Effects of KRAS, STK11, KEAP1, and TP53 mutations on the clinical outcomes of immune checkpoint inhibitors among patients with lung adenocarcinoma

Yao Liang; Osamu Maeda; Chiaki Kondo; Kazuki Nishida; Yuichi Ando

doi:10.1371/journal.pone.0307580

Abstract

Background

This study aimed to identify the associations between individual KRAS, STK11, KEAP1, or TP53 mutations, as well as the comutation status of these genes, and the tumor mutation burden (TMB) with clinical outcomes of lung adenocarcinoma patients treated with immune checkpoint inhibitors (ICIs).

Methods

We collected data from patients with lung adenocarcinoma treated with ICIs from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database between June 2019 and August 2023. The main endpoints were the treatment response and overall survival (OS).

Results

Among 343 patients with lung adenocarcinoma, 61 (18%), 69 (20%), 41 (12%), and 222 (65%) patients had KRAS, STK11, KEAP1, and TP53 mutations, respectively. An overall objective response was observed in 94 of 338 patients (28%), including 2 (1%) who achieved a complete response and 92 (27%) who achieved a partial response. Patients with STK11, KEAP1, or TP53 mutations had a significantly greater TMB (P<0.001). According to the univariate analysis, the treatment response was significantly correlated with TP53 mutation in both the general (P = 0.041) and KRAS wild-type (P = 0.009) populations. KEAP1 and TP53 mutations were associated with worse OS among assessable patients (hazard ratio (HR) = 2.027, P = 0.002; HR = 1.673, P = 0.007, respectively) and among patients without KRAS mutations (HR = 1.897, P = 0.012; HR = 1.908, P = 0.004, respectively). According to the multivariate analysis, KEAP1 (HR = 1.890, P = 0.008) and TP53 (HR = 1.735, P = 0.011) mutations were found to be independent factors for OS.

Conclusions

STK11, KEAP1, and TP53 mutations are significantly associated with a high TMB. TP53 mutation could affect the treatment response to some degree, and both KEAP1 and TP53 mutations resulted in inferior OS in the general patient population and in those with KRAS-wild-type lung adenocarcinoma, indicating that KEAP1 and TP53 mutations might act as prognostic factors for ICI treatment in lung adenocarcinoma patients.

Citation: Liang Y, Maeda O, Kondo C, Nishida K, Ando Y (2024) Effects of KRAS, STK11, KEAP1, and TP53 mutations on the clinical outcomes of immune checkpoint inhibitors among patients with lung adenocarcinoma. PLoS ONE 19(7): e0307580. https://doi.org/10.1371/journal.pone.0307580

Editor: Alvaro Galli, CNR, ITALY

Received: March 25, 2024; Accepted: July 8, 2024; Published: July 22, 2024

Copyright: © 2024 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its supporting information files.

Funding: This work was financially supported by JST SPRING, Grant Number JPMJSP2125.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Yuichi Ando reports research funds and personal fees from Chugai Pharmaceutical Co., Ltd., research funds from BeiGene. Ltd and Geo Holdings Corporation, and personal fees from Bayer Holding Ltd. The other authors have no conflicts of interest to declare.

Introduction

Over the last decade, the treatment for non-small cell lung cancer (NSCLC) has improved by the use of immune checkpoint inhibitors (ICIs), especially inhibitors targeting programmed death-1 (PD-1) or programmed death-ligand 1 (PD-L1) [1]. Although ICIs have provided favorable clinical outcomes, such as improved progression-free survival (PFS) and overall survival (OS), an objective response (OR) was not observed in most patients with NSCLC [2, 3]. Although PD-L1 and the tumor mutation burden (TMB) are known to be predictive biomarkers of the response to ICIs, given that their predictive ability is limited in clinical practice [4], other biomarkers of the response to ICIs and the association between those biomarkers and the TMB must be explored to optimize treatment for patients with NSCLC.

Alterations in several tumor genes, such as mutations in KRAS, STK11, and KEAP1, have been suggested as potential biomarkers for the ICI response in patients with NSCLC [5]. KRAS mutations are the most common clonal oncogenic driver in NSCLC and are present in 35% of lung adenocarcinomas [6]. STK11 encodes the tumor suppressor liver kinase B1 (LKB1), which suppresses tumor growth, and its mutation occurs in approximately 30% of KRAS-mutant lung adenocarcinoma cases; this mutation can promote KRAS-driven cancer growth and early metastasis [7, 8]. KEAP1 encodes Kelch-like ECH-associated protein 1 (KEAP1), which negatively regulates nuclear factor erythroid 2-related factor 2 (NRF2), a regulator of cell survival. Loss-of-function mutations in KEAP1, which account for approximately 20% of KRAS-mutant NSCLC cases, lead to NRF2 activation, resulting in accelerated tumor growth and chemoresistance [7, 9]. TP53 encodes the p53 tumor suppressor protein, a master regulator of the cell cycle and cell death [10]. TP53 mutations were found in approximately 42% of patients with KRAS-mutant NSCLC, and comutation with KRAS was linked to an inflammatory tumor microenvironment [5, 11]. Moreover, KRAS mutation can upregulate NRF2 signal transduction, contributing to oncogenic transformation and senescence evasion [12]. Compared to individual STK11, KEAP1, or TP53 mutations, comutation of KRAS and STK11, KEAP1, or TP53 was associated with worse or better clinical responses to ICIs in patients with NSCLC [7, 8, 13]. However, only a few studies have explored the impact of STK11, KEAP1, and TP53 mutations on clinical outcomes among populations with wild-type KRAS. In this study, we aimed to identify the associations between individual mutations in KRAS, STK11, KEAP1, or TP53, as well as the comutation status of these genes, and the TMB with clinical outcomes of patients with lung adenocarcinoma treated with immune checkpoint inhibitors (ICIs).

Materials and methods

Patients

We collected data from patients with lung cancer who had undergone ICI therapy and who had received a cancer genomic medicine test, including OncoGuide™ NCC Oncopanel, FoundationOne^® CDx and FoundationOne^® Liquid CDx, who were enrolled in the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database between June 1, 2019, and August 30, 2023. All authors had access to information that could identify individual participants during or after data collection. This study was conducted in accordance with the Ethical Guidelines for Medical and Biological Research Involving Human Subjects (Ministry of Health, Labor and Welfare, Japan) and the Declaration of Helsinki. This study was approved by the Institutional Review Board of Nagoya University Hospital (approval no. 2022–0025) and by the review board of C-CAT (C-CAT Control Number: CDU2022-030N). Written consent was obtained from all participants before study initiation.

Data analysis

The TMB was defined as the total number of mutations per coding area of a tumor genome [14]. TMB-high was defined as ≥10 mutations/Mb, and TMB-low was defined as <10 mutations/Mb for OncoGuide™ NCC Oncopanel and FoundationOne^® CDx, while the cutoff value was 16 mutations/Mb for FoundationOne^® Liquid CDx [15–17]. PD-L1 expression was tested using the Dako PD-L1 immunohistochemistry (IHC) 22C3 pharmDx assay for pembrolizumab, the Ventana OptiView PD-L1 (SP142) assay for atezolizumab, and the Ventana OptiView PD-L1 (SP263) assay for adjuvant atezolizumab. PD-L1 positivity was defined as ≥1% of tumor cells staining positive for PD-L1. The main endpoints were the best objective response during ICI treatment and OS. The objective response rate was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 [18]. OS was defined as the time from the date of receiving ICI to the date of death from any cause or the last confirmation of survival. In this study, age, sex, Eastern Cooperative Oncology Group performance status (ECOG PS), smoking history, PD-L1 positivity status, TMB status, treatment line, and regimen were considered potential clinical factors associated with the treatment response and OS. The associations of these clinical variables and target genes, including KRAS, STK11, KEAP1, TP53, and other driver genes, with the treatment response and OS were assessed.

Statistical analysis

The influences of the mutation status of a single gene, including KRAS, STK11, KEAP1, and TP53, on the TMB were compared with the Mann‒Whitney U test, while the effects of the STK11, KEAP1, and TP53 mutation statuses according to KRAS on the TMB were analyzed with Friedman’s test followed by the Bonferroni correction. Univariate analyses with two-sided Fisher’s exact tests were conducted to evaluate the impacts of KRAS, STK11, KEAP1, and TP53 mutation statuses on the PD-L1 status and treatment response. Multivariate analysis was performed to test the associations between clinical and genetic variables and the treatment response, and the results are shown as odds ratios (ORs) with 95% confidence intervals (CIs). The Kaplan‒Meier method was used to analyze and compare OS between groups with differences in the mutation status of the four genes. Hazard ratios (HRs) in univariate and multivariate analyses of OS, with the corresponding 95% CIs, were calculated using the Cox proportional hazard model. All clinical and genetic variables in the multivariate models were selected using a stepwise method. Given the exploratory nature of this study, which aimed to identify potential associations, P values were not adjusted for multiple comparisons to avoid missing potential signals. All reported P values less than 0.05 were considered to indicate statistical significance. All the statistical analyses were performed using IBM SPSS Statistics version 29.0 (IBM Japan Ltd., Tokyo, Japan).

Results

A total of 509 patients with lung cancer received ICIs, 343 of whom had lung adenocarcinoma (Table 1). KRAS, STK11, KEAP1, and TP53 mutations were detected in 62 (18%), 70 (20%), 42 (12%), and 222 (65%) patients with lung adenocarcinoma, respectively. A total of 77 (22%), 71 (21%), 71 (21%), 39 (11%), and 36 (10%) patients had alterations in other driver genes, including EGFR, HER2, NTRK, MET, and ROS1, respectively. A TMB-high status and PD-L1 positivity were present in 103 (30%) and 160 (47%) patients, respectively. The overall best objective response was found in 96 of 343 patients (28%), including 2 (1%) who achieved a complete response (CR) and 94 (27%) who achieved a partial response (PR).

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Table 1. Patient characteristics, n (%).

https://doi.org/10.1371/journal.pone.0307580.t001

Among the 296 patients whose PD-L1 expression status was recorded, we explored the associations between the KRAS, STK11, KEAP1, and TP53 statuses and the PD-L1 expression status (S1 Table). A tendency for patients with STK11 mutant-type (OR, 0.664, 95% CI: 0.380–1.161, P = 0.157) and STK11 mutant but KRAS wild-type (OR, 0.614, 95% CI: 0.326–1.157, P = 0.150) phenotypes to be less likely to exhibit PD-L1 positivity was observed, whereas patients with TP53 mutant-type (OR, 1.583, 95% CI: 0.976–2.568, P = 0.066) and those with comutations of KRAS (OR, 4.857, 95% CI: 1.450–16.266, P = 0.013) tended to exhibit PD-L1 positivity. We also investigated the effects of the KRAS, STK11, KEAP1, and TP53 statuses on the TMB among all patients with lung adenocarcinoma (Fig 1). Patients with STK11, KEAP1, or TP53 mutations had a significantly greater TMB (both P<0.001). When the TMB distribution was evaluated according to the KRAS status, we found that patients carrying TP53 mutations had a significantly greater TMB in the KRAS-wild-type population than in the other three subtypes (all P<0.05, Fig 1). In addition, an increased TMB was observed among patients with single mutations in STK11 (P<0.001), KEAP1 (P = 0.002), or TP53 (P<0.001) compared with patients with comutation of KRAS.

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Fig 1. TMB according to KRAS, STK11, KEAP1, and TP53 statuses.

(A) TMB according to the single-gene status of KRAS, STK11, and KEAP1. (B) TMB according to the comutation status of KRAS and STK11. (C) TMB according to the comutation status of KRAS and KEAP1. (D) TMB according to the comutation status of KRAS and TP53. Note: * P<0.05 was considered to indicate statistical significance. Abbreviations: TMB, tumor mutation burden; WT, wild-type; MUT, mutant-type.

https://doi.org/10.1371/journal.pone.0307580.g001

Through the univariate analysis, we evaluated whether the KRAS, STK11, KEAP1, and TP53 statuses correlated with the treatment response among the 313 patients with known data and found that mutant TP53 (OR, 1.767, 95% CI: 1.045–2.991, P = 0.041) and mutant TP53 but wild-type KRAS (OR, 2,321, 95% CI: 1.240–4.347, P = 0.009) were significantly associated with the objective response (Table 2). Multivariate analysis was performed to explore the relationships between covariables, including age, sex, ECOG PS, smoking history, PD-L1 positivity, TMB status, treatment line, regimen, mutation statuses of KRAS, STK11, KEAP1, and TP53, and treatment response. Patients who were aged 65 years or older (OR, 1.945, 95% CI: 1.089–3.474, P = 0.025), who were PD-L1-positive (OR, 2.444, 95% CI: 1.354–4.409, P = 0.003), who had a high TMB (OR, 2.706, 95% CI: 1.478–4.954, P = 0.001), who received ICIs as a first-line therapy (OR, 2.093, 95% CI: 1.077–4.065, P = 0.029) or who received ICIs in combination with chemotherapy (OR, 0.409, 95% CI: 0.205–0.814, P = 0.011) tended to achieve an objective response (Table 3). No significant association existed between mutations in the four genes and the treatment response.

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Table 2. Univariate analysis of the treatment response according to the KRAS, STK11, KEAP1, and TP53 status.

https://doi.org/10.1371/journal.pone.0307580.t002

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Table 3. Multivariate analysis of the treatment response (n = 264).

https://doi.org/10.1371/journal.pone.0307580.t003

OS was calculated according to the status of KRAS, STK11, KEAP1, and TP53 for 290 patients whose survival data were available (Fig 2). No significant difference was observed in OS among patients with or without KRAS or STK11 mutations, whereas OS was significantly shorter in patients with KEAP1 (HR, 2.027, 95% CI: 1.287–3.191, P = 0.002) or TP53 (HR, 1.673, 95% CI: 1.148–2.438, P = 0.007) mutations than in those without these mutations. When OS was evaluated according to the KRAS status, we observed no association between the STK11 status and OS, while KEAP1 (HR, 1.897, 95% CI: 1.152–3.123, P = 0.012) and TP53 (HR, 1.908, 95% CI: 1.228–2.965, P = 0.004) mutations contributed to significantly a shorter OS among patients with wild-type KRAS (Fig 3). Additionally, patients with KRAS and KEAP1 mutations tended to have worse OS (HR, 2.691; 95% CI: 0.888–8.155; P = 0.080). The multivariate analysis of OS revealed that ECOG PS 1 or worse (HR, 1.762, 95% CI: 1.192–2.606, P = 0.005), treatment with ICIs plus chemotherapy (HR, 0.470, 95% CI: 0.316–0.698, P<0.001), mutation of KEAP1 (HR, 1.890, 95% CI: 1.179–3.031, P = 0.008) or TP53 (HR, 1.735, 95% CI: 1.135–2.654, P = 0.011), and alterations in any other driver gene (HR, 2.244, 95% CI: 1.444–3.487, P<0.001) were associated with inferior OS (Table 4).

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Fig 2. OS according to the single-gene status of KRAS, STK11, KEAP1, and TP53.

Kaplan–Meier curves of OS in patients with lung adenocarcinoma according to the single-gene status of (A) KRAS, (B) STK11, (C) KEAP1 and (D) TP53. Note: * P<0.05 was considered to indicate statistical significance. Abbreviations: OS, overall survival; CI, confidence interval; WT, wild-type; MUT, mutant-type; HR, hazard ratio.

https://doi.org/10.1371/journal.pone.0307580.g002

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Fig 3. OS according to the comutation status of KRAS and STK11, KEAP1 or TP53.

Kaplan–Meier curves of OS according to the STK11 status among patients with (A) KRAS^MUT and (B) KRAS^WT lung adenocarcinoma. Kaplan–Meier curves of OS according to the KEAP1 status among patients with (C) KRAS^MUT and (D) KRAS^WT lung adenocarcinoma. Kaplan-Meier curves of OS according to the TP53 status among patients with (E) KRAS^MUT and (F) KRAS^WT lung adenocarcinoma. Note: * P<0.05 was considered to indicate statistical significance. Abbreviations: OS, overall survival; CI, confidence interval; WT, wild-type; MUT, mutant-type; HR, hazard ratio.

https://doi.org/10.1371/journal.pone.0307580.g003

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Table 4. Multivariate analysis of OS (n = 245).

https://doi.org/10.1371/journal.pone.0307580.t004

A sensitivity analysis was performed to investigate the impacts of KRAS, STK11, KEAP1, and TP53 mutations on OS among patients treated with first- and second-line anti-PD-(L)1 antibodies separately (S2 Table). A significant association between KEAP1 mutation and worse OS was observed among patients receiving anti-PD-(L)1 antibodies as the first- (P = 0.050) or second-line (P<0.001) treatment. Patients carrying TP53 mutations who received first-line anti-PD-(L)1 antibodies tended to have shorter OS (P = 0.070). When analyzing the relationships between gene mutations and OS according to the KRAS status, STK11 and KRAS comutant-type (P = 0.019) and KEAP1 mutant but KRAS wild-type (P = 0.024) showed statistically significant differences in the OS of patients treated with second-line therapy. We also evaluated the influences of mutations in the four genes on the treatment response and OS among subgroups treated with or without ICIs in combination with chemotherapy combination (S3 and S4 Tables). Patients with mutant KEAP1 (P = 0.010) and those with mutant TP53 but wild-type KRAS (P = 0.012) were more likely to achieve a better response to ICIs alone than to ICIs in combination with chemotherapy. We found no association between comutations and the treatment response, regardless of the regimen. Comutation of KRAS and KEAP1 was correlated with inferior OS in patients treated with ICIs alone (P = 0.043), whereas no association existed between comutations and OS in patients treated with ICIs plus chemotherapy. KEAP1 or TP53 mutation contributed to worse OS in the general patient population (P = 0.006, P = 0.019, respectively) and in the wild-type KRAS population (P = 0.004, P = 0.030, respectively) after treatment with ICIs plus chemotherapy.

Because of the small number of patients for whom the FoundationOne® Liquid CDx was used, univariate analyses were only performed to test the associations between the statuses of the four genes and the TMB, the TMB and the treatment response, and the TMB and OS according to two other cancer genomic medicine test methods (data not shown). No association between KRAS mutation and the TMB was observed in patients tested using FoundationOne® CDx, but significant associations between STK11, KEAP1, and TP53 mutations and the TMB were identified. We obtained similar results for the OncoGuide™ NCC Oncopanel, except for TP53. The treatment response was associated with a high TMB in the FoundationOne^® CDx group but not in the OncoGuide™ NCC Oncopanel group. Both methods presented no association between the TMB and OS.

Discussion

In this study, we retrospectively explored the impact of single-gene mutations in KRAS, STK11, KEAP1, or TP53, as well as STK11, KEAP1, or TP53 mutations according to the KRAS status, on the clinical outcomes of patients with lung adenocarcinoma who received ICIs according to data from the C-CAT database. Overall, STK11-, KEAP1-, or TP53-mutant patients presented a significantly greater TMB. On the other hand, patients with PD-L1-positive and TMB-high lung adenocarcinoma tended to exhibit a superior treatment response, which was consistent with previous studies [19] and supported the reliability of the C-CAT database. The benefit of TP53 mutation among the general and KRAS wild-type patient populations in terms of the TMB translated into a minor improvement in the objective response. KEAP1 mutation promotes immune evasion and immunotherapy resistance [20]. In our study, although a significant association between the gene status and treatment response of any of the evaluated patients was not observed, KEAP1 mutation contributed to an inferior OS, which supported the findings of a previous report indicating that KEAP1 mutation is a prognostic factor for patients with lung adenocarcinoma receiving ICIs. Although the effect of TP53 on the OS of patients treated with ICIs remains controversial [5, 11, 21], our results indicated a negative prognostic role for TP53 mutation, which was consistent with the role of TP53 in immune evasion through the regulation of immune checkpoint expression [22].

In addition, although the difference was not significant, nonnegligible differences in the median OS were observed between the subgroups with and without STK11 mutations, regardless of the KRAS mutation status. Among the KRAS mutation subgroups, the Kaplan‒Meier curves of patients with or without KEAP1 mutations were separated after the early stage, and those with KEAP1 and KRAS comutation tended to have worse OS. The absence of a statistically significant association of the comutation might be due to the small sample size. Our results were consistent with the consensus that STK11 and KEAP1 mutations are associated with an immunosuppressive tumor microenvironment [23, 24], but the findings were inconsistent with a retrospective study investigating whether the KRAS status could affect the efficacy of PD-(L)1 inhibitors among 1,261 patients with STK11- or KEAP1-mutant lung adenocarcinoma [25]. These results suggested that both STK11 and KEAP1 mutations could worsen the objective response, progression-free survival (PFS), and OS among patients with KRAS mutations but not among those without KRAS mutations, while we only observed that patients with KEAP1 mutations but wild-type KRAS had a shorter OS. Another retrospective study evaluating the clinical outcomes of first-line pembrolizumab according to the KRAS and TP53 statuses among 696 patients with ≥ 50% PD-L1-positive NSCLC reported that TP53 mutation increased the response rates, PFS, and OS in a KRAS-mutant population but not in a KRAS-wild-type population [26]. However, we found that TP53 mutation improved the response but decreased OS among patients without KRAS mutation but not among those with KRAS mutation. Compared to those studies, our study showed a much longer OS period for each subgroup, possibly due to the heterogeneity of treatment lines and treatment types.

In contrast to the findings of a previous study in which STK11 and KEAP1 mutation carriers exhibited decreased tumor proportion scores (TPSs) for PD-L1 expression [25], we only observed that STK11-mutant patients and STK11-mutant but KRAS wild-type patients might be less likely to exhibit PD-L1 positivity. A possible interpretation might be that the statistical analysis for PD-L1 expression was conducted only using recorded data for PD-L1 positivity or negativity since patients’ TPSs of PD-L1 expression were inaccessible. However, compared to single mutations of KRAS, KRAS and TP53, comutations were more strongly associated with PD-L1 positivity, which was consistent with the findings of a previous study [13]. On the other hand, we reported similar results showing that STK11, KEAP1 and TP53 mutations were associated with an increased TMB [5, 25]. Generally, an increased TMB is associated with longer survival in cancer patients receiving ICIs [27]. However, our results suggested that despite the TMB and PD-L1 status, KEAP1 and TP53 mutations were independently associated with decreased OS. A study identifying the relationship between gene mutations, the TMB, and survival in patients with lung adenocarcinoma reported similar findings [28]. Single-gene mutations and comutations in KEAP1, STK11, PBRM1, and SMARCA4 in patients were accompanied by an elevated TMB and were associated with shorter OS, possibly due to the induction of an immune-cold microenvironment by mutations in those genes.

According to the sensitivity analysis, KEAP1 mutation was the sole factor associated with a poor prognosis for patients treated with first- or second-line anti-PD-(L)1 antibodies, whereas TP53 mutation tended to decrease OS only in patients treated with first-line anti-PD-(L)1 antibodies. Although the differences in the findings of these two genes according to the KRAS status among patients receiving first- or second-line treatment should be considered, these results should be interpreted cautiously due to the small sample size of each subtype. Regardless of treatment with ICIs alone or in combination with chemoimmunotherapy, comutation of KRAS and STK11 or KEAP1 was associated with a worse objective response rate and OS, and KEAP1 mutation among KRAS-wild-type NSCLC patients was correlated with inferior OS after chemoimmunotherapy [25, 29]. In our study, we only observed positive associations between mutations in KEAP1 or TP53 but wild-type KRAS and the treatment response to ICIs alone. No apparent difference in the response to immunotherapy or chemoimmunotherapy was identified according to the comutation status. Despite the trend of associations of KEAP1 and TP53 mutations among the general and KRAS-wild populations with worse OS in patients treated with ICIs alone, these mutations had more apparent prognostic effects on patients treated with chemoimmunotherapy. Among the comutation subgroups, only the KRAS and KEAP1 comutation subgroup of patients treated with the ICIs alone showed a similar outcome to that in a previous study [25]. A possible interpretation of the discrepancy between our comutation results and those of previous studies might be the small sample sizes of each comutation subgroup and the inclusion of different treatment lines.

In the present study, KRAS mutations were detected in 18% of patients with lung adenocarcinoma, but this rate is only approximately 10% among Japanese patients [30]. We did not observe any association between KRAS mutation and the clinical response to ICIs. Indeed, findings on the correlation between KRAS mutation and ICI efficacy in NSCLC patients are inconsistent [31, 32], making it challenging to draw a definitive conclusion.

To our knowledge, the present study is the first to investigate the impacts of KRAS, STK11, KEAP1, and TP53 mutations on the clinical outcomes of Japanese patients with lung adenocarcinoma treated with ICIs using the C-CAT database. Our findings revealed associations between mutations in specific genes and the TMB and clinical outcomes of patients with lung adenocarcinoma treated with ICIs, which demonstrated the usefulness of the C-CAT database and may be meaningful in personalized medicine. However, some limitations in this retrospective study should be noted. First, the sample sizes of populations with comutations were small, especially in the KRAS and KEAP1 comutation population. Second, although we compared results from different cancer genomic medicine tests that showed similar results, the results were not completely consistent. Possible interpretations might be the small sample size of the group analyzed using the OncoGuide™ NCC Oncopanel and bias due to genomic testing methods. Third, this study included individuals with alterations in other driver genes in addition to KRAS mutations, which could affect the contributions of the four target genes to the outcomes. Fourth, although we performed additional analyses of treatment lines and types, the main analyses included a heterogeneous population, and since we did not analyze subsequent treatment after ICIs, which could affect OS, the conclusion was not sufficiently strong. Fifth, we did not perform a PFS analysis for all patients since data on disease progression were not recorded in the C-CAT database.

In conclusion, STK11, KEAP1, and TP53 mutations are significantly associated with a high TMB. TP53 mutation could affect the treatment response to some degree, and both KEAP1 and TP53 mutations resulted in inferior OS in the general patient population and in those with KRAS-wild-type lung adenocarcinoma, indicating that KEAP1 and TP53 mutations might act as prognostic factors for ICI treatment in lung adenocarcinoma patients.

Supporting information

S1 Table. Univariate analysis of the PD-L1 expression status according to the KRAS, TP53, STK11, and KEAP1 statuses.

Abbreviations: PD-L1, programmed death-ligand 1; OR, odds ratio; CI, confidence interval; MUT, mutant-type; WT, wild-type.

https://doi.org/10.1371/journal.pone.0307580.s001

(DOCX)

S2 Table. Univariate analysis of OS according to the KRAS, STK11, KEAP1, and TP53 statuses in patients treated with first- and second-line anti-PD-(L)1 antibodies.

Abbreviations: OS, overall survival; PD-(L)1, programmed death-(ligand) 1; HR, hazard ratio; CI, confidence interval.

https://doi.org/10.1371/journal.pone.0307580.s002

(DOCX)

S3 Table. Univariate analysis of the treatment response according to the KRAS, STK11, KEAP1, and TP53 statuses in patients treated with ICIs in combination with/without chemotherapy.

Abbreviations: ICI, immune checkpoint inhibitor; OR, odds ratio; CI, confidence interval.

https://doi.org/10.1371/journal.pone.0307580.s003

(DOCX)

S4 Table. Univariate analysis of OS according to the KRAS, STK11, KEAP1, and TP53 statuses in patients treated with ICIs in combination with/without chemotherapy.

Abbreviations: OS, overall survival; ICI, immune checkpoint inhibitor; HR, hazard ratio; CI, confidence interval.

https://doi.org/10.1371/journal.pone.0307580.s004

(DOCX)

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Effects of KRAS, STK11, KEAP1, and TP53 mutations on the clinical outcomes of immune checkpoint inhibitors among patients with lung adenocarcinoma

Effects of KRAS, STK11, KEAP1, and TP53 mutations on the clinical outcomes of immune checkpoint inhibitors among patients with lung adenocarcinoma

Correction

Figures

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Patients

Data analysis

Statistical analysis

Results

Discussion

Supporting information

S1 Table. Univariate analysis of the PD-L1 expression status according to the KRAS, TP53, STK11, and KEAP1 statuses.

S2 Table. Univariate analysis of OS according to the KRAS, STK11, KEAP1, and TP53 statuses in patients treated with first- and second-line anti-PD-(L)1 antibodies.

S3 Table. Univariate analysis of the treatment response according to the KRAS, STK11, KEAP1, and TP53 statuses in patients treated with ICIs in combination with/without chemotherapy.

S4 Table. Univariate analysis of OS according to the KRAS, STK11, KEAP1, and TP53 statuses in patients treated with ICIs in combination with/without chemotherapy.

References