Severe Immune-Related Adverse Events in Patients Treated with Nivolumab for Metastatic Renal Cell Carcinoma Are Associated with PDCD1 Polymorphism

Evaluation of the associations of interlukin-7 genetic variants with toxicity and efficacy of immune checkpoint inhibitors: A replication study of a Japanese population, based on the findings of a European genome-wide association study

AIM

Recent genome-wide association studies of European populations have identified rs16906115, a single-nucleotide polymorphism in the interleukin-7 gene, as a predictor of immune-related adverse events (irAEs) and the therapeutic efficacy of immune checkpoint inhibitors. We evaluated this single-nucleotide polymorphism in a Japanese population.

METHODS

From January 2021, we stored host DNA from individuals who received various types of immune checkpoint inhibitors. From this population, we categorized 510 participants into cases (grade ≥2 irAEs) and controls (received ≥3 immune checkpoint inhibitor doses, follow-up ≥12 weeks, no irAEs), and divided 339 hepatocellular carcinoma patients treated with atezolizumab/bevacizumab into responders and non-responders, evaluated using the modified response evaluation criteria in solid tumors. We compared the minor allele frequencies of rs16906115 between cases and controls, and responders and non-responders.

RESULTS

In the irAE prediction analysis of 234 cases and 276 controls, the minor allele frequency was 0.244 in the case group and 0.265 in the control group. This difference is not significant. In the analysis predicting the therapeutic efficacy for hepatocellular carcinoma patients, the responders had a significantly lower minor allele frequency of 0.220, compared with 0.300 for the non-responders (p = 0.022). Univariate and multivariate analyses identified the minor allele homozygosity as a significant predictor of treatment response, with odds ratios of 0.292 (p = 0.015) in the univariate analysis and 0.315 (p = 0.023) in the multivariate analysis.

CONCLUSIONS

In our Japanese cohort, no association was found between the rs16906115 minor allele and irAEs or treatment efficacy. The minor allele homozygosity may be associated with a negative therapeutic outcome.

CLINICAL TRIAL REGISTRATION

UMIN Clinical Trials Registry with the number UMIN000043798.

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Biomarkers for immune-related adverse events in cancer patients treated with immune checkpoint inhibitors

Although immune checkpoint inhibitors have greatly improved cancer therapy, they also cause immune-related adverse events, including a wide range of inflammatory side effects resulting from excessive immune activation. Types of immune-related adverse events are diverse and can occur in almost any organ, with different frequencies and severities. Furthermore, immune-related adverse events may occur within the first few weeks after treatment or even several months after treatment discontinuation. Predictive biomarkers include blood cell counts and cell surface markers, serum proteins, autoantibodies, cytokines/chemokines, germline genetic variations and gene expression profiles, human leukocyte antigen genotype, microRNAs and the gut microbiome. Given the inconsistencies in research results and limited practical utility, there is to date no established biomarker that can be used in routine clinical practice, and additional investigations are essential to demonstrate efficacy and subsequently facilitate integration into routine clinical use.

rs822336 binding to C/EBPβ and NFIC modulates induction of PD-L1 expression and predicts anti-PD-1/PD-L1 therapy in advanced NSCLC

Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 (PD-1) or its ligand 1 (PD-L1) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1, differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPβ and NFIC. As a result, silencing of C/EBPβ and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPβ and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients.

Review - The impact of pharmacogenetics on the outcome of immune checkpoint inhibitors

The development of immune checkpoint inhibitors (ICIs) has a tremendous effect on the treatment options for multiple types of cancer. Nonetheless, there is a large interpatient variability in response, survival, and the development of immune-related adverse events (irAEs). Pharmacogenetics is the general term for germline genetic variations, which may cause the observed interindividual differences in response or toxicity to treatment. These genetic variations can either be single-nucleotide polymorphisms (SNPs) or structural variants, such as gene deletions, amplifications or rearrangements. For ICIs, pharmacogenetic variation in the human leukocyte antigen molecules has also been studied with regard to treatment outcome. This review presents a summary of the literature regarding the pharmacogenetics of ICI treatment, discusses the most important known genetic variations and offers recommendations on the application of pharmacogenetics for ICI treatment.

Identification of Immune-Related Genes as Biomarkers for Uremia

Purpose

Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression.

Methods

The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis.

Conclusion

PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

Immune checkpoint inhibitors: maximizing benefit whilst minimizing toxicity

INTRODUCTION

The advent of immunotherapy has revolutionized the treatment of cancer; anti-tumor efficacy has been observed with immune checkpoint inhibitors (ICI) in ~20 different cancer types with durable responses in some cases. However, the risk of toxicity in the form of immune-related adverse events (irAE) partially counterbalances these benefits, and there are no FDA-approved biomarkers to categorize patients by likelihood of response or risk of irAEs.

AREAS COVERED

We conducted a thorough review of the literature of clinical studies regarding ICI and their toxicities. In this review, we synthesize the current body of literature about ICI treatment and irAE by summarizing the classes and uses of ICI, how to identify patients at risk for irAE, present the current understanding of irAE development, describe ongoing research into biomarkers of irAE, examine opportunities for irAE prevention, described management of steroid refractory irAE, and highlight future directions for development of prevention and management strategies.

EXPERT OPINION

While ongoing biomarker studies are promising, it is unlikely that there will be a 'one-size-fits-all' approach to categorizing irAE risk. In contrast, improved management and irAE prophylaxis are potentially in reach, and ongoing trials will help elucidate best practices.

Immune checkpoint biology in health & disease: Immune checkpoint biology and autoimmunity in cancer patients

Immune checkpoints (ICs) play a central role in maintaining immune homoeostasis. The discovery that tumours use this physiological mechanism to avoid elimination by the immune system, opened up avenues for therapeutic targeting of ICs as a novel way of treating cancer. However, this therapy a new array of autoimmune side effects, termed immune-related adverse events (irAEs). In this narrative review, we first recapitulate the physiological function of ICs that are approved targets for cancer immunotherapy (CTLA-4, PD-(L)1 and LAG-3), as the groundwork to critically discuss current knowledge on irAEs. Specifically, we summarize clinical aspects and examine a molecular classification and predisposing factors of irAEs. Finally, we discuss irAE treatment, particularly emphasizing how molecular knowledge is changing the current treatment paradigm.

Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events

Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site.