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Background:
Systematic Review

The Association between Baseline Proton Pump Inhibitors, Immune Checkpoint Inhibitors, and Chemotherapy: A Systematic Review with Network Meta-Analysis

by
Yu Chang
1,†,
Wan-Ying Lin
2,†,
Yu-Cheng Chang
3,
Chin-Hsuan Huang
3,
Huey-En Tzeng
4,
Eahab Abdul-Lattif
5,
Tsu-Hsien Wang
3,
Tzu-Hsuan Tseng
6,
Yi-No Kang
7,8,* and
Kuan-Yu Chi
3,9,*
1
Section of Neurosurgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701401, Taiwan
2
Department of Family Medicine, Taipei Medical University Hospital, Taipei 100229, Taiwan
3
Department of Education, Center for Evidence-Based Medicine, Taipei Medical University Hospital, Taipei 100229, Taiwan
4
Division of Hematology and Oncology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 100229, Taiwan
5
Department of Internal Medicine, Lower Bucks Hospital, Bristol, PA 19007, USA
6
Department of Family Medicine, Wan Fang Hospital, Taipei Medical University, Taipei 106339, Taiwan
7
Cochrane Taiwan, Taipei Medical University, Taipei 106339, Taiwan
8
Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei 100229, Taiwan
9
Department of Internal Medicine, Taipei Medical University Hospital, Taipei 100229, Taiwan
*
Authors to whom correspondence should be addressed.
†
The authors contributed equally to the manuscript.
Cancers 2023, 15(1), 284; https://doi.org/10.3390/cancers15010284
Submission received: 6 December 2022 / Revised: 27 December 2022 / Accepted: 28 December 2022 / Published: 31 December 2022
(This article belongs to the Section Systematic Review or Meta-Analysis in Cancer Research)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

Proton pump inhibitors (PPIs) are the most commonly used gastric acid suppressants in cancer patients. However, long-term use of PPIs can cause dysbiosis effects disrupting gut microbiota with subsequent impairment of the effectiveness of immune checkpoint inhibitors (ICIs). Our study demonstrates that, in advanced non-small cell lung cancer and urothelial carcinoma, patients receiving ICIs with concomitant PPIs are associated with poorer survival outcomes, when compared not only with those without PPIs but also with patients treated with chemotherapy, implying that PPIs could compromise the effectiveness of ICIs, causing them to be less effective than chemotherapy. As a result, we suggest that clinicians should avoid unnecessary PPI prescription in these patients. Conversely, there is little survival association with PPI in patients with melanoma, renal cell carcinoma, hepatocellular carcinoma, and squamous cell carcinoma of the neck and head. Nevertheless, future high quality prospective studies including more cancer types are warranted.

Abstract

(1) Although emerging evidence suggests that proton pump inhibitor (PPI)-induced dysbiosis negatively alters treatment response to immune checkpoint inhibitors (ICIs) in cancer patients, no study systematically investigates the association between PPIs, ICIs, and chemotherapy; (2) Cochrane Library, Embase, Medline, and PubMed were searched from inception to 20 May 2022, to identify relevant studies involving patients receiving ICIs or chemotherapy and reporting survival outcome between PPI users and non-users. Survival outcomes included overall survival (OS) and progression-free survival (PFS). Network meta-analyses were performed using random-effects models. p-scores, with a value between 0 and 1, were calculated to quantify the treatment ranking, with a higher score suggesting a higher probability of greater effectiveness. We also conducted pairwise meta-analyses of observational studies to complement our network meta-analysis; (3) We identified 62 studies involving 26,484 patients (PPI = 8834; non-PPI = 17,650), including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC), melanoma, renal cell carcinoma (RCC), hepatocellular carcinoma (HCC), and squamous cell carcinoma (SCC) of the neck and head. Eight post-hoc analyses from 18 randomized–controlled trials were included in our network, which demonstrated that, in advanced NSCLC and UC, patients under ICI treatment with concomitant PPI (p-score: 0.2016) are associated with both poorer OS (HR, 1.49; 95% CI, 1.37 to 1.67) and poorer PFS (HR, 1.41; 95% CI, 1.25 to 1.61) than those without PPIs (p-score: 1.000). Patients under ICI treatment with concomitant PPI also had poorer OS (HR, 1.18; 95% CI, 1.07 to 1.31) and poorer PFS (HR, 1.30; 95% CI, 1.14 to 1.48) in comparison with those receiving chemotherapy (p-score: 0.6664), implying that PPIs may compromise ICI’s effectiveness, making it less effective than chemotherapy. Our pairwise meta-analyses also supported this association. Conversely, PPI has little effect on patients with advanced melanoma, RCC, HCC, and SCC of the neck and head who were treated with ICIs; (4) “PPI-induced dysbiosis” serves as a significant modifier of treatment response in both advanced NSCLC and UC that are treated with ICIs, compromising the effectiveness of ICIs to be less than that of chemotherapy. Thus, clinicians should avoid unnecessary PPI prescription in these patients. “PPI-induced dysbiosis”, on the other hand, does not alter the treatment response to ICIs in advanced melanoma, RCC, HCC, and SCC of the head and neck.

1. Introduction

Although clinical trials have shown that immune checkpoint inhibitors (ICIs) provide significant efficacy over non-ICIs comparators in many advanced cancers reshaping their therapeutic landscape [1,2,3,4], only a certain proportion of cancer patients have demonstrated a meaningful treatment response to ICIs. Considering metastatic castration-resistant prostate cancer as an example, pembrolizumab, the only FDA approved ICI for prostate cancer, is used predominantly in patients with high microsatellite instability (MSI-H) [5]. Therefore, identifying prognostic predictors and biomarkers for treatment response in patients taking ICIs is crucial, as it helps determine the subgroups of cancer patients who would benefit most from ICIs, improving the precision of therapeutic management. In fact, a variety of clinicopathological features, including age, gender, the Eastern Cooperative Oncology Group (ECOG) performance status, tumor mutation burden, and programmed death ligand 1 (PD-L1) expression, have been discovered as potential modifiers of treatment response in patients treated with ICIs [6,7,8]. Concomitant use of various medications has also been investigated to determine whether they alter the effectiveness of ICIs [9,10]. Notably, PPIs, commonly prescribed gastric acid suppressants for cancer patients, are well-known for their dysbiosis effects that disrupts gut microbiota, which theoretically impairs patients’ response to both ICIs and chemotherapy [11,12].
Six meta-analyses [13,14,15,16,17,18] have investigated the survival impacts of concomitant PPI on the effectiveness of ICI, but their results are contradictory. One [14] demonstrated negligible survival influence of PPI on ICI. Another three [15,17,18] alluded that PPI could negatively affect ICI in advanced cancers, while the remaining two syntheses [13,16] implied melanoma patients may derive survival benefits due to lower disease recurrence. Despite these six meta-analyses, there has been little investigation and discussion on the interaction of PPI against ICI and chemotherapy. Although a subgroup analysis of trials of IMvigor 211 [19] and IMpower150 [20] showed that PPIs significantly compromised the benefit of ICIs over chemotherapy, another exploratory analysis of OAK [21] and POPLAR [22] demonstrated insignificant interaction.
Given the significant increased indications of ICIs due to actively broadening cancer types and the extensive use of PPIs, an updated synthesis is urgently warranted. Thus, our study contains two main objectives; firstly, we aim to investigate the association among PPIs, ICIs, and chemotherapy by conducting a network meta-analysis (NMA); secondly, we will update the comparative survival outcomes of PPI users and non-users in patients receiving ICIs and also in patients taking chemotherapy with a wider range of cancers.

2. Methods

We performed the present systematic review and meta-analysis based on the Cochrane Handbook for Systematic Reviews of Interventions [23] and reported results in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Network Meta-analysis extension statement, and Meta-analysis Of Observational Studies in Epidemiology guidelines (Supplementary Method 1–3). The study was registered on PROSPERO (CRD 42021258800).

2.1. Study Selection

PubMed, Medline, Embase, and Cochrane Library were searched, from inception until 20 May 2022. Three investigators (W.Y.L, Y.C, and Y.C.C) independently identified relevant studies, and discrepancies were addressed by reaching a consensus with senior reviewers (K.Y.C and Y.N.K). Search details are presented in Supplementary Method 4.

2.2. Eligibility Criteria

Three predefined criteria for evidence selection were as follows: (1) randomized–controlled trials (RCTs), prospective, or retrospective cohort studies; (2) studies involving adult patients aged over 18 with cancers receiving ICIs or chemotherapy without concurrent radiotherapy; (3) studies reporting at least one comparative survival outcome between PPI users and non-users, overall survival (OS), or progression-free survival (PFS) (PPI versus non-PPI users), irrespective of indications.

2.3. Data Extraction

Three investigators (H.C.C., E.A, and T.H.W) independently extracted relevant information from eligible articles. Details are available in Supplementary Method 5.

2.4. Quality Assessment

Two investigators (T.H.T and K.Y.C) independently completed a critical appraisal of the included literature by using the Cochrane Risk of Bias tool 2.0 for RCTs, and the Risk of Bias in Non-randomised Studies–of Interventions (ROBINS-I) tool for non-RCTs. Any discrepancy was addressed through discussion with the third investigator (Y.N.K).

2.5. Assessment of Transitivity Assumption

A pivotal concept of NMA is that patients in the network are jointly randomizable. One plausible assessment of transitivity assumption is to place included trials under scrutiny to examine whether important effect modifiers are similarly distributed throughout the network [24]. We pre-specified age, gender, ECOG performance status, and PDL-1 expression status as effect modifiers since these factors are known prognostic factors for cancer patients.

2.6. Main Outcomes and Statistical Analysis

OS and PFS were pooled by obtaining the unadjusted hazard ratio (HR) extracted directly from each reference. When studies did not report the HR but presented Kaplan–Meier survival curves instead, we acquired an estimated HR from the curves through a well-established method [25], i.e., by using a calculation spreadsheet developed by Tierney and colleagues [26]. For baseline effect modifiers, we used weighted mean difference (WMD) and risk ratio (RR) through an inverse variance method to pool continuous and binary characteristics, respectively. When continuous outcomes are not reported as mean with standard difference but instead median with interquartile or range format, which cannot be used for quantitative syntheses, we utilized a well-established and well-validated tool [27] to convert the data to an appropriate format for syntheses. All estimated effects were presented with a 95% confidence interval (CI).
Network and head-to-head meta-analysis were conducted using RStudio with the ‘netmeta’ and ‘meta’ package, respectively (Supplementary Method 5). For the NMA, we produced a network graph to illustrate the structure of evidence followed by league tables for summary of frequentist NMA using a random-effect model. Regarding the league table, interventions were ranked by their p-scores with the netrank function; p-scores were a value between 0 and 1, with a higher score suggesting a higher probability of greater effectiveness. Forest plots of HRs and 95% CIs were generated with “chemotherapy” as the reference. Inconsistency was evaluated through the netsplit function https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2773396, accessed on 6 June 2022—zoi200800r45 and displayed via heat plots with the netheat command. We also performed sensitivity analyses by excluding trials of potential source of inconsistency, unknown PDL-1, and treatment line. Function ‘netmeta::funnel’ was used for depicting the comparison-adjusted funnel plot, which is a common method for depicting publication bias in NMA.
For pairwise meta-analysis, the pooled estimate was based on random-effects with the restricted maximum likelihood method due to inevitable between-trial variance. Heterogeneity was assessed using I-square [28], with values of I2 < 25%, 25% < I2 <50%, and I2 > 50% indicating low, moderate, and high heterogeneity, respectively. Pre-specified sensitivity analyses include subgroup analyses based on cancer types as well as treatment line, and exclusion of studies subject to high risk of bias. Determination of statistical significance in these analyses followed common threshold (p < 0.05). Additionally, function ‘funnel’ and ‘metabias’ are used for examining publication bias when a head-to-head meta-analysis with more than 10 studies.

3. Results

The study identified 21,059 references, with 102 studies for full-text inspection, among which 40 studies did not meet the eligibility criteria (Supplementary Results 1). Eventually, we included a total of 62 studies [9,10,19,20,21,22,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83] for qualitative and quantitative syntheses (Figure 1).

3.1. Characteristics of Included Studies

Table 1 demonstrates there are thirty-six retrospective studies [9,10,29,30,31,34,37,38,40,41,42,47,49,50,52,55,56,57,58,59,60,61,62,64,65,67,68,69,70,72,74,75,76,77,79,80] and eight were post hoc analyses [35,36,44,45,46,53,54,78] from 18 RCTs [19,20,21,22,32,33,39,43,48,51,63,66,71,73,83,84,85,86,87,88,89,90,91], involving 26,484 patients (PPI = 8834; non-PPI = 17,650) enrolled between 2000 and 2022.
Eligibility criteria of 18 RCTs [19,20,21,22,32,33,39,43,48,51,63,66,71,73,81,82,83] are elaborated in Supplementary Table S1; notably, patients with pregnancy, known CNS metastases, clinically significant cardiovascular disease, bleeding events, coagulopathy, and the use of anticoagulants or antiplatelets were excluded.
There were 42 studies [9,10,19,20,21,22,29,30,31,32,34,37,38,40,41,42,47,49,50,51,52,55,56,57,58,59,60,61,62,65,67,68,69,71,74,75,76,77,79,80,81,82,83] which primarily investigated cancer patients receiving ICI, among which 17 studies [20,21,22,31,34,37,38,47,51,56,61,65,67,69,71,74,75], 8 studies [19,32,58,62,68,76,79,80], 7 studies [29,40,41,59,75,81,82,83], 3 studies, 2 studies [52,55], 1 study, and 10 studies [9,10,30,41,42,49,50,59,60,75] reported survival outcomes of advanced non-small cell lung cancer (NSCLC), urothelial carcinoma (UC), melanoma, renal cell carcinoma (RCC), hepatocellular carcinoma (HCC), squamous cell carcinoma (SCC) of the head and neck, and uncategorized cancer cohorts, respectively. Information on uncategorized cancers is listed on Supplementary Table S2. There were 11 studies (retrospective: 4 [64,70,72,77] and post hoc analysis: 7 [33,39,43,48,63,66,73]) which investigated the effect of PPIs on patients with colorectal cancer (CRC) receiving chemotherapy. Prespecified effect modifiers, including age, sex, ECOG, and PDL expression are comparable between PPI users and non-users across the network (Supplementary Table S3). Details of PPI are presented in Supplementary Table S4. The sources of risk of bias arise mostly from bias due to confounding (Supplementary Figure S1). No study was evaluated as critical risk of bias. Supplementary Results 2 provides detailed elaboration of ROBINS-I of each domain.

3.2. Network Meta-Analysis

Eight post hoc analysis [35,36,44,45,46,53,54,78] of 18 RCTs [19,20,21,22,32,33,39,43,48,51,63,66,71,73,81,82,83] enrolling advanced cancers (advanced NSCLC, UC, melanoma, CRC, and gastroesophageal adenocarcinoma) are included in the network. Through the visualization of network plot (Figure 2), patients under ICI treatment with concomitant PPI (red node) are associated with poor OS and PFS, compared not only with those under ICI without PPI, but also with those with chemotherapy. Details of NMA are presented in Supplementary Figures S2 and S3. However, both the netsplit and netheat plots demonstrated significant inconsistency throughout the comparisons (Supplementary Figures S4–S7). After excluding trials of melanoma (Supplementary Results 4), NMA also indicates that patients under ICI treatment with concomitant PPI are associated with poor OS (HR, 1.49; 95% CI, 1.37 to 1.67) and PFS (HR, 1.41; 95% CI, 1.25 to 1.61), compared with those without PPI, and with worse OS (HR, 1.18; 95% CI, 1.07 to 1.31) and PFS (HR, 1.30; 95% CI, 1.14 to 1.48), compared with those with chemotherapy (Supplementary Figures S8–S10), with a substantial decrease in inconsistency (Supplementary Figures S11–S14). According to the net league table with p-scores, ICI without PPI is ranked highest, followed by chemotherapy without PPI, ICI with PPI, and chemotherapy with PPI (Table 2). The funnel plot (Supplementary Figures S15 and S16) demonstrated general symmetry through inspection, which was further supported by the Egger’s test (p = 0.71), indicating no publication bias from small study effects.

3.3. Prespecified Sensitivity Analyses (Supplementary Results 5 and 6)

We excluded data of IMpower 130, IMpower 131 [51], IMvigor 210, TRIO-013/LOGiC [43], AVF2107g, N016966, and Carrato 2013 [33] because these trials enrolled treatment-naïve patients (Supplementary Figures S17–S23). Regarding the PDL-1 expression, we excluded data of Chu 2017 [36] and Kichenadasse 2021 [53] because of unavailable PDL-1 information (Supplementary Figures S24–S30). After the sensitivity analyses, the association among PPI, ICI, and chemotherapy remains the same, with no significant inconsistency.

3.4. Pairwise Meta-Analysis for ICI Cohorts (Figure 3 and Supplementary Results 7)

In patients with NSCLC, the use of PPIs poses a 36% higher risk of death (HR, 1.36; 95% CI, 1.28 to 1.45; I2 = 0%) and disease progression (HR, 1.36; 95% CI, 1.26 to 1.47; I2 = 0%) than those without PPIs. The same trend is observed in patients with UC regarding OS (HR, 1.71; 95% CI, 1.49 to 1.96; I2 = 0%) and PFS (HR, 1.55; 95% CI, 1.37 to 1.75; I2 = 0%). There is no significant difference in either of the survival outcomes with respect to advanced melanoma, RCC, HCC, and SCC of the head and neck. Scatters in the funnel plot with Egger’s test suggest no publication bias in either OS or PFS (Supplementary Figures S31 and S32). Sensitivity analysis of excluding studies subject to high risk of bias yields the same association with the significant decrease in the statistical heterogeneity (Supplementary Figures S33 and S34).

3.5. Pairwise Meta-Analysis for Chemotherapy Cohorts (Supplementary Results 9)

The use of PPIs is associated with a significant 12% higher all-cause mortality in patients with advanced NSCLC receiving chemotherapy, with a marginally significant higher progression rate (Supplementary Figures S35 and S36). For GI cancers, using a PPI also confers a significantly higher risk of death and disease progression rate (Figures S35 and S36) but with substantial heterogeneity.
When CRC cancer patients are stratified based on chemotherapeutic regimens, concomitant PPI use negatively affects the effectiveness of these regimens in patients with FU-based regimens (Supplementary Figures S37 and S38). Conversely, survival outcomes are similar between PPI users and non-users in patients receiving capecitabine-based regimens (Supplementary Figures S37 and S38).

3.6. Meta-Analysis Using Adjusted HR (Supplementary Results 10)

Supplementary Table S5 provides adjusted variables for adjusted outcomes and Supplementary Figures S39–S44 provide the results of the meta-analysis using adjusted HR. No significant discrepancy is noted between unadjusted and adjusted outcomes. We finally summarize the meta-analyses findings with both unadjusted and adjusted results in Supplementary Table S6.

4. Discussion

Although various meta-analyses have investigated the influence of PPIs on the effectiveness of ICIs, no synthesis, to date, has explored the interaction among PPIs, ICIs, and chemotherapy. Our NMA demonstrated that baseline PPI use not only has a negative prognostic influence on advanced cancer patients treated with ICIs but is shown to compromise the effectiveness of ICI, causing it to be even worse than chemotherapy. It is also noteworthy that trials in the network not only included advanced cancer patients with ECOG PS < 2 but explicitly excluded patients with medical conditions warranting long-term antiplatelets or anticoagulants that may require baseline PPI to prevent GI bleeding, such as coronary artery disease and thromboembolism, which reflect high burdens of co-morbidities and which can obviously confound PPI’s survival impact. Therefore, there is a low risk of unmeasured confounding bias for our network, although included trials contain no pertinent information on the indications of PPI. However, through the visualization of netheat plots (Supplementary Figures S6 and S7), significant inconsistency exists in the network, with melanoma deemed to be the source of the inconsistency, which decreased substantially after the removal of melanoma trials. This, together with the head-to-head meta-analyses, provides a plausible reason as to why melanoma contributes to the source of inconsistency, as melanoma was shown to be neutrally affected by PPI. Conversely, only advanced NSCLC and UC patients treated with ICIs are negatively affected by PPI. Although effect modifiers were comparably distributed and no significant inconsistency was detected in our NMA after the removal of melanoma trials, conceptual heterogeneity was inevitable as different cancers encompass distinct histopathological features, clinical behaviors, and responses to therapy. Ideally, for the most precise synthesis, every cancer type should possess their own network. However, the number of trials to date are too limited for us to construct an individual network for respective cancer types. We acknowledge this as our major limitation, and the findings of our NMA should be interpreted together with pairwise meta-analyses supplemented by real-world evidence.
On the basis of our syntheses, “PPI-induced dysbiosis” serves as a significant modifier of treatment response to ICIs in both advanced NSCLC and UC, and various basic science studies have already laid a solid foundation for this clinical observation. The higher diversity of gut microbiota correlates with a higher response to ICIs owing to its positive correlation with T-cell numbers and activity [11]. Notably, PPI users were found to have lower diversity, regardless of indications, compared with non-users, with Firmicutes being the most strongly affected species [84]. Moreover, Routy et al. [11] indicated that NSCLC patients responded well to anti-PD1 agents that had an over-presentation of Firmicutes and higher memory T-helper cell reactivity against commensals in the peripheral blood. Another possible explanation for the negative association between the effectiveness of PPIs and ICIs may be related to H. pylori infection. Oster and colleagues [85] unveiled that H. pylori infection negatively altered the response to ICI in a pre-clinical setting. They also demonstrated that NSCLC patients with seropositive H. pylori were associated with significantly lower OS and PFS when compared with those with H. pylori seronegativity. Consequently, PPI could be a potential surrogate marker for H. pylori infection causing the negative association observed in our study. Another study also implied that dysbiosis effects were minimal when regimens were administered in a later-line context [45], due to the development of immunosurveillance evasion in advanced cancers [86]. However, we demonstrate that the detrimental effects of PPIs may be independent of the treatment line on the basis of the prespecified sensitivity analyses.
On the other hand, “PPI-induced dysbiosis” does not seem to not alter the treatment response to ICIs in patients with advanced melanoma, RCC, HCC, and SCC of the head and neck. This result contradicts previous syntheses [13,16] that demonstrated the potential benefit of PPIs for melanoma patients treated with ICIs. There are two feasible explanations for such a distinctive observation. Firstly, only two studies were included in previous syntheses, which underlies the convincing mechanism, as a small body of evidence can underpower the result, giving rise to a false positive association. Secondly, the quality assessment tool used in their syntheses was the Newcastle–Ottawa Scale, which is now considered outdated and unreliable due to the advent of ROBINS-I, which is the gold-standard tool in current evidence-based medicine. When ROBINS-I was used in the appraisal of these two studies, it was discovered that the study [40] contributing to the positive effect of PPI on melanoma was subject to high risk of bias. Note that after excluding this study [40], statistical heterogeneity decreased substantially, as we demonstrated in Supplementary Results 8. In fact, pre-clinical studies have shed much light on potential dysbiosis effects on melanoma. For those treated with ICI, responders were found to have a significantly higher alpha diversity of gut microbiota than non-responders [87,88], and using antibiotics during the treatment window was associated with a shorter PFS [89,90]. However, PPIs were shown to exert a pro-apoptotic effect on melanoma cells through the inhibition of vacuolar ATPase, an ATP-dependent proton pump [91,93], which disturbs pH gradient across melanoma cells, resulting in caspase-dependent cell death [94]. We assumed that these two aforementioned mechanisms counterbalance each other and contribute to neutral survival impacts of PPI on melanoma. For patients with advanced RCC, our result resonates with a large pooled analysis [95] of individual data from clinical trials illustrating that PPIs conferred to negligible survival influence on RCC patients treated with tyrosine kinase inhibitors (TKIs), presumably due to insignificant effects on bioavailability of TKIs. The same neutral association is observed in patients with HCC, and SCC of the head and neck as well. However, such association in these cancers currently lacks a plausible mechanism, and clinicians should keep in mind that it exhibits a false negative due to small number of studies and the small sample size. We anticipate more RCTs or large observational studies of these cancers for larger pooled analysis in the future.
Regarding the chemotherapeutic modalities, they varied in CRC among included trials. We believe that the high variance in therapeutic modalities among CRC trials accounts for the main origins of heterogeneity, not only in pairwise analysis but also in NMA. Nonetheless, our study shows that patients treated with capecitabine-based therapy are not affected by baseline PPIs. Capecitabine is almost completely metabolized to fluorouracil after being absorbed through the GI walls, and it was believed that PPI hampers the solubility, absorption, and distribution of capecitabine by increasing the intragastric pH level [96]. However, in vitro data appear not to support this interaction, as capecitabine does not become ionized until it reaches a pH level of 8.8, when it is poorly absorbed, which is obviously higher than that induced by PPI [97,98]. In contrast, fluorouracil-based agents are negatively affected by concomitant PPI, although with considerable heterogeneity. This appears to arise from complex combinations with other chemotherapeutic agents and target therapy. Unfortunately, to date, scarce pharmacokinetic studies explore the effect of PPIs on oxaliplatin, irinotecan, and target therapy, and our findings suggest that interactions between PPIs and these modalities should be addressed in the future, warranting further clinical and pharmacokinetic investigations.
One caveat of incorporating CRC into the network is that ICIs currently have limited roles in the CRC treatment field. Although a recent trial, KEYNOTE-177 [99], has established the antitumor efficacy of ICIs in treatment-naïve advanced CRC, only patients harboring deficient-MMR/MS-instable tumors are eligible for using ICIs, with the rationale being that a high mutational burden from deficient-MMR/MS-instability results in overexpression of immunogenic antigens and upregulation of immune checkpoint proteins [100]. On the other hand, the role of ICI in advanced CRC with preserved-MMR/MS-stable diseases is still under exploration, with multiple active recruiting trials investigating the combination of ICI and traditional regimens, which is hypothesized to evoke immunogenic responses.
Another limitation to our network is in regard to the PDL-1 status. Although PDL-1 expression is well-balanced across our network, and sensitivity analyses demonstrated no significant impact of PDL-1 status on our NMA, readers should bear in mind that they suffer from three limitations. Firstly, different cancers contain their own exclusive immune profile and present distinctive responses to different ICIs with a varying correlation to the PDL-1 expression. For instance, in POPLAR and OAK trials, advanced NSCLC patients with higher PDL-1 expression treated with atezolizumab derived higher survival benefits over chemotherapy [21,22] Conversely, in advanced UC, the issue of whether ICI has survival superiority over chemotherapy varied among different agents. Pembrolizumab was shown to result in better survival benefits than chemotherapy, [101,102] whereas atezolizumab did not [19]. The role of PDL-1 expression remains undetermined in UC to date, with nivolumab (CheckMate 275) [103], durvalumab [104], and pembrolizumab (KeyNote 045) [101,102] predicting a better survival response rate using PDL-1, unlike atezolizumab (IMvigor 211) which does not. Furthermore, for CRC patients, Checkmate 142 [105] suggested the PDL-1status is not a predictive biomarker, but a mismatch repair/DNA microsatellite instability (MMR/MS) is. Secondly, PDL-1 expression can be evaluated by different methodologies. There are four commonly used immunohistochemistry assays, including 22C3, 28-8, SP142, and SP263, to stain tumors, and these scoring systems were not directly comparable metrics of PDL-1 expression level. Thirdly, it should be noted that studies of unknown PDL-1 status correspond exactly to CRC studies, which are also a source of heterogeneity. Thus, it implies our sensitivity analysis could be potentially biased.
In summary, the present study contains following novelties. Firstly, we investigated the interaction of PPIs against ICIs versus chemotherapy, which has allowed us to gain further insight into the detrimental magnitude of PPIs in affected cancers. Secondly, only NSCLC and UC are negatively affected by PPI-induced dysbiosis and melanoma, whereas RCC, HCC, and SCC do not seem to be affected. Therefore, when cancer patients have indications of using gastric acid suppressants, such as antiemesis for ICI and chemotherapy, peptic ulcers, and reflux esophagitis, H2-blockers may be considered given PPI’s potential detrimental effects on ICI in certain cancers and on specific chemotherapeutic regimens. On the other hand, several limitations should be addressed in the future. Firstly, constructing respective networks for different cancers would provide greater knowledge of the interaction between PPIs and therapeutic modalities. Secondly, the included patients were mostly ECOG-PS 0-2, which hinders the generalizability to other cancer patients. Thirdly, information on PDL-1 expression is scarce, so we are unable to look further into the magnitude of PPIs’ effects on ICI-treated patients with different PDL-1 expressions. Last but not least, there is still inadequate data for probing the impact of PPIs on other types of malignancy, such as prostate cancer, CRC, and HCC, as ICIs are still looking for their niche in these cancers.

5. Conclusions

“PPI-induced dysbiosis” serves as a significant modifier of treatment response in both advanced NSCLC and UC that are treated with ICIs, compromising the effectiveness of ICIs to be less than that of chemotherapy. Thus, clinicians should avoid unnecessary PPI prescription in these patients. “PPI-induced dysbiosis”, on the other hand, does not alter the treatment response to ICIs in advanced melanoma, RCC, HCC, and SCC of the head and neck. Future high quality prospective studies including more cancer types, and more detailed PDL-1 status are warranted.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers15010284/s1, Supplementary Method 1. PRISMA NMA Checklist of Items to Include When Reporting A Systematic Review Involving a Network Meta-analysis. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; NMA, network meta-analysis; Supplementary Method 2. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist; Supplementary Method 3. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) checklist; Supplementary Method 4. Search strategy; Supplementary Method 5. Data extraction and data synthesis; Supplementary Results 1. Details of full-text inspection; Supplementary Results 2. Details of risk of bias assessment for survival outcome (Figure S1); Supplementary Results 3. Details of NMA of overall survival and progression-free survival (Figures S2–S7); Supplementary Results 4. Network meta-analysis after excluding melanoma trials (Figures S8–S16); Supplementary Results 5. Sensitivity analyses of NMA, excluding first line therapy (Figures S17–S23); Supplementary Results 6. Sensitivity analyses of NMA, excluding unknown PDL-1 status (Figures S24–S30); Supplementary Results 7. Assessment of publication bias in pairwise meta-analysis of ICI cohorts (Figures S31 and S32); Supplementary Results 8. Sensitivity analysis of excluding studies of high risk of bias (Figures S33 and S34); Supplementary Results 9. Pairwise meta-analyses of chemotherapy cohorts (Figures S35–S38); Supplementary Results 10. Pairwise meta-analyses using adjusted hazard ratios (HR) (Figures S39–S44); Table S1. Eligibility criteria of trial patients included in NMA; Table S2. Details of uncategorized cancers; Table S3. Effect modifiers across the network; Table S4. Details of proton pump inhibitors; Table S5. Covariates of studies reporting adjusted estimates; Table S6. Summary of findings of our meta-analysis.

Author Contributions

Conceptualization, Y.C. and K.-Y.C.; methodology, Y.-N.K. and K.-Y.C.; formal analysis, W.-Y.L., Y.-C.C. and K.-Y.C.; investigation, W.-Y.L., Y.-C.C. and C.-H.H.; data curation, C.-H.H., E.A.-L., T.-H.W. and T.-H.T.; writing—original draft preparation, W.-Y.L. and C.-H.H.; writing—review and editing, Y.C., Y.-C.C., H.-E.T., Y.-N.K. and K.-Y.C.; supervision, Y.-N.K. and K.-Y.C. All authors have read and agreed to the published version of the manuscript. Please refer to the CRediT taxonomy for the term explanation.

Funding

This research received no external funding.

Acknowledgments

Our team would like to thank Tim Stubbings for manuscript proofreading.

Conflicts of Interest

The authors declare no conflict of interest.

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Cancers 15 00284 g001 550
Figure 1. PRISMA flowchart diagram. We initially extracted a total of 21,059 potential references, including 12,060 from PubMed, 7424 from Embase, 932 from Medline, and 643 from CENTRAL. After a duplicate exclusion, 18,962 studies were identified. Screening the titles and abstracts yielded 102 full-text articles, the eligibility of which was assessed. Forty studies were excluded after reading whole texts owing to reasons elaborated in Supplementary Results 1. Eventually, 62 studies fulfilled the eligibility criteria and were included for qualitative and quantitative syntheses. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Figure 1. PRISMA flowchart diagram. We initially extracted a total of 21,059 potential references, including 12,060 from PubMed, 7424 from Embase, 932 from Medline, and 643 from CENTRAL. After a duplicate exclusion, 18,962 studies were identified. Screening the titles and abstracts yielded 102 full-text articles, the eligibility of which was assessed. Forty studies were excluded after reading whole texts owing to reasons elaborated in Supplementary Results 1. Eventually, 62 studies fulfilled the eligibility criteria and were included for qualitative and quantitative syntheses. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Cancers 15 00284 g001
Cancers 15 00284 g002 550
Figure 2. Network plot for comparative association among PPI, ICI, and chemotherapy in terms of (A) overall survival and (B) progression-free survival. The thickness of the connecting line corresponds to the number of trials among comparators. We specifically highlight the arm ICI with baseline PPI as red node, the red arrow as its significant comparison with ICI, and chemotherapy without baseline PPI to reiterate our main findings. Blue arrows also indicate significant survival association between two nodes. Conversely, gray arrows suggest little association between two arms. PPI, proton pump inhibitor; ICI, immune checkpoint inhibitor; and HR, hazard ratio.
Figure 2. Network plot for comparative association among PPI, ICI, and chemotherapy in terms of (A) overall survival and (B) progression-free survival. The thickness of the connecting line corresponds to the number of trials among comparators. We specifically highlight the arm ICI with baseline PPI as red node, the red arrow as its significant comparison with ICI, and chemotherapy without baseline PPI to reiterate our main findings. Blue arrows also indicate significant survival association between two nodes. Conversely, gray arrows suggest little association between two arms. PPI, proton pump inhibitor; ICI, immune checkpoint inhibitor; and HR, hazard ratio.
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Cancers 15 00284 g003 550
Figure 3. Forest plot of comparative (A) overall survival and (B) progression-free survival in cancer patients treated with ICI between PPI users and non-users. The size of squares is proportional to the weight of each study. Horizontal lines indicate the 95% CI of each study; diamond, the pooled estimate with 95%; CI, confidential interval; HCC, hepatocellular carcinoma; HR, hazard ratio; NSCLC, non-small cell lung cancer; PPI, proton pump inhibitor; RCC, renal cell carcinoma; SCC, squamous cell carcinoma; and UC, urothelial carcinoma.
Figure 3. Forest plot of comparative (A) overall survival and (B) progression-free survival in cancer patients treated with ICI between PPI users and non-users. The size of squares is proportional to the weight of each study. Horizontal lines indicate the 95% CI of each study; diamond, the pooled estimate with 95%; CI, confidential interval; HCC, hepatocellular carcinoma; HR, hazard ratio; NSCLC, non-small cell lung cancer; PPI, proton pump inhibitor; RCC, renal cell carcinoma; SCC, squamous cell carcinoma; and UC, urothelial carcinoma.
Cancers 15 00284 g003
Table
Table 1. Study characteristics.
Table 1. Study characteristics.
Included StudiesStudy TypeCountryInclusion
Period		Sample Size, nTherapeutic ModalityTreatment-Naïve,
n (%)		PPI *,
n (%)		PPI Use WindowH2-Blocker,
n (%)
Immune checkpoint inhibitors (n= 36) CTLA-4, n (%)PD-1, n (%)PD-L1, n (%)
Advanced NSCLC (n = 17)
IMpower130 [71]RCT8 countries2015–201748300483 (100)483 (100)N/A¶Following RxN/A
IMpower131 [51]RCT26 countries2015–201768100681 (100)681 (100)N/A¶Following RxN/A
IMpower150 [20]RCT26 countries2015–201680200802 (100)N/A290 (36.2)30 d before/after RxN/A
POPLAR [22]RCT USA/Europe2013–201414400144 (100)0 (0)N/A30 d before/after RxN/A
OAK [21]RCT31 countries2014–201561300613 (100)0 (0)N/A30 d before/after RxN/A
Baek 2022 [31]Retrospective South Korea2017–201816460 1595 (97)51 (3)0 (0)823 (50)30 d before RxN/A
Takada 2022 [67]RetrospectiveJapan2016–201995085 (89.5)10 (10.5)N/A37 (39)N/AN/A
Alessio 2021 [38]Retrospective Italy 2017–20209500950 (100)0950 (100)474 (49.9)N/AN/A
Balado 2021 [34]RetrospectiveSpain2017–202049049 (100)049 (100)26 (53.1)30 d before/after RxN/A
Kostine 2021 [75]RetrospectiveFrance2015–2017150N/AN/AN/AN/AN/A30 d before/after RxN/A
Muira 2021 [56]RetrospectiveJapan2016–20183000300 (100)040 (13.3)163 (54.3)N/AN/A
Rounis 2021 [61]Retrospective Greece2017~201966066 (1000)00 (0)23 (34.8)90 d before RxN/A
Verschueren 2021 [69]RetrospectiveNetherlands2015~20192210214 (97)7 (3)84 (38)96 (43.4)30 d before/after RxN/A
Estevez 2020 [37]RetrospectiveSpain2015~201870064 (91.4)6 (8.6)0 (0)59 (84.3)90 d before RxN/A
Hossain 2020 [47]RetrospectiveAustralia2015~201963N/AN/AN/AN/A34 (54)28 d after RxN/A
Svaton 2020 [65]RetrospectiveCzech2015~20192240224 (100)09 (4.0)64 (28.6)30 d before/after RxN/A
Zhao 2019 [74]RetrospectiveChina 2016–20181090109 (100)028 (25.7)40 (36.7)30 d before/after RxN/A
Advanced UC (n = 8)
IMvigor 210 [32]RCT USA2014~201542900429 (100)119 (22.5)141 (32.9)30 d before/after RxN/A
IMvigor 211 [19]RCT USA2015~201746700467 (100)0 (0)145 (31.0)30 d before/after RxN/A
Fukuokaya 2022 [79]RetrospectiveJapanN/A227N/AN/AN/AN/A56 (24.7)N/AN/A
Kunimitsu 2022 [76]RetrospectiveJapan2017~2020790079 (100)0 (0)34 (43.0)30 d before/60 d after RxN/A
Okuyama 2022 [58]Retrospective Japan2015~202115500155 (100)0 (0)99 (63.9)30 d before RxN/A
Tomisaki 2022 [68]RetrospectiveJapan2018–202140040 (100)00 (0)15 (37.5)60 d before/30 d after RxN/A
Bañobre 2021 [62]RetrospectiveSpain2016~2020119039 (32.7)80 (67.3)22 (18.5)54 (45.4)N/AN/A
Lida 2021 [80]RetrospectiveJapan2018~202111500115 (100)0 (0)N/A30 d before/after RxN/A
Advanced melanoma (n = 8)
CheckMate 066 [81]RCT80 centers2013~2021210Nivolumab: 210 (100)210 (100)49 (23.3)30 d before RxN/A
CheckMate 067 [82]RCT21 countries 2013~now945Ipilimumab + Nivolumab: 314 (33.2);
Ipilimumab: 315 (33.3); Nivolumab: 316 (33.4)		945 (100)161 (17.0)30 d before RxN/A
CheckMate 069 [83]RCTFrance/
USA		2013~2021142Ipilimumab + Nivolumab: 95 (67.0); Ipilimumab: 47 (33.1) 142 (100)33 (23.3)30 d before RxN/A
Gaucher 2021 [41]Retrospective Brazil2010~201911015 (13.6)68 (61.8)27 (24.6)110 (100)39 (35.5)60 d after RxN/A
Kostine 2021 [75]RetrospectiveFrance2015~2017293N/AN/AN/AN/AN/A30 d before/after RxN/A
Peng 2021 [59]RetrospectiveUSA2014~20192330233 (100)095 (40.8)89 (38.2)30 d before/after RxN/A
Afzal 2019 [29]RetrospectiveLebanonN/A120Ipilimumab and/or PembrolizumabN/A29 (24.2)N/AN/A
Failing 2016 [40]RetrospectiveUSA2011~2014159Ipilimumab:159 (100)80 (50) §39 (24.5)N/A9 (6)
Advanced RCC (n = 3)
Mollica 2022 [57]Retrospective USA2010~20216363 (100)063 (100)63 (100)25 (39.7)N/AN/A
Mollica 2022 [57]Retrospective USA2010~202115600156 (100)110 (70.5)88 (56.4)N/AN/A
Kostine 2021 [75]RetrospectiveFrance2015~201783N/AN/AN/AN/AN/A30 d before/after RxN/A
Peng 2021 [59]RetrospectiveUSA2014~20192330233 (100)095 (40.8)89 (38.2)30 d before/after RxN/A
HCC (n = 2)
Jun 2020 [52]RetrospectiveUSA2017~201931421 (7)293 (93)0137 (43.6)110 (35.0)30 d before Rx45 (14.3)
Lee 2020 [55]RetrospectiveTaiwan2017~201994N/AN/AN/AN/A30 (31.9)30 d before RxN/A
Uncategorized cancers † (n = 10)
Araujo 2021 [30]RetrospectiveBrazilN/A21635 (16.2)130 (60.2)27 (12.5)0 (0)114 (52.8)60 d before/after RxN/A
Buti 2021 [9]RetrospectiveItaly2014~201921713 (6.0)186 (85.7)18 (8.3)45 (20.7)104 (47.9)N/AN/A
Gaucher 2021 [41]Retrospective Brazil2010~201937025 (5.4)357 (94.6)087 (23.4)149 (40.1)60 d after RxN/A
Giordan 2021 [42]RetrospectiveFrance2018~201915400154 (100)64 (41.6)47 (30.5)30 d before RxN/A
Husain 2021 [49]RetrospectiveUSA2011~20191091274 (25.1)817 (74.9)N/A415 (38.0)N/AN/A
Peng 2021 [59]RetrospectiveUSA2014~20192330233 (100)095 (40.8)89 (38.2)30 d before/after RxN/A
Alessio 2020 [10]RetrospectiveItaly2014~202010120956 (94.5)56 (5.5)396 (39.1)491 (48.5)N/A56 (5.5)
Santamaria 2020 [50]RetrospectiveSpain2015~20181021 (1.0)86 (84.3)15 (14.7)73 (71.6)78 (77.2)30 d before/after RxN/A
Ruiz 2020 [60]RetrospectiveSpainfrom 201525331 (12.3)222 (87.7)0 (0)73 (28.9)135 (53.4)60 d before/30 d after RxN/A
Kostine 2021 [75]RetrospectiveFrance2015~20176353 (0.5)435 (68.5)66 (10)N/A293 (46.1)30 d before/after RxN/A
Chemotherapy (n = 19)
Advanced NSCLC (n = 6)
Verschueren 2021 [69]RetrospectiveNetherlands2015–2019221Platinum-based agents84 (38)101 (45.7)30 d before/after RxN/A
Impower 130 [71]RCT8 countries2015–2017240Platinum-based agents240 (100)N/A¶Following RxN/A
IMpower 131 [51]RCT26 countries2015–2017340Platinum-based agents340 (100)N/A¶Following RxN/A
IMpower 150 [20]RCT26 countries2015–2016400Platinum-based agentsN/A151 (37.8)30 d before/after RxN/A
POPLAR [22]RCT USA/Europe2013~2014143 Docetaxel 0 (0)N/A30 d before/after RxN/A
OAK [21]RCT31 countries2014~2015612 Docetaxel 0 (0)N/A30 d before/after RxN/A
Advanced UC (n = 1)
IMvigor 211 [19]RCTUSA2015~2017464Platinum-based agents0 (0)185 (39.9)30 d before/after RxN/A
Advanced melanoma (n = 1)
CheckMate 066RCT80 centers2013~2021208Decarbazine 0 (0)48 (23.1)30 d before RxN/A
Uncategorized cancers † (n = 1)
Alessio 2021 [38]Retrospective Italy 2017~2020595Platinum-based agents595 (100)321 (53.7)N/AN/A
FOLFOX, n (%)FOLFIRI/
IFL, n (%)		Cape-based, n (%)
Gastroesophageal carcinoma (n = 1)
TRIO013/
LOGiC [43]		RCT 22 countries 2008~201227400274 (100)274 (100)119 (43.4)20% overlapping RxN/A
Early-stage colorectal cancer (n = 3)
Kitazume 2022 [77]RetrospectiveJapan2009~201460600606 (100)606 (100)54 (8.9)20% overlapping RxN/A
Wong 2019 [72]Retrospective Canada2004~2013389175 (45)0214 (55)389 (100)99 (25.4)During RxN/A
Sun 2016 [64]Retrospective Canada2008~201229800298 (100) *298 (100)77 (26.0)During RxN/A
Advanced colorectal cancer (n = 8)
Wang 2017 [70]RetrospectiveChina2010~2014671307 (45.8)0364 (54.2)N/A474 (70.6)During RxN/A
AXEPT [73]RCTAsia2013~20154820243 (50.4)239 (49.6)0 (0)49 (10.2)20% overlapping RxN/A
HORIZON III [92]RCT28 countries2006~2009666666 (100)00666 (100)87 (13.0)During Rx15 (2.2)
N016966 [63]RCT N/A2004~200520351018 (50)01017 (50)2035 (100)327 (32.1)During Rx115 (5.7)
AVF2107g [48]RCT 3 countries 2000~20027800780 (100)0780 (100)156 (20.0)During Rx129 (16.5)
Carrato 2013 [33]RCT N/A2007~20103480348 (100)0348 (100)39 (11.0)During Rx15 (4.2)
VELOUR [39]RCT 28 countries2007~20105840584 (100)00 (0)105 (18.0)During Rx16 (2.8)
RAISE [66]RCT 24 countries 2010~20139460946 (100)00 (0)232 (24.5)During Rx36 (3.8)
Post hoc analysis of RCTs (n = 8)
Hopkins 2022 [44]Analysis of 5 trials [20,21,22,51,71]4458Advanced NSCLCImmune checkpoint and chemotherapyN/A1225 (27.5)¶30 d before/after RxN/A
Hopkins 2021 [46]Analysis of IMpower 150 [20]1202Advanced NSCLCImmune checkpoint and chemotherapyN/A441 (36.7)30 d before/after RxN/A
Homicsko 2022 [78]Analysis of CheckMate 066 [81]/067 [82]/069 [83]1505Advanced MelanomaImmune checkpoint and chemotherapy1505 (100)291 (19.3)30 d before RxN/A
Chalabi 2020 [35]Analysis of OAK7 and POPLAR [21,22]757Advanced NSCLCImmune checkpoint and chemotherapy0 (0)234 (30.9)30 d before/after RxN/A
Hopkins 2020 [45]Analysis of IMvigor 210 [24] and 211 [19,32]1360Advanced UCImmune checkpoint and chemotherapy119 (8.75)471 (34.6)30 d before/after RxN/A
Kim 2021 [54]Analysis of AXEPT [73]482Advanced CRCChemotherapy0 (0)49 (10.2)20% overlapping RxN/A
Kichenadasse 2021 [53]Analysis of 6 trials [33,39,48,63,66,92]5359Advanced CRCChemotherapy3829 (71.4)946 (17.7)During RxN/A
Chu 2017 [36]Analysis of TRIO013/LOGiC [43]274Advanced GECChemotherapy274 (100)119 (43.4)20% overlapping RxN/A
Abbreviations: RCT, randomised–controlled trial; PPI, proton pump inhibitor; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; PD-1, programmed cell death protein 1; PDL-1, programmed death ligand 1; Rx, therapy; N/A, non-available; NSCLC, non-small cell lung cancers; RCC, renal cell carcinoma; UC, urothelial carcinoma; GEC, gastroesophageal carcinoma; CRC, colorectal cancer; Cape-based, capecitabine-based; IFL, irinotecan/leucovorin/5-FU; FOLFIRI, 5-FU/folinic acid/irinotecan; and FOLFOX, 5-FU/leucovorin/ oxaliplatin. † The details of cancer type (including the type and treatment line of immune therapy and the PDL-1 expression information) are available in Supplementary Table S2 * Details of PPI are presented in Supplementary Table S3, ¶ Although both IMpower 130 and 131 did report the use of PPI in their study population, they did not separately provide the number of PPI use in patients taking ICI and chemotherapy. Thus, we noted it as N/A but the overall use of PPI was provided in post hoc analysis (Hopkins 2022). § Failing 2016, only used cohort receiving first-line therapy for survival analyses.
Table
Table 2. League table of pairwise comparisons in the network for the hazard ratio (with 95% CIs) of OS (A) and PFS (B).
Table 2. League table of pairwise comparisons in the network for the hazard ratio (with 95% CIs) of OS (A) and PFS (B).
(A) Overall Survival
Immune check point inhibitors
(p-score, 1.0000)		0.76 (0.67–0.85)0.70 (0.62–0.78) 0.64 (0.53–0.78)
0.79 (0.72–0.86)Chemotherapy
(p-score, 0.6664)		0.85 (0.70–1.04)0.82 (0.76–0.89)
0.67 (0.60–0.73)0.85 (0.76–0.94)Immune check point inhibitors and PPI
(p-score, 0.2016)		1.07 (0.92–1.24)
0.66 (0.60–0.72)0.84 (0.78–0.90)0.99 (0.89–1.09)Chemotherapy and PPI
(p-score, 0.1319)
(B) Progression-Free Survival
Immune check point inhibitors
(p-score, 0.9706)		0.84 (0.70–1.00)0.90 (0.71–1.13)0.72 (0.61–0.85)
0.92 (0.81–1.04)Chemotherapy
(p-score, 0.6958)		0.84 (0.77–0.92)0.81 (0.64–1.03)
0.80 (0.70–0.91)0.87 (0.80–0.94)Chemotherapy and PPI
(p-score, 0.3217)		0.83 (0.68–1.02)
0.71 (0.62–0.80)0.77 (0.67–0.88)0.89 (0.78–1.01)Immune check point inhibitors and PPI
(p-score, 0.0119)
Note: Treatments are ranked by their p-score of overall survival with the top left representing the best, whereas the bottom right represents the worst. Estimates in the upper right triangle are direct comparisons, while those in the lower left are from the network meta-analysis.
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Chang, Y.; Lin, W.-Y.; Chang, Y.-C.; Huang, C.-H.; Tzeng, H.-E.; Abdul-Lattif, E.; Wang, T.-H.; Tseng, T.-H.; Kang, Y.-N.; Chi, K.-Y. The Association between Baseline Proton Pump Inhibitors, Immune Checkpoint Inhibitors, and Chemotherapy: A Systematic Review with Network Meta-Analysis. Cancers 2023, 15, 284. https://doi.org/10.3390/cancers15010284

AMA Style

Chang Y, Lin W-Y, Chang Y-C, Huang C-H, Tzeng H-E, Abdul-Lattif E, Wang T-H, Tseng T-H, Kang Y-N, Chi K-Y. The Association between Baseline Proton Pump Inhibitors, Immune Checkpoint Inhibitors, and Chemotherapy: A Systematic Review with Network Meta-Analysis. Cancers. 2023; 15(1):284. https://doi.org/10.3390/cancers15010284

Chicago/Turabian Style

Chang, Yu, Wan-Ying Lin, Yu-Cheng Chang, Chin-Hsuan Huang, Huey-En Tzeng, Eahab Abdul-Lattif, Tsu-Hsien Wang, Tzu-Hsuan Tseng, Yi-No Kang, and Kuan-Yu Chi. 2023. "The Association between Baseline Proton Pump Inhibitors, Immune Checkpoint Inhibitors, and Chemotherapy: A Systematic Review with Network Meta-Analysis" Cancers 15, no. 1: 284. https://doi.org/10.3390/cancers15010284

APA Style

Chang, Y., Lin, W. -Y., Chang, Y. -C., Huang, C. -H., Tzeng, H. -E., Abdul-Lattif, E., Wang, T. -H., Tseng, T. -H., Kang, Y. -N., & Chi, K. -Y. (2023). The Association between Baseline Proton Pump Inhibitors, Immune Checkpoint Inhibitors, and Chemotherapy: A Systematic Review with Network Meta-Analysis. Cancers, 15(1), 284. https://doi.org/10.3390/cancers15010284

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