Abstract
Background
Immune checkpoint inhibitors (ICI) have revolutionized cancer therapy; however, immune-related adverse events (irAEs) remain a critical concern. Proton pump inhibitors (PPIs) are frequently co-administered to patients with advanced lung cancer and PPI-induced alterations in the gut microbiota may impair immune responses, potentially affecting ICI efficacy and prognosis. However, the association between PPI use and irAE development remains unclear.
Methods
We retrospectively analyzed 228 patients with advanced non-small cell lung cancer who received first-line ICI therapy between April 2017 and December 2024. The impact of baseline PPI use on the incidence of irAEs, classified as checkpoint inhibitor pneumonitis (CIP) or irAEs without CIP (non-CIP irAEs), was evaluated.
Results
Multivariate logistic regression analysis showed a non-significant association between PPI use and the incidence of overall irAEs. (odds ratio [OR] = 0.625; 95% confidence interval [CI], 0.348–1.120; p = 0.117). Notably, PPI exposure was significantly associated with a reduced incidence of non-CIP irAEs (OR = 0.510; 95% CI, 0.274–0.947; p = 0.033), whereas no significant association was observed with the incidence of CIP. Median overall survival (OS) was shorter in the PPI-exposed group than in the PPI-unexposed group, but the difference was not statistically significant (16.7 vs. 24.7 months; p = 0.065). In multivariate Cox regression analysis, PPI exposure was not identified as an independent prognostic factor for OS (hazard ratio [HR] = 1.120; 95% CI, 0.768–1.634; p = 0.556). In contrast, the occurrence of non-CIP irAEs (HR = 0.574; 95% CI, 0.368–0.895; p = 0.014) was significantly associated with improved OS.
Conclusions
PPI use was potentially associated with a lower incidence of non-CIP irAEs, whereas no significant effect was observed for CIP. In contrast, PPI use was not an independent prognostic factor, suggesting that irAE occurrence is influenced by multiple factors beyond PPI use. This study highlights the importance of investigating the interplay between PPI exposure, irAE occurrence, and gut microbiome alterations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12885-026-15646-7.
Keywords: Immune checkpoint inhibitors, Proton pump inhibitors, Immune-related adverse events, Non-small cell lung cancer
Background
Immune checkpoint inhibitors (ICIs) have become a cornerstone in the treatment of various solid tumors, including lung cancer, profoundly transforming therapeutic strategies over the past decade. Combination therapy with ICIs and cytotoxic chemotherapy is now widely established as the standard first-line treatment for advanced lung cancer. However, due to their mechanism of enhancing immune activation, ICIs can also induce immune-related adverse events (irAEs).
Patients with advanced lung cancer frequently receive multiple concomitant medications to manage comorbidities and tumor-related symptoms. Among these, proton pump inhibitors (PPIs) are particularly common, with reports indicating that over one-fourth of patients with cancer are prescribed PPIs [1]. PPIs may disrupt the gut microbiota, reduce beneficial commensal bacteria, and facilitate colonization by oral bacteria by increasing gastric pH [2]. Certain commensal gut bacteria, such as Faecalibacterium and members of the Ruminococcaceae family, promote the proliferation of CD4⁺ and CD8⁺ cytotoxic T lymphocytes, thereby enhancing antitumor immunity [3, 4]. A reduction in these bacterial populations may lead to decreased immune cell activity, resulting in diminished treatment responsiveness and poorer prognosis in patients with cancer. These alterations in gut microbiota impair immune regulation and are associated with unfavorable clinical outcomes in patients with solid tumors receiving ICI therapy [5–7].
Although the mechanisms underlying irAEs development remain incompletely understood, they are believed to involve immune-mediated tissue injury caused by activated T cells and other immune cells through the release of cytokines and chemokines [8, 9]. PPI-induced reductions in the gut microbiota diversity may attenuate immune cell activation and consequently decrease the incidence of irAEs. Although irAEs can be life-threatening, their occurrence has been associated with tumor regression and improved survival outcomes [10, 11]. However, the current evidence regarding the relationship between PPI use and irAE development remains limited.
In this study, we investigated the association between baseline PPI use and the incidence and subtypes of irAEs in patients with advanced non-small cell lung cancer (NSCLC) receiving first-line ICI therapy. Additionally, we assessed the impact of PPI use and irAE occurrence on clinical outcomes and prognosis.
Methods
Study population and design
This retrospective, single-center observational study was conducted at Nihon University Itabashi Hospital (Tokyo, Japan) between April 2017 and December 2024. We reviewed the medical records of all patients with advanced or recurrent NSCLC who received either ICI monotherapy or ICI plus chemotherapy as first-line treatment. The data cutoff date was June 30, 2025.
Patients were excluded if they received ICI therapy as second-line or later treatment, concurrent chemoradiotherapy followed by durvalumab maintenance, perioperative ICI therapy, molecular targeted therapy as first-line treatment for driver gene mutations, if concomitant medications at ICI initiation were unknown, or if they had pre-existing interstitial lung disease (ILD) before ICI initiation.
This study was approved by the Ethics Committee of Nihon University School of Medicine Itabashi Hospital (approval number RK-180911-1). This study was conducted as an ancillary study on biomarkers of ICIs, and written informed consent was obtained from all the participants. All patients with NSCLC who were receiving PPIs at ICI initiation were classified as the “PPI-exposed group” whereas those not receiving a PPI at that time were classified as the “PPI-unexposed group,” consistent with a previous study [12].
Data collection
We collected patient information including PPI type, age, sex, histology, Programmed cell death-ligand 1 (PD-L1) expression, Eastern Cooperative Oncology Group Performance Status (ECOG-PS), TNM stage, gene mutation status, irAE onset, and treatment patterns at ICI initiation. PD-L1 tumor proportion scores (TPS) were categorized into four groups: ≥ 50%, 1–49%, < 1%, and unknown. The neutrophil-to-lymphocyte ratio (NLR) was obtained from pretreatment peripheral venous blood tests performed 7 days before ICI treatment initiation. Treatment response was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 [13]. Patients without evaluable treatment response according to the RECIST were classified as not evaluable (NE).
An irAE was defined as an adverse event occurring from ICI initiation to the data cutoff date and was judged by the attending physician to be causally related to ICI therapy. Adverse events attributed to cytotoxic anticancer drugs were excluded. When clinical manifestations were similar, the diagnosis was made considering the timing of onset, clinical course after concomitant use or discontinuation of cytotoxic agents, and response to supportive therapy. Although irAEs in general have been associated with tumor regression and improved survival outcomes, immune checkpoint inhibitor-related pneumonitis (CIP) is associated with poor prognosis [14, 15]. Therefore, in this study, irAEs were classified as either “CIP” or “irAEs without CIP (non-CIP irAEs). The Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 was used to grade irAE severity.
Statistical analysis
The associations between categorical variables were evaluated using the chi-square or Fisher’s exact tests. Cutoff values based on previous reports were set as follows: ECOG PS ≥ 2, NLR < 3, and PD-L1 TPS ≥ 50% [16–18]. Factors associated with the occurrence of irAEs were analyzed using multivariate logistic regression, and odds ratios (OR) with 95% confidence intervals (95% CI) were calculated. Progression-free survival (PFS) was defined as the time from the initial treatment until documented disease progression or death. Overall survival (OS) was defined as the time from treatment initiation to death from any cause. PFS and OS were evaluated using the Kaplan–Meier method, and between-group comparisons were performed using the log-rank test. Multivariate Cox proportional hazards regression was performed to identify variables potentially associated with PFS and OS, and hazard ratios (HR) with corresponding 95% CI were calculated. The immortal time bias was considered using a landmark analysis at 6 months after treatment initiation. In the Cox proportional hazards analysis, only irAEs that occurred before the landmark were considered. All statistical analyses were performed using EZR version 1.61 (Saitama Medical Center, Jichi Medical University, Saitama, Japan [19]. A p-value < 0.05 was considered statistically significant.
Results
Patient characteristics
Of 453 patients with advanced NSCLC who received ICI therapy at our institution between 2017 and 2024, 228 met the eligibility criteria and were included in the analysis. Among them, 81 (35.5%) and 147 (64.5%) patients were assigned to the PPI-exposed and PPI-unexposed groups, respectively (Fig. 1). The PPI used in this study were esomeprazole, omeprazole, vonoprazan, lansoprazole, and rabeprazole (Supplementary Fig. 1). The baseline characteristics according to PPI exposure are summarized in Table 1. The combination of anti-programmed cell death protein-1 (PD-1)/PD-L1 antibodies plus anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies and chemotherapy tended to be more frequently administered in the PPI-unexposed group, which also had a significantly higher proportion of patients with NLR < 3. The overall incidence of irAEs was significantly higher in the PPI-unexposed group than in the PPI-exposed group (55.1% vs. 39.5%; p = 0.034), which was mainly attributable to non-CIP irAEs (45.6% vs. 27.2%; p = 0.010). In contrast, the incidence of CIP did not differ significantly between the two groups (9.5% vs. 12.3%; p = 0.661).
Fig. 1.
Flow diagram of the patient exclusion procedure in this study
Table 1.
Baseline characteristics of patients according to proton pump inhibitors exposure
| Characteristics | PPI-exposed, (%) | PPI-unexposed, (%) | p value |
|---|---|---|---|
| Number of cases | 81 | 147 | |
| Age, years | 0.168 | ||
| Median (range) | 73 (41–89) | 70 (36–96) | |
| Sex | 0.760 | ||
| Male | 65 (80.2) | 114 (77.6) | |
| Female | 16 (19.8) | 33 (22.4) | |
| Histology | 0.140 | ||
| Adenocarcinoma | 55 (67.9) | 94 (63.9) | |
| Squamous cell carcinoma | 24 (29.6) | 39 (26.5) | |
| Others | 2 (2.5) | 14 (9.5) | |
| PD-L1 expression | 0.561 | ||
| ≥ 50% | 34 (42.0) | 48 (32.7) | |
| 1–49% | 22 (27.2) | 44 (29.9) | |
| < 1% | 16 (19.8) | 36 (24.5) | |
| Unknown | 9 (11.1) | 19 (12.9) | |
| Performance status | 0.304 | ||
| 0–1 | 65 (80.2) | 127 (86.4) | |
| ≥ 2 | 16 (19.8) | 20 (13.6) | |
| Treatment pattern | 0.079 | ||
| Anti-PD-1 | 23 (28.4) | 27 (18.4) | |
| Anti-PD-1 + anti-CTLA-4 | 2 (2.5) | 2 (1.4) | |
| Anti-PD-1/PD-L1 + anti-CTLA-4 + chemotherapy | 6 (7.4) | 25 (17.0) | |
| Anti PD-1/PD-L1 + chemotherapy | 50 (61.7) | 93 (63.3) | |
| Pulmonary complications | 0.779 | ||
| COPD/pulmonary emphysema | 32 (39.5) | 62 (42.2) | |
| None | 49 (60.5) | 85 (57.8) | |
| Neutrophil-to-lymphocyte ratio | 0.015 | ||
| < 3 | 19 (23.5) | 59 (40.1) | |
| 3 ≤ | 62 (76.5) | 87 (59.2) | |
| Unknown | 0 (0.0) | 1 (0.7) | |
| Best overall response | 0.648 | ||
| CR | 2 (2.5) | 1 (0.7) | |
| PR | 32 (39.5) | 57 (38.8) | |
| SD | 29 (35.8) | 58 (39.5) | |
| PD | 13 (16.0) | 20 (13.6) | |
| NE | 5 (6.2) | 11 (7.5) | |
| IrAE occurrence | 0.034 | ||
| (+) | 32 (39.5) | 81 (55.1) | |
| (-) | 49 (60.5) | 66 (44.9) | |
| Checkpoint inhibitor-related pneumonitis | 0.661 | ||
| (+) | 10 (12.3) | 14 (9.5) | |
| (-) | 71 (87.7) | 133 (90.5) | |
| Non-CIP irAEs | 0.010 | ||
| (+) | 22 (27.2) | 67 (45.6) | |
| (-) | 59 (72.8) | 80 (54.4) | |
Abbreviations: PPI Proton pump inhibitors, PD-L1 Programmed cell death-ligand 1, PD-1 Programmed cell death protein-1, CTLA-4 cytotoxic T-lymphocyte-associated protein 4, COPD Chronic obstructive pulmonary disease, CR Complete response, PR Partial response, SD Stable disease, PD Progressive disease, NE Not evaluable, irAEs immune-related adverse events, CIP Checkpoint inhibitor pneumonitis
Table 2 summarizes the baseline characteristics stratified by irAE type. A higher proportion of patients with ECOG-PS ≥ 2 was observed in the no irAEs group, whereas objective response rates were higher among patients who developed irAEs, including CIP.
Table 2.
Baseline characteristics of patients according to irae type
| Characteristics | Non-CIP irAEs, (%) | CIP, (%) | No-irAEs, (%) | p value |
|---|---|---|---|---|
| Number of cases | 89 | 24 | 115 | |
| Age, years | 0.025 | |||
| Median (range) | 69 (36–87) | 73.5 (47–86) | 72 (40–96) | |
| Sex | 0.269 | |||
| Male | 65 (73.0) | 20 (83.3) | 94 (81.7) | |
| Female | 24 (27.0) | 4 (16.7) | 21 (18.3) | |
| Histology | 0.090 | |||
| Adenocarcinoma | 57 (64.0) | 21 (87.5) | 71 (61.7) | |
| Squamous cell carcinoma | 23 (25.8) | 3 (12.5) | 37 (32.2) | |
| Others | 9 (10.1) | 0 (0.0) | 7 (6.1) | |
| PD-L1 expression | 0.746 | |||
| ≥ 50% | 33 (37.1) | 6 (25.0) | 43 (37.4) | |
| 1–49% | 23 (25.8) | 7 (29.2) | 36 (31.3) | |
| < 1% | 20 (22.5) | 8 (33.3) | 24 (20.9) | |
| Unknown | 13 (14.6) | 3 (12.5) | 12 (10.4) | |
| Performance status | 0.014 | |||
| 0–1 | 80 (89.9) | 23 (95.8) | 89 (77.4) | |
| ≥ 2 | 9 (10.1) | 1 (4.2) | 26 (22.6) | |
| Treatment pattern | 0.161 | |||
| Anti-PD-1 | 16 (18.0) | 6 (25.0) | 28 (24.3) | |
| Anti-PD-1 + anti-CTLA-4 | 1 (1.1) | 1 (4.2) | 2 (1.7) | |
| Anti-PD-1/PD-L1 + anti-CTLA-4 + chemotherapy | 19 (21.3) | 3 (12.5) | 9 (7.8) | |
| Anti PD-1/PD-L1 + chemotherapy | 53 (59.6) | 14 (58.3) | 76 (66.1) | |
| Pulmonary complications | 0.882 | |||
| COPD/pulmonary emphysema | 36 (40.4) | 9 (37.5) | 49 (42.6) | |
| None | 53 (59.6) | 15 (62.5) | 66 (57.4) | |
| Neutrophil-to-lymphocyte ratio | 0.133 | |||
| < 3 | 36 (40.4) | 10 (41.7) | 32 (27.8) | |
| 3 ≤ | 53 (59.6) | 14 (58.3) | 82 (71.3) | |
| Unknown | 0 (0.0) | 0 (0.0) | ༑(0.9) | |
| Best overall response | < 0.00001 | |||
| CR | 3 (3.4) | 0 (0.0) | 0 (0.0) | |
| PR | 47 (52.8) | 11 (45.8) | 31 (27.0) | |
| SD | 31 (34.8) | 13 (54.2) | 43 (37.4) | |
| PD | 5 (5.6) | 0 (0.0) | 28 (24.3) | |
| NE | 3 (3.4) | 0 (0.0) | 13 (11.3) | |
Abbreviations: CIP Checkpoint inhibitor pneumonitis, irAEs Immune-related adverse events, PD-L1 Programmed cell death-ligand 1, PD-1 Programmed cell death protein-1, CTLA-4 cytotoxic T-lymphocyte-associated protein 4, COPD Chronic obstructive pulmonary disease, CR Complete response, PR Partial response, SD Stable disease, PD Progressive disease, NE Not evaluable
Multivariate analysis of factors associated with irae occurrence
Table 3 summarizes the results of multivariate analysis of factors associated with the occurrence of irAEs. For overall irAEs, PPI exposure was not significantly associated with the incidence of irAEs (OR, 0.625; 95% CI, 0.348–1.120; p = 0.117). ECOG-PS ≥ 2 was significantly associated with a lower incidence of irAEs, whereas the combination of anti-PD-1/PD-L1 and anti-CTLA-4 antibodies and chemotherapy was significantly associated with higher incidence.
Table 3.
Multivariate logistic regression analysis of factors associated with the occurrence of IrAEs and non-CIP IrAEs
| Characteristics | Overall irAEs | non-CIP irAEs | ||
|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | |
| Age | ||||
| < 75 | ||||
| ≥ 75 | 0.617 (0.344–1.110) | 0.105 | 0.597 (0.324–1.100.324.100) | 0.100 |
| Sex | ||||
| Male | 0.676 (0.338–1.350) | 0.269 | 0.563 (0.280–1.130) | 0.107 |
| Female | ||||
| Histology | ||||
| Adenocarcinoma | 1.300 (0.710–2.370) | 0.398 | 0.815 (0.441–1.510) | 0.515 |
| Non-Adenocarcinoma | ||||
| PD-L1 expression | ||||
| ≥ 50% | 1.200 (0.659–2.200.659.200) | 0.547 | 1.650 (0.880–3.100) | 0.118 |
| Others | ||||
| Performance status | ||||
| 0–1 | ||||
| ≥ 2 | 0.347 (0.150–0.805) | 0.014 | 0.449 (0.188–1.070) | 0.072 |
| Treatment pattern | ||||
| Anti-PD-1/PD-L1 + anti-CTLA-4 + chemotherapy | 2.500 (1.040–6.050) | 0.041 | 2.760 (1.180–6.460) | 0.019 |
| Others | ||||
| Pulmonary complications | ||||
| COPD/pulmonary emphysema | 1.170 (0.644–2.110) | 0.612 | 1.140 (0.615–2.100.615.100) | 0.684 |
| None | ||||
| Neutrophil-to-lymphocyte ratio | ||||
| < 3 | 1.300 (0.712–2.380) | 0.392 | 1.240 (0.671–2.290) | 0.491 |
| 3 ≤ | ||||
| PPI exposure | ||||
| (+) | 0.625 (0.348–1.120) | 0.117 | 0.510 (0.274–0.947) | 0.033 |
| (-) | ||||
Abbreviations: irAEs Immune-related adverse events, CIP Checkpoint inhibitor pneumonitis, OR Odds ratio, CI Confidence interval, PD-L1 Programmed cell death-ligand 1, PD-1 Programmed cell death protein-1, CTLA-4 Cytotoxic T-lymphocyte-associated protein 4, COPD Chronic obstructive pulmonary disease, PPI Proton pump inhibitors
PPI exposure was significantly associated with a reduced incidence of non-CIP irAEs (OR, 0.510; 95% CI, 0.274–0.947; p = 0.033). In a separate analysis adjusted for factors known to influence CIP development, such as pembrolizumab use and chronic obstructive pulmonary disease/emphysema [20–23], PPI exposure was not significantly associated with CIP (OR, 1.290; 95% CI, 0.543–3.070; p = 0.563; Supplementary Table 1).
Type of IrAEs by PPI exposure
The identified irAEs included adrenal insufficiency, thyroid dysfunction, diabetes mellitus, colitis, hepatitis, pancreatitis, skin-related reactions, pneumonitis, nephritis, arthritis, cytokine release syndrome, myocarditis, and encephalitis (Fig. 2). Skin-related reactions were the most frequent irAEs (41 patients, 18.0%), followed by pneumonitis (24 patients, 10.5%). Pneumonitis was the most common irAE in the PPI-exposed group (10 cases, 12.3%), whereas skin-related reactions were the most frequent in the PPI-unexposed group (32 cases, 21.8%). A higher incidence of multiple irAEs was observed in the PPI-unexposed group than in the PPI-exposed group, with skin-related reactions occurring significantly more frequently (21.8% vs. 11.1%, p = 0.049). Excluding rare irAEs such as pancreatitis, arthritis, and myocarditis, pneumonitis was more frequent in the PPI-exposed group than in the PPI-unexposed group. In contrast, no significant difference was observed in the incidence of grade ≥ 3 pneumonitis between the groups (PPI-exposed group: 4 cases [4.9%]; PPI-unexposed group: 6 cases [4.3%]; p = 0.746).
Fig. 2.
Distribution of immune-related adverse events according to proton pump inhibitors exposure
Clinical outcomes
The median follow-up period for all patients was 34.4 months (95% CI: 27.9–40.6 months). At the data cutoff, 127 patients died, 68 survived, and 33 were lost to follow-up. The median PFS did not differ significantly between the PPI-exposed and PPI-unexposed groups (median PFS: 8.2 vs. 8.6 months; p = 0.586; Fig. 3a). The PPI-exposed group had a shorter median OS than the PPI-unexposed group, although the difference was not statistically significant (median OS: 16.7 vs. 24.7 months; p = 0.065; Fig. 3b). When stratified by the presence of irAEs, both patients who developed non-CIP irAEs and those who developed CIP had significantly longer PFS than those without irAEs (non-CIP irAEs vs. no irAEs: 12.5 vs. 5.8 months, p < 0.000002; CIP vs. no irAEs: 10.1 vs. 5.8 months, p = 0.006; Fig. 3c). In contrast, no significant difference was observed in PFS between patients with non-CIP irAEs and those with CIP (12.5 vs. 10.1 months, p = 0.881; Fig. 3c). Similarly, OS was significantly longer in patients who developed non-CIP irAEs and CIP than in those without irAEs (non-CIP irAEs vs. no irAEs: 34.4 vs. 11.5 months, p < 0.000000004; CIP vs. no irAEs: 40.8 vs. 11.5 months, p = 0.002; Fig. 3d). However, no significant difference in OS was observed between patients with non-CIP irAEs and those with CIP (34.4 vs. 40.8 months, p = 0.796; Fig. 3d). In the multivariate analysis adjusted for patient characteristics, ECOG-PS ≥ 2 was significantly associated with poorer PFS and OS. In contrast, adenocarcinoma, PD-L1 expression ≥ 50%, NLR < 3, and non-CIP irAEs were significantly associated with better OS (Table 4). Notably, PPI exposure was not identified as an independent factor associated with either PFS or OS.
Fig. 3.
Kaplan–Meier curves for (a) progression-free survival according to proton pump inhibitor exposure, (b) overall survival according to proton pump inhibitor exposure, (c) progression-free survival according to irAE status, and (d) overall survival according to irAE status
Table 4.
Multivariate Cox proportional hazards analysis of factors associated with progression-free survival and overall survival
| Characteristics | Progression-free survival | Overall survival | ||
|---|---|---|---|---|
| HR (95% CI) | p value | HR (95% CI) | p value | |
| Age | ||||
| < 75 | ||||
| ≥ 75 | 1.026 (0.737–1.427) | 0.881 | 1.140 (0.775–1.675) | 0.506 |
| Sex | ||||
| Male | 1.209 (0.804–1.816) | 0.362 | 1.511 (0.925–2.470) | 0.099 |
| Female | ||||
| Histology | ||||
| Adenocarcinoma | 0.866 (0.622–1.207) | 0.397 | 0.553 (0.381–0.802) | 0.002 |
| Non-Adenocarcinoma | ||||
| PD-L1 expression | ||||
| ≥ 50% | 0.706 (0.492–1.013) | 0.058 | 0.650 (0.427–0.989) | 0.044 |
| Others | ||||
| Performance status | ||||
| 0–1 | ||||
| ≥ 2 | 2.815 (1.791–4.426) | < 0.000008 | 3.997 (2.419–6.604) | < 0.00000007 |
| Treatment pattern | ||||
| Anti-PD-1/PD-L1 + anti-CTLA-4 + chemotherapy | 1.051 (0.653–1.692) | 0.837 | 1.160 (0.665–2.023) | 0.602 |
| Others | ||||
| Pulmonary complications | ||||
| COPD/pulmonary emphysema | 1.092 (0.782–1.526) | 0.605 | 1.400 (0.952–2.060) | 0.087 |
| None | ||||
| Neutrophil-to-lymphocyte ratio | ||||
| < 3 | 0.780 (0.554–1.098) | 0.154 | 0.612 (0.404–0.925) | 0.020 |
| 3 ≤ | ||||
| Checkpoint inhibitor-related pneumonitis | ||||
| (+) | 1.030 (0.844–1.254) | 0.781 | 1.020 (0.813–1.280) | 0.862 |
| (-) | ||||
| Non-CIP irAEs | ||||
| (+) | 0.777 (0.544–1.109) | 0.165 | 0.574 (0.368–0.895) | 0.014 |
| (-) | ||||
| PPI exposure | ||||
| (+) | 0.826 (0.587–1.163) | 0.273 | 1.120 (0.768–1.634) | 0.556 |
| (-) | ||||
Abbreviations: HR Hazard ratio, CI Confidence interval, PD-L1 Programmed cell death-ligand 1, PD-1 Programmed cell death protein-1, CTLA-4 Cytotoxic T-lymphocyte-associated protein 4, COPD Chronic obstructive pulmonary disease, irAEs Immune-related adverse events, CIP Checkpoint inhibitor pneumonitis, PPI Proton pump inhibitors
Discussion
This retrospective study evaluated the effects of baseline PPI exposure on irAE development and the clinical outcomes in patients with NSCLC treated with ICI. PPI exposure was associated with a reduced incidence of non-CIP irAEs, whereas no significant difference was observed in the incidence of CIP. Additionally, patients who developed either CIP or non-CIP irAEs had significantly longer survival than those without irAEs; however, PPI exposure was not an independent prognostic factor. Given the limited evidence regarding the association between PPI use and irAE development in lung cancer, these findings provide clinically relevant insights into the influence of widely used medications on irAE occurrence.
Growing attention has focused on the effects of concomitant medications on the efficacy of ICI. Both PPI and antibiotics can alter the gut microbiota and may impair antitumor immunity [24–26]. Particularly, PPIs warrant attention because of their widespread and often prolonged use. Previous studies have suggested that PPI may interfere with ICI efficacy to a greater extent than antibiotics [27]. Long-term or inappropriate PPI use may also increase the risk of adverse events through drug–drug interactions and could compromise the effectiveness of subsequent therapies [28, 29]. Nara et al. conducted a multicancer study where PPI use was associated with reduced response rates, poor survival, and a potential decrease in irAE incidence [30]. However, as multivariate adjustment was not performed, the observed association between PPI use and irAE incidence should be interpreted with caution.
In this study, multivariate analysis revealed that PPI exposure was significantly associated with a lower incidence of non-CIP irAEs. This finding suggests that the PPI-related suppression of immune activation may contribute to fewer irAEs. Significant differences were observed in the incidences of skin-related reactions. Although previous studies reported a higher incidence of colitis and nephritis among patients receiving PPI [30–33], no such differences were observed in the present study. Consistent with earlier findings [30], PPI exposure was not significantly associated with CIP. Notably, CIP incidence was higher in the PPI-exposed group than in the unexposed group. PPI use has been linked to an increased risk of bacterial-induced pneumonia [34–36], and infection has been proposed as a risk factor for irAE development [37, 38]. PPI-associated immune modulation may not be sufficient to reduce CIP occurrence in the context of infection-related risk, and this possibility warrants further investigation. Additionally, in this study, ECOG-PS ≥ 2 was significantly associated with a lower incidence of irAEs. Although patients with poor performance status are generally reported to have a higher incidence of irAEs [39], patients with ECOG-PS ≥ 2 in our study had a markedly poor median OS of 3.9 months. Therefore, it is possible that many of these patients discontinued treatment or died before irAE development.
In this study, the occurrence of irAEs was significantly associated with better survival outcomes, which is consistent with previous reports [10, 11]. Therefore, we hypothesized that PPIs, which decrease irAE development, may worsen the clinical outcomes in patients receiving ICI therapy. Although the PPI-exposed group tended to have a shorter median OS, PPI exposure was not identified as an independent prognostic factor in the multivariate analysis. These findings may be explained by the fact that the etiology underlying the occurrence of irAEs is multifactorial and not limited to PPI exposure alone. In addition, in the landmark analysis, non-CIP irAEs were associated with improved survival, and CIP was not a poor prognostic factor. Although CIP is generally considered to confer a poor prognosis in clinical practice, previous studies have reported that its impact on survival varies considerably depending on its severity [40, 41]. Pre-existing ILD is a known risk factor for CIP and for severe CIP [40, 42]. Because patients with pre-existing ILD were excluded from this study, the absence of a difference in the survival curves between the CIP and non-CIP irAE groups may be attributable to this exclusion. In addition, although CIP appeared to be associated with relatively favorable survival in the Kaplan–Meier analysis, 8 of the 24 CIP cases occurred after the 6-month landmark time and were therefore excluded from the Cox proportional hazards analysis. As a result, CIP may not have been identified as a favorable prognostic factor.
This study had several limitations. First, this was a single-center retrospective study conducted in Japan with a modest sample size, limiting its generalizability, particularly regarding irAE incidence, and introducing a potential selection bias. Second, although ECOG-PS did not differ significantly between the groups, the PPI-exposed group had fewer patients receiving combined anti–PD-1/PD-L1 plus anti–CTLA-4 antibodies and chemotherapy, and a higher proportion of patients with elevated NLR. These characteristics suggest that patients in the PPI-exposed group may have had poorer baseline conditions, which potentially affected their survival outcomes. Third, variations in the duration and type of PPI use as well as concurrent medications and polypharmacy may have influenced irAE development and survival outcomes, and residual confounding factors cannot be fully excluded. Fourth, irAE occurrence was evaluated using logistic regression, which does not incorporate the time-to-event nature of irAE onset or account for the competing risk of death. Because death may preclude the observation of irAEs, particularly those occurring later during follow-up, our estimates may be influenced by competing risks and informative censoring. Nevertheless, these findings underscore the need for multicenter prospective studies to clarify the effects of PPI exposure on irAE development and clinical outcomes. Furthermore, this study highlights the importance of investigating the interplay among PPI exposure, irAE occurrence, and gut microbiome alterations.
Conclusion
In this study of first-line ICI therapy for advanced NSCLC, PPI use was potentially associated with a lower incidence of non-CIP irAEs, whereas no significant effect was observed on CIP, which requires careful clinical management. PPI exposure was not an independent prognostic factor, suggesting that irAE occurrence was influenced by multiple factors beyond PPI use. This study highlights the importance of investigating the interplay between PPI exposure, irAE occurrence, and gut microbiome alterations.
Supplementary Information
Acknowledgements
We thank all the patients who participated in the study, along with their families. We would also like to thank Editage (www.editage.com) for English language editing.
Authors’ contributions
Ippei Miyamoto: Conceptualization, Methodology, Investigation, Formal analysis, Writing - Original Draft, Data Curation. Tetsuo Shimizu: Conceptualization, Methodology, Resources, Writing - review and editing, Supervision, Project administration. Mizuki Hanamura: Investigation, Writing - review and editing. Ryoma Tanaka: Writing - review and editing. Ryo Kusahana: Resources, Writing - review and editing. Masayuki Nomoto: Software, Resources, Writing - review and editing. Sugaya Kenichi: Resources, Writing - review and editing. Yoshiko Nakagawa: Conceptualization, Investigation, Resources, Writing - review and editing. Yasuhiro Gon: Writing - review and editing. All authors read and approved the final manuscript.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Data availability
The datasets generated in this study are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
This study was performed in line with the principles of the Declaration of Helsinki. This study was approved by the Ethics Committee of Nihon University School of Medicine Itabashi Hospital (approval number RK-180911-1). This study was conducted as an ancillary study on biomarkers of ICIs, and written informed consent was obtained from all the participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Raoul JL, Guérin-Charbonnel C, Edeline J, Simmet V, Gilabert M, Frenel JS. Prevalence of proton pump inhibitor use among patients with cancer. JAMA Netw Open. 2021;4(6):e2113739. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hussain N, Naeem M, Pinato DJ. Concomitant medications and immune checkpoint inhibitor therapy for cancer: causation or association? Hum Vaccin Immunother. 2021;17(1):55–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC, Karpinets TV, et al. Gut Microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018;359(6371):97–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lu Y, Yuan X, Wang M, He Z, Li H, Wang J, et al. Gut microbiota influence immunotherapy responses: mechanisms and therapeutic strategies. J Hematol Oncol. 2022;15(1):47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Deng R, Zhang H, Li Y, Shi Y. Effect of antacid use on immune checkpoint inhibitors in advanced solid cancer patients: A systematic review and Meta-analysis. J Immunother. 2023;46(2):43–55. [DOI] [PubMed] [Google Scholar]
- 6.Baek YH, Kang EJ, Hong S, Park S, Kim JH, Shin JY. Survival outcomes of patients with nonsmall cell lung cancer concomitantly receiving proton pump inhibitors and immune checkpoint inhibitors. Int J Cancer. 2022;150(8):1291–300. [DOI] [PubMed] [Google Scholar]
- 7.Chalabi M, Cardona A, Nagarkar DR, Dhawahir Scala A, Gandara DR, Rittmeyer A, et al. Efficacy of chemotherapy and Atezolizumab in patients with non-small-cell lung cancer receiving antibiotics and proton pump inhibitors: pooled post hoc analyses of the OAK and POPLAR trials. Ann Oncol. 2020;31(4):525–31. [DOI] [PubMed] [Google Scholar]
- 8.Poto R, Troiani T, Criscuolo G, Marone G, Ciardiello F, Tocchetti CG, et al. Holistic approach to immune checkpoint Inhibitor-Related adverse events. Front Immunol. 2022;13:804597. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hu X, Wang L, Shang B, Wang J, Sun J, Liang B, et al. Immune checkpoint inhibitor-associated toxicity in advanced non-small cell lung cancer: an updated Understanding of risk factors. Front Immunol. 2023;14:1094414. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Cook S, Samuel V, Meyers DE, Stukalin I, Litt I, Sangha R, et al. Immune-Related adverse events and survival among patients with metastatic NSCLC treated with immune checkpoint inhibitors. JAMA Netw Open. 2024;7(1):e2352302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Haratani K, Hayashi H, Chiba Y, Kudo K, Yonesaka K, Kato R, et al. Association of Immune-Related adverse events with nivolumab efficacy in Non-Small-Cell lung cancer. JAMA Oncol. 2018;4(3):374–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Cortellini A, Tucci M, Adamo V, Stucci LS, Russo A, Tanda ET et al. Integrated analysis of concomitant medications and oncological outcomes from PD-1/PD-L1 checkpoint inhibitors in clinical practice. J Immunother Cancer. 2020;8(2):e001361. [DOI] [PMC free article] [PubMed]
- 13.Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47. [DOI] [PubMed] [Google Scholar]
- 14.Suresh K, Psoter KJ, Voong KR, Shankar B, Forde PM, Ettinger DS, et al. Impact of checkpoint inhibitor pneumonitis on survival in NSCLC patients receiving immune checkpoint immunotherapy. J Thorac Oncol. 2019;14(3):494–502. [DOI] [PubMed] [Google Scholar]
- 15.Fukihara J, Sakamoto K, Koyama J, Ito T, Iwano S, Morise M, et al. Prognostic impact and risk factors of Immune-Related pneumonitis in patients with Non-Small-Cell lung cancer who received programmed death 1 inhibitors. Clin Lung Cancer. 2019;20(6):442–e504. [DOI] [PubMed] [Google Scholar]
- 16.Lee PY, Oen KQX, Lim GRS, Hartono JL, Muthiah M, Huang DQ et al. Neutrophil-to-Lymphocyte ratio predicts development of immune-Related adverse events and outcomes from immune checkpoint blockade: A Case-Control study. Cancers (Basel). 2021;13(6):1308. [DOI] [PMC free article] [PubMed]
- 17.Ruste V, Goldschmidt V, Laparra A, Messayke S, Danlos FX, Romano-Martin P, et al. The determinants of very severe immune-related adverse events associated with immune checkpoint inhibitors: A prospective study of the French REISAMIC registry. Eur J Cancer. 2021;158:217–24. [DOI] [PubMed] [Google Scholar]
- 18.Egami S, Kawazoe H, Hashimoto H, Uozumi R, Arami T, Sakiyama N, et al. Peripheral blood biomarkers predict immune-related adverse events in non-small cell lung cancer patients treated with pembrolizumab: a multicenter retrospective study. J Cancer. 2021;12(7):2105–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transpl. 2013;48(3):452–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Zhou P, Zhao X, Wang G. Risk factors for immune checkpoint Inhibitor-Related pneumonitis in cancer patients: A systemic review and Meta-Analysis. Respiration. 2022;101(11):1035–50. [DOI] [PubMed] [Google Scholar]
- 21.Atchley WT, Alvarez C, Saxena-Beem S, Schwartz TA, Ishizawar RC, Patel KP, et al. Immune checkpoint Inhibitor-Related pneumonitis in lung cancer: Real-World Incidence, risk Factors, and management practices across six health care centers in North Carolina. Chest. 2021;160(2):731–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Jia X, Zhang Y, Liang T, Du Y, Li Y, Mao Z, et al. Comprehensive nomogram models for predicting checkpoint inhibitor pneumonitis. Am J Cancer Res. 2023;13(6):2681–701. [PMC free article] [PubMed] [Google Scholar]
- 23.Chao Y, Zhou J, Hsu S, Ding N, Li J, Zhang Y, et al. Risk factors for immune checkpoint inhibitor-related pneumonitis in non-small cell lung cancer. Transl Lung Cancer Res. 2022;11(2):295–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Buti S, Bersanelli M, Perrone F, Tiseo M, Tucci M, Adamo V, et al. Effect of concomitant medications with immune-modulatory properties on the outcomes of patients with advanced cancer treated with immune checkpoint inhibitors: development and validation of a novel prognostic index. Eur J Cancer. 2021;142:18–28. [DOI] [PubMed] [Google Scholar]
- 25.Elkrief A, El Raichani L, Richard C, Messaoudene M, Belkaid W, Malo J, et al. Antibiotics are associated with decreased progression-free survival of advanced melanoma patients treated with immune checkpoint inhibitors. Oncoimmunology. 2019;8(4):e1568812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Hopkins AM, Kichenadasse G, Karapetis CS, Rowland A, Sorich MJ. Concomitant proton pump inhibitor use and survival in urothelial carcinoma treated with Atezolizumab. Clin Cancer Res. 2020;26(20):5487–93. [DOI] [PubMed] [Google Scholar]
- 27.Ruiz-Bañobre J, Molina-Díaz A, Fernández-Calvo O, Fernández-Núñez N, Medina-Colmenero A, Santomé L, et al. Rethinking prognostic factors in locally advanced or metastatic urothelial carcinoma in the immune checkpoint Blockade era: a multicenter retrospective study. ESMO Open. 2021;6(2):100090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Raoul JL, Moreau-Bachelard C, Gilabert M, Edeline J, Frénel JS. Drug-drug interactions with proton pump inhibitors in cancer patients: an underrecognized cause of treatment failure. ESMO Open. 2023;8(1):100880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Yucel E, Sancar M, Yucel A, Okuyan B. Adverse drug reactions due to drug-drug interactions with proton pump inhibitors: assessment of systematic reviews with AMSTAR method. Expert Opin Drug Saf. 2016;15(2):223–36. [DOI] [PubMed] [Google Scholar]
- 30.Nara K, Taguchi S, Buti S, Kawai T, Uemura Y, Yamamoto T et al. Associations of concomitant medications with immune-related adverse events and survival in advanced cancers treated with immune checkpoint inhibitors: a comprehensive pan-cancer analysis. J Immunother Cancer. 2024;12(3):e008806. [DOI] [PMC free article] [PubMed]
- 31.Laurent L, Abbar B, Bihan K, Dumas E, Jochum F, Lebrun-Vignes B, et al. Comedications associated with Immune-Related adverse events from Immune-Checkpoint inhibitors. Clin Pharmacol Ther. 2025;118(3):593–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Miao J, Herrmann SM. Immune checkpoint inhibitors and their interaction with proton pump inhibitors-related interstitial nephritis. Clin Kidney J. 2023;16(11):1834–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Lasagna A, Mascaro F, Figini S, Basile S, Gambini G, Klersy C, et al. Impact of proton pump inhibitors on the onset of Gastrointestinal immune-related adverse events during immunotherapy. Cancer Med. 2023;12(19):19530–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Laheij RJ, Sturkenboom MC, Hassing RJ, Dieleman J, Stricker BH, Jansen JB. Risk of community-acquired pneumonia and use of gastric acid-suppressive drugs. JAMA. 2004;292(16):1955–60. [DOI] [PubMed] [Google Scholar]
- 35.Eom CS, Jeon CY, Lim JW, Cho EG, Park SM, Lee KS. Use of acid-suppressive drugs and risk of pneumonia: a systematic review and meta-analysis. CMAJ. 2011;183(3):310–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Gulmez SE, Holm A, Frederiksen H, Jensen TG, Pedersen C, Hallas J. Use of proton pump inhibitors and the risk of community-acquired pneumonia: a population-based case-control study. Arch Intern Med. 2007;167(9):950–5. [DOI] [PubMed] [Google Scholar]
- 37.Grabska S, Grabski H, Makunts T, Abagyan R. Co-Occurring infections in cancer patients treated with checkpoint inhibitors significantly increase the risk of Immune-Related adverse events. Cancers (Basel). 2024;16(16):2820. [DOI] [PMC free article] [PubMed]
- 38.Makunts T, Burkhart K, Abagyan R, Lee P. Retrospective analysis of clinical trial safety data for pembrolizumab reveals the effect of co-occurring infections on immune-related adverse events. PLoS ONE. 2022;17(2):e0263402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Yan T, Long M, Liu C, Zhang J, Wei X, Li F, et al. Immune-related adverse events with PD-1/PD-L1 inhibitors: insights from a real-world cohort of 2523 patients. Front Pharmacol. 2025;16:1519082. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Tone M, Izumo T, Awano N, Kuse N, Inomata M, Jo T, et al. High mortality and poor treatment efficacy of immune checkpoint inhibitors in patients with severe grade checkpoint inhibitor pneumonitis in non-small cell lung cancer. Thorac Cancer. 2019;10(10):2006–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Li Y, Liang S, Du Y, Yao J, Jiang Y, Lu W, et al. Analysis of baseline interstitial lung abnormality on the risk of checkpoint inhibitor-related pneumonitis and survival in advanced non-small cell lung cancer patients treated with first-line PD-1/PD-L1 inhibitors. Transl Lung Cancer Res. 2025;14(3):912–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Seok J, Park S, Yoon EC, Yoon HY. Clinical outcomes of interstitial lung abnormalities: a systematic review and meta-analysis. Sci Rep. 2024;14(1):7330. [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
Data Availability Statement
The datasets generated in this study are available from the corresponding author upon request.