Research ArticleOriginal Article

Alterations in DNA damage response and repair genes as potential biomarkers for immune checkpoint blockade in gastrointestinal cancer

Yujiao Wang, Xi Jiao, Shuang Li, Huan Chen, Xin Wei, Chang Liu, Jifang Gong, Xiaotian Zhang, Xicheng Wang, Zhi Peng, Changsong Qi, Zhenghang Wang, Yanni Wang, Na Zhuo, Jianling Zou, Henghui Zhang, Jian Li, Lin Shen and Zhihao Lu
Cancer Biology & Medicine August 2022, 19 (8) 1139-1149; DOI: https://doi.org/10.20892/j.issn.2095-3941.2020.0708

Abstract

Objective: Immune checkpoint inhibitors (ICIs) have achieved remarkable results in cancer treatments. However, there is no effective predictive biomarker for gastrointestinal (GI) cancer.

Methods: We conducted integrative analyses of the genomic and survival data of ICI-treated GI cancer patients from the Memorial Sloan Kettering Cancer Center cohort (MSK-GI, n = 227), the Janjigian cohort (n = 40), and the Peking University Cancer Hospital & Institute cohort (PUCH, n = 80) to determine the possible associations between DNA damage response and repair (DDR) gene mutations and clinical outcomes. Data from The Cancer Genome Atlas database were analyzed to determine the possible correlations between DDR gene mutations and the tumor microenvironment.

Results: In the MSK cohort, the presence of ≥ 2 DDR gene mutations was correlated with prolonged overall survival (OS). The Janjigian and PUCH cohorts further confirmed that subgroups with ≥ 2 DDR gene mutations displayed a prolonged OS and a higher durable clinical benefit. Furthermore, the DDR gene mutation load could be considered as an independent prognostic factor, and exhibited a potential predictive value for survival in GI cancer patients treated with ICIs. Mechanistically, we showed that the presence of ≥ 2 DDR gene mutations was correlated with higher levels of tumor mutation burden, neoantigen, and T cell infiltration.

Conclusions: The DDR gene mutation status was correlated with favorable clinical outcomes in GI cancer patients receiving ICIs, which could serve as a potential biomarker to guide patient selection for immunotherapy.

keywords

Introduction

Gastrointestinal (GI) cancer ranks among the world’s most frequent cancer and accounts for a significant proportion of cancer-related deaths1,2. Immunotherapy represents a landmark therapeutic innovation in anticancer therapy, and has been approved in the treatment of various types of tumors, including GI cancer3,4. However, only a small proportion of patients with GI cancer benefit from immune checkpoint inhibitors (ICIs), because of a lack of optimal biomarkers5. To date, only high microsatellite instability (MSI-H) has been validated as a predictive biomarker in clinical trials6. Programmed death ligand-1 (PD-L1) shows a limited predictive value in colorectal cancer (CRC) and esophageal cancer (EC) due to challenges such as antibody uniformity and expression heterogeneity7–12. In addition, tumor mutation burden (TMB) is an important but controversial biomarker for GI cancer patients in different trials13,14. Therefore, it is imperative to develop additional biomarkers for immune checkpoint blockade in GI cancer to identify patients likely to respond to immunotherapy.

Previous studies have shown that the prominent role of DNA damage response and repair (DDR) alterations enhanced antitumor immunity through the production of neoantigens, favoring immune cell recruitment15–17. Classically, deficiency of DDR genes has been shown to guide clinical practice such as chemotherapy and radiotherapy in the treatment of cancer16,18–21. Emerging evidence has verified that DDR gene mutations have been correlated with prolonged survival in non-small cell lung cancer (NSCLC) and urethral carcinoma patients receiving PD(L)-1 antibodies22,23. However, a comprehensive knowledge of DDR gene mutations in GI cancer patients remains unclear and needs to be further investigated to guide ICI therapy.

In the current study, we collected next-generation sequencing data, evaluated the predictive value of DDR gene mutations in different cohorts, and elucidated the effects of DDR mutation status on the tumor microenvironment (TME). Our results may provide a biomarker to help predict the response and survival benefit from ICIs in GI cancer patients.

Materials and methods

Study design and patients

We utilized the genomic and clinical data of ICI-treated GI cancer patients from 3 independent clinical cohorts: (1) the Memorial Sloan Kettering Cancer Center (MSK) cohort14 (http://www.cbioportal.org/study?id=tmb_mskcc_2018) included 1,610 patients receiving ICIs in its pan-cancer analysis, and its GI subgroup contained 227 patients; (2) the Janjigian cohort (https://www.cbioportal.org/study/summary?id=egc_msk_2017) was comprised of 40 patients with esophagogastric cancer who received treatment with a programmed cell death-1 (PD-1) inhibitor alone or together with a cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) inhibitor24; and (3) the Peking University Cancer Hospital & Institute (PUCH) cohort included 80 GI cancer patients who received ICI treatment from August 2015 to May 2019 (Table 1). Responses to immunotherapy were measured by a clinical radiographic assessment based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and modified RECIST 1.1 for immune based therapeutics (iRECIST). Durable clinical benefit (DCB) was defined as complete response (CR), partial response (PR), or stable disease (SD) lasting ≥ 6 months; no durable benefit (NDB) was defined as progressive disease (PD) or SD lasting < 6 months after the beginning of treatment25. We also identified 92 stage II–III gastric cancer patients from the PUCH cohort who received primary gastric cancer resection and adjuvant platinum/5-fluorouracil-based chemotherapy (PUCH-ACT) as a nonimmunotherapy cohort to further validate the prognostic value of the DDR gene mutation load26.

View this table:
Table 1

Clinical characteristics of patients from the 3 immunotherapeutic cohorts

For further analyses during this study, we obtained data from The Cancer Genome Atlas (TCGA) cohort of GI cancer (esophageal cancer, n = 184; gastric cancer, n = 439; colorectal cancer, n = 380) to determine the possible correlations between DDR mutation status and the tumor microenvironment.

Targeted tumor next-generation sequencing

Tumors from the MSK-GI and Janjigian cohorts were analyzed using the Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) clinical sequencing assay, which is a next-generation sequencing platform based on hybridization capture14,24. Whole-exome sequencing (WES) of DNA was analyzed in tumors together with white blood cell samples of the patients in the PUCH cohort. The TMB was measured by analyzing somatic mutations per megabase (mutation/Mb). We selected a cutoff of the top 25% of the TMB as defining a tumor as TMB-High in each cohort (MSK-GI: 10.8 mutations/Mb; Janjigian cohort: 10 mutations/Mb; PUCH cohort: 10 mutations/Mb).

Assessment of DDR mutation status

Based on PubMed searches and the National Center for Biotechnology Information Gene and BioSystems Databases, the MSK-IMPACT panel including a total of 34 genes was previously considered as DDR gene-related (Supplementary Table S1)21,23. A DDR gene list assembled using 6 major DDR pathways was defined as shown in Supplementary Table S121,23. All loss-of-function alterations were classified as deleterious, such as nonsense mutations, frameshift mutations, or splice site alterations.

Detection of PD-L1 expression

In the PUCH cohort, PD-L1 expression was detected by immunohistochemical staining of FFPE sections using an anti-PD-L1 antibody (rabbit, clone SP142, 1:100; Spring Bioscience, Pleasanton, CA, USA). PD-L1 positivity was defined as a staining cell percentage ≥ 1% of tumor and immune cells.

Correlations between DDR gene mutations and the TME

We obtained genomic and mRNA data on GI cancer from TCGA on the website (https://gdc.cancer.gov/about-data/publications/pancanatlas). The expression data for mRNA in RNA-Seq by expectation-maximization (RSEM) values were transformed into log10(RSEM + 1). Previously published immune-related signatures were used to characterize the tumor immune microenvironment (Supplementary Table S2). We calculated the signature scores of patients by averaging of the included genes in the corresponding signature gene sets. To quantify the infiltration of immune cells in the TME, we used single-sample gene set enrichment analysis (ssGSEA) by the Gene Set Variation Analysis package to predict the distributions of various types of immune cells in tumors27,28. In addition, we obtained the tumor neoantigens of GI cancer patients from TCGA cohort data directly using the methods based on a previous study29. The mutation was possibly considered antigenic if it was predicted to generate a neopeptide with affinity less than 500 nM, and its corresponding gene expressed more than 10 transcripts per million.

Statistical analysis

R statistical software, version 3.6.1 (The R Project for Statistical Computing, Vienna, Austria) and SPSS statistical software for Windows, version 23.0 (SPSS, Chicago, IL, USA) were used for the analyses. Categorical data were analyzed by the chi-squared test or Fisher’s exact test, as appropriate. Kaplan-Meier curves were used to determine the survival outcomes including overall survival (OS) and progression-free survival (PFS) estimations. Time-dependent receiver operating characteristic (ROC) curve analysis was used to assess the predictive accuracy of the DDR and other potential biomarkers for immunotherapy. Univariate Cox regression analysis was conducted to evaluate the prognostic value of current biomarkers for patient survival. Multivariate Cox regression analysis was conducted to determine the independent prognostic biomarkers of OS. Student’s t-test was used to determine the difference between 2 groups; and nonparametric tests were used when data were not normally distributed. P-values < 0.05 were assumed to be statistically significant.

Results

Patient characteristics

This study was based on 3 cohorts of GI cancer patients receiving ICI treatment. The MSK-GI cohort is a publicly available dataset consisting of 227 patients with esophagogastric cancer (n = 118) and colorectal cancer (n = 109). The median age of the cohort was 59-years-old (ranging from 19–87 years) with the majority being males (67.0%), which is reflective of GI cancer patients. The other data from the Janjigian and PUCH cohorts included 40 and 80 patients, respectively. Table 1 provides a summary of the patient characteristics from these 3 immunotherapeutic cohorts.

Association between DDR mutation status and GI cancer prognosis in the MSK cohort

We first analyzed the MSK pan-cancer cohort, consisting of 1,610 primary tumors assayed by the MSK-IMPACT sequencing panel to assess the prognostic value of DDR gene mutations. We found that the presence of DDR gene mutations predicted clinical survival in pan-cancer, and noticed that only when setting the DDR gene mutation number as 2, the prognostic value of the DDR gene mutation load was statistically significant in both esophagogastric cancer and the CRC subgroups (Figure 1A and Supplementary Figure S1). We next performed time-dependent ROC analysis to consider the number of DDR gene mutations as a continuous variable, and the highest value of the Youden index was for a cutoff value of 2 (Supplementary Figure S2). We therefore set DDR gene mutations = 2 as a cutoff value in our subsequent studies. Figure 1B and 1C shows that patients with ≥ 2 DDR gene mutations had improved OS in both esophagogastric cancer (n = 118, median OS: 27 vs. 13 months, P = 0.033) and colorectal cancer [n = 109, median OS: not reached (NR) vs. 12 months, P = 0.001] (Figure 1B and 1C). Notably, different DDR pathways may have distinct prognostic values for immunotherapy survival in the MSK cohort (Supplementary Figure S3). The mutations of genes involved in mismatch repair (MMR), nucleotide excision repair (NER), Fanconi anemia (FA), and homologous recombination (HR) were associated with a favorable prognosis, but mutations in the Checkpoint pathway did not show these relationships. The number and frequency of DDR gene mutations are displayed in Supplementary Figure S4 and Supplementary Table S3.

Figure 1
Figure 1

The association between DNA damage response and repair (DDR) gene mutations and survival outcomes of the Memorial Sloan Kettering Cancer Center (MSK) cohort. (A) Forest plot of hazard ratios (univariate analysis) describing the association between the number of DDR gene mutations (≥ 2 vs. < 2) and overall survival (OS) in pan-cancer from the MSK cohort. (B, C) Kaplan-Meier survival curves of overall survival comparing ≥ 2 DDR gene mutations with < 2 DDR gene mutations in esophagogastric cancer (n = 118) and colorectal cancer (n = 109). GI cancer, gastrointestinal cancer; Mut, mutation; HR, hazard ratio; CI, confidence interval.

The prognostic value of DDR gene mutations in validation cohorts

To further investigate the prognostic significance of the DDR mutation status, 2 independent cohorts of ICI-treated GI cancer patients were analyzed. In the Janjigian cohort, 10 patients with ≥ 2 DDR gene mutations had a better OS and PFS than those with < 2 DDR gene mutations (median OS: NR vs. 4.8 months, P = 0.021; median PFS: 4.5 vs. 1.8 months, P = 0.011; Figure 2A and 2B). Moreover, patients with mutations in ≥ 2 DDR genes showed a considerably higher DCB than those with < 2 mutated DDR genes (70% vs. 13.3%, P = 0.002; Figure 2C). Similarly, in our PUCH cohort, patients with ≥ 2 DDR gene mutations had improved survival outcomes than patients with < 2 DDR gene mutations (median OS: NR vs. 9.8 months, P = 0.027; median PFS: NR vs. 2.2 months, P = 0.002; Figure 2D and 2E). Furthermore, patients with ≥ 2 DDR gene mutations demonstrated a significantly higher DCB than those with < 2 mutated DDR genes (81.8% vs. 34.8%, P = 0.009; Figure 2F).

Figure 2
Figure 2

Clinical outcomes of patients with ≥ 2 and < 2 DNA damage response and repair (DDR) gene mutations in the validation cohorts. (A–B) Kaplan-Meier survival curves of the overall survival (OS) (A) and progression-free survival (PFS) (B) comparing ≥ 2 DDR gene mutations with < 2 DDR gene mutations in gastric cancer patients from the Janjigian cohort. (C) Percentage of DCB in patients with < 2 DDR gene mutations (n = 30) and ≥ 2 DDR gene mutations (n = 10) in the Janjigian cohort. (D–E) Kaplan-Meier curves for OS (D) and PFS (E) between ≥ 2 DDR gene mutations and < 2 DDR gene mutation subgroups in the Peking University Cancer Hospital & Institute (PUCH) cohort. (F) The DCB was compared between patients with ≥ 2 DDR gene mutations (n = 11) and those with < 2 DDR gene mutations (n = 69) in the PUCH cohort. (G–I) Time-dependent receiver operating characteristic curve analysis of the DDR gene mutation load and the tumor mutation burden in predicting survival outcomes in MSK-GI (2-year OS, G), Janjigian (2-year OS, H), and PUCH cohort (1-year OS, I). DCB, durable clinical benefit; NDB, no durable benefit.

Our univariant and multivariant Cox analyses confirmed that DDR gene mutations and the TMB were independent prognostic factors across 3 cohorts (Table 2). To compare the predictive power of these biomarkers, we used time-dependent ROC curve analysis. Our data revealed that DDR gene mutations indicated the higher area under the curve value than the TMB in all 3 cohorts (MSK-GI cohort: 0.797 vs. 0.690; Janjigian cohort: 0.773 vs. 0.705; PUCH cohort: 0.665 vs. 0.583; Figure 2G–2I). However, PD-L1 expression displayed a moderate predictive power in the Janjigian and PUCH cohorts (Supplementary Figure S5).

View this table:
Table 2

Univariate and multivariate Cox analysis for overall survival in 3 cohorts

To determine whether the DDR gene mutation load could be considered as a biomarker for nonimmunotherapy treatment, we analyzed the WES and clinical data of GI cancer in TCGA dataset and PUCH-ACT cohort containing 92 gastric cancer patients26. Intriguingly, the DDR gene mutation was not significantly associated with improved OS in either TCGA or the PUCH-ACT cohort (Supplementary Figure S6).

Correlation between DDR mutation status and the TME

To further identify the mechanisms involving the impact of DDR gene mutations on clinical outcomes of ICI-treated GI cancer patients, we next investigated the influence of DDR gene mutations on the TME. First, a significantly increased level of TMB was observed in patients with ≥ 2 DDR gene mutations compared to < 2 DDR gene mutations in the MSK-GI, Janjigian, PUCH, and TCGA-GI cohorts (P < 0.001, Figure 3A–3D). The DDR gene mutation load was positively correlated with the TMB in these 3 cohorts (Supplementary Figure S7). Furthermore, gastric cancer and colorectal cancer patients from TCGA with ≥ 2 DDR gene mutations had higher neoantigen levels than those with fewer mutations (P < 0.001, Figure 3E).

Figure 3
Figure 3

Correlation between DNA damage response and repair (DDR) gene mutations and the tumor microenvironment. (A–D) The tumor mutation burden between patients with ≥ 2 DDR gene mutations and < 2 DDR gene mutations in the MSK-GI cohort (A), the Janjigian cohort (B), the PUCH cohort (C) and TCGA-GI cancer cohort (D). (E) The number of neoantigens comparing patients with ≥ 2 DDR gene mutations and < 2 from TCGA database. (F) Comparison of the immune cell infiltration between the ≥ 2 and < 2 DDR gene mutation subgroups in the analysis of GI cancers. (G) Heat map showing the differentially expressed immune signatures between the subgroup of patients with ≥ 2 DDR gene mutations and the subgroup with < 2 DDR gene mutations in TCGA. GI, gastrointestinal; TMB, tumor mutation burden; GEP, gene expression profile; Teff, effector T cell; TLS, tertiary lymphoid structures; EC, esophageal cancer; GC, gastric cancer; CRC, colorectal cancer.

We next focused on the relationship between DDR gene mutation and immune cell infiltration using ssGSEA methodology. Patients with ≥ 2 DDR gene mutations had greater infiltration of effective immune cells, such as activated dendritic cells (DCs), CD4+ T cells, and CD8+ T cells, but had fewer immunosuppressive cells than patients with < 2 DDR gene mutations (Figure 3F, P < 0.001). Moreover, tumors with ≥ 2 DDR gene mutations exhibited significant enrichment in immune-related signatures (Figure 3G and Supplementary Figure S8, P < 0.001). We also explored the influence of mutations in different DDR pathways on the immune environment in TCGA dataset, which showed that patients with any DDR pathway mutations had a favorable immune infiltration and enhanced immune-related signatures (Supplementary Figure S9). Together, these results indicated that the presence of DDR gene mutations could predict T cell inflammation phenotypes in GI cancer.

Discussion

In this multicohort study, we investigated the prognostic role of DDR gene mutations in GI cancer patients receiving ICIs. Moreover, our study showed that the presence of ≥ 2 DDR gene mutations induced a distinct immune-activated microenvironment with an increased infiltration of immune cells, TMB, and neoantigens.

To date, several studies have been conducted to determine the efficacy of immunotherapy in GI cancer patients, but only approximately 10%–30% of patients benefit from ICIs30,31. Although the U.S. Food and Drug Administration has also approved mismatch repair deficient (dMMR)/MSI-H status as a biomarker for pembrolizumab utilization in treating solid tumors, less than 5% of advanced GI cancer patients harbor this marker7,32. TMB remains a controversial biomarker in GI cancer33,34, encountering several issues, including the lack of consensus regarding the cutoff point and the distinct immunologic impact of each gene mutation35,36. Emerging studies have demonstrated that mutations in some specific pathways or genes may exert positive or negative effects on the outcomes of ICI treatment37–41. Among these aberrations, the DDR gene mutation is a critical parameter predicting immunogenicity, and has been established as a promising biomarker of immunotherapy in urothelial cancer and NSCLC22,23, while its predictive value in GI cancer remains unclear.

Notably, DDR gene alterations are relatively common, occurring in approximately 17% of GI carcinomas, which is higher than the prevalence of dMMR/MSI-H42. To our knowledge, this study is the first to show a correlation between DDR gene mutations and clinical benefits from ICIs in GI cancer patients. We first divided patients into two subgroups by DDR mutation status. DDR gene mutation positivity (≥ 1) generally estimated the clinical outcomes and prognoses with different predictive values in various tumors (Supplementary Figure S1). However, with this kind of grouping, the prognostic value of DDR gene mutation (cutoff = 1) was not evident in esophagogastric cancer patients. We speculated that only 1 DDR gene mutation was not sufficient to contribute to a favorable immune environment, or to confer a survival benefit to GI cancer patients treated with ICIs. In fact, an increasing number of DDR gene alterations have shown a trend toward increased TMB and response to ICIs in urothelial carcinoma and NSCLC21,22. We therefore adjusted the cutoff points of the DDR gene mutation number, and found that setting the DDR gene mutations = 2 stratified GI cancer patients with distinct prognoses across 3 cohorts (Figure 1A–1C, Figure 2A–2F, Supplementary Figure S1 and S2)43. Importantly, our results further suggested that DDR gene mutation load was an independent prognostic factor (Table 2), showing a powerful predictive value for survival of GI cancer patients (Figure 2G–2I). In addition, the relationship between DDR gene mutation load and outcomes in GI cancer patients might vary by treatment, with the prognostic value of DDR gene mutation mainly observed in patients treated by ICIs.

Mechanistically, extensive efforts have been made to understand how DDR gene mutations influence the TME. Tumors with somatic DDR gene mutations manifest as an immune activation profile with increased TMB and neoantigens in multiple cancer types43,44, including GI cancer, as shown in the present study. In addition, DDR gene deficiency has been related to high levels of the chemokines, CXCL10 and CCL5, which are important for immune cell trafficking17. These findings suggested that DDR gene alterations in cancer cells generate a proinflammatory environment. Consistent with previous studies, we showed that tumors with ≥ 2 DDR gene mutations exhibited an enrichment of DCs, CD4+ T cells, CD8+ T cells, and immune-related signatures (Figure 3F and 3G). Among these enhanced signatures, the T cell inflammation gene expression profile (GEP) and tertiary lymphoid structures (TLS) were previously shown to predict benefits with ICI treatment45,46. Taken together, these results indicated that DDR gene mutations could predict T cell infiltration in GI cancer and hence predict the immunotherapeutic benefits.

There were several limitations in our study. (1) Due to the retrospective nature of this analysis, our study requires further validation in prospective clinical trials. (2) Our DDR gene profile may not have included all DDR-related genes. However, the 34 genes used in our study were the most representative genes reflective of the DDR system, which has been verified in previous studies21,23.

Conclusions

In summary, our study elucidates that the presence of ≥ 2 DDR gene mutations correlated with improved survival in ICI-treated patients, and increased levels of T cell inflammation. However, further prospective studies are needed to validate this observation across multiple GI cancer types.

Supporting Information

Grant support

This work was supported by the National Key Research and Development Program of China (Grant Nos. 2018YFC1313302 and 2017YFC1308900), the Beijing Municipal Science and Technology Commission Program (Grant No. Z141107002514013), and the Digestive Medical Coordinated Development Center of Beijing Hospitals Authority (Grant No. XXT19).

Conflict of interest statement

No potential conflicts of interest are disclosed.

Author contributions

Conceived and designed the analysis: Lin Shen, Zhihao Lu, Jian Li.

Collected the data: Chang Liu, Jifang Gong, Xiaotian Zhang, Xicheng Wang, Zhi Peng, Changsong Qi, Zhenghang Wang.

Contributed data or analysis tools: Yujiao Wang, Xi Jiao, Shuang Li, Huan Chen, Xin Wei, Yanni Wang, Na Zhuo, Jianling Zou, Henghui Zhang.

Performed the analysis: Xin Wei, Xi Jiao, Yujiao Wang.

Wrote the paper: Yujiao Wang, Xi Jiao, Shuang Li.

Footnotes

  • ↵*These authors contributed equally to this work.

  • Received November 18, 2020.
  • Accepted April 6, 2021.
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References

  1. 1.↵
    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018; 68: 394–424.
    OpenUrlCrossRefPubMed
  2. 2.↵
    Abdul-Latif M, Townsend K, Dearman C, Shiu K-K, Khan K. Immunotherapy in gastrointestinal cancer: the current scenario and future perspectives. Cancer Treat Rev. 2020; 88: 102030.
  3. 3.↵
    Bender EJN. Cancer immunotherapy. Nature. 2017; 552: S61.
    OpenUrl
  4. 4.↵
    Wang J, Reiss KA, Khatri R, Jaffee E, Laheru D. Immune Therapy in GI Malignancies: a review. J Clin Oncol. 2015; 33: 1745–53.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    Kono K, Nakajima S, Mimura K. Current status of immune checkpoint inhibitors for gastric cancer. Gastric cancer. 2020; 23: 565–78.
    OpenUrl
  6. 6.↵
    Dudley JC, Lin M-T, Le DT, Eshleman JR. Microsatellite instability as a biomarker for PD-1 blockade. Clin Cancer Res. 2016; 22: 813–20.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    Fuchs CS, Doi T, Jang RW, Muro K, Satoh T, Machado M, et al. Safety and efficacy of pembrolizumab monotherapy in patients with previously treated advanced gastric and gastroesophageal junction cancer: phase 2 clinical KEYNOTE-059 trial. JAMA Oncol. 2018; 4: e180013.
  8. 8.
    Huang J, Xu J, Chen Y, Zhuang W, Zhang Y, Chen Z, et al. Camrelizumab versus investigator’s choice of chemotherapy as second-line therapy for advanced or metastatic oesophageal squamous cell carcinoma (ESCORT): a multicentre, randomised, open-label, phase 3 study. Lancet Oncol. 2020; 21: 832–42.
    OpenUrlCrossRefPubMed
  9. 9.
    Kato K, Cho BC, Takahashi M, Okada M, Lin CY, Chin K, et al. Nivolumab versus chemotherapy in patients with advanced oesophageal squamous cell carcinoma refractory or intolerant to previous chemotherapy (ATTRACTION-3): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol. 2019; 20: 1506–17.
    OpenUrlCrossRefPubMed
  10. 10.
    Mishima S, Kawazoe A, Nakamura Y, Sasaki A, Kotani D, Kuboki Y, et al. Clinicopathological and molecular features of responders to nivolumab for patients with advanced gastric cancer. J Immunother Cancer. 2019; 7: 24.
    OpenUrlAbstract/FREE Full Text
  11. 11.
    Shah M, Adenis A, Enzinger P, Kojima T, Muro K, Bennouna J, et al. Pembrolizumab versus chemotherapy as second-line therapy for advanced esophageal cancer: phase 3 KEYNOTE-181 study. J Clin Oncol. 2019; 37: 4010.
    OpenUrl
  12. 12.↵
    Wang F, Wei XL, Wang FH, Xu N, Shen L, Dai GH, et al. Safety, efficacy and tumor mutational burden as a biomarker of overall survival benefit in chemo-refractory gastric cancer treated with toripalimab, a PD-1 antibody in phase Ib/II clinical trial NCT02915432. Ann Oncol. 2019; 30: 1479–86.
    OpenUrlCrossRefPubMed
  13. 13.↵
    Maleki Vareki S. High and low mutational burden tumors versus immunologically hot and cold tumors and response to immune checkpoint inhibitors. J Immunother Cancer. 2018; 6: 157.
    OpenUrlAbstract/FREE Full Text
  14. 14.↵
    Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019; 51: 202–6.
    OpenUrlCrossRefPubMed
  15. 15.↵
    Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, et al. Genomic and molecular landscape of DNA damage repair deficiency across the cancer genome atlas. Cell Rep. 2018; 23: 239–54.
    OpenUrlCrossRefPubMed
  16. 16.↵
    Vidotto T, Nersesian S, Graham C, Siemens DR, Koti M. DNA damage repair gene mutations and their association with tumor immune regulatory gene expression in muscle invasive bladder cancer subtypes. J Immunother Cancer. 2019; 7: 148.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    Parkes EE, Walker SM, Taggart LE, McCabe N, Knight LA, Wilkinson R, et al. Activation of STING-dependent innate immune signaling by S-phase-specific DNA damage in breast cancer. J Natl Cancer Inst. 2016; 109: djw199.
  18. 18.↵
    Conway JR, Kofman E, Mo SS, Elmarakeby H, Van Allen E. Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Med. 2018; 10: 93.
    OpenUrlCrossRefPubMed
  19. 19.
    McLaughlin M, Patin EC, Pedersen M, Wilkins A, Dillon MT, Melcher AA, et al. Inflammatory microenvironment remodelling by tumour cells after radiotherapy. Nat Rev Cancer. 2020; 20: 203–17.
    OpenUrlCrossRefPubMed
  20. 20.
    Pearl LH, Schierz AC, Ward SE, Al-Lazikani B, Pearl FMG. Therapeutic opportunities within the DNA damage response. Nat Rev Cancer. 2015; 15: 166–80.
    OpenUrlCrossRefPubMed
  21. 21.↵
    Teo MY, Bambury RM, Zabor EC, Jordan E, Al-Ahmadie H, Boyd ME, et al. DNA damage response and repair gene alterations are associated with improved survival in patients with platinum-treated advanced urothelial carcinoma. Clin Cancer Res. 2017; 23: 3610–8.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    Ricciuti B, Recondo G, Spurr LF, Li YY, Lamberti G, Venkatraman D, et al. Impact of DNA damage response and repair (DDR) gene mutations on efficacy of PD-(L)1 immune checkpoint inhibition in non–small cell lung cancer. Clin Cancer Res. 2020; 26: 4135–42.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    Teo MY, Seier K, Ostrovnaya I, Regazzi AM, Kania BE, Moran MM, et al. Alterations in DNA damage response and repair genes as potential marker of clinical benefit from PD-1/PD-L1 blockade in advanced urothelial cancers. J Clin Oncol. 2018; 36: 1685–94.
    OpenUrlPubMed
  24. 24.↵
    Janjigian YY, Sanchez-Vega F, Jonsson P, Chatila WK, Hechtman JF, Ku GY, et al. Genetic predictors of response to systemic therapy in esophagogastric cancer. Cancer Discov. 2018; 8: 49–58.
    OpenUrlAbstract/FREE Full Text
  25. 25.↵
    Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science. 2015; 348: 124–8.
    OpenUrlAbstract/FREE Full Text
  26. 26.↵
    Huang WW, Zhao SH, Zhang C, Li ZW, Ge S, Lian BF, et al. Identification of “regulation of RhoA activity panel” as a prognostic and predictive biomarker for gastric cancer. Aging (Albany NY). 2020; 13: 714–34.
    OpenUrl
  27. 27.↵
    Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009; 462: 108–12.
    OpenUrlCrossRefPubMed
  28. 28.↵
    Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017; 18: 248–62.
    OpenUrlCrossRefPubMed
  29. 29.↵
    Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015; 160: 48–61.
    OpenUrlCrossRefPubMed
  30. 30.↵
    Samaan M, Pavlidis P, Papa S, Powell N, Irving P. Gastrointestinal toxicity of immune checkpoint inhibitors: from mechanisms to management. Nat Rev Gastroenterol Hepatol. 2018; 15: 222–34.
    OpenUrlCrossRefPubMed
  31. 31.↵
    Chau I. Clinical development of PD-1/PD-L1 immunotherapy for gastrointestinal cancers: facts and hopes. Clin Cancer Res. 2017; 23: 6002–11.
    OpenUrlAbstract/FREE Full Text
  32. 32.↵
    Nash GM, Gimbel M, Cohen AM, Zeng ZS, Ndubuisi MI, Nathanson DR, et al. KRAS mutation and microsatellite instability: two genetic markers of early tumor development that influence the prognosis of colorectal cancer. Ann Surg Oncol. 2010; 17: 416–24.
    OpenUrlCrossRefPubMed
  33. 33.↵
    Overman MJ, Lonardi S, Wong KYM, Lenz HJ, Gelsomino F, Aglietta M, et al. Durable clinical benefit with nivolumab plus ipilimumab in DNA mismatch repair-deficient/microsatellite instability-high metastatic colorectal cancer. J Clin Oncol. 2018; 36: 773–9.
    OpenUrlCrossRefPubMed
  34. 34.↵
    O’Neil BH, Wallmark JM, Lorente D, Elez E, Raimbourg J, Gomez-Roca C, et al. Safety and antitumor activity of the anti-PD-1 antibody pembrolizumab in patients with advanced colorectal carcinoma. PLoS One. 2017; 12: e0189848.
  35. 35.↵
    Sha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA. Tumor mutational burden as a predictive biomarker in solid tumors. Cancer Discov. 2020; 10: 1808–25.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    Wellenstein MD, de Visser KE. Cancer-cell-intrinsic mechanisms shaping the tumor immune landscape. Immunity. 2018; 48: 399–416.
    OpenUrlCrossRefPubMed
  37. 37.↵
    Zhou H, Liu J, Zhang Y, Huang Y, Shen J, Yang Y, et al. PBRM1 mutation and preliminary response to immune checkpoint blockade treatment in non-small cell lung cancer. NPJ Precis Oncol. 2020; 4: 6.
    OpenUrl
  38. 38.
    Papillon-Cavanagh S, Doshi P, Dobrin R, Szustakowski J, Walsh AM. Correction: STK11 and KEAP1 mutations as prognostic biomarkers in an observational real-world lung adenocarcinoma cohort. ESMO Open. 2020; 5: e000706corr1.
  39. 39.
    Jiao X, Wei X, Li S, Liu C, Chen H, Gong J, et al. A genomic mutation signature predicts the clinical outcomes of immunotherapy and characterizes immunophenotypes in gastrointestinal cancer. NPJ Precis Oncol. 2021; 5: 36.
    OpenUrl
  40. 40.
    Okamura R, Kato S, Lee S, Jimenez RE, Sicklick JK, Kurzrock R. ARID1A alterations function as a biomarker for longer progression-free survival after anti-PD-1/PD-L1 immunotherapy. J Immunother Cancer. 2020; 8: e000438.
  41. 41.↵
    Fan Y, Ying H, Wu X, Chen H, Hu Y, Zhang H, et al. The mutational pattern of homologous recombination (HR)-associated genes and its relevance to the immunotherapeutic response in gastric cancer. Cancer Biol Med. 2020; 17: 1002–13.
    OpenUrlAbstract/FREE Full Text
  42. 42.↵
    Parikh AR, He Y, Hong TS, Corcoran RB, Clark JW, Ryan DP, et al. Analysis of DNA damage response gene alterations and tumor mutational burden across 17,486 tubular gastrointestinal carcinomas: implications for therapy. Oncologist. 2019; 24: 1340–7.
    OpenUrlAbstract/FREE Full Text
  43. 43.↵
    Wang Z, Zhao J, Wang G, Zhang F, Zhang Z, Zhang F, et al. Comutations in DNA damage response pathways serve as potential biomarkers for immune checkpoint blockade. Cancer Res. 2018; 78: 6486–96.
    OpenUrlAbstract/FREE Full Text
  44. 44.↵
    Tian W, Shan B, Zhang Y, Ren Y, Liang S, Zhao J, et al. Association between DNA damage repair gene somatic mutations and immune-related gene expression in ovarian cancer. Cancer Med. 2020; 9: 2190–200.
    OpenUrl
  45. 45.↵
    Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017; 127: 2930–40.
    OpenUrlCrossRefPubMed
  46. 46.↵
    Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science. 2018; 362: eaar3593.
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