USH2A mutation and specific driver mutation subtypes are associated with clinical efficacy of immune checkpoint inhibitors in lung cancer

USH2A mutation and specific driver mutation subtypes are associated with clinical efficacy of immune checkpoint inhibitors in lung cancer USH2A 突变和其他关键驱动基因突变亚型与免疫检查点抑制剂在肺癌患者中临床联系的关联研究 1 4 equal first-author 1 equal 1 1 2 5 6 1 corresp Dajing XIA, dxia@zju.edu.cn Dajing XIA, dxia@zju.edu.cn dxia@zju.edu.cn https://orcid.org/0000-0003-1645-9046 1 3 corresp Yihua WU, georgewu@zju.edu.cn Yihua WU, georgewu@zju.edu.cn georgewu@zju.edu.cn https://orcid.org/0000-0001-7952-4090 浙江大学医学院公共卫生学院毒理学系， 附属妇产医院妇科肿瘤科， 中国杭州市， 310058 浙江大学医学院公共卫生学院毒理学系， 附属妇产医院妇科肿瘤科， 中国杭州市， 310058 Department of Toxicology of School of Public Health, and Department of Gynecologic Oncology of Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China Department of Toxicology of School of Public Health, and Department of Gynecologic Oncology of Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China 浙江大学附属第一医院胸外科， 中国杭州市， 310003 浙江大学附属第一医院胸外科， 中国杭州市， 310003 Department of Thoracic Surgery, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China Department of Thoracic Surgery, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China 中国医学科学院肿瘤病理智能分型和精准治疗创新单元(2019RU042)， 中国杭州市， 310058 中国医学科学院肿瘤病理智能分型和精准治疗创新单元(2019RU042)， 中国杭州市， 310058 Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy, Chinese Academy of Medical Sciences (2019RU042), Hangzhou 310058, China Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy, Chinese Academy of Medical Sciences (2019RU042), Hangzhou 310058, China 德克萨斯大学安德森癌症中心， 德克萨斯大学健康科学中心麦戈文医学院生物化学与分子生物学系， 美国休斯顿， TX 77030 德克萨斯大学安德森癌症中心， 德克萨斯大学健康科学中心麦戈文医学院生物化学与分子生物学系， 美国休斯顿， TX 77030 Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center and UTHealth, Houston; Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030, USA Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center and UTHealth, Houston; Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030, USA 浙江大学附属第二医院消化内科，中国杭州市， 310009 浙江大学附属第二医院消化内科，中国杭州市， 310009 Department of Gastroenterology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China Department of Gastroenterology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China 多伦多大学达拉拉娜公共卫生学院流行病学系， 加拿大多伦多， M5S 2E8 多伦多大学达拉拉娜公共卫生学院流行病学系， 加拿大多伦多， M5S 2E8 Epidemiology Stream, Dalla Lana School of Public Health, University of Toronto, Toronto, M5S 2E8, Canada Epidemiology Stream, Dalla Lana School of Public Health, University of Toronto, Toronto, M5S 2E8, Canada This study aimed to identify subtypes of genomic variants associated with the efficacy of immune checkpoint inhibitors (ICIs) by conducting systematic literature search in electronic databases up to May 31, 2021. The main outcomes including overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and durable clinical benefit (DCB) were correlated with tumor genomic features. A total of 1546 lung cancer patients with available genomic variation data were included from 14 studies. The Kirsten rat sarcoma viral oncogene homolog G12C ( KRAS G12C ) mutation combined with tumor protein P53 ( TP53 ) mutation revealed the promising efficacy of ICI therapy in these patients. Furthermore, patients with epidermal growth factor receptor ( EGFR ) classical activating mutations (including EGFR L858R and EGFR Δ19 ) exhibited worse outcomes to ICIs in OS (adjusted hazard ratio (HR), 1.40; 95% confidence interval (CI), 1.01‍‒‍1.95; P =0.0411) and PFS (adjusted HR, 1.98; 95% CI, 1.49‍‒‍2.63; P < 0.0001), while classical activating mutations with EGFR T790M showed no difference compared to classical activating mutations without EGFR T790M in OS (adjusted HR, 0.96; 95% CI, 0.48‍‒‍1.94; P =0.9157) or PFS (adjusted HR, 0.72; 95% CI, 0.39‍‒‍1.35; P =0.3050). Of note, for patients harboring the Usher syndrome type-2A ( USH2A ) missense mutation, correspondingly better outcomes were observed in OS (adjusted HR, 0.52; 95% CI, 0.32‍‒‍0.82; P =0.0077), PFS (adjusted HR, 0.51; 95% CI, 0.38‍‒‍0.69; P < 0.0001), DCB (adjusted odds ratio (OR), 4.74; 95% CI, 2.75‍‒‍8.17; P < 0.0001), and ORR (adjusted OR, 3.45; 95% CI, 1.88‍‒‍6.33; P < 0.0001). Our findings indicated that, USH2A missense mutations and the KRAS G12C mutation combined with TP53 mutation were associated with better efficacy and survival outcomes, but EGFR classical mutations irrespective of combination with EGFR T790M showed the opposite role in the ICI therapy among lung cancer patients. Our findings might guide the selection of precise targets for effective immunotherapy in the clinic. 本研究以探索与免疫检查点抑制剂（ICIs）效果有关联的基因突变亚型为目标，进行了系统文献检索（电子数据库截至2021年5月31日）。与肿瘤基因特征相关联的主要结局事件包括：总生存期、无进展生存期、客观反应率、持久临床获益。我们从14项研究中总计提取了1546个有基因突变数据的肺癌患者，发现ICIs治疗在 KRAS G12C 联合 TP53 双突变的患者中有更好的疗效，而在携带 EGFR 经典激活突变（包括 EGFR L585R 和 EGFR Δ19 ）的患者中的效果则截然相反：总生存期（调整后HR, 1.40; 95% CI, 1.01–1.95; P =0.0411），无进展生存期（调整后HR, 1.98; 95% CI, 1.49–2.63; P < 0.0001）。另外，ICIs治疗在 EGFR 经典突变联合 EGFR T790M 双突变患者与仅有 EGFR 经典突变的患者在总生存期（调整后HR, 0.96; 95% CI, 0.48–1.94; P =0.9157）与无进展生存期（调整后HR, 0.72; 95% CI, 0.39–1.35; P =0.3050）中均无明显差异。更重要的是，我们发现ICIs在携带 USH2A 错义突变的患者中可能有更好的疗效：总生存期（调整后HR, 0.52; 95% CI, 0.32–0.82; P =0.0077），无进展生存期（调整后HR, 0.51; 95% CI, 0.38–0.69; P < 0.0001），持久临床获益（调整后OR, 4.74; 95% CI, 2.75–8.17; P < 0.0001）与客观反应率（调整后OR, 3.45; 95% CI, 1.88–6.33; P < 0.0001）。综上，我们的研究发现在使用ICIs疗法的肺癌患者中， USH2A 错义突变、 KRAS G12C 联合 TP53 双突变与更好的疗效和生存结局有关，而 EGFR 经典突变无论是否合并 EGFR T790M 突变都预示着不良结局，我们的结果可能会对在肿瘤ICIs的精准治疗方案提供新的依据和靶标。 免疫检查点抑制剂（ICIs） 肺癌 USH2A 错义突变 KRAS G12C 与 TP53 双突变 EGFR 突变 Immune checkpoint inhibitor (ICI) Lung cancer Usher syndrome type-2A ( USH2A ) missense mutation Kirsten rat sarcoma viral oncogene homolog G12C ( KRAS G12C ) mutation combined with tumor protein P53 ( TP53 ) mutation Epidermal growth factor receptor ( EGFR ) mutation 1 Introduction 1 Recent studies have indicated that immune checkpoint inhibitors (ICIs), including antibodies targeting programmed death-‍(ligand) 1 (PD-(L)1) or cytotoxic T-lymphocyte antigen-4 (CTLA-4), have prolonged the survival of lung cancer patients to some extent ( Brahmer, 2013 Brahmer, 2013 ; Steven et al., 2016 Steven et al., 2016 ; Gao et al., 2022 Gao et al., 2022 ). However, objective response and durable clinical benefit (DCB) only occur in a minority of patients ( Yarchoan et al., 2017 Yarchoan et al., 2017 ), and effective genomic biomarkers associated with the potential response of ICIs still remain poorly characterized in the present guidelines. To identify novel genomic predictors of response to ICIs, a string of recent research has performed high-throughput genomic profiling of tumor samples from patients treated with ICIs, by either whole-exome sequencing (WES) or targeted next-generation sequencing (NGS). However, a considerable number of studies were restricted by the inclusion of small patient cohorts, which resulted in contradictory conclusions ( Jeanson et al., 2019 Jeanson et al., 2019 ; Mazieres et al., 2019 Mazieres et al., 2019 ; Gao et al., 2020 Gao et al., 2020 ; Arbour et al., 2021 Arbour et al., 2021 ) and limited their power to detect novel genomic biomarkers. Therefore, a comprehensive analysis was indispensable to assess the potential value of established genomic biomarkers and to enable the discovery of biomarkers with sufficient sensitivity. In this present study, we performed individual patient-level analyses from published clinical research data to examine the genomic features correlated with clinical and survival outcomes, including the DCB, objective response rate (ORR), overall survival (OS), as well as progression-free survival (PFS) data of lung cancer patients treated with ICIs. In particular, our study demonstrated the predictive value of the mutation subtypes of canonical driver genes, including Kirsten rat sarcoma viral oncogene homolog ( KRAS ) and epidermal growth factor receptor ( EGFR ), in immunotherapy. More importantly, we identified novel target genes such as Usher syndrome type-2A ( USH2A ), which could be robust biomarker and serve to predict the clinical efficacy of immunotherapy. 2 Methods 1 2.1 Study eligibility and data acquisition 2 We conducted systematic literature search in the databases of Embase, PubMed, and Cochrane Library to identify relevant studies published up to May 31, 2021. The prospective pooled analysis protocol was registered on the PROSPERO database on September 6, 2021, and was regularly updated (ID: CRD42021271903). Two investigators (YANG and FENG) independently performed search on the databases. The search terms were as follows: (“lung cancer” or “lung carcinoma” or “cancer of lung” or “lung neoplasm”) AND (“mutation” OR “mutations” OR “genomic alteration” OR “genomic variant” OR “genomic alterations” OR “genomic variants”) AND (“immune checkpoint inhibitor” OR “immune checkpoint inhibitors” OR “ICI” OR “ICIs” OR “immune checkpoint blockers” OR “immune checkpoint blockade” OR “ICB” OR “ICBs” OR “PD-1” OR “PD-L1” OR “CTLA-4” OR “Ipilimumab” OR “Tremelimumab” OR “Nivolumab” OR “Pembrolizumab” OR “Lambrolizumab” OR “Atezolizumab” OR “Avelumab” OR “Durvalumab”). When duplicate reports were identified, the one with larger sample size and more detailed clinical profiling was included. In addition, to discern potentially missing articles, the references in the included articles were also reviewed. After the articles were obtained, studies were accessed according to the following inclusion criteria: (1) patients were diagnosed with lung cancer and treated with ICIs; (2) individual patient-level clinical profile of objective response, when DCB, OS, or PFS was given in the article; (3) variant-level genomic profiling of patient cohorts was available; (4) the size of patients attainable for analysis was at least five; (5) studies published in English. Two investigators (YANG and FENG) inspected the retrieved articles to screen potentially applicable studies. Disagreements over particular articles were discussed and the consensus of all investigators was eventually reached. 2.2 Data extraction 2 Two investigators (YANG and FENG) independently extracted data from each screened study. Disagreements or inconsistencies were discussed and determined with the corresponding author, and consensus was reached with all investigators. The information extracted from each study included title, first author, year of publication, sample size, and sequencing method. First, we extracted the individual patient-level information from each study: patient ID, sample source, histological type, gender, age, smoking status, treatment, disease outcomes (objective response, DCB, OS, and PFS), and sequencing method. When duplicate patients were identified, the one with detailed clinical information was included. “Responder” (R) was defined as response evaluation criteria in solid tumors (RECIST) version 1.1 criteria-based response ( Eisenhauer et al., 2009 Eisenhauer et al., 2009 ) with complete response (CR) or partial response (PR); “Non-responder” (NR) was defined as stable disease (SD) or progressive disease (PD); DCB was defined as CR, PR, or SD lasting longer than six months; all other patients were considered to have no durable benefit (NDB) ( Hellmann et al., 2018 Hellmann et al., 2018 ). The following variant-level genomic data were extracted from each study: patient ID, chromosome, start position, reference allele, and variant allele, which were based on the hg19 human genome. The analysis and interpretation of genomic variants were performed by SNPnexus ( https://www. https://www. ‍snp-nexus.‍org) ( Oscanoa et al., 2020 Oscanoa et al., 2020 ). The top 20 most frequently mutated genes were included in our study. 2.3 Statistical analysis 2 The hazard ratios (HRs) of OS and PFS were calculated by applying both univariate and multivariate Cox’s proportional hazard regression analyses. The odds ratios (ORs) of ORR and DCB were estimated by applying both univariate and multivariate logistic regression analyses. To adjust for potential confounding factors, including age and gender, multivariate models were employed. For OR equal to 0, Fisher’s exact test was performed to estimate its 95% confidence interval (CI). For survival analyses, the log-rank test was performed to compare the Kaplan-Meier survival curves. All statistical analyses were performed using R 4.1.0 and GraphPad Prism 7. Data visualization was conducted by the R packages “ComplexHeatmap” ( Gu et al., 2016 Gu et al., 2016 ), ggplot2, forestplot, and survminer. 3 Results 1 3.1 Genomic biomarker landscape of immunotherapy efficacy in lung cancer 2 We performed systematic literature research of the PubMed, Embase, and Cochrane Library databases to identify studies reporting the association between genomic variation and the efficacy of ICIs, which were published up to May 31, 2021. The pooled analysis (Fig. S1) included a total of 1546 patients across 14 studies ( Table 1 Table 1 ) ( Rizvi NA et al., 2015 Rizvi NA et al., 2015 ; Gandara et al., 2018 Gandara et al., 2018 ; Hellmann et al., 2018 Hellmann et al., 2018 ; Miao et al., 2018 Miao et al., 2018 ; Rizvi H et al., 2018 Rizvi H et al., 2018 ; Samstein et al., 2019 Samstein et al., 2019 ; Abou Alaiwi et al., 2020 Abou Alaiwi et al., 2020 ; Anagnostou et al., 2020 Anagnostou et al., 2020 ; Jia et al., 2020 Jia et al., 2020 ; Moding et al., 2020 Moding et al., 2020 ; Reuss et al., 2020 Reuss et al., 2020 ; Fang et al., 2021 Fang et al., 2021 ; Frigola et al., 2021 Frigola et al., 2021 ; Pender et al., 2021 Pender et al., 2021 ). WES: whole-exome sequencing; WGS: whole-genome sequencing; MSK-IMPACT: Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets; ctDNA: circulating tumor DNA; CAPP-Seq: cancer personalized profiling by deep sequencing; EGFR : epidermal growth factor receptor; KRAS : Kirsten rat sarcoma viral oncogene homolog; USH2A : Usher syndrome type-2A. 2 Study 2 Sequencing platform 2 Sample size after screening 6 Percentage of all lung cancer patients (%) Age≥65 years Male gender Ever smoker EGFR mutation KRAS mutation USH2A mutation Miao et al., 2018 WES 30 20.00 36.67 73.33 43.33 26.67 33.33 Pender et al., 2021 WGS 26 19.23 38.46 7.69 30.77 30.77 Abou Alaiwi et al., 2020 Oncopanel 331 47.43 10.88 40.48 Samstein et al., 2019 MSK-IMPACT 139 62.59 46.76 15.11 37.41 Frigola et al., 2021 WES 44 36.36 63.64 81.82 9.09 25.00 43.18 Hellmann et al., 2018 WES 75 52.00 49.33 80.00 17.33 32.00 40.00 Rizvi et al., 2015 WES 34 32.35 44.12 79.41 5.88 23.53 5.88 Rizvi et al., 2018 MSK-IMPACT 239 53.97 49.37 80.75 10.88 35.98 Gandara et al., 2018 Targeted gene panel 429 43.36 26.81 82.05 10.02 9.09 Fang et al., 2021 WES 69 49.28 15.94 5.80 24.64 Anagnostou et al., 2020 WES 89 49.44 51.69 39.33 6.74 32.58 24.72 Jia et al., 2020 ctDNA-WES 10 70.00 90.00 80.00 20.00 0 30.00 Moding et al., 2020 CAPP-Seq 23 78.26 17.39 8.70 4.35 Reuss et al., 2020 WES-IMPACT 8 37.50 75.00 100.00 0 50.00 25.00 The genomic variations covering missense mutations, nonsense mutations, frameshifts, in-frame alterations (deletions and insertions), splice site alterations, and other alterations (silent, intron alterations, untranslated region alterations, etc.) include the mutant forms with important structural and functional differences ( Sabapathy and Lane, 2018 Sabapathy and Lane, 2018 ). Therefore, we estimated the effect size of each gene categorized by its mutation types (subtypes less than 15 cases were not included). Then, both univariable logistic regression analyses and univariable Cox’s proportional hazard regression analyses were performed to evaluate the effect size for clinical and survival outcomes, including DCB, ORR, OS, and PFS. Multivariable models were also applied to reweigh the potential confounding effects (age and gender) (Tables S1–S8). In order to reveal gene subtypes with significantly higher ORs of DCB and response after adjustment, we visualized the results via heatmaps ( Fig. 1 Fig. 1 , multivariable analyses). Consistent results were exhibited for ORR and DCB in univariable analyses (Figs. S2c and S2d) and survival outcomes (Figs. S2a and S2b, multivariable analyses; Figs. S2e and S2f, univariable analyses). The findings were in line with reported research—that is, patients typically with missense mutations of tumor protein P53 ( TP53 ) ( Sun et al., 2020 Sun et al., 2020 ), titin ( TTN ) ( Jia et al., 2019 Jia et al., 2019 ), mucin 16 ( MUC16 ) ( Yu et al., 2020 Yu et al., 2020 ; Zhang et al., 2020 Zhang et al., 2020 ), FAT atypical cadherin 1 ( FAT1 ) ( Fang et al., 2019 Fang et al., 2019 ; Zhu et al., 2021 Zhu et al., 2021 ), type 2 ryanodine receptor ( RYR2 ), or zinc finger homeobox 4 ( ZFHX4 ) ( Cai et al., 2018 Cai et al., 2018 ) showed favorable clinical outcomes compared to wild type, while patients with EGFR missense mutations gained significantly less benefit ( Lee et al., 2017 Lee et al., 2017 ; Fang et al., 2019 Fang et al., 2019 ). Employing this method, we also discovered several novel potential biomarkers (such as USH2A missense mutation, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4 ( SMARCA4 ) missense mutation, and protein tyrosine phosphatase receptor type D ( PTPRD ) mutation) that could be used for lung cancer patients to significantly benefit from ICI therapy. Among the signatures listed above, the USH2A missense mutation was found to be associated with the highest OR with significance in most subgroups. 3.2 Association between favorable outcomes in response to ICIs and KRAS G12C -TP53 co-mutation 2 In order to investigate the treatment efficacy of ICIs in KRAS mutant patients, patients were stratified by three major subtypes ( Fig. 2 Fig. 2 a). Among the 291 KRAS mutant patients in total, 285 (97.9%) carried missense variants, 161 (55.3%) harbored G12C , and 55 (18.9%) and 69 (23.7%) harbored G12D and other missense mutations (excluding G12C and G12D ), respectively. The OS and PFS of KRAS mutant patients that harbored G12C missense mutation were proved to be prolonged across most examined subgroups compared with KRAS wild-type lung cancer patients. Notably, significantly superior OS (adjusted HR 0.70, 95% CI 0.49‍–‍0.99, P =0.05; HR 0.68, 95% CI 0.29–0.96, P =0.03) and PFS (adjusted HR 0.48, 95% CI 0.29‍–0.79, P =0.004; HR 0.48, 95% CI 0.29‍–‍0.79, P =0.004) were observed for KRAS G12C - TP53 co-mutation patients compared to other mutation subtypes ( Figs. 2 Figs. 2 b and S3a). For all KRAS subtypes other than G12C , few significant differences were observed. The likelihood of response to ICIs was stratified by KRAS mutational status, and a strong association was found between the existence of KRAS G12C mutation and higher ORR ( P < 0.05, irrespective of most subgroups but not in TP53 wild-type or PD-‍(L)1+CTLA-4) and DCB (adjusted OR, 2.33; 95% CI, 1.05‍–‍5.18; P =0.04, in TP53 mutant subgroup) (Fig. S4), which were in line with the results for HR. We further examined the correlation of KRAS G12C with clinical outcomes in ICI-treated lung cancer and non-small-cell lung cancer (NSCLC), separately (Figs. S3b‍–‍S3i), in TP53 mutant patients. Compared with the KRAS wild-type, KRAS G12C mutations accumulated in responders (adjusted OR, 3.89; 95% CI, 1.89‍–‍7.98; P < 0.001, all lung cancer) and DCB (adjusted OR, 2.23; 95% CI, 1.05‍–‍5.18; P =0.04, all lung cancer) (Figs. S3b, S3c, S3f, and S3g), and these were also correlated with prolonged survival in both OS (adjusted HR, 0.70; 95% CI, 0.49–0.99; P =0.05, all lung cancer) and PFS (adjusted HR, 0.48; 95% CI, 0.29–0.79; P =0.004, all lung cancer) (Figs. S3d, S3e, S3h, and S3i), where NSCLC patients showed consistent outcomes, as mutually proven by reported research ( Assoun et al., 2019 Assoun et al., 2019 ; Moding et al., 2020 Moding et al., 2020 ). This indicated that KRAS G12C - TP53 co-mutation was associated with favorable outcomes in ICI therapy. 3.3 Negative effects of EGFR classical activating mutations on prognosis and response to ICIs 2 To our knowledge, lung cancer patients with classical EGFR mutations ( EGFR L858R missense mutation, EGFR L858R , and in-frame deletions in exon 19 of EGFR ( EGFR Δ19 )) showed worse outcomes for PD-‍(L)1 blockade treatment ( Hastings et al., 2019 Hastings et al., 2019 ). Thus, we estimated the effect size of EGFR classical activating mutations in our cohort to verify the effectiveness of our method and to investigate the potential predictive value of EGFR mutation subtypes in immunotherapy. We first conducted the investigation on individuals harboring EGFR classical activating mutations, and found that lung cancer patients with these mutations showed worse clinical and survival outcomes ( Figs. 3 Figs. 3 a‍–‍3h) in terms of DCB (multivariable analysis adjusted OR 0.17, 95% CI 0.07‍–‍0.44, P < 0.001; univariable analysis OR 0.18, 95% CI 0.07‍–‍0.46, P < 0.001), ORR ( n =36/38; multivariable analysis adjusted OR 0.18, 95% CI 0.04‍–‍0.76, P =0.02; univariable analysis OR 0.18, 95% CI 0.04‍–‍0.74, P =0.02), OS (multivariable analysis adjusted HR 1.40, 95% CI 1.01‍–1.95, P =0.04; univariable analysis HR 1.32, 95% CI 0.96–1.82, P =0.09), and PFS (multivariable analysis adjusted HR 2.08, 95% CI 1.56‍–‍2.76, P< 0.001; univariable analysis HR 1.98, 95% CI 1.49‍–‍2.63, P < 0.001), as supported by a previous report ( Cai et al., 2018 Cai et al., 2018 ). The outcomes of NSCLC patients were concordant with those of all lung cancer patients. The forest plots containing detailed data in all stratified groups were shown in Fig. S5. Next, patients with classical activating mutations were divided by whether they further developed T790M missense mutation to evaluate clinical efficacy of ICIs in tyrosine kinase inhibitors (TKIs)‍-resistant patients (Figs. 3i‍–‍3l). T790M is the most commonly acquired resistant mutation, which mainly occurs due to treatment with first-generation or second-generation TKIs for years ( Sequist et al., 2011 Sequist et al., 2011 ; Yu et al., 2013 Yu et al., 2013 ; Wu and Shih, 2018 Wu and Shih, 2018 ). From the total of 58 EGFR classical activating mutation patients, 17 (29.31%) were combined with T790M missense mutation. The DCB for activating mutation with T790M ( n =14/16; multivariable analysis adjusted OR 1.53, 95% CI 0.21‍–‍11.26, P =0.68; univariable analysis OR 1.53, 95% CI 0.21‍–‍11.26, P =0.68) exhibited no difference compared with that for activating mutation without T790M . Among all lung cancer patients, non-responders were not more frequently found when activating mutation with T790M was present ( n =15/15; OR 0.00, 95% CI 0.00‍–‍3.30, P =0.51; Fisher’s exact test) compared with activating mutation without T790M . For survival outcome, the results showed no significant difference between activating mutation with and without T790M in OS (adjusted HR 0.96, 95% CI 0.48–1.94, P =0.31, all lung cancer) or PFS (adjusted HR 0.72, 95% CI 0.39‍–‍1.35, P =0.92, all lung cancer). Taken together, we observed no significant difference in clinical or survival outcomes between EGFR activating mutant patients with and without T790M , which indicated that patients harboring the EGFR classical activating mutation presented similar clinical efficacy of ICIs regardless of acquiring drug resistance to TKIs. In addition, we examined the categorized effect size of missense mutation inside or outside the tyrosine kinase domain (codon 713‍–‍965) in EGFR , and all subtypes listed above were integrated into forest plots (Figs. S6 and S7). For most applicable data, patients carrying activating mutations failed to derive significant clinical benefit from ICIs, while no significance was observed among other missense mutations inside or outside the tyrosine kinase domain. 3.4 Association between USH2A missense mutation and favorable clinical outcome in ICI-treated lung cancer patients 2 After systematically estimating genes proposed as classical mutations related to benefits from ICIs, we sought to identify novel biomarkers via pooled analyses. The USH2A missense mutation was one of the top 20 most frequently mutated genes among lung cancer patients (114/404, 28.22%). Interestingly, the stratified analysis illustrated that USH2A missense mutation strongly correlated with better outcomes (Figs. 1 1 a, 1 1 b, and S2), though there had been no previous report showing the association between USH2A and the efficacy of ICIs. Therefore, we questioned whether USH2A could serve as a robust biomarker in predicting the clinical efficacy of ICIs. By applying our method described above, patients harboring USH2A missense mutation were included for further analyses (Fig. S8). As shown in Fig. 4 Fig. 4 , USH2A missense mutated patients exhibited significantly higher ORR and rate of DCB and a promising survival outcome among both lung cancer and NSCLC patients. For lung cancer patients with USH2A missense mutation ( Figs. 4 Figs. 4 a‍–‍4d), the rate of DCB reached 70% (71.76% in all lung cancer patients; adjusted OR 4.74, 95% CI 2.75‍–‍8.17, P < 0.001) and the ORR exceeded 50% (51.56% in all lung cancer patients; adjusted OR 3.45, 95% CI 1.88‍–‍6.33, P < 0.001). Following clinical benefit and objective response, patients harboring the USH2A missense mutation had significantly prolonged OS (adjusted HR, 0.52; 95% CI, 0.32–0.82; P =0.006, all lung cancer) and PFS (adjusted HR, 0.51; 95% CI, 0.38‍–‍0.69; P < 0.001, all lung cancer) compared with USH2A wild-type patients. The results were in parallel with NSCLC patients ( Figs. 4 Figs. 4 e‍–‍4h). These findings supported the predictive utility of USH2A in ICI-treated lung cancer patients. 4 Discussion 1 The development of ICIs has revolutionized the therapeutic landscape in many solid malignancies, including lung cancer. Nonetheless, those who can acquire benefits constitute only a small proportion of patients ( Yarchoan et al., 2017 Yarchoan et al., 2017 ), emphasizing the urgent need for biomarkers to predict the sensitivity and resistance of ICIs. Driven by the advances in high-throughput sequencing methods, clinical scientists began to perform genomic sequencing to elucidate the genomic features associated with ICI efficacy. Nevertheless, the inclusion of small patient cohorts restricted the power of this technology to identify novel biomarkers and even led to debatable conclusions ( Fang et al., 2019 Fang et al., 2019 ; Jeanson et al., 2019 Jeanson et al., 2019 ; Mazieres et al., 2019 Mazieres et al., 2019 ; Gao et al., 2020 Gao et al., 2020 ). This study is the first to perform individual-level pooled analyses to comprehensively characterize the predictive values of commonly mutated genes in ICI-treated lung cancer patients. Firstly, we focused on the roles of two well-established driver mutations, KRAS and EGFR , in ICI-treated lung cancer patients. The roles of different KRAS mutation subtypes in immunotherapy are still under debate ( Wu and Shih, 2018 Wu and Shih, 2018 ; Gao et al., 2020 Gao et al., 2020 ; Liu et al., 2020 Liu et al., 2020 ). Therefore, we performed stratified analyses and found that patients harboring KRAS G12C - TP53 showed favorable clinical and survival outcomes. Previous studies based on published datasets suggested that TP53 - KRAS co-mutated patients exhibited inflamed tumor microenvironment (TME) characteristics, including elevated expression of PD-L1 and accumulation of cluster of differentiation 8-positive (CD8 + ) T cells ( Dong et al., 2017 Dong et al., 2017 ). Further in silico analysis showed that the alterations of TP53 and KRAS were associated with deficient mismatch repair or DNA damage repair, leading to increased tumor mutational burden (TMB) and immunogenicity ( Dong et al., 2017 Dong et al., 2017 ; Canon et al., 2019 Canon et al., 2019 ). Therefore, combined treatment with ICIs and KRAS G12C inhibitor might provide new opportunities for KRAS G12C - TP53 co-mutated patients. Patients with EGFR classical activating mutations, including EGFR Δ19 and EGFR L858R , have been consistently reported to rarely derive benefits from ICIs ( Gainor et al., 2016 Gainor et al., 2016 ; Cai et al., 2018 Cai et al., 2018 ; Kumagai et al., 2021 Kumagai et al., 2021 ), which is consistent with our results. Tumor cells with EGFR activating mutations commonly induced immunosuppressive TME ( Peng et al., 2015 Peng et al., 2015 ; Sugiyama et al., 2020 Sugiyama et al., 2020 ; Cho et al., 2021 Cho et al., 2021 ). The non-inflamed TME established by aberrant EGFR signaling resulted in resistance to ICIs, which was also observed in EGFR T790M mutant tumors ( Hata et al., 2017 Hata et al., 2017 ). Thus, to investigate the treatment potential of ICIs in EGFR -mutated patients, it is necessary to target the immunosuppressive pathway caused by dysregulated EGFR signaling. Importantly, we firstly identified the predictive value of USH2A missense mutations in immunotherapy. Usherin protein, encoded by USH2A , is a transmembrane protein, whose extracellular domain occupies about 97% of the whole protein ( Weston et al., 2000 Weston et al., 2000 ; Toualbi et al., 2020 Toualbi et al., 2020 ). Previous studies have revealed that the dysfunction of Usherin caused by alterations of USH2A was associated with retinitis pigmentosa ( Rivolta et al., 2002 Rivolta et al., 2002 ) and USH2A ( Eudy et al., 1998 Eudy et al., 1998 ). In lung adenocarcinoma, the USH2A mutation is also among the most frequently mutated genes to predict neoantigens ( Cai et al., 2018 Cai et al., 2018 ). Sun et al. (2021) Sun et al. (2021) illustrated that USH2A mutant tumors exhibited higher TMB and immune cell infiltration into tumor tissues with stronger immunogenicity, as well as the excessive expression of immune checkpoint genes, including PD-1 , PD-L1 , and CTLA-4 in colon adenocarcinoma. Moreover, USH2A has been studied as a top mutated gene with predicted human leukocyte antigen-DR β1 (HLA-DRB1)‍-binding neo-peptides at the RNA level in lung adenocarcinoma patients, suggesting that USH2A may play a role in identifying CD4 + T cell epitopes for immune surveillance. Here, it was speculated that the mutated USH2A could serve to derive a neoantigen peptide that recruits immune cells and elicits a subsequent anti-tumor immune response. Detecting mutations of USH2A and other critical genes with next-generation sequencing is simple enough, and their relevance for predicting clinical outcomes in response to ICIs is known. Compared to relatively discordant results from different methods and platforms to reveal the expression of PD-L1 and TMB ( Tsao et al., 2018 Tsao et al., 2018 ; Addeo et al., 2019 Addeo et al., 2019 ), the combination of USH2A and genes listed above may be a better choice as biomarkers for clinical decisions on ICI therapy. Taken together, to our current knowledge, this study is the largest and most comprehensive pooled individual-level analyses in lung cancer so far, providing evidence for the potential predictive value of two common driver mutations in lung cancer. More importantly, we found that patients harboring USH2A missense mutations could derive promising clinical and survival benefits from ICIs. Thus, our work might provide valuable clues for precision cancer immunotherapy. However, this study has several limitations. Firstly, more than 70% of the included samples were sequenced by targeted gene panels, which only covered 300–500 genes. Compared with WES, the restricted detection of genomic variations lowered the statistical power to screen novel biomarkers. Secondly, the clinical and survival outcomes of some included patients are not available, which might result in some potential bias. Thirdly, most of the available genomic profiling is in mutation annotation format (MAF), which can only provide relatively limited information on genomic variations. Ideally, it would be promising to obtain the raw genomic data of these included cohorts in FASTQ or BAM format, from which we could possibly infer copy number variations ( Talevich et al., 2016 Talevich et al., 2016 ), HLA class I haplotypes ( Szolek et al., 2014 Szolek et al., 2014 ), or tumor-filtrating T cell fractions ( Bentham et al., 2021 Bentham et al., 2021 ) for each sample. Integrating these features into the current study might provide new insights into precision cancer immunology. References Abou Alaiwi S , Nassar AH , Xie WL , et al. , 2020 . 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Cancers (Basel) , 13 ( 6 ): 1397 . https://doi.org/10.3390/cancers13061397 https://doi.org/10.3390/cancers13061397 Mutations associated with no durable clinical benefit to immune checkpoint blockade in non-s-cell lung cancer Yihua WU and Dajing XIA contributed to the conception and design of the study. Yihua WU, Dexin YANG, and Yuqin FENG developed the workflow and methodology. Dexin YANG, Yuqin FENG, Haohua LU, Kelie CHEN, Jinming XU, Peiwei LI, and Tianru WANG contributed to data collection, data analysis, and interpretation. Yuqin FENG, Dexin YANG, Yihua WU, and Dajing XIA contributed to writing and review of the manuscript. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data. This work was supported by the National Natural Science Foundation of China (Nos. 21976155, 81802881, and 81773016), the Zhejiang Provincial Natural Science Foundation of China (No. LY18C060001), the Fundamental Research Funds for the Central Universities, and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) (No. 2019-I2M-5-044), China. 10.1631/jzus.B2200292 1673-1581（2023）02-0143-15 杨德馨,冯愉沁,鲁昊骅等. USH2A 突变和其他关键驱动基因突变亚型与免疫检查点抑制剂在肺癌患者中临床联系的关联研究[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2023,24(02):143-157. 杨德馨,冯愉沁,鲁昊骅等.USH2A突变和其他关键驱动基因突变亚型与免疫检查点抑制剂在肺癌患者中临床联系的关联研究[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2023,24(02):143-157. This work was supported by the National Natural Science Foundation of China (Nos. 21976155, 81802881, and 81773016), the Zhejiang Provincial Natural Science Foundation of China (No. LY18C060001), the Fundamental Research Funds for the Central Universities, and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) (No. 2019-I2M-5-044), China. 2022-05-20 2022-10-08 2023-02-15 2023-02-09 2022-12-12 Copyright © Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2022 2023 浙江大学出版社 Dexin YANG, Yuqin FENG, Haohua LU, Kelie CHEN, Jinming XU, Peiwei LI, Tianru WANG, Dajing XIA, and Yihua WU declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects performed by any of the authors. Compliance with ethics guidelines Supplementary information 1 Figs. S1‒S8; Tables S1‒S8 浙江大学学报（英文版）（B辑：生物医学和生物技术） 2 24