Dataset Information

Cytokines and Immune Cell Phenotype in Acute Kidney Injury Associated With Immune Checkpoint Inhibitors.

ABSTRACT:

Introduction

Immune checkpoint inhibitors (ICIs) induce impressive antitumor responses but may lead to acute kidney injury (AKI) associated with ICI therapy (AKI-ICI). Biomarkers distinguishing AKI-ICI from AKI because of other causes (AKI-other) are currently lacking. Because ICIs block immunoregulatory pathways, we hypothesized that biomarkers related to immune cell dysregulation, including tumor necrosis factor alpha (TNF-α) and other markers of B and T cell activation in the systemic circulation and kidney tissue, may aid with the diagnosis of AKI-ICI.

Methods

This is a prospective study consisting of 24 participants who presented with AKI during ICI therapy, adjudicated to either have AKI-ICI (n = 14) or AKI-other (n = 10). We compared markers of kidney inflammation and injury (neutrophil gelatinase-associated lipocalin, kidney injury molecule-1) as well as plasma and urine levels of T cell-associated cytokines (TNF-α, interferon-γ, interleukin (IL)-2, IL-4, IL-6, IL-8, IL-9, and IL-10) between groups. We also compared T-cell responses in the systemic circulation and in kidney tissue across groups, using mass cytometry systems.

Results

We observed increase in several specific immune cells, including CD4 memory, T helper cells, and dendritic cells in the kidney tissue, as well as in the urine cytokines IL-2, IL-10, and TNF-α, in patients who developed AKI-ICI compared to patients with AKI-other (P < 0.05 for all). The discriminatory ability of TNF-α on AKI cause was strong (area under the curve = 0.814, 95% confidence interval: 0.623-1.00. The CD4+ T cells with memory phenotype formed the dominant subset.

Conclusion

These results suggest that specific T-cell responses and their respective cytokines may be indicative of AKI associated with ICI therapy and may help to differentiate AKI-ICI from AKI-other. Urine TNF-α is a promising biomarker for AKI-ICI, which is most often caused by acute interstitial nephritis (AIN), and TNF-α pathway may serve as a potential target for therapeutic intervention.

SUBMITTER: Farooqui N

PROVIDER: S-EPMC10014345 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Cytokines and Immune Cell Phenotype in Acute Kidney Injury Associated With Immune Checkpoint Inhibitors.

Farooqui Naba N Zaidi Mark M Vaughan Lisa L McKee Trevor D TD Ahsan Eram E Pavelko Kevin D KD Villasboas Jose C JC Markovic Svetomir S Taner Timucin T Leung Nelson N Dong Haidong H Alexander Mariam P MP Herrmann Sandra M SM

Kidney international reports 20221205 3

<h4>Introduction</h4>Immune checkpoint inhibitors (ICIs) induce impressive antitumor responses but may lead to acute kidney injury (AKI) associated with ICI therapy (AKI-ICI). Biomarkers distinguishing AKI-ICI from AKI because of other causes (AKI-other) are currently lacking. Because ICIs block immunoregulatory pathways, we hypothesized that biomarkers related to immune cell dysregulation, including tumor necrosis factor alpha (TNF-α) and other markers of B and T cell activation in the systemic ...[more]

PMID: 36938084

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Project description:With the development and introduction of immune checkpoint inhibitors (ICIs) in cancer patients, immune-related side effects have increasingly attracted attention. However, the risks of immune-related renal toxicity are poorly characterized. In this study, we performed a network meta-analysis (NMA) of ICI-related randomized clinical trials (RCTs) to elucidate the comparative risk of acute kidney injury (AKI) in cancer patients receiving different ICIs. We also sought to identify other factors potentially affecting the risk of AKI. PubMed and EMBASE were searched for peer-reviewed trial reports published between January 2000 and May 2021. Eligible studies were RCTs studying ICIs in cancer patients and reporting AKI data. We performed a frequentist NMA to evaluate the risk ratios for grade 1-5 and grade 3-5 AKI between the treatment groups. We also assessed the absolute incidence of AKI in the ICI-containing arm using traditional direct meta-analysis. Once significant heterogeneity was detected in a traditional direct meta-analysis, multivariable meta-regression analysis was applied to identify factors that significantly affected the absolute incidence of AKI. A total of 85 RCTs were included in this study. In the NMA for the risk of grade 1-5 and 3-5 AKI, ipilimumab showed a significantly higher risk than avelumab and durvalumab, whereas 1 mg/kg nivolumab plus 3 mg/kg ipilimumab (N1I3) showed a significantly higher risk than other groups. In terms of treatment ranking, durvalumab ± low-dose tremelimumab and avelumab were consistently among the top three safest treatments for grade 1-5 or 3-5 AKI, whereas N1I3, ipilimumab and tremelimumab were consistently among the top three treatments with the highest risk for grade 1-5 or 3-5 AKI. Compared with other cancers, renal cell carcinoma and urothelial carcinoma showed a significantly higher risk of AKI. The incidence of AKI was significantly higher with ICI+chemotherapy than with ICI monotherapy. In this NMA involving large-scale up-to-date ICI trials, we demonstrated the comparative safety of existing ICI drugs for grade 1-5 and grade 3-5 AKI. Based on data from the ICI arms of these trials, we also revealed several potential risk factors for immune-related AKI, including tumor type and treatment paradigm.

Project description:ObjectivesImmune checkpoint inhibitors (ICPi) have significantly improved survival for patients with advanced cancers. However, the occurrence of ICPi-associated acute kidney injury (AKI) and its clinical impact remains unclear. This study evaluates the effects of ICPi-associated AKI (ICPi-AKI) on mortality, kidney and cardiovascular outcomes in patients undergoing ICPi treatments.DesignThis multicentre retrospective cohort study with propensity score matching to balance baseline characteristics. The International Classification of Diseases, 10th Revision codes were used to identify individuals with cancer and treated with ICPi concurrently. Kaplan-Meier analyses coupled with log-rank tests were conducted to estimate the survival probabilities.SettingData were sourced from the TriNetX database spanning records from 25 March 2011 to 5 April 2024.ParticipantsPatients with cancer aged ≥18 years treated with ICPi with or without AKI occurrence.Primary and secondary outcome measuresThe primary outcome was all-cause mortality, and secondary outcomes included major adverse kidney events (MAKE), major adverse cardiovascular events (MACE), the composite of MAKE or MACE with death, and end-stage renal disease.ResultsThe study identified 926 patients with cancer who developed ICPi-AKI (mean age, 67.1±11.8 years; 57.4% men). The control group consisted of 48 147 patients treated with ICPi but did not develop AKI (mean age, 65.3±13.1 years; 53.7% men). After matching, the ICPi-AKI group exhibited a higher risk of all-cause mortality (HR=1.27; 95% CI 1.02 to 1.61), MAKE (HR=3.83; 95% CI 1.72 to 8.40), MACE (HR=1.35; 95% CI 1.03 to 1.75)) compared with the non-ICPi-AKI group. Subgroup analyses confirmed these findings across various patient's characteristics.ConclusionIndividuals with ICPi-AKI are associated with an increased risk of all-cause mortality, MAKE and MACE. Enhancing awareness and timely intervention for ICPi-AKI are crucial for improving prognosis and reducing complications among patients with cancer.

Project description:Background and objectives: Immune checkpoint inhibitor use in oncology is increasing rapidly. We sought to determine the frequency, severity, cause, and predictors of AKI in a real-world population receiving checkpoint inhibitors.Design, setting, participants, & measurements: We included all patients who received checkpoint inhibitor therapy from May 2011 to December 2016 at Massachusetts General Hospital. Baseline serum creatinine, averaged 6 months before checkpoint inhibitor start date, was compared with all subsequent creatinine values within 12 months of starting therapy. AKI was defined by Kidney Disease: Improving Global Outcomes criteria for fold changes in creatinine from baseline. Sustained AKI events lasted at least 3 days and was our primary outcome. The cause of sustained AKI was determined by chart review. Cumulative incidence and subdistribution hazard models were used to assess the relationship between baseline demographics, comorbidities, and medications, and sustained AKI and potential checkpoint inhibitor-related AKI.Results: We included 1016 patients in the analysis. Average age was 63 (SD 13) years, 61% were men, and 91% were white. Mean baseline creatinine was 0.9 mg/dl (SD 0.4 mg/dl), and 169 (17%) had CKD (eGFR<60 ml/min per 1.73 m2) at baseline. A total of 169 patients (17%) experienced AKI, defined by an increase in creatinine at least 1.5 times the baseline within 12 months; 82 patients (8%) experienced sustained AKI and 30 patients (3%) had potential checkpoint inhibitor-related AKI. The first episode of sustained AKI occurred, on average, 106 days (SD 85) after checkpoint inhibitor initiation. Sixteen (2%) patients experienced stage 3 sustained AKI and four patients required dialysis. Proton pump inhibitor use at baseline was associated with sustained AKI.Conclusions: AKI is common in patients receiving checkpoint inhibitor therapy. The causes of sustained AKI in this population are heterogenous and merit thorough evaluation. The role of PPI and other nephritis-inducing drugs in the development of sustained AKI needs to be better defined.

Project description:Background: The benefits of immune checkpoint inhibitors (ICPis) in the treatment of patients with malignancies emerged recently, but immune-related adverse events (IRAEs), including acute kidney injury (AKI), cannot be ignored. The present study established and validated an ICPi-AKI prediction model based on machine learning algorithms to achieve early prediction of AKI events and timely intervention adjustment. Methods: We performed a retrospective study based on data from the First Medical Center of the PLA General Hospital. Patients with malignancy who received at least one dose of ICPi between January 2014 and December 2019 were included in the study. The characteristics of available variables were included after case review, and the baseline characteristics and clinical data of ICPi AKI and non-AKI patients were compared. After variable preprocessing, eight machine learning algorithms were used to construct a full variable availability model. Variable simplification models were constructed after screening important variables using the random forest recursive feature elimination method, and the performance of different machine learning methods and two types of modeling strategies were evaluated using multiple indicators. Results: Among the 1616 patients receiving checkpoint inhibitors, the overall incidence of AKI was 6.9% during the total follow-up time. Sixty-eight patients were associated with ICPi treatment after chart review, primarily in AKI stage 1 (70.5%), with a median time from first ICPi administration to AKI of 12.7 (IQR 2 to 56) weeks. The demographic characteristics, comorbidities, and proportions of malignancy types were similar between the ICPi-AKI and non-AKI groups, but there were significant differences in multiple characteristics, such as concomitant medications and laboratory test indicators. For model performance evaluation and comparison, the AUC values of all 38 variable availability models ranged from 0.7204−0.8241, and the AUC values of the simplicity model constructed using 16 significant variables ranged from 0.7528−0.8315. The neural networks model (NNs) and support vector machine (SVM) model had the best performance in the two types of modeling strategies, respectively; however, there was no significant difference in model performance comparison (p > 0.05). In addition, compared with the full variable availability model, the performance of the variable simplicity model was slightly improved. We also found that concomitant medications contributed more to the model prediction performance by screening the optimal feature combination. Conclusion: We successfully developed a machine learning-based ICPi-AKI prediction model and validated the best prediction performance of each machine model. It is reasonable to believe that clinical decision models driven by artificial intelligence can improve AKI prediction in patients with malignancies treated with ICPi. These models can be used to assist clinicians in the early identification of patients at high risk of AKI, support effective prevention and intervention, and ultimately improve the overall benefit of antitumor therapy in the target population.

Project description:BackgroundImmune checkpoint inhibitors (ICIs) have been associated with acute kidney injury (AKI). However, the occurrence rate of ICI-related AKI has not been systematically examined. Additionally, exposure to proton pump inhibitors (PPIs) and non-steroidal anti-inflammatory drugs (NSAIDs) were considered as risk factors for AKI, but with inconclusive results in ICI-related AKI. Our aim was to analyse the occurrence rate of all-cause AKI and ICI-related AKI and the occurrence rates of severe AKI and dialysis-requiring AKI, and to determine whether exposure to PPIs and NSAIDs poses a risk for all-cause and ICI-related AKI.MethodsThis study population was adult ICI recipients. A systematic review was conducted by searching MEDLINE, Embase and PubMed through October 2023. We included prospective trials and observational studies that reported any of the following outcomes: the occurrence rate of all-cause or ICI-related AKI, the relationship between PPI or NSAID exposure and AKI development or the mortality rate in the AKI or non-AKI group. Proportional meta-analysis and pairwise meta-analysis were performed. The evidence certainty was assessed using the Grading of Recommendations Assessment, Development and Evaluation framework.ResultsA total of 120 studies comprising 46 417 patients were included. The occurrence rates of all-cause AKI were 7.4% (14.6% from retrospective studies and 1.2% from prospective clinical trials). The occurrence rate of ICI-related AKI was 3.2%. The use of PPIs was associated with an odds ratio (OR) of 1.77 [95% confidence interval (CI) 1.43-2.18] for all-cause AKI and an OR of 2.42 (95% CI 1.96-2.97) for ICI-related AKI. The use of NSAIDs was associated with an OR of 1.77 (95% CI 1.10-2.83) for all-cause AKI and an OR of 2.57 (95% CI 1.68-3.93) for ICI-related AKI.ConclusionsOur analysis revealed that approximately 1 in 13 adult ICI recipients may experience all-cause AKI, while 1 in 33 adult ICI recipients may experience ICI-related AKI. Exposure to PPIs and NSAIDs was associated with an increased OR risk for AKI in the current meta-analysis.

Dataset Information

Cytokines and Immune Cell Phenotype in Acute Kidney Injury Associated With Immune Checkpoint Inhibitors.

Introduction

Methods

Results

Conclusion

Publications

Cytokines and Immune Cell Phenotype in Acute Kidney Injury Associated With Immune Checkpoint Inhibitors.

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