/deletion corrections (32). We speculated that high TMB and NAL were the result of increasing DDR mutation accumulation. Defects in the HRR pathway may lead to an inability to repair the DNA damage caused by BER or MMR deficiency. The underlying mechanism of co-ordination between HRR-BER and HRR-MMR should be further investigated in the future.
Co-mutation is a potential predictive biomarker for the use of PD-1 blockades in patients with advanced NSCLC. Both the tumor response rate and median PFS were improved in the co-mut+ subgroup. In melanoma, the median OS was longer in the co-Research.
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Author Manuscript Published OnlineFirst on August 31, 2018; DOI: 10.1158/0008-5472.CAN-18-1814 mut+ subgroup, while the difference in ORR was not significant. Approximately 36.8% (14/38) of the patients in the Hugo cohort had received MAPK inhibitors, which may mediate cross-resistance to ICBs (6). By comparing co-mut+ with PD-L1 expression and TMB in clinical NSCLC and melanoma cohorts, we found that co-mut+ might be a clinically practicable biomarker predictive of response to ICBs and survival.
Currently, PD-L1 expression is a standard predictive biomarker for first-line pembrolizumab (3). To date, the treatment for patients with PD-L1 expression < 50% is platinum-based chemotherapy, either alone (33) or in combination with pembrolizumab (34). Biomarker selection for the use of ICBs in the population remains debatable. We have demonstrated that co-mut+ patients may benefit from anti-PD-1 therapy even if the PD-L1 expression is less than 50%. Similarly, in the TMB-low subset, the co-mutation status may enrich a patient population with a higher response rate to ICBs. Co-mutation is a qualitative biomarker that does not require a cut-off value. Standard next-generation sequencing (NGS) may be able to determine comutation status accurately. This method would be relatively simple and cost-effective compared with measurement of TMB. Prevalence of co-mut+ status in the 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 co-mutation status is detectable in plasma cell-free DNA (cfDNA). These features may make comutations in the DDR pathways an attractive biomarker for ICBs. Future prospective studies are warranted. DDR genes in predicting a clinical benefit in NSCLC or melanoma treated with anti-PD-1 (Supplementary Fig. S8A, B). One important potential reason was that the TMB and the corresponding NAL caused by a single DDR mutation were not high enough to lead to significantly improved immunogenicity. According to our results, patients with mutations in a single DDR pathway did not show a superior response to ICBs compared with that of those with wild-type DDR. The median TMB levels of patients with single mutations in DDR genes were 52 (TCGA) and 53 (ICGC), which were very different from the previously reported predictive TMB cut-off values (6,10,21,22). Based on these data, co-mutations in DDR pathways could better predict ICB efficacy than a single mutated DDR pathway could.
This study is first limited by the retrospective profiling of the analysis. However, TCGA is the most reliable public dataset, and validation using the ICGC database supports the reliability of our results. Moreover, we here did not differentiate whether the DDR gene mutations were functional. Our attempts to recruit functional DDR mutations into our co-mut+ pattern was handicapped by the limited information available regarding the functions of different mutations and the lack of hotspots in DDR gene mutations. In addition, some benign mutations may also influence protein function (35). Therefore, it is very difficult to clearly differentiate functional DDR mutations, and thus, missense DDR mutations were utilized in the present study. Finally, we could not fully exclude the functions of co-mutations in other DDR pathways. However, the necessity of at least 2 pathways and the co-mut+ pattern provided optimal accuracy for TMB estimation and efficacy prediction for ICB delivery.     response-related gene mRNA expression. A, Impact of co-mut status on immune response-related pathways. B, Heatmap depicting the mean differences in immune response-related gene mRNA expression between co-mut+ and co-mut-across different tumor types. The x-axis indicates different cancer types, and the y-axis indicates gene names. Each square represents the difference of mean expression level of each indicated immune-related genes between co-mut+ and co-mut-in each cancer types. Red indicates that the gene's mean expression level was higher in the co-mut+ group than in the co-mut-group. Green indicates that the gene's mean expression level was lower in the co-mut+ group than in the co-mut-group. C-F, Comparison of the mRNA expression of genes related to immune checkpoints, tumor microenvironment, INF-γ pathway and T-effector, and T-cell receptor signature between co-mut+ and comut-groups in the pan-cancer analysis. ***FDR < 0.001. Abbreviations: co-mut+, comutation of HRR-BER or HRR-MMR. TME, tumor microenvironment; TCR, T-cell receptor. Comparison of the objective response rates between the co-mut+ and co-mut-groups from the Hugo cohort. Abbreviations: PFS, progression-free survival; OS, overall survival; NR, not reached.  Comparison of the objective response rates between the co-mut+ and co-mut-groups stratified by tumor mutational burden (low or high). C, Kaplan-Meier survival curves of PFS comparing the co-mut+ and co-mut-groups stratified by PD-L1 expression (< 50% or ≥ 50%). D, Comparison of the objective response rate between the co-mut+ and co-mut-groups stratified by PD-L1 expression (< 50% or ≥ 50%)