Prognostic analysis and beneficiary population exploration of subsequent treatment regimens after third-generation EGFR-TKIs failure in EGFR-mutated advanced non-small cell lung cancer: a retrospective cohort study

Introduction

Lung cancer is the most common malignant tumor (1). According to the latest China Cancer Report, lung cancer ranks first in both incidence and mortality, with approximately 1,060,600 new cases and 733,300 deaths annually (2). In the latest United States report, it is estimated that there are approximately 226,650 new lung cancer cases and 124,730 lung cancer-related deaths annually (3). Given the high incidence and mortality rates of lung cancer, effective treatment regimens need to be established.

Lung cancer can be classified into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) based on its pathological type, with NSCLC accounting for approximately 85% of all cases (1). In China, lung adenocarcinoma accounts for 55–60% of NSCLC cases (4).

The epidermal growth factor receptor (EGFR) is one of the most common driver genes in NSCLC. It is located on the cell surface, and its binding to ligands triggers autophosphorylation, which promotes the proliferation and metastasis of cancer cells via the mitogen-activated protein kinase (MAPK) cascade signaling pathway (5). The administration of third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) has significantly prolonged the survival of patients with EGFR mutations and those with acquired resistance to first- and second-generation EGFR-TKIs. However, patients who receive third-generation EGFR-TKIs inevitably develop drug resistance, which greatly reduces therapeutic efficacy.

The application of immune checkpoint inhibitors (ICIs) has significantly prolonged the survival of lung cancer patients. Studies such as IMpower150 and ORIENT-31 have provided novel treatment options for lung cancer patients after the failure of EGFR-TKIs treatment, confirming the clinical value and application potential of immunotherapy (6,7). Current clinical evidence lacks systematic head-to-head efficacy comparisons among diverse post-resistance treatment regimens, leading to highly empirical and inconsistent clinical decision-making in the sequential treatment of EGFR-TKIs-resistant NSCLC.

With the growing adoption of precision tumor therapy, the formulation of individualized treatment regimens based on patient baseline characteristics such as tumor stage and metastasis sites represents an urgent clinical need. However, systematic research identifying the patients most likely to benefit from various salvage treatment regimens (e.g., immunotherapy, targeted combination therapy, and antibody-drug conjugate therapy) following resistance to third-generation EGFR-TKIs is lacking, making it difficult to implement clinically precise, stratified treatment.

This study aimed to evaluate the efficacy of different subsequent treatment regimens, and to identify the patients most likely to benefit from each regimen among those at our center who developed resistance to third-generation EGFR-TKIs. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0500/rc).

Methods

Study subjects

A total of 16,089 lung cancer patients who were treated at the Jiangsu Cancer Hospital from January 1, 2017 to December 31, 2023 were retrospectively enrolled in this study. Patients were screened in accordance with the following inclusion and exclusion criteria. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of Jiangsu Cancer Hospital (No. KY-2025-013) and informed consent was taken from all the patients.

Inclusion criteria

Patients were included in the study if they met the following inclusion criteria:

• Aged 18–79 years;
• Eastern Cooperative Oncology Group (ECOG) performance status score of 0 to 2;
• Pathologically confirmed adenocarcinoma;
• Presence of an EGFR mutation; and
• Disease progression after treatment with third-generation EGFR-TKIs, and receipt of two or more subsequent drugs after disease progression.

Exclusion criteria

Patients were excluded from the study if they met the following exclusion criteria:

• A history of other malignant tumors (except for non-melanoma skin cancer or other cancers cured for ≥5 years);
• Active autoimmune disease or prior use of immunosuppressive therapy; and/or
• Loss to follow-up.

Ultimately, 225 patients who met the above inclusion and exclusion criteria were enrolled in this study. Based on the different treatment modalities, the patients were divided into the ICI (I+O) group (N=131) and the No ICIs group (N=94). The ICI group was further subdivided into three subgroups: the ICI plus bevacizumab plus chemotherapy (IBC) subgroup (n=79), the ICIs plus anlotinib (IA) subgroup (n=38), and the ICIs plus chemotherapy (IC) subgroup (n=14). The No ICIs group was further subdivided into three subgroups: the bevacizumab plus chemotherapy (BC) subgroup (n=48), the chemotherapy alone (C) subgroup (n=31), and the targeted therapy subgroup based on re-biopsy genetic testing (based on mutation) subgroup (n=15) (Figure 1).

Figure 1 Study flowchart and inclusion/exclusion criteria. ECOG, Eastern Cooperative Oncology Group; EGFR-TKIs, epidermal growth factor receptor-tyrosine kinase inhibitors; ICIs, immune checkpoint inhibitors; I+O, immune checkpoint inhibitors and other treatments.

Study methods

Data were retrospectively collected from the electronic medical record system. The following baseline patient data were collected: (I) demographic characteristics, clinical stage, etc.; (II) treatment-related data: date of initial diagnosis, date of death, third-generation EGFR-TKIs regimen, treatment initiation time, and time to disease progression; and (III) first-line treatment regimen after resistance to third-generation EGFR-TKIs, treatment initiation time, time to disease progression, and best overall response. The cut-off date for the last follow-up was March 31, 2025.

Clinical efficacy evaluation criteria

Efficacy was assessed according to the Response Evaluation Criteria in Solid Tumors to version 1.1 (RECIST 1.1) (8). The efficacy assessment criteria included complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). The objective response rate (ORR) was defined as the proportion of patients who achieved CR or PR among all patients. The disease control rate (DCR) was defined as the percentage of patients who achieved CR, PR, or SD among all patients.

Statistical analysis

Data processing and analysis were performed using R 4.3.1, Zstats (www.medsta.cn), Python, and CNSknowall (cnsknowall.com). Quantitative data were presented as the mean ± standard deviation or median; comparisons between groups were conducted using the t-test or Mann-Whitney U test. Categorical data were analyzed using the Chi-squared (χ²) test or Fisher’s exact test. The survival analysis was performed using the Kaplan-Meier method and log-rank test. A two-sided P value <0.05 was considered statistically significant.

Results

Patient characteristics

A total of 225 patients with acquired resistance to third-generation EGFR-TKIs who attended the Jiangsu Cancer Hospital from January 1, 2017, to December 31, 2023, were enrolled in this study in accordance with the predefined inclusion and exclusion criteria. The cut-off date for the last follow-up was March 31, 2025. The median progression-free survival (mPFS) of the 225 patients was 7.70 months, and the median overall survival (mOS) was 69.37 months (Figure 2A,2B).

Figure 2 Characteristics of the total patient population. (A,B) PFS and OS survival curves of the 225 patients included in this study. (C) Pie charts showing patient characteristics. (D-F) PFS and OS of patients in the I+O and No ICIs groups. (G,H) Survival curves of patients in each subgroup. BC, bevacizumab plus chemotherapy; C, chemotherapy; CI, confidence interval; EGFR-TKIs, epidermal growth factor receptor-tyrosine kinase inhibitors; HR, hazard ratio; IA, immunotherapy plus anlotinib; IBC, immunotherapy plus bevacizumab plus chemotherapy; IC, immunotherapy plus chemotherapy; ICIs, immune checkpoint inhibitors; I+O, immune checkpoint inhibitors and other treatments; M, metastasis; mOS, median overall survival; mPFS, median progression-free survival; N, node; OS, overall survival; PFS, progression-free survival; T, tumor.

In terms of demographic characteristics, of the 225 patients, 48.44% (109/225) were male, 71.11% (160/225) were aged 51–79 years, and 28.89% (65/225) were aged ≤50 years. Additionally, 41.78% had a body mass index (BMI) of 18.5–23.9 kg/m² (Table 1 and Figure 2C).

In terms of tumor biological characteristics, the tumors were predominantly located in the upper lobes. Specifically, 27.56% (62/225) of the tumors were located in the left upper lobe of the lung, and 28.44% (64/225) were located in the right upper lobe.

In relation to T stage, the majority of patients were classified as Tx (49.55%, 111/225). Specifically, T2 accounted for the highest proportion (17.41%, 39/225), followed by T4 (14.29%, 32/225), T1 (10.27%, 23/225), and T3 (8.48%, 19/225). In relation to N stage, N3 was the most common (62.05%, 139/224), while the N0–N2 and Nx stages showed a relatively balanced distribution (Table 1 and Figure 2C).

In terms of metastatic site distribution, 24.00% of the patients (54/225) had brain metastasis, 18.67% (42/225) had liver metastasis, 49.33% (111/225) had bone metastasis, 54.22% (122/225) had intrapulmonary metastasis (including intrapulmonary dissemination or contralateral lung metastasis), 50.67% (114/225) had pleural metastasis (including malignant pleural effusion), and only 12.89% (29/225) had adrenal metastasis (Table 1 and Figure 2C).

In relation to molecular pathological and disease progression characteristics, no statistically significant difference was observed in the distribution of EGFR mutation sites (P=0.27). Specifically, 44.00% (99/225) of the patients had the exon 19 deletion (19del), and 38.22% (86/225) had the exon 21 L858R mutation. Most patients (80.44%, 181/225) were initially diagnosed with stage IV disease.

In terms of treatment-related characteristics, the predominant pattern of tumor progression was local progression (56.44% of patients), while distant metastasis and mixed local plus distant progression accounted for 16.44% and 24.89%, respectively (Table 1 and Figure 2C).

The patients were divided into the following two groups based on whether or not they received ICIs: the I+O group and the No ICIs group. The mPFS of the I+O group was approximately 9.10 months, which was significantly longer than that of the No ICIs group (mPFS =7.15 months, P<0.001). However, no statistically significant difference in OS was observed between the two groups (71.47 months in the I+O group vs. 65.10 months in the No ICIs group, P=0.44) (Figure 2D-2F).

The I+O group was further subdivided into the following three subgroups based on treatment regimens: the IBC subgroup, IA subgroup, and IC subgroup. The No ICIs group was further subdivided into the following three subgroups: the BC subgroup, C subgroup, and Based on mutation subgroup.

To evaluate the efficacy of different treatment regimens, the progression-free survival (PFS) and overall survival (OS) of each subgroup were analyzed using Kaplan-Meier curves. The PFS curves of the IBC, IA, IC, BC, C, and Based on mutation subgroups differed significantly (P<0.001). The mPFS of each of these subgroups was 11.40, 7.10, 7.20, 8.20, 5.07, and 4.07 months, respectively (Figure 2G). Conversely, no such significant difference was observed between the OS curves of each subgroup (P=0.58) (Figure 2H).

IBC yielded remarkable efficacy

First, we compared each subgroup in the I+O group with the No ICIs group. The mPFS of the IBC subgroup was 11.40 months, which was longer than that of the No ICIs group (7.15 months) (P<0.001), with a hazard ratio (HR) of 2.226 (Figure 3A). After multivariate Cox regression adjustment, No ICIs treatment was associated with a significantly higher risk of endpoint events compared with the IBC regimen [HR =2.24, 95% confidence interval (CI): 1.57–3.20, P<0.001] (Figure S1A). Thus, the clinical efficacy of the IBC subgroup was significantly superior to that of the No ICIs group. Conversely, the IA and IC subgroups had mPFS of 7.10 and 7.20 months, respectively, showing no significant difference compared with the 7.15 months of the No ICIs group (P=0.25 and P=0.88) (Figure 3B,3C). Thus, the combination regimen of IBC significantly prolonged mPFS compared with the No ICIs regimen. Additionally, no significant survival difference was observed between the IA and IC subgroups and the No ICIs group. However, the OS outcomes were unsatisfactory. The IBC, IA, and IC subgroups had mOS values of 69.53, 82.47, and 64.53 months, respectively (Figure 3D-3F). There was no significant difference in the mOS of these three subgroups compared with the 65.10 months of the No ICIs group (P=0.90, 0.12, and 0.76, respectively).

Figure 3 Efficacy analysis of the IBC regimen. (A-F) PFS and OS survival analyses of each ICI group versus the No ICIs group. (G-L) PFS and OS survival analyses of the IBC subgroup versus each No ICIs group. (M,N) Intergroup survival analyses of all treatment regimens (**, P<0.01; ***, P<0.001). (O-Q) Analysis of the best overall response in the IBC subgroup. BC, bevacizumab plus chemotherapy; C, chemotherapy; CI, confidence interval; DCR, disease control rate; HR, hazard ratio; IA, immunotherapy plus anlotinib; IBC, immunotherapy plus bevacizumab plus chemotherapy; IC, immunotherapy plus chemotherapy; ICIs, immune checkpoint inhibitors; mOS, median overall survival; mPFS, median progression-free survival; ORR, objective response rate; OS, overall survival; PD, progressive disease; PFS, progression-free survival; PR, partial response; SD, stable disease.

To further verify the remarkable clinical efficacy of the IBC regimen, we conducted pairwise comparisons between the IBC subgroup and each subgroup in the No ICIs group. The mPFS of the IBC subgroup was significantly prolonged compared with that of the BC subgroup (11.40 vs. 8.20 months, respectively) (P=0.005) (Figure 3G). After multivariate adjustment, all three factors remained independent prognostic indicators. Compared with IBC regimen, BC regimen was linked to higher event risk (HR =1.88, 95% CI: 1.25–2.82, P=0.002) (Figure S1B). The mPFS of the C subgroup was approximately 5.07 months. Notably, the mPFS of the IBC subgroup was significantly longer than that of the C subgroup (P<0.001) (Figure 3H). After multivariate Cox adjustment, compared with IBC regimen, regimen C was independently associated with a 3-fold increased risk of endpoint events (HR =3.00, 95% CI: 1.81–4.96, P<0.001) (Figure S1C). The patients in the Based on mutation subgroup had an mPFS of 4.07 months, which was significantly shorter than the 11.40 months of the IBC subgroup (P<0.001) (Figure 3I). After multivariate Cox regression adjustment, patients receiving mutation-directed treatment exhibited a substantially higher risk of clinical endpoints relative to those treated with the IBC regimen (HR =3.09, 95% CI: 1.65–5.79, P<0.001), with their event risk elevated by more than two-fold (Figure S1D).

Similarly, we performed statistical analyses of OS outcomes between the IBC subgroup and each No ICIs subgroup. The Kaplan-Meier survival curves for OS showed no significant difference between the IBC subgroup and the BC, C, and Based on mutation subgroups. The mOS values were 69.53 vs. 68.37 months, 69.53 vs. 52.97 months, and 69.53 vs. 75.30 months, respectively (all P>0.05; 0.69, 0.56, and 0.96), indicating no statistically significant differences (Figure 3J-3L). Additionally, the differential bar charts demonstrated that the IBC regimen resulted in significantly prolonged PFS compared with the other regimens, but no significant difference was observed in OS (Figure 3M,3N). Based on the above findings, the I+O group exhibited significantly superior PFS efficacy compared with the No ICIs group; thus, the IBC regimen was identified as the optimal therapeutic strategy.

As shown in the waterfall plot, only 6 patients (1 of whom experienced disease progression in the form of new lesions) were assessed as having PD after two cycles of the IBC regimen, accounting for approximately 7.60% of the IBC subgroup (Figure 3O). Conversely, 29.11% (23/79) of the patients achieved PR, and no patients achieved CR (Figure 3P). The DCR of the IBC group reached as high as 92.40%, while the ORR was approximately 29.11% (Figure 3Q).

In conclusion, following the development of resistance to third-generation EGFR-TKIs, the IBC regimen yielded significantly prolonged mPFS compared with the other therapeutic regimens, while also demonstrating optimal treatment efficacy.

Reason for the lack of an OS benefit with the IBC regimen—disparities in PFS/OS ratios across different treatment phases

In our study, although the IBC regimen significantly prolonged mPFS, no statistically significant difference was observed in mOS. To investigate the underlying cause of this phenomenon, we examined the complete treatment trajectories of patients from diagnosis to death or the time of last follow-up (Figure 4A-4C and Figure S2). All patients received third-generation EGFR-TKIs, the therapy following the development of resistance to third-generation EGFR-TKIs, as well as other treatments (including neoadjuvant therapy, surgery, adjuvant therapy, first- and second-generation EGFR-TKIs therapy, and multiple lines of therapy for advanced disease).

Figure 4 Reason for the lack of an OS benefit in the IBC regimen. (A-C) All treatments from diagnosis until death or the end of follow-up history and regimens of patients in the IBC, BC, and IA subgroups. (D) Box plot of PFS/OS ratios of patients in the six subgroups across different treatment phases. The arrows indicate patients who were alive at the last follow-up. ***, P<0.001. BC, bevacizumab plus chemotherapy; IA, immunotherapy plus anlotinib; IBC, immunotherapy plus bevacizumab plus chemotherapy; OS, overall survival; PFS, progression-free survival; TKI, tyrosine kinase inhibitor.

As the box plot shows, the PFS/OS ratio for the six treatment regimens examined in this study was significantly lower than that observed for third-generation EGFR-TKIs and other treatments (Figure 4D). To further examine this difference, we generated additional box plots based on the PFS/OS ratios of all patients. The median and interquartile ranges of the PFS/OS ratios for the six treatment regimens examined were lower than those of the third-generation EGFR-TKIs and other treatments. Mann–Whitney U tests confirmed that these differences in the PFS/OS ratios between third-generation EGFR-TKIs, other treatments, and the six treatment regimens were statistically significant (all P<0.001) (Figure 4D).

In conclusion, it may be that the IBC subgroup exhibited significant differences in PFS but no OS benefit because the PFS of patients receiving third-generation EGFR-TKIs and other treatments was longer and comprised a larger proportion of OS. This reduced the relative contribution of the IBC regimens to OS, thus resulting in the absence of a significant OS difference.

Exploration of beneficiary populations in each subgroup

Exploration of beneficiary populations for the IBC regimen

First, we performed variable selection on relevant covariates using least absolute shrinkage and selection operator (LASSO) regression. Following LASSO screening, four variables were retained: combination therapy during third-generation EGFR-TKIs treatment, liver metastasis, age, and newly developed pleural metastasis. The remaining variables were excluded due to their minimal predictive contribution to PFS (Figure 5A-5C).

Figure 5 Exploration of beneficiary populations for the IBC regimen. (A-C) LASSO analysis for the IBC regimen. (D) Univariate and multivariate analyses for the IBC regimen. (E-G) Survival curves for variables. CI, confidence interval; EGFR-TKIs, epidermal growth factor receptor-tyrosine kinase inhibitors; HR, hazard ratio; IBC, immunotherapy plus bevacizumab plus chemotherapy; LASSO, least absolute shrinkage and selection operator; mPFS, median progression-free survival; PFS, progression-free survival.

The univariate analysis showed that patients aged 51–79 years had a significantly lower risk of PFS events than those aged ≤50 years (P=0.04). This difference remained statistically significant in the multivariate analysis (P=0.03). Similarly, the univariate analysis showed that patients who did not receive combination therapy during third-generation EGFR-TKIs treatment exhibited a significantly reduced risk of PFS events (P<0.001). This finding was further validated in the multivariate analysis (P=0.001). In relation to baseline metastatic sites, both the univariate and multivariate analyses showed that patients without baseline liver metastasis had a significantly decreased risk of PFS events (univariate Cox analysis: P=0.008; multivariate Cox analysis: P=0.04), suggesting that patients without liver metastasis had more favorable PFS outcomes (Figure 5D). For the other baseline characteristics, all P values exceeded 0.05, indicating that these metastatic sites had no significant impact on PFS (Figure 5D and Figure S3).

Kaplan-Meier curves were used to analyze the key variables associated with PFS. The patients aged 51–79 years had an mPFS of 13.20 months, which was significantly longer than the 7.30 months observed in patients aged ≤50 years (P=0.04, HR =1.901, 95% CI: 1.035–3.493). The patients without baseline liver metastasis had an mPFS of 13.23 months, which significantly exceeded the 7.70 months observed in patients with baseline liver metastasis (P=0.01, HR =2.295, 95% CI: 1.194–4.412). The patients who did not receive combination therapy during third-generation EGFR-TKI treatment had an mPFS of 13.23 months, which was significantly longer than the 6.08 months observed in patients who received combination therapy (P<0.001, HR =2.897, 95% CI: 1.566–5.359) (Figure 5E-5G).

In conclusion, validation using LASSO regression, Cox regression analysis, and Kaplan-Meier curves revealed that patients aged 51–79 years, those who did not receive combination therapy during third-generation EGFR-TKIs treatment, and those without baseline liver metastasis experienced longer PFS. Other baseline characteristics did not significantly affect PFS in patients treated with the IBC regimen. Given that factors such as economic status and physical condition may prevent some patients from receiving combination IBC regimens, we also sought to investigate the beneficiary populations for other treatment regimens.

Exploration of beneficiary populations for the BC regimen

To examine the relationship between baseline characteristics and patient survival outcomes, patients in the BC treatment group were stratified into short- and long-survival groups using PFS cut-off points of 3, 6, 9, and 12 months, as well as the mPFS (8.20 months). A logistic regression analysis was performed using baseline characteristics as variables, and a heatmap was generated based on P values (Figure 6A). The results revealed that sex, prior therapy history before third-generation EGFR-TKIs treatment, and the presence of bone or liver metastasis were strongly correlated with patient survival outcomes. χ² tests comparing the short- and long-survival groups further confirmed that sex, and the presence of bone or liver metastasis were strongly associated with patient survival outcomes (Figure 6B).

Figure 6 Exploration of beneficiary populations for the other regimen. (A) P value heatmap of logistic regression for each variable in the long- and short-survival subgroups of the BC regimen patients. (B) P value heatmap of χ² tests for each variable in the long- and short-survival subgroups of the BC regimen patients. (C-E) Survival curves for each variable. (F) P value heatmap of logistic regression for each variable in the long- and short-survival subgroups of the IA regimen patients. (G) P value heatmap of χ² tests for each variable in the long- and short-survival subgroups of the IA regimen patients. (H,I) Survival curves for each variable. (J) P value heatmap of logistic regression for each variable in the long- and short-survival subgroups of the IC regimen patients. (K) P value heatmap of χ² tests for each variable in the long- and short-survival subgroups of the IC regimen patients. (L-O) Survival curves for each variable. (P) Co-mutation sites of the patients in the based on mutation subgroup. BC, bevacizumab plus chemotherapy; BMI, body mass index; CI, confidence interval; EGFR-TKIs, epidermal growth factor receptor-tyrosine kinase inhibitors; HR, hazard ratio; IA, immunotherapy plus anlotinib; IC, immunotherapy plus chemotherapy; mPFS, median progression-free survival; N, node; PFS, progression-free survival; T, tumor.

To assess the impact of individual variables on survival outcomes, Kaplan-Meier curves were used to analyze patient survival. Among the patients treated with the BC regimen, those without liver metastasis had an mPFS of 8.50 months, while those with liver metastasis had a significantly shorter mPFS of 4.07 months, and the difference between the two groups was statistically significant (P=0.03, 95% CI: 1.065–5.603) (Figure 6C). The patients without bone metastasis had an mPFS of 9.08 months, while those with bone metastasis had an mPFS 7.28 months. Although numerically longer, the survival curves of the two groups overlapped, indicating no statistically significant difference between the groups (P=0.46) (Figure 6D).

Male patients exhibited a trend toward longer mPFS (9.07 months) than female patients (7.75 months), but the difference was not statistically significant (P=0.62) (Figure 6E).

Overall, among patients receiving the BC regimen, the absence of liver metastasis emerged as a significant predictor of improved survival, with such patients experiencing longer PFS and a lower risk of disease progression.

Exploration of beneficiary populations for the IA regimen

To examine the relationship between baseline characteristics and patient survival outcomes, patients in the IA treatment group were stratified into short- and long-survival groups using the approach described above. A logistic regression analysis was performed using baseline characteristics as variables, and a heatmap was generated based on P values (Figure 6F). The results showed that age, prior treatment history before third-generation EGFR-TKIs treatment, primary tumor lobe, N stage, and the presence of pleural or lung metastasis were all significantly correlated with survival outcomes. χ² tests further confirmed that age, tumor lobe involvement, N stage, and the presence of lung metastasis were significantly associated with survival outcomes (Figure 6G).

To further assess the impact of individual variables on survival outcomes, a Kaplan-Meier analysis was conducted. The results showed patients aged 51–79 years had an mPFS of 9.30 months, compared with 5.65 months for patients aged ≤50 years. Although the difference between the two groups did not reach statistical significance (P=0.10), the survival curves showed a clear trend of separation (Figure 6H).

The mPFS of patients with and without baseline lung metastasis was 8.20 and 6.17 months, respectively; however, there was no statistically significant difference between the two groups (Figure 6I). Kaplan-Meier analyses for primary tumor lobe and N stage also showed no statistically significant differences (Figure S4).

Thus, patients aged 51–79 years may represent a potential beneficiary population for IA regimen.

Exploration of beneficiary populations for the IC regimen

The same approach was used to identify the beneficiary populations in the BC and IA groups. The logistic regression analysis indicated that sex, initial diagnosis of stage IV, and the presence of bone metastasis or liver metastasis were associated with survival time (Figure 6J). The χ² tests confirmed that BMI, sex, initial diagnosis of stage IV, and the presence of bone or liver metastasis were significantly associated with survival outcomes (Figure 6K).

Kaplan-Meier curve analyses were also conducted. The results showed that the male patients had an mPFS of 8.50 months, significantly longer than the 4.23 months observed in female patients (P=0.042) (Figure 6L). Stratification by BMI revealed that patients with a BMI >23.9 kg/m² had an mPFS of 9.00 months, significantly exceeding the 6.13 months observed in patients with a BMI of 18.5–23.9 kg/m² (P=0.03) (Figure 6M).

Patients without liver metastasis had an mPFS of 7.87 months, compared with 5.33 months in those with liver metastasis. The survival curves of the two groups showed a clear trend toward separation, but the difference was not statistically significant (P=0.22) (Figure 6N).

The patients initially diagnosed at stage I–III had an mPFS of 9.17 months compared with 6.33 months for stage IV, with the survival curves showing a distinct separation trend (Figure 6O).

In this treatment group, male sex and BMI >23.9 kg/m² were identified as significant predictors of favorable survival outcomes, with these patients achieving longer PFS. Conversely, although patients without liver metastasis and those initially diagnosed at stages I–III showed favorable trends in survival curves, their potential survival benefit requires further validation in a larger sample.

In the Based on mutation subgroup, the distribution characteristics of co-mutation sites after drug resistance was as follows: MET exon 14 skipping mutation was the most common, detected in 8 cases (53.33% of the subgroup), followed by trans-C797S mutations, detected in 5 cases (33.33%). RET translocation and HER-2 mutation were less frequent, with 1 case each (6.67%) (Figure 6P). Due to the relatively limited sample size in this subgroup, a statistical analysis could not be performed to further explore the association between each mutation type and clinical outcomes. The potential prognostic value of these mutations requires further validation in larger cohorts (Figure 6P).

Summary of study findings

In this study, we systematically explored the beneficiary populations of four distinct treatment regimens for lung cancer patients with resistance to third-generation EGFR-TKIs. The key findings are summarized as follows:

• IBC regimen: the beneficiary populations for this regimen included patients aged 51–79 years, those who did not receive combination therapy during third-generation EGFR-TKIs treatment, and those without baseline liver metastasis. Among these, the mPFS of patients without liver metastasis reached 13.23 months, while both patients aged 51–79 years and those without combination therapy had an mPFS of 12.30 months.
• BC regimen: patients without liver metastasis were identified as the beneficiary population for this regimen, with an mPFS of 8.50 months, which was significantly longer than that of patients with liver metastasis.
• IA regimen: patients aged 51–79 years were identified as a potential beneficiary population for this regimen. Although the difference did not reach statistical significance, the survival curves showed a clear separation trend, suggesting further validation in larger cohorts is needed.
• IC regimen: male patients and those with a BMI >23.9 kg/m² were identified as the beneficiary populations for this regimen. The male patients had an mPFS of 8.50 months, and those with a BMI >23.9 kg/m² had an mPFS of 9.00 months, both with statistically significant differences. In addition, patients without liver metastasis and those initially diagnosed at stages I–III exhibited more favorable trends in survival curves (Figure 7).

Figure 7 Summary of beneficiary populations for all treatment regimens [created with BioGDP.com (9)]. BMI, body mass index; EGFR-TKIs, epidermal growth factor receptor-tyrosine kinase inhibitors; ICIs, immune checkpoint inhibitors; mPFS, median progression-free survival.

Discussion

In this study, we found that, following resistance to third-generation EGFR-TKIs, patients treated with the combination IBC regimen experienced significantly longer PFS compared to those treated with other treatment regimens; thus, the IBC regimen demonstrated notably superior efficacy. However, the OS outcomes were less favorable. We subsequently explored the reasons underlying the observed PFS benefit in the absence of a corresponding OS improvement for the IBC regimen. Finally, we identified the beneficiary populations for the IBC, BC, IA, and IC regimens, aiming to guide individualized and precision treatment strategies for these patients.

The clinical application of ICIs has significantly improved the survival outcomes of lung cancer patients. The IMpower150 study reported that the mPFS of the ABCP (atezolizumab plus bevacizumab and chemotherapy) group was significantly longer than that of the BCP (bevacizumab and chemotherapy) group (8.3 vs. 6.8 months, HR 0.61, P<0.001), establishing immunotherapy as a cornerstone in the first-line treatment of advanced lung cancer (10). Nevertheless, while the IMpower150 study validated the clinical efficacy of combination IBC therapy in advanced-stage lung cancer patients receiving first-line treatment, it did not examine the use of ICIs in patients with acquired resistance to EGFR-TKIs. In this context, our study found that the mPFS of patients receiving ICIs after resistance to third-generation EGFR-TKIs reached 9.10 months, which was significantly longer than that of patients not receiving ICIs (mPFS =7.15 months).

Studies have shown that EGFR-mutant NSCLC harbors an immunosuppressive tumor microenvironment (11,12). Regulatory T cells and myeloid-derived suppressor cells, which negatively regulate T cells, express vascular endothelial growth factor (VEGF) receptors, and their inhibitory effect on tumor-specific immune cells are reduced in the presence of VEGF (13,14). Additionally, EGFR-mutant NSCLC cells overexpress VEGF (15), further exacerbating the immunosuppressive state of EGFR-mutant NSCLC. These findings suggest that ICIs and anti-angiogenic agents exert a synergistic therapeutic effect.

The present study showed that the IBC regimen resulted in a significantly longer mPFS than the No ICIs regimens. In addition, the mPFS of the IBC regimen was also significantly longer than that of other immunotherapy-containing regimens. In terms of efficacy, the IBC regimen achieved a DCR of 92.4% and an ORR of 29.11%, exhibiting favorable disease control.

As the line and box plots showed, despite the significant PFS benefit, no significant OS benefit was observed with the IBC regimen in this study. Notably, the substantial contribution of third-generation EGFR-TKIs and other treatments to both PFS duration and OS proportion likely masked the positive impact of the IBC regimen on OS, making it difficult to detect a statistically significant difference in OS.

Thus, we sought to identify the patients most likely to benefit from the combination IBC regimen to enable such patients to receive precision treatment and achieve longer survival benefits after acquiring drug resistance. We found that patients aged 51–79 years, those who did not receive combination therapy during third-generation EGFR-TKIs treatment, and those without baseline liver metastasis had superior PFS outcomes.

Previous research has reported a significant correlation between tumor mutational burden (TMB) and age in patients with solid tumors (16,17). High TMB often correlates with better survival benefits after tumor immunotherapy (18). Research suggests that the combination IBC regimen has poor efficacy in patients with liver metastasis who have developed resistance to third-generation EGFR-TKIs. A phase II clinical trial of atezolizumab plus bevacizumab and chemotherapy in NSCLC patients after TKI failure reported that the mPFS of patients with liver metastasis was approximately 5.4 months, which was significantly shorter than that of patients without liver metastasis (mPFS =7.9 months, P=0.003), with the OS of patients with liver metastasis being 11.8 months shorter than that of the patients without liver metastasis (19). Similarly, our study also found that patients without baseline liver metastasis had better mPFS than those with baseline liver metastasis (13.23 vs. 7.70 months, P=0.01).

In our study, we found that the patients who received combination therapy during third-generation EGFR-TKIs treatment had significantly shorter PFS than those who did not. To explore the underlying reasons, we reviewed the medical records of these patients and found that their tumors grew slowly during third-generation EGFR-TKIs treatment, leading to the treatment of combination therapy with third-generation EGFR-TKIs and other drugs. Previous studies have shown that the mPFS of patients with slow tumor progression treated with anlotinib plus EGFR-TKIs is approximately 10.3 months, which is significantly shorter than the 15.3 months reported in the osimertinib group in the FLAURA study (20,21). These findings suggest that patients who experience slow progression during third-generation EGFR-TKIs treatment and subsequently receive combination therapy may have poor survival outcomes.

Given that factors such as economic constraints and underlying diseases may prevent some patients from receiving combination IBC regimens, we also sought to investigate the beneficiary populations for other treatment regimens. Through multiple validation methods, including logistic regression, χ² tests, and Cox proportional hazards models, we found that the absence of baseline liver metastasis was a favorable prognostic factor for patients treated with the BC regimen. The mPFS of patients without liver metastasis was significantly longer than that of patients with liver metastasis (8.50 vs. 4.07 months). Further, the risk of disease progression in patients with liver metastasis was 2.442-fold higher than that in patients without liver metastasis (P=0.03). By reviewing relevant literature to explore the underlying mechanism, we hypothesized that liver metastases often have more complex vascular networks and microenvironmental disorders, which may reduce the therapeutic effect of bevacizumab, resulting in limited survival benefits (22,23).

In relation to the IA treatment regimen, we found that patients aged 51–79 years were most likely to benefit from the treatment. Consistent with the results of the IBC beneficiary population, patients aged 51–79 years may have higher TMB levels, increasing the efficacy of ICIs (18).

In relation to the IC regimen, we found that male sex and a BMI >23.9 kg/m² were significant predictors for favorable survival outcomes. Notably, the mPFS of the male patients was significantly longer than that of the female patients (8.50 vs. 4.23 months, P=0.04). While the mPFS of patients with a BMI >23.9 kg/m² was significantly longer than that of patients with a BMI of 18.5–23.9 kg/m² (9.00 vs. 6.13 months, P=0.03).

The impact of sex differences on immunotherapy efficacy may be attributed to the high infiltration of natural killer (NK) cells in the tumor microenvironment of male patients. These NK cells maintain robust cytotoxic function and more coordinated cellular programs, which may increase the efficacy of immunotherapy (24).

As an important indicator of nutritional status, a BMI >23.9 kg/m² may reflect stronger immune function and tissue repair capacity. Such patients may be better able to tolerate the adverse reactions caused by IC and better able to maintain treatment continuity (25,26). In addition, research suggests impaired immune editing in the tumor microenvironment of obese animal models may enhance tumor immunogenicity, increasing the benefits of ICI treatment (27).

This study highlighted the potential contribution of immunotherapy in patients with resistance to third-generation EGFR-TKIs; however, it still had several limitations. This study found that some patients who did not initially receive ICIs later switched to immunotherapy, and their OS was subsequently prolonged. However, we did not collect data on subsequent immunotherapy regimens and their therapeutic efficacy. Thus, the survival status of the patients who subsequently switched to immunotherapy and the efficacy of immunotherapy need to be further investigated. Moreover, we found that some patients who developed resistance to immunotherapy underwent immunotherapy re-challenge. The rationality of subsequent immunotherapy re-challenge in patients with acquired immune resistance needs to be further investigated. In clinical practice, the IC and IA regimens are not listed as preferred regimens for patients with resistance to third-generation EGFR-TKIs, resulting in a relatively small number of patients in these two groups and a limited sample size. Some subgroup analyses did not reach statistical significance, which may affect the reliability of the results. This study was a retrospective analysis, which may be subject to selection bias and information loss. Future studies should conduct large-sample, prospective research, integrate patients’ baseline clinical characteristics and molecular biomarkers to construct multi-dimensional prediction models, and further accurately identify the patients most likely to benefit from each treatment regimen. Meanwhile, in-depth exploration of the molecular mechanisms by which different baseline characteristics affect treatment efficacy will provide theoretical support for the development of individualized treatment strategies.

Conclusions

In conclusion, this study compared the PFS of various post-resistance treatment regimens following third-generation EGFR-TKIs treatment, and found that IBC represented the optimal regimen for patients with acquired resistance to third-generation EGFR-TKIs. To improve survival benefits and enable the implementation of precision treatment, we also identified the patients most likely to benefit from the IBC regimen, as well as those most likely to benefit from the BC, IA, and IC regimens.

Acknowledgments

The investigators thank Prof. Xiaohua Wang for her assistance in the preparation and review of the manuscript.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0500/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0500/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0500/prf

Funding: The study received funding from the 2023 Science and Technology Development Fund of the Jiangsu Cancer Hospital (No. ZL202302) and Qunfeng Project of Jiangsu Cancer Hospital (No. GFXK202501).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0500/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 Jiangsu Cancer Hospital (No. KY-2025-013) and informed consent was obtained from all the patients.

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. Molina JR, Yang P, Cassivi SD, et al. Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc 2008;83:584-94. [Crossref] [PubMed]
2. Chen P, Liu Y, Wen Y, et al. Non-small cell lung cancer in China. Cancer Commun (Lond) 2022;42:937-70. [Crossref] [PubMed]
3. Siegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025. CA Cancer J Clin 2025;75:10-45. [Crossref] [PubMed]
4. Shi Y, Au JS, Thongprasert S, 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;9:154-62. [Crossref] [PubMed]
5. Bethune G, Bethune D, Ridgway N, et al. Epidermal growth factor receptor (EGFR) in lung cancer: an overview and update. J Thorac Dis 2010;2:48-51.
6. Lu S, Wu L, Jian H, et al. Sintilimab plus chemotherapy for patients with EGFR-mutated non-squamous non-small-cell lung cancer with disease progression after EGFR tyrosine-kinase inhibitor therapy (ORIENT-31): second interim analysis from a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Respir Med 2023;11:624-36. [Crossref] [PubMed]
7. Socinski MA, Nishio M, Jotte RM, et al. IMpower150 Final Overall Survival Analyses for Atezolizumab Plus Bevacizumab and Chemotherapy in First-Line Metastatic Nonsquamous NSCLC. J Thorac Oncol 2021;16:1909-24. [Crossref] [PubMed]
8. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45:228-47. [Crossref] [PubMed]
9. Jiang S, Li H, Zhang L, et al. Generic Diagramming Platform (GDP): a comprehensive database of high-quality biomedical graphics. Nucleic Acids Res 2025;53:D1670-6. [Crossref] [PubMed]
10. Socinski MA, Jotte RM, Cappuzzo F, et al. Atezolizumab for First-Line Treatment of Metastatic Nonsquamous NSCLC. N Engl J Med 2018;378:2288-301. [Crossref] [PubMed]
11. Madeddu C, Donisi C, Liscia N, et al. EGFR-Mutated Non-Small Cell Lung Cancer and Resistance to Immunotherapy: Role of the Tumor Microenvironment. Int J Mol Sci 2022;23:6489. [Crossref] [PubMed]
12. Hu LY, Zhuang WT, Chen MJ, et al. EGFR Oncogenic Mutations in NSCLC Impair Macrophage Phagocytosis and Mediate Innate Immune Evasion Through Up-Regulation of CD47. J Thorac Oncol 2024;19:1186-200. [Crossref] [PubMed]
13. Benmebarek MR, Oguz C, Seifert M, et al. Anti-vascular endothelial growth factor treatment potentiates immune checkpoint blockade through a BAFF- and IL-12-dependent reprogramming of the TME. Immunity 2025;58:926-945.e10. [Crossref] [PubMed]
14. Xiong Z, Chan SL, Zhou J, et al. Targeting PPAR-gamma counteracts tumour adaptation to immune-checkpoint blockade in hepatocellular carcinoma. Gut 2023;72:1758-73. [Crossref] [PubMed]
15. Nilsson MB, Robichaux J, Herynk MH, et al. Altered Regulation of HIF-1α in Naive- and Drug-Resistant EGFR-Mutant NSCLC: Implications for a Vascular Endothelial Growth Factor-Dependent Phenotype. J Thorac Oncol 2021;16:439-51. [Crossref] [PubMed]
16. Yu Z, Wang J, Feng L, et al. Association of tumor mutational burden with age in solid tumors. J Clin Oncol 2020;38:e13590.
17. Li J, Pan Y, Guo F, et al. Patterns in genomic mutations among patients with early-onset colorectal cancer: an international, multicohort, observational study. Lancet Oncol 2025;26:1055-66. [Crossref] [PubMed]
18. Gandara DR, Agarwal N, Gupta S, et al. Tumor mutational burden and survival on immune checkpoint inhibition in >8000 patients across 24 cancer types. J Immunother Cancer 2025;13:e010311. Erratum in: J Immunother Cancer 2025;13:e010311corr1. [Crossref] [PubMed]
19. Watanabe S, Furuya N, Nakamura A, et al. A phase II study of atezolizumab with bevacizumab, carboplatin, and paclitaxel for patients with EGFR-mutated NSCLC after TKI treatment failure (NEJ043 study). Eur J Cancer 2024;197:113469. [Crossref] [PubMed]
20. Planchard D, Jänne PA, Cheng Y, et al. Osimertinib with or without Chemotherapy in EGFR-Mutated Advanced NSCLC. N Engl J Med 2023;389:1935-48. [Crossref] [PubMed]
21. Chen HJ, Tu HY, Hu Y, et al. A phase II trial of anlotinib plus EGFR-TKIs in advanced non-small cell lung cancer with gradual, oligo, or potential progression after EGFR-TKIs treatment (CTONG-1803/ALTER-L001). J Hematol Oncol 2025;18:3. [Crossref] [PubMed]
22. Zheng Y, Zhou R, Cai J, et al. Matrix Stiffness Triggers Lipid Metabolic Cross-talk between Tumor and Stromal Cells to Mediate Bevacizumab Resistance in Colorectal Cancer Liver Metastases. Cancer Res 2023;83:3577-92. [Crossref] [PubMed]
23. Qi M, Fan S, Huang M, et al. Targeting FAPα-expressing hepatic stellate cells overcomes resistance to antiangiogenics in colorectal cancer liver metastasis models. J Clin Invest 2022;132:e157399. [Crossref] [PubMed]
24. Zhang L, Liu Z, Ding Y, et al. Natural killer cell infiltration elicits gender-dependent dichotomous clinical outcomes in urothelial carcinoma. Cancer 2025;131:e70196. [Crossref] [PubMed]
25. Chen T, Zhai W, Song J, et al. Obesity Paradox in Cancer: A Complex Interplay Between Risk and Therapeutic Outcomes. MedComm (2020) 2025;6:e70494.
26. Zhang T, Li S, Chang J, et al. Impact of BMI on the survival outcomes of non-small cell lung cancer patients treated with immune checkpoint inhibitors: a meta-analysis. BMC Cancer 2023;23:1023. [Crossref] [PubMed]
27. Piening A, Ebert E, Gottlieb C, et al. Obesity-related T cell dysfunction impairs immunosurveillance and increases cancer risk. Nat Commun 2024;15:2835. [Crossref] [PubMed]