Immunohistochemical analysis of p53 and LKB1 as predictive biomarkers of immune checkpoint inhibitor response in non-small cell lung cancer

Introduction

Immunotherapy has become the standard treatment for various malignancies. Immune checkpoint inhibitors (ICIs), particularly programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) antibodies, elicit durable responses in some patients with advanced cancer. However, primary resistance and limited response duration remain common challenges. With regard to first-line chemotherapy for advanced non-small cell lung cancer (NSCLC) without driver gene mutations, regimens that include PD-1 and PD-L1 antibodies are recommended for patients with a good performance status. Especially, PD-L1 expression and tumor mutation burden (TMB) are established predictive biomarkers of ICI treatment efficacy (1-4). For patients exhibiting PD-L1 expression levels of ≥50%, the efficacy of ICI monotherapy is supported by substantial evidence (1). TMB assessment using next-generation sequencing (NGS) can also yield valuable information; however, the requirements for adequate tissue and long turnaround times limit its applicability in routine clinical settings.

Given the risk of serious immune-related adverse events, accurate biomarkers are urgently needed to identify patients most likely to benefit from ICIs and avoid unnecessary treatment in non-responders. Effective ICI therapy depends on antigen presentation and activation of CD8-positive effector T cells (5,6). However, tumors often evade immune surveillance by impairing T cell infiltration or suppressing T cell function through immunoregulatory mechanisms, contributing to treatment resistance (7-9). Evaluating both the tumor and its immune microenvironment may help predict ICI efficacy.

Genomic profiling provides insights into tumor-immune interactions. For instance, TP53 mutations are associated with improved ICI response, while co-mutations in TP53 and epidermal growth factor receptor (EGFR) correlate with reduced sensitivity to EGFR-tyrosine kinase inhibitors (TKIs) (10). Combination immunotherapies—such as those comprising PD-1/PD-L1 and CTLA-4 inhibitors—have been explored in select populations (11-13). Although early studies have yielded promising outcomes, predictive biomarkers for identifying patients who may benefit from additional CTLA-4 blockade remain undefined. KRAS mutations co-occurring with STK11 or KEAP1 mutations have been linked to a poor response to PD-1/PD-L1 monotherapy, suggesting a potential role for CTLA-4 combination therapy in these subgroups (14).

Despite its utility, NGS is often impractical for first-line treatment planning in advanced NSCLC owing to time constraints. Therefore, in this study, we aimed to evaluate protein expression in tumors and the tumor microenvironment of archived diagnostic samples, with immunohistochemistry (IHC)-based assessment in a real-world clinical cohort, which offers a pragmatic and cost-effective alternative to sequencing-based approaches. We also assessed associations with clinical outcomes and responses to ICI therapy. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-782/rc).

Methods

Patients and sample collection

Patients diagnosed with NSCLC and treated with ICIs or EGFR-TKIs as first-line therapy at our hospital between March 2017 and March 2025 were included, and their clinical records and archival data were retrospectively analyzed. Patients who were indicated for radical resection or irradiation, transferred to other hospitals, receiving best supportive care, deceased before treatment, or treated with TKIs other than those for EGFR (ALK fusion gene-positive, ROS1 fusion gene-positive, BRAF gene-positive) as first-line therapy were excluded (Figure 1). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The research protocol was approved by the Ethics Committee of Shizuoka Saiseikai General Hospital (No. 20240506; date: 29-05-2024), and the requirement for informed consent was waived via an opt-out form posted on the hospital website.

Figure 1 Flow diagram of the study and representative images of immunohistochemical analyses. BSC, best supportive care; CRT, chemoradiotherapy; EGFR-TKI, epidermal growth factor receptor-tyrosine kinase inhibitor; ICI, immune checkpoint inhibitor; NSCLC, non-small cell lung cancer.

The collected data included sex, age, Brinkman index, Eastern Cooperative Oncology Group Performance Status (ECOG PS), clinical stage (cStage) at treatment initiation, driver mutation status, histology, and PD-L1 tumor proportion score (TPS). cStage was determined according to the eighth edition of the TNM classification (15). PD-L1 TPS was assessed using the 22C3 antibody. Driver mutation status was evaluated using the Cobas EGFR Mutation Test v2, the Oncomine Dx target test, or the AmoyDx pan-lung cancer PCR panel.

IHC

Immunostaining was performed using antibodies against proteins (p53, LKB1, NRF2, CD8, FOXP3 and CTLA4), and staining intensity was evaluated in diagnostic tissue. The following commercially available antibodies were used at recommended dilutions: anti-p53 (1:300, DO-7, Agilent, RRID:AB_2537130), LKB1 (1:400, D60C5F10, Cell Signaling Technology, Danvers, MA, USA, RRID:AB_2716796), NRF2 (1:400, GTX103322, GeneTex, Irvine, CA, USA, RRID:AB_1950993), CD8 (1:50, C8/144B, Agilent Technologies, Santa Clara, CA, USA, RRID:AB_2075537), FOXP3 (1:50, ab22510, Abcam, Cambridge, UK, RRID:AB_447114), and CTLA4 (1:200, ab237712, Abcam, RRID:AB_2905652). Two observers (K.T. and K.H.) independently assessed p53 protein expression in tumor areas using the H-score, which was calculated by multiplying the intensity (0, absent; 1, weak; 2, moderate; 3, strong) by the percentage area of positive cells (0–100%), resulting in scores from 0 to 300. The cutoff for over expression was defined as an H-score of 200 using the minimum P value method. An H-score of 0 was defined as a null pattern, and an H-score >200 as overexpression; both were considered aberrant patterns, while the others were classified as normal. LKB1 staining intensity was assessed, and LKB1 loss was defined as the complete absence of detectable protein expression. NRF2 expression distribution in tumor cells was evaluated; a nuclear-dominant NRF2 expression was defined as greater protein expression in the nucleus than in the cytoplasm (Figure 2). Sections containing fewer than 50 cancer cells were excluded from analysis. Tumor-infiltrating lymphocytes (TILs), including CD8⁺ cytotoxic T cells, FOXP3⁺ regulatory T cells, and CTLA4⁺ regulatory T cells, were evaluated within the tumor nest under high-power fields (×200). The number of lymphocytes stained with each antibody was assessed semi-quantitatively on a 4-point scale from 0 to 3 (Figure S1).

Figure 2 Representative immunohistochemical images of p53, LKB1, and NRF2 expression. (A) p53 expression patterns. Left, null phenotype with complete loss of staining; center, overexpression with strong nuclear positivity in most tumor cells; right, normal expression with scattered weakly positive nuclei. (B) LKB1 expression. Left, loss with absent staining; right, normal with uniform cytoplasmic positivity. (C) NRF2 expression. Left, nuclear-dominant pattern; right, cytoplasmic-dominant pattern.

Statistical analysis

Discrete variables are expressed as numbers (percentages), unless otherwise specified. A heavy smoker was defined as a patient with a Brinkman index ≥600. The overall response rate was estimated in accordance with Response Evaluation Criteria in Solid Tumors guideline (version 1.1). Progression-free survival (PFS) was calculated from the start of first-line immunotherapy until the first evidence of disease progression, all-cause mortality, or the last follow-up (censored). Overall survival (OS) was calculated from the start of first-line immunotherapy to all-cause mortality or the last follow-up (censored). The follow-up period ended on April 30, 2025. Median PFS and OS were compared using the Kaplan-Meier method and log-rank tests. Univariate and multivariate Cox proportional hazard models were used to calculate hazard ratios (HRs) after adjusting for potential confounding factors. Statistical analyses were performed using GraphPad Prism Version 8 (GraphPad Software, San Diego, CA, USA, RRID: SCR_002798) and EZR software (Saitama Medical Center, Jichi Medical University, Saitama, Japan), with statistical significance set at P<0.05.

Results

Patient characteristics

A total of 168 patients were screened for eligibility, of whom 92 were excluded. Ultimately, 46 and 30 patients were assigned to the ICI-containing chemotherapy and EGFR-TKI groups, respectively (Figure 1). According to recent reports, patients with EGFR mutations have shown reduced responsiveness to immunotherapy (ICI treatment). In this study, the cohort with EGFR mutations was incorporated into the baseline table for the assessment of disparities in protein expression between tumors and the tumor microenvironment. Among the patients receiving ICI-containing therapy, 17 received ICI monotherapy and 29 received a combination of ICI and chemotherapy (Table 1).

Within the ICI group, three patients were excluded from the IHC analysis of tumor cells because of insufficient tumor cell counts (<50) on the slides. TIL markers were evaluated in 41 cases for CD8, 42 for FOXP3, and 42 for CTLA4. In the EGFR-mutant group, certain IHC markers were unevaluable in 1–4 samples because of insufficient tumor material. Baseline characteristics, including sex ratio, smoking index, histological subtype, and PD-L1 TPS, differed substantially between the two groups (Table 1).

Association between tumor-associated protein expression and immune cell infiltration

Among the 43 evaluable cases in the ICI group, aberrant p53 expression was observed in 20 cases (46.5%), LKB1 loss in six cases (13.9%), and nuclear-dominant NRF2 expression in 15 cases (34.9%). In the EGFR-TKI group, aberrant p53 expression was observed in nine cases (33%), LKB1 loss in six cases (22%), and nuclear-dominant NRF2 expression in only one case (3%), indicating lower NRF2 activation in this group. The LKB1 loss group exhibited a lower PD-L1 TPS and a higher prevalence of EGFR mutations. Conversely, squamous cell carcinoma was more frequent in the nuclear-dominant NRF2 group (Figure 3, Table 2).

Figure 3 Overall characteristics of tumor-associated protein expression and immune cell infiltration. (A) Heatmap showing the expression of tumor-suppressive proteins and TIL markers. (B) Tumor-associated protein expression and immune cell infiltration in patients treated with ICIs. (C) Tumor-associated protein expression and immune cell infiltration in patients treated with EGFR-TKIs. ICIs, immune checkpoint inhibitors; TILs, tumor-infiltrating lymphocytes; EGFR, epidermal growth factor receptor; TKI, tyrosine kinase inhibitor.

TIL profiles were analyzed according to the tumor-associated protein expression patterns. In the aberrant p53 group, the infiltration of CD8⁺, FOXP3⁺, and CTLA4⁺ TILs was higher. Conversely, TIL infiltration was reduced in the LKB1 loss group (Figure 4).

Figure 4 Association between tumor-associated protein expression and immune cell infiltration. (A) TILs in the aberrant p53 group. (B) TILs in the LKB1 loss group. (C) TILs in the nuclear-dominant NRF2 group. TILs, tumor-infiltrating lymphocytes.

Prognostic significance of tumor and immune markers for ICI response

The prognostic impact of protein expression in tumor cells and the tumor microenvironment on ICI therapy efficacy was evaluated. Given that the primary objective of this study was to evaluate the ICI treatment response on the basis of differences in protein expression between tumors and the tumor microenvironment, the EGFR mutation group was excluded from survival rate analyses. The median follow-up period was 11.5 months. As summarized in Table 3, aberrant p53 expression was observed for fewer progressive disease cases than normal cases. For LKB1, loss of expression was associated with lower response rates than was normal expression. For NRF2, nuclear-dominant expression was associated with a higher partial response rate. Univariate analysis for PFS showed that aberrant p53 was associated with better outcomes (P=0.03), whereas LKB1 loss was correlated with worse outcomes (P=0.01) (Figure 5A-5F and Figure S2). Multivariate analysis adjusted for potential prognostic factors (cStage, performance status, treatment type, and PD-L1 expression) revealed that cStage [HR 4.254; 95% confidence interval (CI): 1.328–13.63; P=0.02], treatment (HR 0.120; 95% CI: 0.027–0.525; P=0.005), PD-L1 TPS (HR 0.223; 95% CI: 0.067–0.734; P=0.01), p53 (HR 0.159; 95% CI: 0.050–0.503; P=0.001), and LKB1 (HR 4.096; 95% CI: 1.160–14.47; P=0.03) were independent prognostic factors (Figure 5G).

Figure 5 PFS according to biomarker status (p53, LKB1, NRF2, CD8, FOXP3, and CTLA4) and forest plot of univariate and multivariate Cox regression analyses. (A-F) PFS analysis according to the prognostic significance of tumor- and immune-related markers in ICI-treated patients. (G) Univariate and multivariate Cox proportional hazards model analysis for PFS. CI, confidence interval; HR, hazard ratio; ICI, immune checkpoint inhibitors; mPFS, median PFS; PD-L1, programmed death ligand-1; PFS, progression-free survival.

Conversely, univariate analysis showed no significant differences in OS; however, nuclear-dominant NRF2 expression tended to correlate with worse outcomes (P=0.07) (Figure 6A-6F). Multivariate analysis adjusted for potential prognostic factors confirmed that nuclear-dominant NRF2 was an independent negative prognostic factor (HR 2.294; 95% CI: 1.010–5.210; P=0.047) (Figure 6G).

Figure 6 OS according to biomarker status (p53, LKB1, NRF2, CD8, FOXP3, and CTLA4) and forest plot of univariate and multivariate Cox regression analyses. (A-F) OS analysis according to the prognostic significance of tumor- and immune-related markers in ICI-treated patients. (G) Univariate and multivariate Cox proportional hazards model analyses for OS. CI, confidence interval; HR, hazard ratio; ICI, immune checkpoint inhibitors; mOS, median OS; OS, overall survival; PD-L1, programmed death ligand-1.

We generated stratified Kaplan-Meier plots for PD-L1 TPS categories (<49%, ≥50%) within the p53/LKB1 subgroups. In the PD-L1 high-expression group, the median PFS differed between patients with and those without abnormal p53 expression (27.85 vs. 5.82 months). Similarly, in the PD-L1 high-expression group, the median PFS differed between patients with and those without LKB1 loss (3.32 vs. 12.43 months) (Figure S3).

Discussion

Key findings

Our findings demonstrate an association between aberrant p53 expression, an inflammatory tumor immune microenvironment, and improved PFS, as well as a correlation between LKB1 loss and both immunosuppressive features and poor response to ICIs. Additionally, nuclear-dominant NRF2 expression was associated with inferior OS, suggesting a potential resistance to later-line therapies. Taken together, these results indicate that IHC-based assessment of p53, LKB1, and NRF2 may serve as a practical surrogate for genomic data, providing clinically relevant information on tumor biology and therapeutic response.

Strengths and limitations

To our knowledge, this is the first study to investigate the association between the expression of tumor suppressor proteins and the therapeutic efficacy of ICIs in patients with NSCLC. However, several limitations should be acknowledged. First, there was a methodological limitation. The small cohort size limited the statistical power, and the study design was retrospective. Second, most specimens were small biopsy samples, limiting TIL evaluation and potentially underestimating intratumoral heterogeneity. Third, genomic sequencing was not performed, precluding a direct correlation between IHC findings and mutation status. Nonetheless, the observed protein expression patterns largely corresponded with known genomic and immunologic associations, supporting the validity of the IHC-based surrogate approach.

Comparison with similar research

TP53 missense mutations typically cause protein accumulation (nuclear overexpression), while nonsense or frameshift mutations generally result in complete loss of expression (16-19). Herein, aberrant p53 expression was defined using H-score-based thresholds as either complete absence or marked overexpression. This classification identified a subgroup of tumors exhibiting increased CD8-positive T cell infiltration, indicative of an inflamed tumor immune microenvironment and potentially enhanced ICI responsiveness. Our findings align with previous reports indicating that TP53 mutations may sensitize tumors to PD-1/PD-L1 blockade (20-22). Although standardized scoring criteria for p53 IHC are lacking, our results support quantitative IHC as a surrogate marker for TP53 mutation status and as a potential tool for patient stratification in immunotherapy.

Herein, LKB1 loss was associated with lower PD-L1 expression and reduced immune infiltration, consistent with previous reports that LKB1 inactivation promotes immune evasion via non-PD-1/PD-L1 pathways. LKB1 regulates cytokine production and angiogenesis; its loss impairs these processes and contributes to a “cold” tumor immune phenotype (23-27). Our definition of LKB1 loss—complete absence of protein expression by IHC—correlated with significantly reduced ICI efficacy, suggesting that this protein-level assessment may identify patients less likely to benefit from PD-1/PD-L1 inhibitors (28,29). Preclinical studies suggest that dual checkpoint blockade (anti-PD-1 plus anti-CTLA-4) may overcome resistance in LKB1-deficient models, supporting potential tailored ICI regimens for this subgroup (30).

In this study, nuclear-dominant expression of NRF2 was more frequently observed in squamous cell carcinoma, consistent with the findings of previous reports (31). Recent studies have demonstrated that KEAP1/NRF2 alterations play a critical role in tumor progression and therapeutic resistance in advanced disease. Constitutive NRF2 activation enhances metabolic adaptability and redox control, thereby conferring resistance to chemotherapy, TKIs, and radiotherapy (32-35). Because KEAP1 mutations stabilize NRF2 and promote its nuclear accumulation, nuclear-dominant NRF2 protein expression may serve as a surrogate marker of KEAP1 alterations. In addition, NRF2 itself has been associated with a “cold” tumor microenvironment and reduced responsiveness to immunotherapy (36,37). In the present cohort, nuclear NRF2 expression was not associated with PFS; this suggests that it may only partially reflect the underlying KEAP1 mutation status (38,39). Indeed, nuclear-dominant NRF2 protein expression can also be induced under cellular stress conditions, and such external factors may have influenced the results. In contrast, nuclear NRF2 expression was significantly associated with worse OS; this likely reflected the aggressive biology and multidrug resistance conferred by KEAP1/NRF2 pathway dysregulation.

Conclusions

IHC evaluation of p53 and LKB1 may provide clinically relevant information on tumor immunogenicity and the likelihood of ICI response in NSCLC. These markers can help guide therapeutic decisions, particularly in the absence of genomic data. Further validation in larger prospective cohorts is required to confirm the utility of and refine biomarker-driven immunotherapeutic strategies.

Acknowledgments

We are grateful to the patients and the sample donors for their participation in this study. We thank the staff of the Department of Pathology for their technical assistance. We also extend our gratitude to the Department of Respiratory Units for their assistance.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-782/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-782/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-782/prf

Funding: This work was supported by the Shizuoka Saiseikai Medical Research Center.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-782/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Shizuoka Saiseikai General Hospital (No. 20240506; date: 29-05-2024). The requirement for informed consent was waived via an opt-out form posted on the hospital website.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med 2016;375:1823-33. [Crossref] [PubMed]
2. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med 2017;377:2500-1. [Crossref] [PubMed]
3. Ricciuti B, Wang X, Alessi JV, et al. Association of High Tumor Mutation Burden in Non-Small Cell Lung Cancers With Increased Immune Infiltration and Improved Clinical Outcomes of PD-L1 Blockade Across PD-L1 Expression Levels. JAMA Oncol 2022;8:1160-8. [Crossref] [PubMed]
4. Mok TSK, Lopes G, Cho BC, et al. Associations of tissue tumor mutational burden and mutational status with clinical outcomes in KEYNOTE-042: pembrolizumab versus chemotherapy for advanced PD-L1-positive NSCLC. Ann Oncol 2023;34:377-88. [Crossref] [PubMed]
5. Raskov H, Orhan A, Christensen JP, et al. Cytotoxic CD8(+) T cells in cancer and cancer immunotherapy. Br J Cancer 2021;124:359-67. [Crossref] [PubMed]
6. Waldman AD, Fritz JM, Lenardo MJ. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat Rev Immunol 2020;20:651-68. [Crossref] [PubMed]
7. Schoenfeld AJ, Hellmann MD. Acquired Resistance to Immune Checkpoint Inhibitors. Cancer Cell 2020;37:443-55. [Crossref] [PubMed]
8. Jenkins RW, Barbie DA, Flaherty KT. Mechanisms of resistance to immune checkpoint inhibitors. Br J Cancer 2018;118:9-16. [Crossref] [PubMed]
9. Zhang C, Zhang C, Wang H. Immune-checkpoint inhibitor resistance in cancer treatment: Current progress and future directions. Cancer Lett 2023;562:216182. [Crossref] [PubMed]
10. Vokes NI, Chambers E, Nguyen T, et al. Concurrent TP53 Mutations Facilitate Resistance Evolution in EGFR-Mutant Lung Adenocarcinoma. J Thorac Oncol 2022;17:779-92. [Crossref] [PubMed]
11. Paz-Ares L, Ciuleanu TE, Cobo M, et al. First-line nivolumab plus ipilimumab combined with two cycles of chemotherapy in patients with non-small-cell lung cancer (CheckMate 9LA): an international, randomised, open-label, phase 3 trial. Lancet Oncol 2021;22:198-211. [Crossref] [PubMed]
12. Hellmann MD, Paz-Ares L, Bernabe Caro R, et al. Nivolumab plus Ipilimumab in Advanced Non-Small-Cell Lung Cancer. N Engl J Med 2019;381:2020-31. [Crossref] [PubMed]
13. Johnson ML, Cho BC, Luft A, et al. Durvalumab With or Without Tremelimumab in Combination With Chemotherapy as First-Line Therapy for Metastatic Non-Small-Cell Lung Cancer: The Phase III POSEIDON Study. J Clin Oncol 2023;41:1213-27. [Crossref] [PubMed]
14. Probing Mutant KRAS. STK11, KEAP1 in NSCLC. Cancer Discov 2022;12:2226. [Crossref] [PubMed]
15. Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2016;11:39-51. [Crossref] [PubMed]
16. Sung YN, Kim D, Kim J. p53 immunostaining pattern is a useful surrogate marker for TP53 gene mutations. Diagn Pathol 2022;17:92. [Crossref] [PubMed]
17. Ozaki Y, Muto S, Takagi H, et al. Tumor mutation burden and immunological, genomic, and clinicopathological factors as biomarkers for checkpoint inhibitor treatment of patients with non-small-cell lung cancer. Cancer Immunol Immunother 2020;69:127-34. [Crossref] [PubMed]
18. Cha YJ, Kim HR, Lee CY, et al. Clinicopathological and prognostic significance of programmed cell death ligand-1 expression in lung adenocarcinoma and its relationship with p53 status. Lung Cancer 2016;97:73-80. [Crossref] [PubMed]
19. Olivares-Hernández A, Del Barco Morillo E, Miramontes-González JP, et al. Immunohistochemical Assessment of the P53 Protein as a Predictor of Non-Small Cell Lung Cancer Response to Immunotherapy. Front Biosci (Landmark Ed) 2022;27:88. [Crossref] [PubMed]
20. Mathiot L, Nigen B, Goronflot T, et al. Prognostic Impact of TP53 Mutations in Metastatic Nonsquamous Non-small-cell Lung Cancer. Clin Lung Cancer 2024;25:244-253.e2. [Crossref] [PubMed]
21. Biton J, Mansuet-Lupo A, Pécuchet N, et al. TP53, STK11, and EGFR Mutations Predict Tumor Immune Profile and the Response to Anti-PD-1 in Lung Adenocarcinoma. Clin Cancer Res 2018;24:5710-23. [Crossref] [PubMed]
22. Dong ZY, Zhong WZ, Zhang XC, et al. Potential Predictive Value of TP53 and KRAS Mutation Status for Response to PD-1 Blockade Immunotherapy in Lung Adenocarcinoma. Clin Cancer Res 2017;23:3012-24. [Crossref] [PubMed]
23. Tanaka I, Koyama J, Itoigawa H, et al. Metabolic barriers in non-small cell lung cancer with LKB1 and/or KEAP1 mutations for immunotherapeutic strategies. Front Oncol 2023;13:1249237. [Crossref] [PubMed]
24. Koyama S, Akbay EA, Li YY, et al. STK11/LKB1 Deficiency Promotes Neutrophil Recruitment and Proinflammatory Cytokine Production to Suppress T-cell Activity in the Lung Tumor Microenvironment. Cancer Res 2016;76:999-1008. [Crossref] [PubMed]
25. Della Corte CM, Byers LA. Evading the STING: LKB1 Loss Leads to STING Silencing and Immune Escape in KRAS-Mutant Lung Cancers. Cancer Discov 2019;9:16-8. [Crossref] [PubMed]
26. Bonanno L, Zulato E, Pavan A, et al. LKB1 and Tumor Metabolism: The Interplay of Immune and Angiogenic Microenvironment in Lung Cancer. Int J Mol Sci 2019;20:1874. [Crossref] [PubMed]
27. Lv L, Miao Q, Zhan S, et al. LKB1 dictates sensitivity to immunotherapy through Skp2-mediated ubiquitination of PD-L1 protein in non-small cell lung cancer. J Immunother Cancer 2024;12:e009444. [Crossref] [PubMed]
28. Calles A, Sholl LM, Rodig SJ, et al. Immunohistochemical Loss of LKB1 Is a Biomarker for More Aggressive Biology in KRAS-Mutant Lung Adenocarcinoma. Clin Cancer Res 2015;21:2851-60. [Crossref] [PubMed]
29. Skoulidis F, Goldberg ME, Greenawalt DM, et al. STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma. Cancer Discov 2018;8:822-35. [Crossref] [PubMed]
30. Skoulidis F, Araujo HA, Do MT, et al. CTLA4 blockade abrogates KEAP1/STK11-related resistance to PD-(L)1 inhibitors. Nature 2024;635:462-71. [Crossref] [PubMed]
31. Solis LM, Behrens C, Dong W, et al. Nrf2 and Keap1 abnormalities in non-small cell lung carcinoma and association with clinicopathologic features. Clin Cancer Res 2010;16:3743-53. [Crossref] [PubMed]
32. Adinolfi S, Patinen T, Jawahar Deen A, et al. The KEAP1-NRF2 pathway: Targets for therapy and role in cancer. Redox Biol 2023;63:102726. [Crossref] [PubMed]
33. Islam SS, Qassem K, Islam S, et al. Genetic alterations of Keap1 confers chemotherapeutic resistance through functional activation of Nrf2 and Notch pathway in head and neck squamous cell carcinoma. Cell Death Dis 2022;13:696. [Crossref] [PubMed]
34. Dempke WCM, Reck M. KEAP1/NRF2 (NFE2L2) mutations in NSCLC - Fuel for a superresistant phenotype? Lung Cancer 2021;159:10-7. [Crossref] [PubMed]
35. Rojo de la Vega M, Chapman E, Zhang DD. NRF2 and the Hallmarks of Cancer. Cancer Cell 2018;34:21-43. [Crossref] [PubMed]
36. Arbour KC, Jordan E, Kim HR, et al. Effects of Co-occurring Genomic Alterations on Outcomes in Patients with KRAS-Mutant Non-Small Cell Lung Cancer. Clin Cancer Res 2018;24:334-40. [Crossref] [PubMed]
37. Duan J, Zhang Y, Chen R, et al. Tumor-immune microenvironment and NRF2 associate with clinical efficacy of PD-1 blockade combined with chemotherapy in lung squamous cell carcinoma. Cell Rep Med 2023;4:101302. [Crossref] [PubMed]
38. Li QK, Singh A, Biswal S, et al. KEAP1 gene mutations and NRF2 activation are common in pulmonary papillary adenocarcinoma. J Hum Genet 2011;56:230-4. [Crossref] [PubMed]
39. Singh A, Misra V, Thimmulappa RK, et al. Dysfunctional KEAP1-NRF2 interaction in non-small-cell lung cancer. PLoS Med 2006;3:e420. [Crossref] [PubMed]