Skip to main content
NCBI home page
Thoracic Cancer logoLink to Thoracic Cancer
. 2023 May 23;14(19):1843–1856. doi: 10.1111/1759-7714.14929

Abnormal activation of NF‐κB and MAPK signaling pathways affect osimertinib resistance and influence the recruitment of myeloid‐derived suppressor cells to shape the immunosuppressive tumor immune microenvironment

Chao Wang 1,2, Kailun Fei 1,2, Lihui Liu 1,2, Jianchun Duan 1,2, Zhijie Wang 1,2, Sini Li 1, Jiachen Xu 1,2,3, Xue Zhang 1, Yanhua Tian 1, Yan Qu 1, Hua Bai 1,2,✉, Jie Wang 1,2,✉
PMCID: PMC10317600  PMID: 37221702

Abstract

Background

Osimertinib is the first‐line treatment for patients with epidermal growth factor receptor (EGFR) mutations, but the treatment options after drug resistance are limited. Previous studies have suggested that EGFR is in an immunosuppressive tumor immune microenvironment (TIME). However, the evolution of TIME after osimertinib resistance and whether this resistance can be overcome by targeting TIME needs to be further investigated.

Methods

The remodeling process and mechanism of TIME during the treatment with osimertinib were studied.

Results

The proportion of EGFR L858R+T790M mutant tumor immune infiltrating cells was extremely low. Osimertinib treatment transiently triggered inflammatory cells, but several immunosuppressive cells infiltrated after drug resistance and formed a myeloid‐derived suppressor cell (MDSC)‐enriched TIME. The programmed cell death protein‐1 monoclonal antibody was not able to reverse the MDSC‐enriched TIME. Further analysis revealed that the activation of nuclear factor‐kappa B (NF‐κB) and mitogen‐activated protein kinase (MAPK) pathways recruited a large number of MDSCs via cytokines. Finally, MDSC secreted high levels of interleukin‐10 and arginase‐1 and created an immunosuppressive TIME.

Conclusions

Thus, our findings lay the foundation for the evolution of TIME in osimertinib treatment, establish the mechanism of immunosuppressive TIME after osimertinib resistance, and propose potential solutions.

Keywords: EGFR, lung cancer, MDSC, osimertinib resistance, TIME

The abnormal activation of MAPK and NF‐kB signaling pathways after osimertinib resistance recruits MDSC infiltration, thus mediating the formation of immunosuppressive microenvironment.

graphic file with name TCA-14-1843-g003.jpg

INTRODUCTION

Epidermal growth factor receptor (EGFR) mutations are found in 15% of Caucasians and 30%–50% of Asian patients with non‐small cell lung cancer (NSCLC). 1 , 2 Several clinical trials have shown that EGFR‐tyrosine kinase inhibitors (EGFR‐TKIs) can prolong the survival time of patients with tumors harboring EGFR mutations. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 The median progression‐free survival (PFS) with the use of first‐generation EGFR‐TKIs is approximately 10 months, but the overall survival (OS) is not significantly prolonged. 5 , 8 , 11 , 12 According to the FLAURA study, the third‐generation EGFR‐TKIs represented by osimertinib not only overcome the T790M mutation but also show significantly prolonged median PFS (18.9 months vs. 10.2 months) and median OS (38.6 months vs. 31.8 months). 13 Therefore, osimertinib is recommended as the first‐line treatment for patients with EGFR mutation. Although the drug significantly improves the prognosis of patients who are EGFR positive, almost all patients still face the problem of drug resistance. The mechanisms of osimertinib resistance include MET amplification, HER‐2 amplification, MET mutation, HER‐2 mutation, PI3K mutation, and new mutations such as EGFR‐C797S, EGFR‐L792H, and EGFR‐G796R. 14 , 15 Analysis of tumor expression after resistance suggests that the expression levels of IL‐6, IL‐8, VEGF, HGF, CXCL16, TGF‐β, NF‐κB, and mitogen‐activated protein kinase (MAPK) in EGFR‐TKI resistant NSCLC are higher than those in EGFR‐TKI sensitive NSCLC, thereby causing the resistance of EGFR‐TKIs and inducing an immunosuppressive tumor immune microenvironment (TIME). 16 , 17 , 18 , 19 , 20 , 21 , 22

Owing to the complex mechanism of osimertinib resistance, which is caused by multiple factors including immunosuppressive TIME, extensive and effective follow‐up therapy measures are presently lacking. Although immune checkpoint blockers have significantly improved the efficacy of NSCLC treatment, many studies have shown that patients who are EGFR positive do not benefit from immune checkpoint blockade (ICB). 23 , 24 This problem limits the subsequent treatment options for patients who are resistant to osimertinib, especially those who are T790M positive, whose ICB treatment effect is worse than that of patients who are EGFR positive but not T790M positive. 25 Previous studies have suggested that for patients who are EGFR positive, the use of EGFR‐TKIs can activate the antitumor immune response in TIME and provide a possible opportunity for PD‐1 monoclonal antibody intervention. 26 However, some studies have shown that in the case of EGFR L858R mutation, immune response in TIME is activated during the treatment with first‐generation EGFR‐TKIs but is accompanied by tumor regression and that the addition of ICB does not improve the efficacy. 27 Therefore, it is urgent to explore the characteristics of TIME in T790M‐positive tumors as well as the changes in TIME after drug resistance in the treatment with osimertinib.

Previous studies have divided TIME into three phenotypes, namely the inflamed phenotype, the immune‐excluded phenotype, and the immune‐desert phenotype, and have proposed that the different phenotypes of tumor ICB differ in their efficacies. 28 Studies on the TIME of EGFR‐positive tumors suggest that these tumors belong to the uninflamed phenotype, which explains why the ICB treatment effect is poor in patients who are EGFR positive. 29 Several studies on TIME during the treatment with EGFR‐TKIs have proposed that the levels of immunosuppressive cells, such as M2 and myeloid‐derived suppressor cells (MDSCs), are increased after the resistance. 30 , 31 On the contrary, the levels of immune active cells, such as DCs are CD8+ T cells, are decreased after the resistance to EGFR‐TKIs. 32 , 33 Therefore, this article focuses on the following three aspects: (1) How does TIME change in patients who have tumors with EGFR‐sensitive and T790M mutations before, during, and after osimertinib resistance? (2) How do tumor cells mediate the remodeling of TIME during the whole course of treatment? (3) Is there an appropriate target and time for intervention as the patient's TIME changes? Can the prognosis of such patients be improved? To address these issues, a cell line of EGFR L858R combined with T790M was constructed. Furthermore, a model of osimertinib resistance was built to characterize the changes in tumor signaling pathways, the proportion of immune infiltrating cells, and cytokines throughout the process (Figure S1).

METHODS

Reagents and antibodies

Osimertinib (AZD9291, cat no. S7297), QNZ (EVP4593, NK‐κB inhibitor, cat no. S4902), SCH772948 (MAPK/extracellular signal‐regulated kinase [ERK] inhibitor, cat no. S7101), and corn oil (cat no. cat no. S6701) were purchased from Selleck Chemicals. Dulbecco's modified Eagle's medium (DMEM, high glucose, containing glutamine, cat no. 06‐1055‐57‐1A) were purchased from Biological Industries. Fetal bovine serum (FBS, cat no. 12662029) was purchased from Gibco. Puromycin dihydrochloride (cat no. P8833) was purchased from Sigma‐Aldrich Company. Osimertinib was dissolved in phosphate buffered saline (PBS) and dimethyl sulfoxide (DMSO) (final DMSO concentration <0.1%) and stored at −20°C. QNZ and SCH772948 were dissolved in corn oil and DMSO (final DMSO concentration <0.1%) and stored at −20°C. Anti‐PD‐1 mAb (CD279, clone RMP1‐14, cat no.BE0146), InVivoPlus anti‐mouse Ly6G/Ly6C (Gr‐1, clone RB6‐8C5, cat no. BP0075), and InVivoPure pH 7.0 dilution buffer (cat no.IP0070) were purchased from BioXcell. BD Pharm Lys lysing buffer (cat no.555899) was purchased from BD Biosciences. Antibodies used for flow cytometry were purchased from Tonbo Biosciences and Biolegend. Foxp3/transcription factor staining buffer kit (cat no.TNB‐0607) was purchased from Tonbo Biosciences. Collagenase type IV (cat no.C5138) and deoxyribonuclease I (cat no. D4263) were purchased from Sigma‐Aldrich Company and used to prepare single‐cell suspensions from tumor tissue. Antibodies for western blotting and immunohistochemistry (IHC) were purchased from Cell Signaling Technology, Abcam, and Invitrogen. The information on the antibodies is listed in Table S1. Tris‐glycine running buffer 5× (cat no.T1070), WB transfer buffer 10× (cat no.D1060), and Cole's hematoxylin solution (cat#G1140) were purchased from Solarbio. Endogenous peroxidase blocking buffer (cat no. ZLI‐9311) and normal goat serum for blocking (cat no.9056) were purchased from ZSGB‐BIO. HIER buffer 20× (cat no. GT100410) and GTVisionTM III detection system/Mo&Rb (including DAB) were purchased from the Gene Tech Company. The Enhanced BCA protein assay kit (cat no. P0010), SDS‐PAGE sample loading buffer 5× (cat no. P0015), SDS‐PAGE gel preparation kit (cat no. P0012A), RIPA lysis buffer (cat no. P0013B), phosphatase inhibitor cocktail A 50× (cat no. P1081), and phenylmethanesulfonyl fluoride (PMSF, cat no. ST506) were purchased from Beyotime Biotechnology. PageRuler Prestained Protein Ladder (cat no. 26616) and SuperSignal West Pico chemiluminescent substrate (cat no. 34580) were purchased from Thermo Scientific. Mouse‐IL‐10 ELISA Kit (cat no. TAE‐371m), Mouse‐IFNγ ELISA kit (cat no. TAE‐366m), Mouse‐IL‐4 ELISA kit (cat no. TAE‐384m), Mouse‐IL‐13 ELISA kit (cat no. TAE‐373m), Mouse‐M‐CSF ELISA kit (cat no. TAE‐416m), and Mouse‐IL‐34 ELISA kit (cat no. TAE814m) were purchased from Anoric Biotechnology. Recombinant mouse IL‐2 protein (cat no. 402‐ML‐020) and recombinant mouse GM‐CSF protein (cat no. 415‐ML‐005/CF) were purchased from the R&D systems. Purified anti‐mouse CD3ε antibody (cat no. 100301) and purified anti‐mouse CD28 antibody (cat no. 102101) were purchased from Biolegend. EasySep Mouse MDSC (CD11b+Gr1+) isolation kit (cat no. 19867) and EasySep Mouse CD8+ T cell isolation kit (cat no. 19853) were purchased from Stemcell Technologies.

Cell lines and culture condition

KLN‐205 mouse lung cancer cell line was obtained from the American Type Culture Collection (ATCC). The human EGFR mutation (L858R + T790M) overexpressing KLN‐205 cell (termed H6714) was obtained from Obio Technology. These cells were cultured in DMEM supplemented with 10% FBS, 1% penicillin–streptomycin, and 2 μg/mL puromycin in a humidified incubator with 5% CO2 at 37°C.

Establishment of lung cancer syngeneic mouse models

DBA‐2J male mice (age: 5–6 weeks, weight: 18–21 g) were purchased from Beijing HFK Bioscience Co. (Beijing, China). We implanted 1 × 106 H6714 cells into the right flanks of the experimental mice via subcutaneous injection. Tumor volume was measured using a caliper and calculated using the formula, volume = L × W2/2, where L is the length and W is the width of the tumor. The mice were randomly assigned to different treatment groups, and the drug treatments were initiated when the tumor volume reached 100 ± 25 (mean ± SD) mm3. The animals were fed a standard commercial diet produced by the Experimental Animal Center of the Chinese Academy of Medical Sciences and maintained under specific pathogen‐free conditions with a 12‐h light–dark schedule. The temperature and humidity of the animal house were maintained at 26–28°C and 60% ± 5%, respectively. The mice were treated with saline, osimertinib, anti‐PD‐1 mAb, SCH772948, QNZ, and anti‐Gr‐1 mAb. When the tumor size was larger than that in the previous administration, the case was regarded as a case of drug resistance. The animal studies were conducted as specified by the Animal Care and Use Committee of the Cancer Hospital of the Chinese Academy of Medical Sciences.

Flow cytometry analysis

The experimental mice were sacrificed on the indicated days and their tumors were harvested and rinsed once with prechilled PBS. The tumors were then shredded into pieces and digested in FBS‐free DMEM containing 1 mg/mL collagenase type‐IV and 1 μg/mL deoxyribonuclease I at 37°C for 90 min. After incubation, single tumor cells were filtered through 100‐μm cell strainers (Corning Incorporated) twice to remove any debris and then treated with the RBC lysis buffer for 5 min at room temperature (RT). Next, single tumor cells were blocked with anti‐mouse CD16/CD32 for 10 min at 4°C to block the Fc receptors and then stained with the appropriate fluorescein isothiocyanate‐conjugated antibodies for 30 min at 4°C, followed by once washing with PBS. Intracellular Foxp3+ Treg staining was performed in accordance with the manufacturer's instructions. To prepare single‐cell spleen tissue suspensions, the collected spleens were crushed with tweezers, filtered through cell strainers, and incubated with RBC lysis buffer at RT for 5 min. After washing the cells, they were subjected to analyses by the CytoFLEX Cytometer (Beckman Coulter) equipped with the CytExpert software and then the FlowJo software (TreeStar). The gating strategy is illustrated in Table S2.

Western blotting (WB)

The tumor tissues were homogenized with a homogenizer and then lysed with RIPA lysis buffer on an ice bath. Whole‐cell extracts were separated with SDS–polyacrylamide gel electrophoresis and blotted onto a polyvinylidene fluoride (PVDF) membrane. After blocking the membrane, it was probed with a primary antibody and then rinsed twice with TBST, followed by incubation with a horseradish peroxidase‐conjugated secondary antibody. The membrane was then washed and visualized by an enhanced chemiluminescence detection system and the Amersham Imager 600 (GE Healthcare).

Enzyme‐linked immunosorbent assay (ELISA)

The concentrations of IL‐4, IL‐10, IL13, IL‐34, M‐CSF, and IFN‐γ were examined by ELISA as per the manufacturer's instructions.

IHC

The tumor tissues were fixed in 4% paraformaldehyde, embedded in paraffin, and sectioned into 5‐μm slides for IHC. The slides were deparaffinized with xylene, rehydrated, and antigen‐retrieved in a microwave oven for 20 min. After the inhibition of the endogenous peroxidase activity, individual slides were incubated overnight at 4°C with the antibody. The slides were then enumerated by the GTVisionTM III detection system/Mo&Rb (including DAB) as suggested by the manufacturer. Finally, these sections were counterstained with hematoxylin for observation.

MDSCs and CD8+ T cell isolation and culture

The bone marrows were isolated from the femur of the tumor‐bearing mice under sterile conditions and washed with a serum‐free medium. Then, the MDSCs were isolated using the MDSC isolation kit according to the manufacturer's instruction. The spleen of the mice was ground to pieces under sterile conditions, and the splenocytes were isolated by passing through a 100‐μm cell strainer. Then, CD8+ T cells were isolated using the CD8+ T cell isolation kit as per the manufacturer's instruction. MDSCs were cultured in DMEM supplemented with 10% FBS, 20 ng/mL GM‐CSF, 1% penicillin–streptomycin, and 2 μg/mL puromycin in a humidified incubator with 5% CO2 at 37°C. CD8+ T cells were seeded in 12‐well plates and stimulated with anti‐CD3 antibody (5 μg/mL), anti‐CD8 antibody (0.5 μg/mL), and IL‐2 (20 ng/mL) at 37°C under 5% CO2 atmosphere for 4 days.

RNA sequence data analysis

The RNA‐sequence data of the H1975 cell line treated with osimertinib were downloaded from the Gene Expression Omnibus (GEO) database (GSE146850). Data analysis was performed using the R package. Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the differential genes was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/). The correlations among the signaling pathways, cytokines, and immune cells were analyzed by using the Tumor IMmune Estimation Resource (TIMER2.0) (http://timer.cistrome.org/).

Statistical analysis

All data analyses were performed using the GraphPad Prism version 8.0 (GraphPad Software, Inc). Student's t‐test or a one‐way analysis of variance (ANOVA) test was performed to evaluate the statistical significance, considering p < 0.05 to indicate statistical significance.

RESULTS

Osimertinib transiently activates TIME of EGFR T790M + L858R mutant tumors

A subcutaneous transplanted tumor animal model was used to study osimertinib resistance in vivo. The strategy of the in vivo experiment is depicted in Figure 1a. Compared with the control group, the tumor with the EGFR mutation was reduced after osimertinib treatment. However, the tumor increased after osimertinib treatment for 30 days, which signifies resistance to the drug (Figure 1b).

FIGURE 1.

FIGURE 1

Open in a new tab

Transformation of the microenvironmental phenotype in the whole treatment of osimertinib. (a) Mice were subcutaneously injected with 1 × 106 H6714 cells and treated with saline as control (200 μL, gavage administration every 2 days), osimertinib (30 mg/kg, gavage administration every 2 days) when the tumor size reached approximately 100 mm3. On the days 0, 15, and 40 of osimertinib treatment, the tumors were harvested for analyses. (b) Tumor growth curve on the treatment of saline, osimertinib. (c) Left panel: Representative flow cytometry plot showing the percentage of tumor‐infiltrated immune cells. Right panel: Histograms of tumor‐infiltrated immune cells during osimertinib treatment. (d) Representative images of tumor sections stained for tumor‐infiltrated immune cells by flow cytometry during osimertinib treatment. (e) Representative images of tumor sections stained for tumor‐infiltrated immune cells by immunohistochemistry during osimertinib treatment. ns, not significant. (f) The expressions of M‐CSF, IL‐4, IL‐10, IL13, IL‐34, and IFN‐γ during osimertinib treatment were detected by ELISA. ns, not significant. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

According to the tumor growth curve of the mouse tumor model, flow cytometry was performed on the tumor tissues at three stages: before osimertinib treatment, the stage of osimertinib sensitivity, and the stage of osimertinib resistance. The proportion of total tumor infiltrated immune cells (tTIIs) accounted for only 0.45% of the total number of cells, which was lower than that of small cell lung cancer whose TIME belongs to the “immune‐desert phenotype.” 34 The proportion of tTILs increased after osimertinib treatment and further increased after osimertinib resistance (Figure 1c). As for TIL subtypes, the proportions of immune‐activated CD4+ T cells, CD8+ T cells, M1s, and DCs increased at the stage of osimertinib sensitivity and decreased at the stage of osimertinib resistance. The proportions of immunosuppressive cells such as MDSCs, M2, and Tregs decreased at the stage of osimertinib sensitivity and increased significantly at the stage of osimertinib resistance, especially MDSC, which reached over 40% after the resistance (Figure 1d). To verify the results of flow cytometry, IHC was performed to detect the immunocytes (Figure 1e). The results were found to be consistent with those from flow cytometry. Hence, it was concluded that an MDSC enriched “immune‐excluded” TIME is created after osimertinib resistance.

As MDSC increased significantly at the drug resistance stage, the subtypes of MDSC were further analyzed. The proportion of polymorphonuclear‐MDSC (PMN‐MDSC) was not significantly different among the three stages. However, the proportion of monocyte‐MDSC (M‐MDSC) was the lowest at the stage of osimertinib sensitivity and the highest at the stage of osimertinib resistance (Figure S2).

As for the cytokines, the levels of IL‐4 and IL‐13 decreased at the stage of osimertinib sensitivity, while the levels of IL‐4, IL‐10, IL‐34, and Arg‐1 increased at the stage of osimertinib resistance (Figure 1f). These cytokines might participate in the transformation of TIME. The level of IFN‐γ increased at the stage of osimertinib sensitivity and decreased at the stage of osimertinib resistance (Figure 1f). These findings suggest that the function of T lymphocytes is inhibited after drug resistance.

PD‐1 monoclonal antibody cannot reverse the immunosuppressive TIME

The level of PD‐L1 in the tumor cells was measured using flow cytometry, and it was found that the level increased during the period of osimertinib sensitivity but did not increase further after resistance was acquired (Figure 2a). Hence, it is suggested that the PD‐1/PD‐L1 pathway is not the main mechanism of the immune state after drug resistance. The possibility of using PD‐1 monoclonal antibodies to alter the immunosuppressive TIME of tumor models was explored. ICB treatment was added at different time points (Figure 2b). However, the use of PD‐1 monoclonal antibody could not improve the treatment effect on the tumors regardless of whether it was used before osimertinib treatment, combined with it, or after osimertinib resistance (Figure 2c). Even when the PD‐1 monoclonal antibody was added to the inflamed TIME during the period of osimertinib sensitivity, the results were not better (Figure 2c). This observation is consistent with the conclusions of previous animal models and clinical studies. 27

FIGURE 2.

FIGURE 2

Open in a new tab

The expression level of PD‐1 at different time points and the therapeutic effect of PD‐1 monoclonal antibody. (a) Left panel: Representative flow cytometry plot showing the percentage of PD‐L1 expressed in tumor and immune cells. Right panel: Histograms of PD‐L1 expressed in tumor cells and immune cells during osimertinib treatment. (b) Mice were subcutaneously injected 1 × 106 H6714 cells and treated with osimertinib (30 mg/kg, gavage administration every 2 days), anti‐PD‐1 mAb (200 μg, intraperitoneal injection, every 3 days), osimertinib plus anti‐PD‐1 mAb when the tumor size reached approximately 100 mm3. After osimertinib resistance, the mouse treated with osimertinib or anti‐PD‐1 mAb plus osimertinib. (c) Tumor growth curve on the treatment of osimertinib, anti‐PD‐1 mAb, osimertinib plus anti‐PD‐1 mAb, anti‐PD‐1mAb plus osimertinib after osimertinib resistance.

Abnormal activation of MAPK and NF‐κB signaling pathways are involved in osimertinib resistance

As ICB could not overcome the immune escape state of tumors in the drug‐resistant stage, the modifications in tumor signaling pathways responsible for the formation of MDSC‐enriched TIME after drug resistance were further explored. First, the RNA‐seq data of the osimertinib‐resistant H1975 cell line was analyzed and compared with the sensitive H1975 cell line based on GEO (GSE146850). The vascular endothelial growth factor (VEGFR) and transforming growth factor beta (TGF‐β) signaling pathways were observed to be activated after osimertinib resistance based on the results of KEGG pathway analysis (Figure 3a). Furthermore, the MAPK and NF‐κB signaling pathways were found to be activated after osimertinib resistance based on the results of GO enrichment (Figure 3b). These findings allude that the changes in these pathways may play an important role in the resistance of tumors.

FIGURE 3.

FIGURE 3

Open in a new tab

Signaling pathways abnormally activated after osimertinib resistance. (a) and (b) Representation of the enrichment analysis results of differential genes before and after osimertinib resistance, namely Kyoto Encyclopedia of Genes and Genomes (KEGG) (a) and gene ontology (GO) (b). The main 30 results for each term are shown, and the different color indicates the significant degree of enrichment and the size indicates the number of genes enriched for each result. (c) The expressions of mitogen‐activated protein kinase (MAPK), nuclear factor‐kappa B (NF‐κB), vascular endothelial growth factor (VEGF), transforming growth factor beta (TGF‐β) and the main signaling pathway‐related proteins during osimertinib treatment were detected by western blotting (WB).

We also detected the expression of epithelial mesenchymal transformation (EMT) related proteins, which is associated with EGFR‐TKIs resistance. We found that the expression of EMT related protein did not significantly change during osimertinib treatment.

To clarify the changes in these pathways before and after drug resistance in mice, the tumor tissues of the in vivo experiment were obtained at three different time points, namely, before osimertinib treatment, the sensitive period, and the resistant period. The tissues were employed to detect the changes in these four signaling pathways and the EGFR downstream signaling pathways using WB. The MAPK and NF‐κB signaling pathways were found to be activated at the stage of osimertinib resistance, but the VEGFR, TGF‐β, and EGFR downstream signaling pathways were not activated (Figure 3c). This result indicates that the activation of MAPK and NF‐κB signaling pathways correlate with osimertinib resistance.

Subsequently, whether the MAPK and NF‐kB signaling pathways play an important role in drug resistance was verified. The small‐molecule inhibitor SCH772948 was used to block the MAPK signaling pathway, and QNZ was used to block the NF‐κB signaling pathway after osimertinib resistance in vivo. The strategy of the in vivo experiment is shown in Figure 4a. SCH772948 or QNZ treatment alone was sufficient to lower the growth of the tumor but could not shrink the tumor. Only the combined use of SCH772948 and QNZ could significantly reduce the tumor volume (Figure 4b). WB showed that the two signaling pathways were suppressed after the combined treatment with SCH772948 and QNZ (Figure 4c).

FIGURE 4.

FIGURE 4

Open in a new tab

Simultaneous inhibition of nuclear factor‐kappa B (NF‐κB) and mitogen‐activated protein kinase (MAPK) could overcome osimertinib resistance and reverse the immunosuppressive microenvironment. (a) The mice were subcutaneously injected with 1 × 106 H6714 cells and treated with osimertinib (30 mg/kg, gavage administration every 2 days), SCH772948 (10 mg/kg, intraperitoneal injection, every 2 days), QNZ (1 mg/kg, intraperitoneal injection, every 2 days), QNZ plus osimertinib after osimertinib resistance, SCH772948 plus osimertinib after osimertinib resistance, QNZ, and SCH772948 plus osimertinib after osimertinib resistance when the tumor size reached approximately 100 mm3. After six administrations of ONZ or SCH772948 tumors were harvested for analyses. (b) Tumor growth curve on treatment of osimertinib, QNZ, SCH772948, QNZ plus osimertinib after osimertinib resistance, SCH772948 plus osimertinib after osimertinib resistance, SCH772948, and QNZ plus osimertinib after osimertinib resistance. (c) The expressions of Arg‐1, NF‐κB, and the MAPK signaling pathway‐related proteins in different groups after osimertinib resistance were detected by western blotting (WB). (d) Representative flow cytometry plot showing the percentage of tumor‐infiltrated immune cells in osimertinib resistance group and blocking the NF‐κB plus MAPK group. (e) Representative images of the tumor sections stained for tumor‐infiltrated immune cells by flow cytometry in osimertinib resistance group and blocking the NF‐κB plus MAPK group. (f) Representative images of the tumor sections stained for tumor‐infiltrated immune cells by immunohistochemistry in osimertinib resistance group and blocking the NF‐κB plus MAPK group. (g) The expressions of IL‐4, IL‐10, IL‐34, and IFN‐γ in different groups after osimertinib resistance were detected by ELISA. ns, not significant; *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

Whether the use of inhibitors can reshape TIME was examined. After using SCH772948 or QNZ individually, there was no significant change in the proportions of immune‐activated and immunosuppressive cells and there was no significant difference in the levels of various cytokines. However, when both NF‐κB and MAPK pathways were inhibited, there was an increase in the proportion of tTIIs (Figure 4d and Figure S3). As for the subtype of the immune cells, the proportion of immune‐activated cells increased after blocking the two signaling pathways, whereas the proportion of immunosuppressive cells, such as MDSCs, decreased (Figure 4e and Figure S3). This result denotes that blocking the two signaling pathways could reduce the immunosuppressive cells and recruit the CD4+ T cells and CD8+ T cells. Moreover, IHC was used to validate the results of flow cytometry, which yielded the same result (Figure 4f). Subsequently, the levels of cytokines secreted by tumor and immune cells were measured using ELISA and WB. The levels of IL‐4, IL‐10, IL‐34, and Arg‐1 were found to decrease after blocking the MAPK and NF‐κB signaling pathways (Figure 4g). These findings assert that blocking the MAPK and NF‐κB signaling pathways could reverse MDSC‐enriched “immune‐excluded” TIME.

Simultaneous inhibition of MDSC and MAPK or MDSC and NF‐κB can overcome osimertinib resistance

Although the simultaneous inhibition NF‐κB and MAPK had a good effect, the mice in the experimental group died of drug toxicity. The above results suggest that MDSC is enriched in TIME after osimertinib resistance and that inhibiting NF‐κB or MAPK alone is not effective. Therefore, the MAPK or NF‐κB pathway and MDSC were simultaneously inhibited to observe its efficacy on the tumors and the alterations in TIME. The strategy of the in vivo experiment is shown in Figure 5a. Inhibiting MDSC and MAPK or inhibiting MDSC and NF‐κB significantly reduced the tumor size after osimertinib resistance (Figure 5b), and no mice died because of the combination treatment. The changes in TIME were reanalyzed, and it was found that after inhibiting MDSC and blocking either of the two signaling pathways, the proportion of immune‐activated cells, such as CD4+ T cells, CD8+ T cells, DCs, and M1s, increased and the proportion of immunosuppressive cells, such as MDSCs, M2, and Tregs, decreased (Figure 5c and Figure S4). Moreover, IHC was used to confirm the results of flow cytometry, which yielded comparable results (Figure 5d). The levels of cytokines and proteins secreted by the tumor and immune cells were also measured using ELISA and WB. The levels of IL‐10 and Arg‐1 were found to be decreased (Figure 5e), but the levels of IL‐4 and IL‐34 were not significantly altered after the treatment.

FIGURE 5.

FIGURE 5

Open in a new tab

Simultaneous inhibition of myeloid‐derived suppressor cells (MDSC) and NF‐κB or mitogen‐activated protein kinase (MAPK) could overcome osimertinib resistance. (a) The mice were subcutaneously injected with 1 × 106 H6714 cells and treated with osimertinib (30 mg/kg, gavage administration every 2 days), QNZ (1 mg/kg, intraperitoneal injection, every 2 days) plus anti‐Gr‐1 mAb after osimertinib resistance, SCH772948 (10 mg/kg, intraperitoneal injection, every 2 days) plus anti‐Gr‐1 mAb after osimertinib resistance when the tumor size reached approximately 100 mm3. After six administrations of anti‐Gr‐1 mAb, the tumors were harvested for analysis. (b) Tumor growth curve on treatment of anti‐MDSC plus QNZ, and anti‐MDSC plus SCH772948. (c) Changes in the proportion of different immune cells after osimertinib resistance and after anti‐MDSC combined with anti‐MAPK or anti‐NF‐κB therapy. (d) Representative images of tumor sections stained for tumor‐infiltrated immune cells by immunohistochemistry in different groups after osimertinib resistance and after anti‐MDSC combined with anti‐MAPK or anti‐NF‐κB therapy. (e) The expressions of Arg‐1 in different groups after osimertinib resistance were detected by western blotting (WB), and the expressions of IL‐4, IL‐10, IL‐34, and IFN‐γ in different groups after osimertinib resistance were detected by ELISA. ns, not significant; *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

MDSC plays an important role in shaping the immunosuppressive TIME after osimertinib resistance

Previous studies have reported that the activation of MAPK and NF‐kB signaling pathways can promote the secretion of cytokines by the tumor cells to reshape TIME. Excessive MDSC infiltration was noted after drug resistance; hence, whether the MDSC‐enriched inhibitory TIME was mediated by the activation of MAPK and NF‐kB pathways in the tumor cells was investigated.

To confirm this theory, the osimertinib‐resistant tumor cells were cultured in vitro, the cell culture supernatant was separated, and the expressions of IL‐4 and IL‐34 were detected using ELISA. The concentrations of IL‐4 and IL‐34 in the osimertinib‐resistant tumor cell supernatant were found to be higher than those in the osimertinib‐untreated tumor cell supernatant (Figure 6a). Furthermore, MDSC was isolated from the mouse bone marrow, and osimertinib‐resistant and osimertinib‐untreated tumor cell culture supernatants were added to the cultured MDSC in vitro, and the expressions of Arg‐1 and IL‐10 were measured using ELISA and WB. The concentrations of IL‐10 and Arg‐1 in the MDSC culture supernatant to which osimertinib‐resistant tumor cell culture supernatant was added were higher than those in the MDSC culture supernatant to which osimertinib‐untreated tumor cell culture supernatant was added (Figure 6b). To verify whether MDSC was capable of inhibiting the antitumor function of CD8+ T cells, the cells were isolated from the mouse spleen, MDSC culture supernatant was added, and the IFN expression of the CD8+ T cells was detected using ELISA. The concentration of IFN‐γ in the CD8+ T cell culture supernatant to which MDSC culture supernatant was added was lower than that in the culture supernatant without MDSC culture supernatant (Figure 6c). In summary, the activation of MAPK and NF‐kB pathways after osimertinib resistance can recruit MDSCs by promoting the secretion of IL‐4 and IL‐34 by the tumor cells, thus shaping the inhibitory TIME dominated by MDSC and leading to tumor immune escape (Figure 7).

FIGURE 6.

FIGURE 6

Open in a new tab

Abnormal activation of NF‐κB or mitogen‐activated protein kinase (MAPK) led to high levels of IL‐4 and IL‐34 expression, thereby stimulating myeloid‐derived suppressor cells (MDSC) to enhance the production of IL‐10 and Arg‐1 and inhibit the expression of CD8+ T cells. (a) The expression of IL‐4 and IL‐34 secreted from tumor before and after osimertinib resistance were detected by ELISA in an in vitro experiment. (b) The expression of IL‐10 secreted from MDSCs added osimertinib resistant tumor cell supernatant and osimertinib untreated tumor cell supernatant was detected by ELISA and Arg‐1 secreted from MDSCs added osimertinib resistant tumor cell supernatant and osimertinib untreated tumor cell supernatant was detected by western blotting (WB). (c) The expression of IFN‐γ secreted from CD8+ T cells added with or without MDSC culture supernatant. ns, not significant; *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

FIGURE 7.

FIGURE 7

Open in a new tab

Immune microenvironment of non‐small cell lung cancer (NSCLC) with osimertinib resistance.

DISCUSSION

Osimertinib is currently the first‐line treatment for EGFR‐sensitive mutations, especially in patients with T790M mutations. 9 , 12 , 13 However, the treatment options for osimertinib resistance are limited, and our knowledge about the evolution of TIME during the treatment is limited. Hence, an EGFR L858R+T790M mutation model was constructed in this study, and the entire TIME was analyzed. The proportion of tTIIs in EGFR mutant tumors was extremely low, and TIME belonged to the “immune‐desert” phenotype. During the period of osimertinib sensitivity, TIME changed to “inflamed” phenotype, and after drug resistance, it changed to “immune‐excluded” phenotype. Subsequently, PD‐1 monoclonal antibody was added at three different time points: before treatment, the sensitive period, and the osimertinib‐resistant period. However, none of these additions could improve the efficacy. Further analysis of the signaling pathways after tumor resistance suggested that NF‐κB and MAPK were abnormally activated. The simultaneous inhibition of these two pathways could inhibit tumor growth, reduce MDSC infiltration, and reverse the immunosuppressive TIME. The study of the underlying mechanism revealed that the activation of either NF‐κB or MAPK pathway led to increased levels of IL‐4 and IL‐34. Furthermore, MDSCs were recruited to produce IL‐10 and Arg‐1, and an MDSC‐enriched “immune‐excluded” TIME was created.

Previous studies have classified tumors into three phenotypes, namely “immune‐desert,” “inflamed,” and “immune‐excluded,” based on the type and proportion of tTIIs. 28 Among these, the “inflamed” phenotype has a good effect, whereas the “immune‐desert” phenotype has a low level of immunogenicity and the “immune‐excluded” phenotype, which is due to immunosuppressive cells, leads to poor efficacy of PD‐1 monoclonal antibody. 35 , 36 , 37 Our results imply that the TIME of patients with EGFR L858R+T790M before treatment and after osimertinib resistance belongs to immune‐desert and immune‐excluded phenotypes, respectively. Further analysis suggested that the immune tolerance after osimertinib resistance is mainly caused by MDSC enrichment rather than the high expression of PD‐L1, thus forming an MDSC‐enriched TIME. This result explains to a certain extent why PD‐1 monoclonal antibodies are not effective in such patients. 25 , 38 , 39 Studies have suggested that EGFR‐TKIs can activate the TIME of EGFR‐mutant tumors and have proposed that the addition of PD‐1 monoclonal antibody at this time may improve the treatment efficacy. 26 However, another study found that for EGFR L858R tumors, the combined use of erlotinib and PD‐1 monoclonal antibody could not further improve the treatment efficacy. 27 Our results confirm that the addition of PD‐1 monoclonal antibodies cannot further improve the efficacy of osimertinib.

Based on this observation, the cause of the immune‐excluded phenotype was explored. The relationship between the activation of MAPK and NF‐κB signaling pathways in lung cancer and the cytokines, such as IL‐4 and IL‐34, secreted by lung cancer cells remains unknown. Our study showed that the activation of MAPK and NF‐κB signaling pathways were positively correlated with the increased secretion of IL‐4 and IL‐34 and that blocking the two signaling pathways could reduce the secretion of IL‐4 and IL‐34. Our analysis of drug‐resistant tumors revealed that the abnormal activation of NF‐κB and MAPK pathways was associated with higher levels of IL‐4 and IL‐34. To determine which pathway was related to the levels of IL‐4 and IL‐34, small molecule inhibitors were used to inhibit MAPK and NF‐κB, and the levels of IL‐4 and IL‐34 did not decrease. However, when the two pathways were simultaneously inhibited, the levels of IL‐4 and IL‐34 decreased, which suggests that the activation of the NF‐κB or MAPK pathway can cause an increase in the levels of IL‐4 and IL‐34. Only when NF‐κB and MAPK were inhibited at the same time, the proportions of MDSC and other immunosuppressive cells were reduced in TIME.

Previous studies have shown that cytokines are involved in immune evasion, but the underlying mechanism is unknown. 40 , 41 , 42 Using in vitro experiments, we verified that high levels of IL‐4 and IL‐34 induced the MDSCs to produce more IL‐10 and Arg‐1. Previous studies have suggested that MDSCs promoted tumor progression and immune evasion via the production of cytokines such as IL‐10 and Arg‐1. 43 , 44 Moreover, MDSCs were differentiated into M2, which also suppressed the immune response by secreting Arg‐1 and IL‐10. 45 , 46 Our results further proved that high levels of IL‐4 and Arg‐1 inhibited the secretion of IFN‐γ by CD8+ T cells. When MDSC and either NF‐κB or MAPK signaling pathways were simultaneously inhibited, it could significantly shrink the tumor and activate TIME. This finding suggests that MDSC may be the core of the immunosuppressive TIME created after osimertinib resistance. The treatment for MDSC may be an option after osimertinib resistance.

The main advantage of our study lies in the fact that the evolution of TIME in EGFR L858R+T790M mutant tumors during osimertinib treatment has been described for the first time. This study has proposed for the first time that the tumor belongs to MDSC‐enriched immune‐excluded TIME after osimertinib resistance. Moreover, our analysis of drug‐resistant tumors explains for the first time how the abnormal activation of tumor signaling pathways leads to an immunosuppressive TIME. Finally, our study has established that MDSC is an important factor in the immune‐excluded TIME after osimertinib resistance.

However, our study has certain limitations. First, animal models were constructed using transgenic EGFR mutant cell lines, which could not fully simulate TIME in the patients. Second, the mechanism of how the abnormal activation of NF‐κB and MAPK signaling pathways increases the levels of IL‐4 and IL‐34 needs to be explored. There are multiple types of EGFR mutation patients, and we have only constructed a model of L858R combined with T790M mutation, which cannot represent all types of T790M mutations. Finally, whether the treatment for MDSC can be used as a therapeutic option after osimertinib resistance warrants further clinical research.

In conclusion, our findings lay the foundation for the evolution of TIME in osimertinib treatment and the association between TIME and osimertinib resistance. Our findings also shed light on the core role of MDSC in the immunosuppressive TIME and on the treatments targeting MDSC that can reactivate TIME.

AUTHOR CONTRIBUTIONS

Experimental analysis, data collection, and analysis and manuscript writing: CW, KF, and LL. Data collection and statistical analyses: SL, JD, ZW, JX, XZ, YT, and QY. Study concept and supervision of the whole project: HB and WJ. Read and approved the final version of the manuscript: all authors.

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests.

Supporting information

Figure S1. The depiction of the experimental flow chart.

Figure S2. Histograms of tumor‐infiltrated M‐MDSC and G‐MDSC during osimertinib treatment.

Figure S3. Representative flow cytometry plot showing the percentage of tumor‐infiltrated immune cells in different treatment groups after osimertinib treatment.

Figure S4. Left panel: Representative flow cytometry plot showing the percentage of tumor‐infiltrated immune cells. Right panel: Histograms of tumor‐infiltrated immune cells in different groups after osimertinib resistance.

Click here for additional data file. (580.3KB, pdf)

Table S1. Antibodies for WB and IHC.

Click here for additional data file. (20.9KB, docx)

Table S2. Gating strategy of flow cytometry.

Click here for additional data file. (15.9KB, docx)

ACKNOWLEDGMENTS

The authors thank all the staff at the Department of Medical Oncology for their support during the study.

This work was supported by National Key Research and Development Project 2019YFC1315700; NSFC Key Program (81630071); The Special Research Fund for Central Universities, Peking Union Medical College (3332021029 to Dr Jiachen Xu), NSFC General Program (81871889, 81972905); Aiyou Foundation (KY201701); National Natural Sciences Foundation (82102886 to Dr Jiachen Xu); Beijing Hope Run Special Fund of Cancer Foundation of China (LC2020B09 to Dr Jiachen Xu); Guangdong Association of Clinical Trials (GACT)/Chinese Thoracic Oncology Group (CTONG) and Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer (grant no. 2017B030314120).

Wang C, Fei K, Liu L, Duan J, Wang Z, Li S, et al. Abnormal activation of NF‐κB and MAPK signaling pathways affect osimertinib resistance and influence the recruitment of myeloid‐derived suppressor cells to shape the immunosuppressive tumor immune microenvironment. Thorac Cancer. 2023;14(19):1843–1856. 10.1111/1759-7714.14929

Chao Wang and Kailun Fei have contributed equally to this work and share first authorship.

Contributor Information

Hua Bai, Email: baihuahb@sina.com.

Jie Wang, Email: zlhuxi@163.com.

DATA AVAILABILITY STATEMENT

The datasets used and/or analyzed during the current study are available from the corresponding author open reasonable request.

REFERENCES

  • 1. Jänne PA, Engelman JA, Johnson BE. Epidermal growth factor receptor mutations in non‐small‐cell lung cancer: implications for treatment and tumor biology. J Clin Oncol. 2005;23(14):3227–34. [DOI] [PubMed] [Google Scholar]
  • 2. Shi Y, Au JS‐K, Thongprasert S, Srinivasan S, Tsai C‐M, Khoa MT, et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non‐small‐cell lung cancer of adenocarcinoma histology (PIONEER). J Thorac Oncol. 2014;9(2):154–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Maemondo M, Inoue A, Kobayashi K, Sugawara S, Oizumi S, Isobe H, et al. Gefitinib or chemotherapy for non‐small‐cell lung cancer with mutated EGFR. N Engl J Med. 2010;362(25):2380–8. [DOI] [PubMed] [Google Scholar]
  • 4. Mitsudomi T, Morita S, Yatabe Y, Negoro S, Okamoto I, Tsurutani J, et al. Gefitinib versus cisplatin plus docetaxel in patients with non‐small‐cell lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): an open label, randomised phase 3 trial. Lancet Oncol. 2010;11(2):121–8. [DOI] [PubMed] [Google Scholar]
  • 5. Wu YL, Zhou C, Liam CK, Wu G, Liu X, Zhong Z, et al. First‐line erlotinib versus gemcitabine/cisplatin in patients with advanced EGFR mutation‐positive non‐small‐cell lung cancer: analyses from the phase III, randomized, open‐label: ENSURE study. Ann Oncol. 2015;26(9):1883–9. [DOI] [PubMed] [Google Scholar]
  • 6. Zhou C, Wu YL, Chen G, Feng J, Liu XQ, Wang C, et al. Final overall survival results from a randomised, phase III study of erlotinib versus chemotherapy as first‐line treatment of EGFR mutation‐positive advanced non‐small‐cell lung cancer (OPTIMAL, CTONG‐0802). Ann Oncol. 2015;26(9):1877–83. [DOI] [PubMed] [Google Scholar]
  • 7. Zhou C, Wu Y‐L, Chen G, Feng J, Liu X‐Q, Wang C, et al. Erlotinib versus chemotherapy as first‐line treatment for patients with advanced EGFR mutation‐positive non‐small‐cell lung cancer (OPTIMAL, CTONG‐0802): a multicentre, open‐label, randomised, phase 3 study. Lancet Oncol. 2011;12(8):735–42. [DOI] [PubMed] [Google Scholar]
  • 8. Wu Y‐L, Zhou C, Hu C‐P, Feng J, Lu S, Huang Y, et al. Afatinib versus cisplatin plus gemcitabine for first‐line treatment of Asian patients with advanced non‐small‐cell lung cancer harbouring EGFR mutations (LUX‐lung 6): an open‐label, randomised phase 3 trial. Lancet Oncol. 2014;15(2):213–22. [DOI] [PubMed] [Google Scholar]
  • 9. Soria J‐C, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee KH, et al. Osimertinib in untreated EGFR‐mutated advanced non‐small‐cell lung cancer. N Engl J Med. 2018;378(2):113–25. [DOI] [PubMed] [Google Scholar]
  • 10. Ramalingam SS, Vansteenkiste J, Planchard D, Cho BC, Gray JE, Ohe Y, et al. Overall survival with Osimertinib in untreated, mutated advanced NSCLC. N Engl J Med. 2020;382(1):41–50. [DOI] [PubMed] [Google Scholar]
  • 11. Mok TS, Wu Y‐L, Thongprasert S, Yang C‐H, Chu D‐T, Saijo N, et al. Gefitinib or carboplatin‐paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009;361(10):947–57. [DOI] [PubMed] [Google Scholar]
  • 12. Mok TS, Wu Y‐L, Ahn M‐J, Garassino MC, Kim HR, Ramalingam SS, et al. Osimertinib or platinum‐pemetrexed in EGFR T790M‐positive lung cancer. N Engl J Med. 2017;376(7):629–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Ramalingam SS, Vansteenkiste J, Planchard D, Cho BC, Gray JE, Ohe Y, et al. Overall survival with osimertinib in untreated, EGFR‐mutated advanced NSCLC. N Engl J Med. 2020;382(1):41–50. [DOI] [PubMed] [Google Scholar]
  • 14. Oxnard GR, Hu Y, Mileham KF, Husain H, Costa DB, Tracy P, et al. Assessment of resistance mechanisms and clinical implications in patients with EGFR T790M–positive lung cancer and acquired resistance to osimertinib. JAMA Oncol. 2018;4(11):1527–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Tang Z‐H, Lu J‐J. Osimertinib resistance in non‐small cell lung cancer: mechanisms and therapeutic strategies. Cancer Lett. 2018;420:242–6. [DOI] [PubMed] [Google Scholar]
  • 16. Tamura T, Kato Y, Ohashi K, Ninomiya K, Makimoto G, Gotoda H, et al. Potential influence of interleukin‐6 on the therapeutic effect of gefitinib in patients with advanced non‐small cell lung cancer harbouring EGFR mutations. Biochem Biophys Res Commun. 2018;495(1):360–7. [DOI] [PubMed] [Google Scholar]
  • 17. Jia Y, Li X, Zhao C, Jiang T, Zhao S, Zhang L, et al. Impact of serum vascular endothelial growth factor and interleukin‐6 on treatment response to epidermal growth factor receptor tyrosine kinase inhibitors in patients with non‐small‐cell lung cancer. Lung Cancer. 2018;125:22–8. [DOI] [PubMed] [Google Scholar]
  • 18. Umeguchi H, Sueoka‐Aragane N, Kobayashi N, Nakamura T, Sato A, Takeda Y, et al. Usefulness of plasma HGF level for monitoring acquired resistance to EGFR tyrosine kinase inhibitors in non‐small cell lung cancer. Oncol Rep. 2015;33(1):391–6. [DOI] [PubMed] [Google Scholar]
  • 19. Cho JH, You Y‐M, Yeom YI, Lee DC, Kim B‐K, Won M, et al. RNF25 promotes gefitinib resistance in EGFR‐mutant NSCLC cells by inducing NF‐κB‐mediated ERK reactivation. Cell Death Dis. 2018;9(6):587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Tsukita Y, Fujino N, Miyauchi E, Saito R, Fujishima F, Itakura K, et al. Axl kinase drives immune checkpoint and chemokine signalling pathways in lung adenocarcinomas. Mol Cancer. 2019;18(1):24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Soucheray M, Capelletti M, Pulido I, Kuang Y, Paweletz CP, Becker JH, et al. Intratumoral heterogeneity in EGFR‐mutant NSCLC results in divergent resistance mechanisms in response to EGFR tyrosine kinase inhibition. Cancer Res. 2015;75(20):4372–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Fernando RI, Hamilton DH, Dominguez C, David JM, McCampbell KK, Palena C. IL‐8 signaling is involved in resistance of lung carcinoma cells to erlotinib. Oncotarget. 2016;7(27):42031–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Lee CK, Man J, Lord S, Links M, Gebski V, Mok T, et al. Checkpoint inhibitors in metastatic EGFR‐mutated non‐small cell lung cancer‐a meta‐analysis. J Thorac Oncol. 2017;12(2):403–7. [DOI] [PubMed] [Google Scholar]
  • 24. Lee CK, Man J, Lord S, Cooper W, Links M, Gebski V, et al. Clinical and molecular characteristics associated with survival among patients treated with checkpoint inhibitors for advanced non–small cell lung carcinoma: a systematic review and meta‐analysis. JAMA Oncol. 2018;4(2):210–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Haratani K, Hayashi H, Tanaka T, Kaneda H, Togashi Y, Sakai K, et al. Tumor immune microenvironment and nivolumab efficacy in EGFR mutation‐positive non‐small‐cell lung cancer based on T790M status after disease progression during EGFR‐TKI treatment. Ann Oncol. 2017;28(7):1532–9. [DOI] [PubMed] [Google Scholar]
  • 26. Jia Y, Li X, Jiang T, Zhao S, Zhao C, Zhang L, et al. EGFR‐targeted therapy alters the tumor microenvironment in EGFR‐driven lung tumors: implications for combination therapies. Int J Cancer. 2019;145(5):1432–44. [DOI] [PubMed] [Google Scholar]
  • 27. Ayeni D, Miller B, Kuhlmann A, Ho P‐C, Robles‐Oteiza C, Gaefele M, et al. Tumor regression mediated by oncogene withdrawal or erlotinib stimulates infiltration of inflammatory immune cells in EGFR mutant lung tumors. J Immunother Cancer. 2019;7(1):1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Hegde PS, Chen DS. Top 10 challenges in cancer immunotherapy. Immunity. 2020;52(1):17–35. [DOI] [PubMed] [Google Scholar]
  • 29. Dong Z, Zhong W, Wu Y. EGFR mutation correlates with uninflamed phenotype and weak immunogenicity, causing impaired response to PD‐1 blockade in NSCLC. J Thorac Oncol. 2017;12(11):S2370–1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Zhang B, Zhang Y, Zhao J, Wang Z, Wu T, Ou W, et al. M2‐polarized macrophages contribute to the decreased sensitivity of EGFR‐TKIs treatment in patients with advanced lung adenocarcinoma. Med Oncol. 2014;31(8):127. [DOI] [PubMed] [Google Scholar]
  • 31. Feng P‐H, Yu C‐T, Chen K‐Y, Luo C‐S, Wu SM, Liu C‐Y, et al. S100A9 MDSC and TAM‐mediated EGFR‐TKI resistance in lung adenocarcinoma: the role of RELB. Oncotarget. 2018;9(7):7631–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Venugopalan A, Lee M‐J, Niu G, Medina‐Echeverz J, Tomita Y, Lizak MJ, et al. EGFR‐targeted therapy results in dramatic early lung tumor regression accompanied by imaging response and immune infiltration in EGFR mutant transgenic mouse models. Oncotarget. 2016;7(34):54137–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Isomoto K, Haratani K, Hayashi H, Shimizu S, Tomida S, Niwa T, et al. Impact of EGFR‐TKI treatment on the tumor immune microenvironment in mutation‐positive non‐small cell lung cancer. Clin Cancer Res. 2020;26(8):2037–46. [DOI] [PubMed] [Google Scholar]
  • 34. Sun C, Zhang L, Zhang W, Liu Y, Chen B, Zhao S, et al. Expression of PD‐1 and PD‐L1 on tumor‐infiltrating lymphocytes predicts prognosis in patients with small‐cell lung cancer. Onco Targets Ther. 2020;13:6475–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Hansen A, Massard C, Ott P, Haas N, Lopez J, Ejadi S, et al. Pembrolizumab for advanced prostate adenocarcinoma: findings of the KEYNOTE‐028 study. Ann Oncol. 2018;29(8):1807–13. [DOI] [PubMed] [Google Scholar]
  • 36. Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov. 2019;18(3):197–218. [DOI] [PubMed] [Google Scholar]
  • 37. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGFβ attenuates tumour response to PD‐L1 blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Herbst RS, Baas P, Kim D‐W, Felip E, Pérez‐Gracia JL, Han J‐Y, et al. Pembrolizumab versus docetaxel for previously treated, PD‐L1‐positive, advanced non‐small‐cell lung cancer (KEYNOTE‐010): a randomised controlled trial. Lancet. 2016;387(10027):1540–50. [DOI] [PubMed] [Google Scholar]
  • 39. Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Pembrolizumab for the treatment of non‐small‐cell lung cancer. N Engl J Med. 2015;372(21):2018–28. [DOI] [PubMed] [Google Scholar]
  • 40. Bronte V, Serafini P, De Santo C, Marigo I, Tosello V, Mazzoni A, et al. IL‐4‐induced arginase 1 suppresses alloreactive T cells in tumor‐bearing mice. J Immunol. 2003;170(1):270–8. [DOI] [PubMed] [Google Scholar]
  • 41. Sinha P, Clements VK, Ostrand‐Rosenberg S. Interleukin‐13‐regulated M2 macrophages in combination with myeloid suppressor cells block immune surveillance against metastasis. Cancer Res. 2005;65(24):11743–51. [DOI] [PubMed] [Google Scholar]
  • 42. Blondy T, d'Almeida SM, Briolay T, Tabiasco J, Meiller C, Chéné A‐L, et al. Involvement of the M‐CSF/IL‐34/CSF‐1R pathway in malignant pleural mesothelioma. J Immunother Cancer. 2020;8(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Molon B, Ugel S, Del Pozzo F, Soldani C, Zilio S, Avella D, et al. Chemokine nitration prevents intratumoral infiltration of antigen‐specific T cells. J Exp Med. 2011;208(10):1949–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Shojaei F, Wu X, Qu X, Kowanetz M, Yu L, Tan M, et al. G‐CSF‐initiated myeloid cell mobilization and angiogenesis mediate tumor refractoriness to anti‐VEGF therapy in mouse models. Proc Natl Acad Sci USA. 2009;106(16):6742–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Corzo CA, Condamine T, Lu L, Cotter MJ, Youn J‐I, Cheng P, et al. HIF‐1α regulates function and differentiation of myeloid‐derived suppressor cells in the tumor microenvironment. J Exp Med. 2010;207(11):2439–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Noy R, Pollard JW. Tumor‐associated macrophages: from mechanisms to therapy. Immunity. 2014;41(1):49–61. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. The depiction of the experimental flow chart.

Figure S2. Histograms of tumor‐infiltrated M‐MDSC and G‐MDSC during osimertinib treatment.

Figure S3. Representative flow cytometry plot showing the percentage of tumor‐infiltrated immune cells in different treatment groups after osimertinib treatment.

Figure S4. Left panel: Representative flow cytometry plot showing the percentage of tumor‐infiltrated immune cells. Right panel: Histograms of tumor‐infiltrated immune cells in different groups after osimertinib resistance.

Click here for additional data file. (580.3KB, pdf)

Table S1. Antibodies for WB and IHC.

Click here for additional data file. (20.9KB, docx)

Table S2. Gating strategy of flow cytometry.

Click here for additional data file. (15.9KB, docx)

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author open reasonable request.

Articles from Thoracic Cancer are provided here courtesy of Wiley

RESOURCES