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Abstract

Background:

Proton pump inhibitors (PPIs) have been shown to regulate the gut microbiome and affect the response to immune checkpoint inhibitors (ICIs). Contradictory results on survival have been observed in patients concomitantly treated with ICIs and PPIs. We performed a systematic review and meta-analysis to determine the association between PPI use and survival outcomes in ICI-treated cancer patients.

Methods:

EMBASE, MEDLINE/PubMed, Cochrane Library databases, and major oncology conference proceedings were searched. Studies comparing overall survival (OS) and progression-free survival (PFS) between PPI-treated and PPI-free groups of ICI-treated cancer patients were included. Data regarding study and patient characteristics, ICI and PPI treatments, and survival outcomes were extracted. Hazard ratios (HRs) with 95% confidence interval (CI) were pooled using random effects models. Subgroup meta-analyses and meta-regressions were performed to explore possible factors of heterogeneity among the studies.

Results:

A total of 33 studies were included, comprising 7383 ICI- and PPI-treated patients and 8574 ICI-treated and PPI-free patients. The pooled HR was 1.31 (95% CI, 1.19–1.44; p < 0.001) for OS and 1.30 (95% CI, 1.17–1.46; p < 0.001) for PFS, indicating a significant negative association between PPI use and survival in ICI-treated patients. Subgroup meta-analyses by factors including cancer type, ICI type, and time window of PPI use revealed that ICI and PPI use impacted survival in patients with non-small cell lung or urothelial cancer, patients treated with anti-PD-1/PD-L1 antibodies, and patients receiving PPI as baseline treatment or 60 days before ICI treatment initiation.

Conclusions:

PPI use in patients treated with ICIs was associated with shorter OS and PFS, especially in several specific subgroups of cancer patients. PPIs should be strictly controlled and appear to not impact survival if given temporarily after ICI initiation. These observations could provide the basis for clinical guidelines for concomitant PPI and ICI use.

Keywords

cancer immune checkpoint inhibitors meta-analysis proton pump inhibitor survival

Introduction

Currently, immune checkpoint inhibitors (ICIs) that target programmed cell death protein 1 (PD-1) or its ligand (PD-L1), alone or in combination with ICIs that target cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), have achieved remarkable results in the management of certain cancers, particularly melanoma, lung cancer, and microsatellite instable colon cancer.^1–3 Although ICIs are effective, they are limited by a low response rate, which represents approximately 20–30% of patients.^4,5 Thus, there is an unmet medical need to identify factors that affect the efficacy of ICIs and their underlying mechanisms.

A growing number of studies have demonstrated that an altered gut microbiome can impact homeostasis, systemic immune response, and ICI treatment efficacy in various cancer patient populations, though the exact underlying mechanisms remain unclear. The gut microbiome interacts with cytokines to regulate antigen presentation and by partial functional regulation of effector T cells or myeloid cells in the tumor microenvironment.^6,7

Proton pump inhibitors (PPIs), among the 10 most widely prescribed drugs globally, are also frequently given to cancer patients.⁸ Significant gut microbiome dysregulations and reduced bacterial richness are associated with PPIs.^9,10 Unlike antibiotics, which have been shown to regulate the gut microbiome, which in turn modulates response to ICIs, the effect of PPIs on gut microbiome alterations and on the response to ICIs have been only scarcely researched.¹¹ These observations prompted us to question whether the concomitant use of PPIs affects the treatment efficacy of ICIs. The contradictory survival results recently obtained from patients concomitantly treated with ICIs and PPIs lent credence to the need for our investigation.^12,13 We, therefore, performed a systematic review and meta-analysis to better define the effect of PPIs on cancer patients treated with ICIs.

Methods

Protocol and reporting guidelines

The study protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO CRD42020181618) and INPLASY (Protocol 2020100088) and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) 2020 checklist.¹⁴

Information sources and search strategy

Studies reporting the impact of PPI use on the response to ICIs, including survival data of ICI-treated patients from inception to 22 March 2022 were identified by comprehensively searching the EMBASE, MEDLINE (via PubMed), Cochrane Library databases, and major oncology conference proceedings by two investigators (C.Y. and B.Q.C.) independently. Furthermore, emails requesting additional data were sent to the corresponding authors of the studies of interest. Moreover, reference lists of initially detected articles were hand-searched.

The search queries were built using the following broadly defined medical terms: (‘pembrolizumab’ OR ‘nivolumab’ OR ‘durvalumab’ OR ‘ticilimumab’ OR ‘toripalimab’ OR ‘sintilimab’ OR ‘camrelizumab’ OR ‘ipilimumab’ OR ‘immune checkpoint inhibitor*’ OR ‘ici*’ OR ‘anti-PD-1*’ OR ‘anti-PD-L1*’ OR ‘anti-CTLA-4*’) AND (‘omeprazole’ OR ‘lansoprazole’ OR ‘dexlansoprazole’ OR ‘esomeprazole’ OR ‘pantoprazole’ OR ‘rabeprazole’ OR ‘proton pump inhibitor*’ OR ‘ppi*’), without any filters and limits. The detailed queries of the search strategies for different data sources are shown in Supplemental Table 1.

Inclusion criteria

All available studies (publications, abstracts, posters, and clinical trials) comparing survival between concomitant use of ICIs and PPIs (intervention group) and ICI use alone (comparator group) in cancer patients were preliminarily included. The studies endpoints were defined as hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) and/or progression-free survival (PFS). Only studies reporting HRs with 95% CIs for OS or PFS were included in the meta-analysis; these data were extracted directly from original publications, requested and obtained from the researchers, or calculated using Kaplan–Meier curves based on a recognized method.¹⁵

In case of duplicate data from overlapping cohorts, only the largest and the most up-to-date studies were included. Two investigators (C.Y. and B.Q.C.) independently screened the titles and abstracts and then assessed the full-text articles for eligibility. When discrepancies occurred, a third investigator (D.M.C.) reviewed the study and decided on its eligibility.

Data collection process and the risk of bias

The data from all included studies were extracted and summarized by one investigator (C.Y.) using a standardized spreadsheet, whereas the selection and coding of data were reviewed by two other investigators (B.Q.C. and M.P.D.) independently. When results of univariate and multivariate analyses were both available, the data from the multivariate analysis were included. The following data were collected:

– Source: Author, year and type of publication, type of study.

– Patient and cancer characteristics: Region and number of patients included, type and stage of cancer.

– ICI treatment: Type and treatment line of ICIs (i.e. first-line therapy, non-first-line therapy).

– PPI treatment: Number and percentage of patients receiving PPIs and ICIs, types and time window of PPI use.

– Results: Type of analysis, HRs with 95% CIs for OS and PFS, and statistical significance of the comparison.

The risk of bias in individual studies was assessed using the modified Newcastle–Ottawa Quality Assessment Scale (NOS),¹⁶ which includes three aspects: selection of cohorts, comparability of cohorts, and assessment of outcomes. Given that cohorts differ in population size and treatment options, we further researched the studies’ comparability by assessing and comparing additional variables such as age, gender, race, tumor type, tumor stage, ICI type, Eastern Cooperative Oncology Group-Performance Status, treatment line of immunotherapy, and other concomitant treatments (e.g. antibiotics and corticosteroids). Two investigators (C.Y. and B.Q.C.) independently evaluated the risk of bias and discussed to reach a consensus when disagreements arose.

Statistical analysis

Data synthesis was performed on the effect measure HR with 95% CIs for OS and PFS for each analysis: HR > 1.0 indicated a worse outcome in the PPI-treated arm and two-sided p < 0.05 was considered statistically significant. The Cochrane Q test and the I² index were used to evaluate heterogeneity; p < 0.1 for Q test or I² > 50% indicated significant heterogeneity.¹⁷ When these two methods yielded heterogeneous results, the random effects model was adopted, which was more conservative and reliable for making conclusions¹⁸ because it accounts for differences in population and treatment characteristics. All statistical analyses were performed using Stata (version 16.0, Stata Corp, College Station, TX, USA), and forest plots were used to display the results. Subgroup analyses and meta-regression were performed to investigate possible causes of heterogeneity among study results. Funnel plots with Egger’s regression tests were used to examine publication bias across studies. Sensitivity analyses were performed using the leaving-one-out approach.

Results

Study selection and study characteristics

The preliminary search yielded 826 records. After duplicate studies were removed, titles and abstracts of 613 studies were screened. Reviews, case reports, animal experiments, and studies unrelated to PPI or ICI were discarded (n = 566), and the remaining 47 publications were assessed. Six studies (two commentaries, two repeat studies, and two studies on antacids but not PPI alone) were excluded, and 41 were included in the qualitative synthesis. Eight additional studies were excluded because no HR data were available and the corresponding authors did not respond to our inquiry emails. Finally, 33 studies, of which 24 were peer-reviewed journal publications and 9 were conference abstracts, were included in the systematic review and meta-analysis.^{12,13,19–49} The flow diagram of identifying the eligible studies is shown in Figure 1.

Figure 1.

Flow diagram of included studies.

A total of 33 studies were included, comprising 15,957 patients for OS and 7577 patients for PFS analyses. Characteristics of the studies included in the meta-analysis are presented in Table 1. These 33 studies were subjected to retrospective or, for randomized clinical trials, pooled post hoc analyses. The patient sample sizes of all studies ranged from small to medium (n = 49–3634). The prevalence of PPI use ranged from 21.2 to 84.3%, and there were 7383 PPI-treated patients and 8574 PPI-free patients. In 13 studies, patients with different cancer types were enrolled. Among all included studies, the following ICI regimens were used: anti-PD-1 monoclonal antibodies (mAbs) alone (toripalimab, sintilimab, camrelizumab, pembrolizumab, and nivolumab; 10 studies), anti-PD-L1 mAbs alone (atezolizumab; three studies), anti-PD-1 or anti-PD-L1 (anti-PD-[L]1) mAbs (9), anti-CTLA-4 mAb alone (ipilimumab; one study), or anti-PD-(L)1 combined with anti-CTLA-4 mAbs (one study). The use of PPI ranged from 60 days before ICI initiation to 60 days after ICI initiation in most studies, although other studies reported concomitant or baseline use.

Table 1.

Studies included in the meta-analysis.

Source	Year	Type of publication	Type of study	Region	No. of patients	Cancer type	Stage (%)	ICI(s) used	Treatment line (%)	No. of PPI users (%)	PPI used	(Star day, end day) of PPI use	Type of analysis	HR for OS (95% CI)	HR for PFS (95% CI)
Afzal and Shirai¹⁹	2019	Abstract	RP	Europe	120	Melanoma	Advanced	Anti-PD-1 ± anti-CTLA-4	NA	29 (24.2)	Omeprazole (majority)	(0, NA)	MVA	1.01 (0.4, 2.0)	0.3 (0.1, 0.7)^$
Araujo et al.²⁰	2021	Abstract	RP	Brazil	216	Multiple	Metastatic	Anti-PD-(L)1, anti-CTLA-4	NA	114 (52.8)	NA	(−60, NA)	MVA	1.73 (1.23, 2.44)*	2.36 (1.67, 3.34)*
Baek et al²¹	2022	Publication	RP	Asia	1646	NSCLC	IIIB–IV	Anti-PD-(L)1	Second or beyond	823 (50.0)	NA	(−30, 0)	MVA	1.28 (1.13, 1.46)*	NA
Buti et al.²²	2021	Publication	RP	Europe	217	Multiple	Advanced	Anti-PD-(L)1, anti-CTLA-4	First (20.7), second or beyond (79.3)	104 (47.9)	NA	(−30, NA)	UVA	1.57 (1.13, 2.18)*	NA
Castro Balado et al.²³	2021	Abstract	RP	Europe	49	NSCLC	Metastatic	Anti-PD-1 (pembrolizumab)	First	26 (53.1)	NA	(−30, 30)	UVA	2.50 (1.08, 5.88)*	1.02 (0.45, 2.33)
Chalabi et al.¹³	2020	Publication	Post hoc analyses of OAK/POPLAR	Europe, USA and Asia	757	NSCLC	Advanced	Anti-PD-L1 (atezolizumab)	Second (74.2), third (25.8)	234 (30.9)	Omeprazole (majority)	(−30, 30)	MVA	1.45 (1.20, 1.75)*	1.30 (1.10, 1.53)*
Conde-Estévez et al.²⁴	2021	Publication	RP	Europe	70	NSCLC	Advanced or recurrent	Anti-PD-(L)1	Second (72.9), third (20), fourth (7.1)	59 (84.3)	NA	(0, NA)	MVA	1.32 (0.56, 3.13)	2.91 (0.88, 9.65)
Cortellini et al.²⁵	2020	Publication	RP	Europe	1012	Multiple	IV	Anti-PD-(L)1	First (39.1), non-first (60.9)	491 (48.5)	NA	Baseline	MVA	1.26 (1.04, 1.52)*	1.26 (1.07, 1.48)*
Cortellini et al.²⁶	2021	Publication	RP	Europe	950	NSCLC	Metastatic	Anti-PD-1 (pembrolizumab)	First	474 (49.9)	NA	Baseline	MVA	1.49 (1.26, 1.77)*	1.32 (1.13, 1.54)*
Failing et al.²⁷	2016	Publication	RP	USA	80	Melanoma	IV	Anti-CTLA-4 (ipilimumab)	First	17 (21.2)	Omeprazole, pantoprazole, esomeprazole, lansoprazole	(0, NA)	MVA	0.44 (0.17, 1.15)	0.60 (0.34, 1.06)
Gaucher et al.²⁸	2021	Publication	RP	Europe	372	Multiple	Metastatic (74.2)	Anti-PD-1 ± anti-CTLA-4	First	149 (40.1)	NA	(0, NA) or (60, NA)	MVA	0.80 (0.60, 1.08)	NA
Giordan et al.²⁹	2021	Publication	RP	Europe	220	Multiple	III–IV	Anti-PD-1	First, second, third	76 (34.7)	Pantoprazole, esomeprazole, lansoprazole, rabeprazole, omeprazole	(−30, 0)	MVA	1.89 (1.23, 2.90)*	1.51 (1.11, 2.05)*
Hopkins et al.³⁰	2020	Publication	Post hoc analysis of IMvigor 210&211	Australia	896	UC	Advanced	Anti-PD-L1 (atezolizumab)	NA	286 (31.9)	Omeprazole (majority)	(−30, 30)	MVA	1.52 (1.27, 1.83)*	1.38 (1.18, 1.62)*
Hopkins et al.³¹	2022	Publication	Post hoc analysis of Impower150	Australia	748	NSCLC	Metastatic	Anti-PD-L1 (atezolizumab)	First	290 (38.8)	Omeprazole (39%), pantoprazole (37%)	(−30, 30)	MVA	1.53 (1.21, 1.95)*	1.34 (1.12, 1.61)*
Hossain et al.³²	2020	Abstract	RP	Australia	63	NSCLC	Advanced	NA	NA	34 (53.97)	NA	(NA, 30)	MVA	1.66 (0.81, 3.42)	1.61 (0.88, 2.96)
Husain et al.³³	2021	Abstract	RP	USA	1091	Multiple	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	Multiple	415 (38.0)	NA	(0, NA)	MVA
					NA	Multiple	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	First	NA	NA	(0, NA)	MVA	1.46 (1.11, 1.91)*	NA
					NA	Multiple	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	Second or beyond	NA	NA	(0, NA)	MVA	1.30 (1.10, 1.53)*	NA
Iglesias-Santamaría³⁴	2020	Publication	RP	Europe	101	Multiple	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	First or second (71.6), beyond (28.4)	78 (77.2)	NA	(0, NA)	MVA	0.79 (0.40, 1.56)	0.75 (0.42, 1.34)
Iida et al.³⁵	2021	Abstract	RP	Europe	115	UC	Metastatic	Anti-PD-1 (pembrolizumab)	Second	NA	NA	(−30, 30)	MVA	NA	2.25 (1.41, 3.58)*
Jun et al.³⁶	2021	Publication	RP	Europe, USA, and Asia	314	HCC	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	First (43.6), second (50.3), third or beyond (6.1)	85 (27.1)	Omeprazole, lansoprazole, dexlansoprazole, esomeprazole, pantoprazole, rabeprazole	(−30, NA)	UVA	1.14 (0.84, 1.54)	NA
Kostine et al.³⁷	2021	Publication	RP	Europe	635	Multiple	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	First, second, or beyond	239 (37.6)	Esomeprazole (59%), omeprazole (24%)	(−30, 30)	UVA	1.70 (1.40, 2.08)*	1.37 (1.12, 1.66)*
					293	Melanoma	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	First, second, or beyond	NA	NA	(−30, 30)	UVA	2.18 (1.61, 2.96)*	1.62 (1.20, 2.19)*
					150	NSCLC	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	First, second, or beyond	NA	NA	(−30, 30)	UVA	1.42 (0.96, 2.09)	1.30 (0.88, 1.93)
					83	RCC	Advanced	Anti-PD-(L)1 ± anti-CTLA-4	First, second, or beyond	NA	NA	(−30, 30)	UVA	1.09 (0.60, 1.99)	0.99 (0.59, 1.69)
Kulkarni et al.³⁸	2019	Abstract	RP	Europe	203	Multiple	Advanced	Anti-PD-(L)1	NA	74 (36.5)	NA	(−30, NA) or (0, NA)	UVA
					148	NSCLC	Advanced	Anti-PD-(L)1	NA	57 (38.5)	NA	(−30, NA) or (0, NA)	UVA	1.55 (1.00, 2.40)*	1.15 (0.79, 1.66)
					55	RCC	Advanced	Anti-PD-(L)1	NA	17 (30.9)	NA	(−30, NA) or (0, NA)	UVA	1.01 (0.39, 2.62)	1.03 (0.53, 1.97)
Miura et al.³⁹	2021	Publication	RP	Asia	300	NSCLC	Advanced	Anti-PD-1 (nivolumab or pembrolizumab)	First, second, or beyond	163 (54.3)	Lansoprazole, rabeprazole, esomeprazole	(0, NA)	MVA	1.36 (0.96, 1.91)	NA
Mollica et al.⁴⁰	2021	Publication	RP	Europe and USA	218	RCC	Metastatic	Anti-PD-1 ± anti-CTLA-4	Multiple	113 (51.8)	NA	(−30, 0)	UVA
					62	RCC	Metastatic	Nivolumab + ipilimumab	First	25 (40.3)	NA	(−30, 0)	UVA	1.12 (0.38, 3.27)	1.04 (0.49, 2.20)
					156	RCC	Metastatic	Nivolumab	Second or third	88 (56.4)	NA	(−30, 0)	UVA	0.81 (0.53, 1.24)	1.05 (0.73,1.50)
Nguyen et al.⁴¹	2019	Abstract	RP	Asia	95	Multiple	NA	Anti-PD-1 (nivolumab)	NA	40 (42.0)	NA	(0, NA) ⩾1 4 days	UVA	1.51 (0.87, 2.60)	1.29 (0.80, 2.07)
					23	HNC	NA	Anti-PD-1 (nivolumab)	NA	8 (34.8)	NA	(0, NA) ⩾ 14 days	UVA	2.61 (0.93, 7.28)	1.17 (0.44, 3.14)
					17	GC	NA	Anti-PD-1 (nivolumab)	NA	10 (58.8)	NA	(0, NA) ⩾ 14 days	UVA	1.26 (0.35, 4.56)	1.04 (0.36, 2.97)
					45	Melanoma	NA	Anti-PD-1 (nivolumab)	NA	17 (37.8)	NA	(0, NA) ⩾ 14 days	UVA	1.52 (0.53, 4.31)	1.59 (0.79, 3.20)
					10	RCC	NA	Anti-PD-1 (nivolumab)	NA	4 (40.0)	NA	(0, NA) ⩾ 14 days	UVA	0.92 (0.24, 3.51)	1.38 (0.64, 2.98)
Peng et al.⁴²	2022	Publication	RP	USA	233	Multiple	IV	Anti-PD-1 ± anti-CTLA-4	First, second, or beyond	89 (38.2)	Omeprazole (47.2%), pantoprazole (23.6%)	(−30, 30)	MVA	1.20 (0.77, 1.87)	0.90 (0.64, 1.28)
					117	NSCLC	IV	Anti-PD-1 (nivolumab or pembrolizumab)	First, second, or beyond	46 (39.3)	NA	(−30, 30)	MVA	1.06 (0.60, 1.86)	1.27 (0.82, 1.95)
Routy et al.¹²	2017	Publication	RP	Europe	249	Multiple	Multiple	Anti-PD-(L)1	Multiple	187 (75.1)	NA	(−60, 30)	MVA	1.15 (0.87, 1.53)	1.12 (0.83, 1.51)
					140	NSCLC	Advanced	Anti-PD-(L)1	First, second or third (74), beyond (26)	105 (75.0)	NA	(−60, 30)	MVA	1.08 (0.77, 1.53)	1.01 (0.69, 1.49)
					67	RCC	I–II (27)III–IV (43)	Anti-PD-(L)1	NA	47 (70.1)	NA	(−60, 30)	MVA	1.45 (0.80, 2.62)	1.36 (0.79, 2.34)
					42	UC	NA	Anti-PD-(L)1	NA	35 (83.3)	NA	(−60, 30)	MVA	1.01 (0.35, 2.96)	1.16 (0.48, 2.80)
Ruiz-Bañobr et al.⁴³	2021	Publication	RP	Europe	119	UC	Locally advanced or metastatic	Anti-PD-(L)1	First, second or beyond	54 (45.0)	NA	(−30, NA)	MVA	1.83 (1.11, 3.02)*	1.94 (1.22, 3.09)*
Spakowicz et al.⁴⁴	2020	Publication	RP	USA	689	Multiple	IV (90.0)	Anti-PD-(L)1 ± anti-CTLA-4	NA	255 (37.0)	NA	(−30, NA)	UVA	0.99 (0.85, 1.16)	NA
Stein et al.⁴⁵	2021	Publication	RP	Europe	232	Melanoma	Advanced	Anti-PD-1 (nivolumab, pembrolizumab)	First, non-first	86 (37.1)	NA	Baseline	MVA	1.83 (1.20, 2.78)*	NA
Stokes et al.⁴⁶	2021	Abstract	RP	USA	3634	NSCLC	IV (40.9)	Anti-PD-(L)1	NA	2159 (59.4)	Omeprazole (majority)	(0, 90)	MVA	0.96 (0.89, 1.04)	NA
Svaton et al.⁴⁷	2020	Publication	RP	Europe	224	NSCLC	Advanced	Anti-PD-1 (nivolumab)	First (4), second (48.2), third (24.6), beyond (33.3)	64 (28.6)	Omeprazole, pantoprazole, lansoprazole	(−30, 30)	MVA	1.22 (0.72, 2.05)	1.36 (0.89, 2.06)
Takada et al.⁴⁸	2022	Publication	RP	Asia	95	NSCLC	Postoperative recurrent	Anti-PD-(L)1	NA	37 (38.9)	Omeprazole, lansoprazole, rabeprazole, esomeprazole	(0, NA)	MVA	2.55 (1.31, 4.99)*	4.12 (2.28, 7.46)*
Zhao et al.⁴⁹	2019	Publication	RP	Asia	109	NSCLC	Advanced	Anti-PD-1 (nivolumab or pembrolizumab)	First (25.7), second or beyond (74.3)	40 (36.7)	NA	(−30, 30)	UVA	0.68 (0.33, 1.43)	0.91 (0.54, 1.54)

Studies in which a statistically significant association with shorter OS and/or PFS was observed.

Studies in which a statistically significant association with longer OS and/or PFS was observed; in all other studies no statistical difference was obtained.

CI, confidence interval; CTLA-4, cytotoxic T lymphocyte-associated antigen 4; GC, gastric cancer; HCC, hepatocellular carcinoma; HNC, head and neck cancer; HR, hazard ratio; ICI, immune checkpoint inhibitor; MVA, multivariate analysis; NA, not available; NSCLC, non-small cell lung cancer; OS, overall survival; PD-1, programmed cell death protein 1; PD-L1, programmed death ligand 1; PFS, progression-free survival; PPI, proton pump inhibitor; RCC, renal cell carcinoma; RP, retrospective design, prospective data collection; UC, urothelial cancer; UVA, univariate analysis.

Of the 33 publications, 25 received an NOS assessment score of 7 or higher, and 8 received a score of 5 or 6, suggesting the studies were of high or moderate methodological quality (Supplemental Table 2).

Meta-analysis of OS and PFS

Kulkarni et al. provided HR data of survival for renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC) separately, and Husain et al. and Mollica et al. provided HR data for subgroups categorized by ICI treatment line and ICI type, respectively, instead of the whole population; therefore, we pooled the HR data of these subgroups separately. Thus, there were 35 cohorts providing HR data for OS and 26 cohorts for PFS. HRs for PPI-treated versus PPI-free subgroups ranged from 0.30 (95% CI, 0.10–0.70) to 4.12 (95% CI, 2.28–7.46). In 17 cohorts, a statically significant association was observed between PPI and ICI use and shorter OS,^{13,20–23,25,26,29–31,33,37,43,45,48} and in 11 a significant association was observed between PPI and ICI use and shorter PFS.^{13,20,25,26,29–31,35,37,43,48} In 18 cohorts no impact on OS,^{12,19,24,27,28,32,34,36,38–42,44,46,47,49} and in 14 no impact on PFS,^{12,23,24,27,32,34,38,40–42,47,49} was observed in patients treated with ICI and PPI versus ICI without PPI. However, in one cohort significantly longer PFS was observed in patients treated with PPI and ICI.¹⁹ Additionally, we observed that especially (but not exclusively) in larger studies, a significant association between ICI and PPI use versus ICI without PPI and shorter OS and/or PFS was likely to be detected. Together, these data proved once more the importance of our study question and that a method capable of combining multiple studies together is ideal for answering it.

The between-study heterogeneities were moderate (I² of 72% and 63% for OS and PFS, respectively). Pooled HRs with 95% CIs were calculated using random effects models. For OS a pooled HR of 1.31 (95% CI, 1.19–1.44; p < 0.001) was obtained, which indicated that significantly shorter OS was found in patients treated with ICIs and PPIs (Figure 2(a)). In accordance with OS data, pooled HRs for PFS indicated that PPI use in patients treated with ICIs resulted in shorter PFS (HR, 1.30; 95% CI, 1.17–1.46; p < 0.001; Figure 2(b)).

Figure 2.

Forest plots comparing the OS and PFS of PPI-treated versus PPI-free patients treated with ICIs for cancer. Results for (a) OS and (b) PFS are shown.

Meta-analysis of subgroup factors

To explore possible factors of heterogeneity among study results, subgroup analyses were conducted by multiple factors, including time window of PPI use, cancer type, ICI type, treatment line of ICIs, and patients’ region of residence.

Patient populations were divided into three subgroups according to the time window of PPI use: group A, PPI therapy as baseline treatment; group B, PPI therapy begin within 60 days before ICI treatment initiation (−60, NA); group C, concomitant treatment (0, NA; Supplemental Figure 1). The pooled HRs for OS and PFS in these subgroups are displayed in Figures 3(a) and 4(a). In our comparison of PPI-treated versus PPI-free subgroups, shorter OS and PFS were found in group A (OS: HR, 1.43; 95% CI, 1.21–1.69; p < 0.001; PFS: HR, 1.29; 95% CI, 1.15–1.44; p < 0.001) and group B (OS: HR, 1.35; 95% CI, 1.22–1.51; p < 0.001; PFS: HR, 1.33; 95% CI, 1.20–1.48; p < 0.001). However, there was no significant impact of PPI therapy on survival when the PPI therapy was concomitantly started with ICIs (group C; OS: HR, 1.18; 95% CI, 0.98–1.41; p = 0.09; PFS: HR, 1.19; 95% CI, 0.65–2.17; p = 0.58).

Figure 3.

Forest plots for OS of subgroup analyses. Results for OS by (a) time window of PPI use, (b) cancer type, and (c) ICIs type. Group A, PPI therapy as baseline treatment; group B (−60, NA), PPI therapy beginning within 60 days before ICI treatment initiation; group C (0, NA), PPI therapy beginning concomitantly with initiation of ICI treatment.

Figure 4.

Forest plots for PFS of subgroup analyses. Results for PFS by (a) time window of PPI use, (b) cancer type, and (c) ICIs type. Group A, PPI therapy as baseline treatment; group B (−60, NA), PPI therapy beginning within 60 days before ICI treatment initiation; group C (0, NA), PPI therapy beginning concomitantly with initiation of ICI treatment.

Analyses of NSCLC, RCC, melanoma, and urothelial cancer (UC) were all reported in more than two studies and were included in the subgroup analyses. The HRs for OS of NSCLC and UC patients, respectively, in PPI-treated versus PPI-free groups were 1.33 (95% CI, 1.15–1.54; p < 0.001) and 1.54 (95% CI, 1.30–1.82; p < 0.001). The impact of PPI on the OS of ICI-treated patients with RCC (HR, 1.01; 95% CI, 0.77–1.33; p = 0.92) or melanoma (HR, 1.39; 95% CI, 0.87–2.22; p = 0.16) was not significant (Figure 3(b)). Similar to our OS findings, the use of PPIs resulted in shorter PFS in ICI-treated patients with NSCLC (HR, 1.33; 95% CI, 1.17–1.51; p < 0.001) and UC (HR, 1.63; 95% CI, 1.23–2.15; p = 0.001) but not RCC (HR, 1.11; 95% CI, 0.89–1.38; p = 0.37) and melanoma (HR, 0.89; 95% CI, 0.44–1.81; p = 0.75; Figure 4(b)).

For our analysis based on the type of ICIs, five subgroups (anti-PD-1 alone, anti-PD-L1 alone, anti-PD-[L]1 [anti-PD-1 or anti-PD-L1], anti-CTLA-4, and anti-PD-[L]1 combined with anti-CTLA-4) were identified, and their pooled HRs for OS and PFS are presented in Figures 3(c) and 4(c). In the subgroups treated with anti-PD-1, anti-PD-L1, and anti-PD-(L)1, a negative impact of PPI use was noted on both OS (anti-PD-1: HR, 1.36; 95% CI, 1.12–1.64; p < 0.01; anti-PD-L1: HR, 1.50; 95% CI, 1.33–1.68; p < 0.001; anti-PD-(L)1: HR, 1.28; 95% CI, 1.07–1.52; p < 0.01) and PFS (anti-PD-1: HR, 1.32; 95% CI, 1.16–1.51; p < 0.001; anti-PD-L1: HR, 1.34; 95% CI, 1.22–1.48; p < 0.001; anti-PD-(L)1: HR, 1.52; 95% CI, 1.13–2.03; p < 0.001). Unlike the broadly researched impact of PPI on anti-PD(L)1, the subgroups treated with anti-CTLA-4 and anti-PD-(L)1 combined with anti-CTLA-4 subgroups contained only one study each, and no association was noted between PPI treatment and OS or PFS. Therefore, the impact of PPI on anti-CTLA-4 and anti-PD-(L)1 combined with anti-CTLA-4 subgroups will need to be further investigated if additional studies appear.

PPI use shortened the OS and PFS when ICIs were used in non-first-line therapy rather than in first-line ICI therapy (Supplemental Figure 2A and 2B). Subgroup analysis based on the patients’ geographic region [Europe, America (USA and Brazil), Asia, and Australia] was also performed. PPI use showed a detrimental effect on PFS and OS in studies from Europe and Australia, but no effect on survival was noted in studies from America (USA and Brazil). For patients from Asia treated with ICIs, PPI use shortened OS but had no impact on PFS (Supplemental Figure 3A and 3B).

Test of subgroup differences and meta-regression analysis

Results of our test of subgroup differences demonstrated that subgroup factors including time window of PPI use, cancer type, ICI type, ICI treatment line, and geographic region were not statistically significant factors contributing to studies’ heterogeneity, although ICI type and cancer type showed a non-significant trend as heterogeneity contributors (Figure 5). Besides the above factors, sample size and percentage of patients receiving PPIs were also analyzed in meta-regression, but no factors were identified as heterogeneity contributors (Supplemental Table 3).

Figure 5.

Analyses of subgroup factors and test for subgroup differences. Results for (a) OS and (b) PFS are shown. The subgroups of the time window of PPI use analysis: group A, PPI therapy as baseline treatment; group B (−60, NA), PPI therapy beginning within 60 days before ICI treatment initiation; group C (0, NA), PPI therapy beginning concomitantly with initiation of ICI treatment.

Publication bias and sensitivity analysis

Funnel plots with Egger’s test indicated no publication bias affecting OS or PFS in any study (Supplemental Figure 4). Sensitivity analyses examined by the leave-one-out approach showed that no individual study significantly affected the pooled HRs of OS or PFS, indicating results were stable (Supplemental Figure 5).

Discussion

It is well-known that PPIs can alter the microbiome,¹¹ and it is well-known that the microbiome can affect the survival of cancer patients treated with ICIs.^7,12 The mechanism of this effect on survival is still debated and is probably mediated by the effect of commensal bacteria on the crosstalk between the host immune cells and cancer cells.⁵⁰ In this report, we present new data supporting a negative correlation of PPI therapy and survival of cancer patients treated with ICIs. Our findings reveal some subtle details that may need to be considered in clinical practice when simultaneously treating cancer patients with PPIs and ICIs.

First, we observed that therapy with PPIs shortens the OS and PFS of cancer patients treated with ICIs. Our results contradict the data from some previous meta-analyses performed on this topic. In a first meta-analysis of seven studies, Li et al.⁵¹ observed that PPIs had no impact on survival of cancer patients treated with ICI. In a subsequent meta-analysis including only five studies, Li et al.⁵² found that PPI therapy had no effect on the survival of cancer patients treated with ICIs. Later, in a more recent meta-analysis, Qin et al.⁵³ using data from seven studies reported that PPI therapy shortened the PFS and OS of cancer patients treated with ICIs. Finally, Liu et al.⁵⁴ in a meta-analysis including 17 studies, observed that PPI therapy for cancer patients receiving ICI shortens the OS, but not PFS. Therefore, despite these meta-analysis, the impact of PPIs on ICI therapy still remains controversial. In contrast to these studies, our meta-analysis was based on the largest curated list of studies to date, comprising data from 33 studies and including articles and abstracts collected from conference proceedings. Additionally, we obtained part of the data by directly contacting authors of several studies, some of whom provided additional unpublished data. Overall, we included almost 10,000 more patients compared to the other meta-analyses on this matter. Moreover, our large list of enrolled studies mitigated the risk of publication bias. Confirming this, our publication bias analysis showed symmetrical funnel plots for OS and PFS, and the Egger’s test was not significant for bias toward PFS and borderline non-significant for bias toward OS.

Second, by including 33 different studies, we were able to perform subgroup analyses and better understand the subtle mechanisms of PPI and ICI interplay. We observed that only baseline PPI therapy and PPI therapy preceding ICI treatment shortened OS and PFS. This may be explained by previous findings showing that long-term, but not short-term, use of PPIs decreased the gut microbiome’s bacterial richness and that this decrease was negatively correlated to ICIs’ effect.^11,55,56 Our observation also indirectly implies that PPIs might affect the survival of cancer patients treated with ICIs by altering the microbiome. Moreover, our observation is of clinical value; many patients receiving ICIs develop upper gastrointestinal tract symptoms and need to be treated with PPIs.⁵⁷ Our data underscore that the use of PPI therapy is acceptable if started simultaneously with ICI treatment. One question though remains to be answered: how long can PPIs and ICIs be concomitantly prescribed before PPIs start affecting survival?

Third, the subgroup analysis enabled us to observe that PPI use affected the survival of NSCLC and UC patients, but not melanoma and RCC patients, treated with ICIs. These data are partially in accordance with those of Li et al.⁵¹ who observed in the subgroup analysis that PPI shortened the survival of NSCLC patients. In patients with NSCLC who responded to ICIs, abundant Akkermansia muciniphila and Ruminococcus sp. have been detected in fecal samples.¹² In contrast, A. muciniphila and the family of Ruminococcaceae were found to be decreased in the gut microbiome of patients treated with PPIs.^58,59 Therefore, PPIs may decrease the taxa of A. muciniphila and Ruminococcaceae that are beneficial for response to ICIs in NSCLC patients. Similarly, the Ruminococcus family was also enriched in melanoma patients who responded to ICI therapy.⁷ Thus, the use of PPI is believed to weaken the efficacy of ICIs in melanoma patients. However, another taxon, the Bifidobacteriaceae family, were increased after PPI treatment and were found to positively correlate with response to anti-PD-L1 treatment in melanoma patients.^59,60 These findings partially explain the controversy regarding the effect of PPI use on ICI efficacy in melanoma patients and are in accordance with our finding.

Fourth, our large list of studies made it possible to compare the PPI-ICI crosstalk for different types of ICIs and for different ICI treatment regimens (first line versus non-first line). Previous meta-analyses have proven that ICIs yield more robust OS and PFS outcomes compared with chemotherapy in pretreated cancer patients, although, importantly, heterogeneity between the included studies was noted.^61,62 Our analysis sheds some light on this heterogeneity. We observed that PPI shortened the survival of patients treated with anti-PD-(L)1 and of patients treated with ICIs as non-first line therapy. A few studies have shown that the efficacy of CTLA-4 blockade was influenced by certain microbiota, including Bacteroides fragilis, B. thetaiotaomicron, and Burkholderiales.^63,64 So far, no correlation between these three taxa and PPI treatment has been found. Once more, we believe that these observations could have important clinical impact. A subgroup analysis performed by Li et al.⁵² showed that PPI had no impact on survival in subgroups treated with different ICIs; however, detailed information about the ICI subgroups was not provided in their study. Another meta-analysis showed that the PD-L1 status of tumor cells had an important role regarding the survival benefit of ICI treatment.⁶¹ Unfortunately, only a few of the studies included in our analysis provided detailed information regarding PD-L1 status, and therefore such an analysis was not possible.

Additionally, our study shows that a regional difference existed for the PPI and ICI interaction. One potential explanation for this observation is the complex interaction between diet and the microbiome, which has an important impact on ICIs.⁶⁵

Limitations

This meta-analysis has some important limitations. For some subgroup analyses, we were not able to include the desired number of patients, and these data should be interpreted with caution. Additionally, we would have added additional subgroup analyses, but data were not available for characteristics such as type of PPIs prescribed, patient ethnicity, patient diet, and tumor cells/immune cells PD-L1 status on immunohistochemistry. Hence, we point out that future studies on this topic should report as many clinical and demographic details as possible, including diet, ethnicity, type of PPIs, dosage of PPIs, and indication for PPIs. Furthermore, definitive answers would likely come from comparative studies reporting the composition of the microbiome during ICI treatment with versus without PPI therapy.

Conclusions

This large-cohort meta-analysis revealed a negative correlation of PPI therapy with survival in cancer patients treated with ICIs. The association was observed in several specific subgroups of cancer patients: those who had NSCLC or UC, started PPI use long before ICI, were treated with anti-PD-(L)1 agents, were treated with ICI(s) as non-first-line therapy, and resided in Europe or Australia. PPIs should be strictly controlled and appear not to impact survival if given briefly and after ICI initiation. These observations could provide the basis for future clinical guidelines for the concomitant use of PPIs and ICIs.

Supplemental Material

sj-docx-1-tam-10.1177_17588359221111703 – Supplemental material for Association of proton pump inhibitor use with survival outcomes in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

Supplemental material, sj-docx-1-tam-10.1177_17588359221111703 for Association of proton pump inhibitor use with survival outcomes in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis by Baoqing Chen, Chen Yang, Mihnea P. Dragomir, Dongmei Chi, Wenyan Chen, David Horst, George A. Calin and Qiaoqiao Li in Therapeutic Advances in Medical Oncology

Footnotes

Acknowledgements

The authors specially thank Dr. Spakowicz (Division of Medical Oncology, The Ohio State University Medical Center), Dr. Kulkarni (University of Minnesota, Minneapolis, MN, USA), and Dr. Nguyen (Department of Medical Oncology, Kyoto University) and their teams for generously sharing with us the data, which are not available in the online version of the publications or abstracts. Editorial support was provided by Bryan Tutt, Scientific Editor, Research Medical Library, MD Anderson Cancer Center.

Declarations

ORCID iDs

Chen Yang

Qiaoqiao Li

Supplemental material

Supplemental material for this article is available online.

References

Larkin

Chiarion-Sileni

Gonzalez

Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N Engl J Med 2019; 381: 1535–1546.

Hellmann

Paz-Ares

Bernabe Caro

, et al. Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. New Engl J Med 2019; 381: 2020–2031.

Zhao

Jiang

, et al. Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. J Hematol Oncol 2019; 12: 54.

Das

Johnson

DB.

Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors. J Immunother Cancer 2019; 7: 306.

Mazieres

Drilon

Lusque

, et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the immunotarget registry. Ann Oncol 2019; 30: 1321–1328.

Gong

Chehrazi-Raffle

Placencio-Hickok

, et al. The gut microbiome and response to immune checkpoint inhibitors: preclinical and clinical strategies. Clin Transl Med 2019; 8: 9.

Gopalakrishnan

Spencer

Nezi

Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018; 359: 97–103.

Strand

Kim

Peura

DA.

25 years of proton pump inhibitors: a comprehensive review. Gut Liver 2017; 11: 27–37.

Paroni Sterbini

Palladini

Masucci

, et al. Effects of proton pump inhibitors on the gastric mucosa-associated microbiota in dyspeptic patients. Appl Environ Microbiol 2016; 82: 6633–6644.

10.

Sanduleanu

Jonkers

De Bruine

, et al. Non-Helicobacter pylori bacterial flora during acid-suppressive therapy: differential findings in gastric juice and gastric mucosa. Aliment Pharmacol Ther 2001; 15: 379–388.

11.

Imhann

Bonder

Vich Vila

, et al. Proton pump inhibitors affect the gut microbiome. Gut 2016; 65: 740–748.

12.

Routy

Le Chatelier

Derosa

, et al. Gut microbiome influences efficacy of PD-1–based immunotherapy against epithelial tumors. Science 2018; 359: 91–97.

13.

Chalabi

Cardona

Nagarkar

, 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: 525–531.

14.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71.

15.

Tierney

Stewart

Ghersi

, et al. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials 2007; 8: 16.

16.

Stang

Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010; 25: 603–605.

17.

Higgins

Thompson

Deeks

JJ.

Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557–560.

18.

Nikolakopoulou

Mavridis

Salanti

How to interpret meta-analysis models: fixed effect and random effects meta-analyses. Evid Based Ment Health 2014; 17: 64.

19.

Afzal

Shirai

What impact do the proton pump inhibitors have on the efficacy of immune check point inhibitors in metastatic malignant melanoma?

J Clin Oncol 2019; 37: e21040–e21040.

20.

Araujo

Moniz

CMV

Braghiroli

OFM

, et al. Proton pump inhibitors and antibiotics impact on toxicities and clinical outcomes in cancer patients treated with immunotherapy. J Clin Oncol 2021; 39: 2652–2652.

21.

Baek

Kang

Hong

, et al. Survival outcomes of patients with nonsmall cell lung cancer concomitantly receiving proton pump inhibitors and immune checkpoint inhibitors. Int J Cancer 2022; 150: 1291–1300.

22.

Buti

Bersanelli

Perrone

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.

23.

Castro Balado

Tourís Lores

Álvarez Sánchez

, et al. 4CPS-301 Association of antibiotics and proton pump inhibitors on clinical activity of firstline pembrolizumab for non-small cell lung cancer: 2 years of real world data. Eur J Hosp Pharm 2021; 28: A65.

24.

Conde-Estévez

Monge-Escartín

Ríos-Hoyo

, et al. Prognostic factors and effect on survival of immune-related adverse events in patients with non-small-cell lung cancer treated with immune checkpoint blockage. J Chemother 2021; 33: 32–39.

25.

Cortellini

Tucci

Adamo

, 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: e001361.

26.

Cortellini

Di Maio

Nigro

, et al. Differential influence of antibiotic therapy and other medications on oncological outcomes of patients with non-small cell lung cancer treated with first-line pembrolizumab versus cytotoxic chemotherapy. J Immunother Cancer 2021; 9: e002421.

27.

Failing

Finnes

Kottschade

, et al. Effects of commonly used chronic medications on the outcomes of ipilimumab therapy in patients with metastatic melanoma. Melanoma Res 2016; 26: 609–615.

28.

Gaucher

Adda

Séjourné

, et al. Associations between dysbiosis-inducing drugs, overall survival and tumor response in patients treated with immune checkpoint inhibitors. Ther Adv Med Oncol 2021; 13: 17588359211000591.

29.

Giordan

Salleron

Vallance

, et al. Impact of antibiotics and proton pump inhibitors on efficacy and tolerance of anti-PD-1 immune checkpoint inhibitors. Front Immunol 2021; 12: 716317.

30.

Hopkins

Kichenadasse

Karapetis

, et al. Concomitant proton pump inhibitor use and survival in urothelial carcinoma treated with atezolizumab. Clin Cancer Res 2020; 26: 5487–5493.

31.

Hopkins

Kichenadasse

McKinnon

, et al. Efficacy of first-line atezolizumab combination therapy in patients with non-small cell lung cancer receiving proton pump inhibitors: post hoc analysis of Impower150. Br J Cancer 2022; 126: 42–47.

32.

Hossain

Htut

Pathmanathan

Immunotherapy efficacy and concomitant proton pump inhibitor use in non-small cell lung cancer. Asia Pac J Clin Oncol 2020; 16: 37.

33.

Husain

Patel

, et al. Proton pump inhibitor use (PPI) in patients treated with immune checkpoint inhibitors (ICI) for advanced cancer: survival and prior therapy. J Clin Oncol 2021; 39: 2633–2633.

34.

Iglesias-Santamaría

Impact of antibiotic use and other concomitant medications on the efficacy of immune checkpoint inhibitors in patients with advanced cancer. Clin Transl Oncol 2020; 22: 1481–1490.

35.

Iida

Nagai

Nozaki

Proton pump inhibitor use is a negative prognostic factor for metastatic urothelial carcinoma progression in pembrolizumab-treated patients. J Urol 2021; 206: e766–e767.

36.

Jun

Ozbek

Dharmapuri

, et al. Antacid exposure and immunotherapy outcomes among patients with advanced hepatocellular carcinoma. Ther Adv Med Oncol 2021; 13: 17588359211010937.

37.

Kostine

Mauric

Tison

, et al. Baseline co-medications may alter the anti-tumoural effect of checkpoint inhibitors as well as the risk of immune-related adverse events. Eur J Cancer 2021; 157: 474–484.

38.

Kulkarni

Kumar

Pease

, et al. Impact of antibiotics and proton pump inhibitors on clinical outcomes of immune check point blockers in advanced non-small cell lung cancers and metastatic renal cell cancer. J Clin Oncol 2019; 37: e20520–e20520.

39.

Miura

Sano

Niho

, et al. Impact of concomitant medication on clinical outcomes in patients with advanced non-small cell lung cancer treated with immune checkpoint inhibitors: a retrospective study. Thorac Cancer 2021; 12: 1983–1994.

40.

Mollica

Santoni

Matrana

, et al. Concomitant proton pump inhibitors and outcome of patients treated with nivolumab alone or plus ipilimumab for advanced renal cell carcinoma. Target Oncol 2022; 17: 61–68.

41.

Nguyen

Nomura

Matsumoto

, et al. The effect of proton pump inhibitors on the efficacy of nivolumab monotherapy in different types of cancer. Ann Oncol 2019; 30: vi115.

42.

Peng

Chen

Teply

, et al. Impact of proton pump inhibitor use on the effectiveness of immune checkpoint inhibitors in advanced cancer patients. Ann Pharmacother 2022; 56: 377–386.

43.

Ruiz-Bañobre

Molina-Díaz

Fernández-Calvo

, 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: 100090.

44.

Spakowicz

Hoyd

Muniak

, et al. Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications. BMC Cancer 2020; 20: 383.

45.

Stein

Burtey

Mancini

, et al. Acute kidney injury in patients treated with anti-programmed death receptor-1 for advanced melanoma: a real-life study in a single-centre cohort. Nephrol Dial Transplant 2021; 36: 1664–1674.

46.

Stokes

Behera

Jiang

, et al. Association of proton pump inhibitors with survival in veterans with non-small cell lung cancer receiving immunotherapy. J Clin Oncol 2021; 39: e18729–e18729.

47.

Svaton

Zemanova

, et al. Impact of concomitant medication administered at the time of initiation of nivolumab therapy on outcome in non-small cell lung cancer. Anticancer Res 2020; 40: 2209–2217.

48.

Takada

Shimokawa

Takamori

, et al. The clinical impact of concomitant medication use on the outcome of postoperative recurrent non-small-cell lung cancer in patients receiving immune checkpoint inhibitors. PLoS One 2022; 17: e0263247.

49.

Zhao

Gao

, et al. Antibiotics are associated with attenuated efficacy of anti-PD-1/PD-L1 therapies in Chinese patients with advanced non-small cell lung cancer. Lung Cancer 2019; 130: 10–17.

50.

Routy

Gopalakrishnan

Daillère

, et al. The gut microbiota influences anticancer immunosurveillance and general health. Nat Rev Clin Oncol 2018; 15: 382–396.

51.

Zeng

Yao

, et al. The association between proton pump inhibitors use and clinical outcome of patients receiving immune checkpoint inhibitors therapy. Int Immunopharmacol 2020; 88: 106972.

52.

Xia

, et al. The effect of proton pump inhibitor uses on outcomes for cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Ann Transl Med 2020; 8: 1655.

53.

Qin

Jiao

Zhou

, et al. Effects of concomitant proton pump inhibitor use on immune checkpoint inhibitor efficacy among patients with advanced cancer. OncoImmunology 2021; 10: 1929727.

54.

Liu

Guo

Mao

, et al. An up-to-date investigation into the correlation between proton pump inhibitor use and the clinical efficacy of immune checkpoint inhibitors in advanced solid cancers: a systematic review and meta-analysis. Front Oncol 2022; 12: 753234.

55.

Zhang

Wang

Sun

, et al. Commensal microbiota contributes to predicting the response to immune checkpoint inhibitors in non-small-cell lung cancer patients. Cancer Sci 2021; 112: 3005–3017.

56.

Lee

Kim

Nam

, et al. Gut microbiota and butyrate level changes associated with the long-term administration of proton pump inhibitors to old rats. Sci Rep 2019; 9: 6626.

57.

Tang

Abu-Sbeih

Luo

, et al. Upper gastrointestinal symptoms and associated endoscopic and histological features in patients receiving immune checkpoint inhibitors. Scand J Gastroenterol 2019; 54: 538–545.

58.

Davis

Collier

Mohebbi

, et al. Obesity, Akkermansia muciniphila, and proton pump inhibitors: is there a link? Obes Res Clin Pract 2020; 14: 524–530.

59.

Jackson

Goodrich

Maxan

, et al. Proton pump inhibitors alter the composition of the gut microbiota. Gut 2016; 65: 749–756.

60.

Sivan

Corrales

Hubert

, et al. Commensal bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 2015; 350: 1084–1089.

61.

Petrelli

Ferrara

Signorelli

, et al. Immune checkpoint inhibitors and chemotherapy in first-line NSCLC: a meta-analysis. Immunotherapy 2021; 13: 621–631.

62.

Tartarone

Roviello

Lerose

, et al. Anti-PD-1 versus anti-PD-L1 therapy in patients with pretreated advanced non-small-cell lung cancer: a meta-analysis. Future Oncol 2019; 15: 2423–2433.

63.

Vétizou

Pitt

Daillère

, et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 2015; 350: 1079–1084.

64.

Coutzac

Jouniaux

Paci

, et al. Systemic short chain fatty acids limit antitumor effect of CTLA-4 blockade in hosts with cancer. Nat Commun 2020; 11: 2168.

65.

Lee

Shaw

Bataille

, et al. Role of the gut microbiome for cancer patients receiving immunotherapy: dietary and treatment implications. Eur J Cancer 2020; 138: 149–155.

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