Background: Although clinical practice guidelines for the management of Immune Checkpoint Inhibitor (ICI)-related adverse events have recently been published, precise and nuanced toxicity data for combination ICI therapy are lacking. Therefore, herein we have conducted a systematic review and meta-analysis of published clinical trials on combination ICI to synthesize the treatment-related adverse event (TRAE) profile of combination ICI therapy. Methods: PUBMED, EMBASE, and the Cochrane Database/EBM were searched for eligible studies. Clinical trials evaluating combination immune checkpoint inhibitor therapy in advanced unresectable cancer were included in the analysis based on prespecified criteria. Risk of bias across studies was evaluated using Begg's funnel plot and Egger's regression test. The summary outcomes were pooled risk ratios (RR) and the logit-transformed proportion for incidence data. Results: A total of 18 studies comprising 2,767 patients across 10 cancer types were included in the final analysis. Combination ICI was associated with a slightly higher risk of all-grade adverse events (RR 1.07 [95% CI 1.03–1.11]) and markedly greater risk of grade 3 or higher adverse events (RR 2.21 [95% CI 1.57–3.10]) compared to monotherapy ICI. Subgroup analyses showed significant differences in risk of grade 3 or higher adverse events between treatment types (PD-1 + CTLA-4 and PD-L1 + CTLA-4), among cancer types, and among dosing regimens (N1I3, N3I1, and D20T1). The incidence of all-grade adverse events was 0.905 [95% CI 0.842–0.945], and the ratio of grade 3 or higher events to all-grade adverse events was 0.396 [95% CI 0.315–0.483]. The most common all-grade TRAEs were diarrhea/colitis, fatigue/asthenia, nausea/vomiting, rash, and pruritis. Conclusion: Combination ICI therapy has a significantly different treatment-related adverse event profile compared to monotherapy.

Aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that plays a crucial role in bone remodeling through altering the interplay between bone-forming osteoblasts and bone-resorbing osteoclasts. While effects of AhR signaling in osteoblasts are well understood, the role and mechanism of AhR signaling in regulating osteoclastogenesis is not widely understood. AhR, when binding with exogenous ligands (environmental pollutants such as polycylic aryl hydrocarbon (PAH), dioxins) or endogenous ligand indoxyl-sulfate (IS), has dual functions that are mediated by the nature of the binding ligand, binding time, and specific pathways of distinct ligands. In this review, AhR is discussed with a focus on (i) the role of AhR in osteoclast differentiation and function and (ii) the mechanisms of AhR signaling in inhibiting or promoting osteoclastogenesis. These findings facilitate an understanding of the role of AhR in the functional regulation of osteoclasts and in osteoclast-induced bone destructive conditions such as rheumatoid arthritis and cancer.

Studies suggest associations between the miR-146a single nucleotide polymorphisms (SNPs) and susceptibility to autoimmune diseases. However, the results are inconsistent and inconclusive. Therefore, the aim of this study was to arrive at a conclusion about the association between the three functional miR-146a SNPs and autoimmune disease risk. Studies were identified through PubMed/MEDLINE searches for studies published up to January 2016 using as keywords rs2910164, rs57095329, rs2431697, and miR-146a polymorphisms. Thirty studies were included in the meta-analysis. The SNP rs2910164 G > C was found to be associated with increased risk of multiple sclerosis (CC + CG versus GG, OR = 1.25, 95% CI: 1.01–1.55), with decreased risks of psoriasis (C versus G, OR = 0.81, 95% CI: 0.69–0.96; CC versus GC + GG, OR = 0.73, 95% CI: 0.56–0.94), Behcet’s disease (CC versus GC + GG, OR = 0.60, 95% CI: 0.50–0.73), asthma (C versus G, OR = 0.80, 95% CI: 0.69–0.93; CC versus GC + GG, OR = 0.65, 95% CI: 0.48–0.86), and uveitis (CC + CG versus GG, OR = 0.61, 95% CI: 0.49–0.77). The SNP rs2431697 C > T was found to be associated with an increased risk of SLE (T versus C, OR = 1.26, 95% CI: 1.15–1.38; TC + TT versus CC, OR = 1.28, 95% CI: 1.03–1.58; TT versus TC + CC, OR = 1.40, 95% CI: 1.21–1.62). The SNP rs57095329 A > G was found to be associated with an increased risk of SLE (G versus C, OR = 1.25, 95% CI: 1.17–1.35). The miR-146a SNPs rs2910164, rs57095329, rs2431697 are associated with susceptibility to certain autoimmune diseases. However, for other autoimmune diseases, they may be protective or insignificant.

Cancer patients suffer from worse coronavirus disease-2019 (COVID-19) outcomes. Whether active oncologic treatment is an additional risk factor in this population remains unclear. Therefore, here we have conducted a systematic review and meta-analysis to summarize the existing evidence for the effect of active oncologic treatment on COVID-19 outcomes. Systematic search of databases (PubMed, Embase) was conducted for studies published from inception to July 1, 2020, with a subsequent search update conducted on 10 October 2020. In addition, abstracts and presentations from major conference proceedings (ASCO, ESMO, AACR) as well as pre-print databases (medxriv, bioxriv) were searched. Retrospective and prospective studies reporting clinical outcomes in cancer patients with laboratory confirmation or clinical diagnosis of COVID-19 and details of active or recent oncologic treatment were selected. Random-effects model was applied throughout meta-analyses. Summary outcome measure was the pooled odds ratio (OR) of death for active cancer therapy versus no active cancer therapy for each of the following modalities: recent surgery, chemotherapy, targeted therapy, immunotherapy, or chemoimmunotherapy. Sixteen retrospective and prospective studies (3558 patients) were included in the meta-analysis. Active chemotherapy was associated with higher risk of death compared to no active chemotherapy (OR, 1.60, 95% CI, 1.14-2.23). No significant association with risk of death was identified for active targeted therapy, immunotherapy, chemoimmunotherapy, or recent surgery. Meta-analysis of multivariate adjusted OR of death for active chemotherapy was consistently associated with higher risk of death compared to no active chemotherapy (OR, 1.42, 95% CI, 1.01-2.01). Active chemotherapy appears to be associated with higher risk of death in cancer patients with COVID-19. Further research is necessary to characterize the complex interactions between active cancer treatment and COVID-19.

Systemic chemotherapy is the mainstay of therapy in patients with advanced gastric and esophageal cancer, but has multiple drawbacks including lack of durable efficacy and dose limited toxicities. Recent clinical trials data on the efficacy of immune therapy in this patient group have shed light on its potential as an alternative treatment option. Checkpoint inhibitors, specifically the anti-PD-1/PD-L1 inhibitors, seem to be beneficial for a subgroup of patients with advanced gastric or esophageal cancer who have progressed on multiple systemic chemotherapies. As clinical trials results mature, it will become apparent whether checkpoint inhibitors are effective in other treatment settings such as in first-line therapy or adjuvant therapy. Although the toxicity of checkpoint inhibitors is generally unpredictable, they tend to be more manageable and better tolerated than the toxicities of systemic chemotherapy. Furthermore, recent research in molecular subtyping of esophageal and gastric cancer are paving way for better treatment response prediction and patient selection for checkpoint inhibitor therapies. Compared to checkpoint inhibitors, other types of immune therapies such as cancer vaccines, and adoptive cell therapies have yet to be proven effective in esophageal and gastric cancer and are further away from clinical use. Immune therapy seems poised to take a firm position as part of the therapeutic armamentarium for advanced gastric and esophageal cancer and future clinical trials will show the extent of its application in different treatment settings in this patient population.