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Immune checkpoints are inhibitory receptor/ligand pairs regulating immunity that are exploited as key targets of anti-cancer therapy. Although the PD-1/PD-L1 pair is one of the most studied immune checkpoints, several aspects of its biology remain to be clarified. It has been established that PD-1 is an inhibitory receptor up-regulated by activated T, B, and NK lymphocytes and that its ligand PD-L1 mediates a negative feedback of lymphocyte activation, contributing to the restoration of the steady state condition after acute immune responses. This loop might become detrimental in the presence of either a chronic infection or a growing tumor. PD-L1 expression in tumors is currently used as a biomarker to orient therapeutic decisions; nevertheless, our knowledge about the regulation of PD-L1 expression is limited. The present review discusses how NF-κB, a master transcription factor of inflammation and immunity, is emerging as a key positive regulator of PD-L1 expression in cancer. NF-κB directly induces PD-L1 gene transcription by binding to its promoter, and it can also regulate PD-L1 post-transcriptionally through indirect pathways. These processes, which under conditions of cellular stress and acute inflammation drive tissue homeostasis and promote tissue healing, are largely dysregulated in tumors. Up-regulation of PD-L1 in cancer cells is controlled via NF-κB downstream of several signals, including oncogene- and stress-induced pathways, inflammatory cytokines, and chemotherapeutic drugs. Notably, a shared signaling pathway in epithelial cancers induces both PD-L1 expression and epithelial-mesenchymal transition, suggesting that PD-L1 is part of the tissue remodeling program. Furthermore, PD-L1 expression by tumor infiltrating myeloid cells can contribute to the immune suppressive features of the tumor environment. A better understanding of the interplay between NF-κB signaling and PD-L1 expression is highly relevant to cancer biology and therapy.The potential of tumor three-dimensional (3D) in vitro models for the validation of existing or novel anti-cancer therapies has been largely recognized. During the last decade, diverse in vitro 3D cell systems have been proposed as a bridging link between two-dimensional (2D) cell cultures and in vivo animal models, both considered gold standards in pre-clinical settings. The latest awareness about the power of tailored therapies and cell-based therapies in eradicating tumor cells raises the need for versatile 3D cell culture systems through which we might rapidly understand the specificity of promising anti-cancer approaches. Yet, a faithful reproduction of the complex tumor microenvironment is demanding as it implies a suitable organization of several cell types and extracellular matrix components. The proposed 3D tumor models discussed here are expected to offer the required structural complexity while also assuring cost-effectiveness during pre-selection of the most promising therapies. As neuroblastoma is an extremely heterogenous extracranial solid tumor, translation from 2D cultures into innovative 3D in vitro systems is particularly challenging. In recent years, the number of 3D in vitro models mimicking native neuroblastoma tumors has been rapidly increasing. However, in vitro platforms that efficiently sustain patient-derived tumor cell growth, thus allowing comprehensive drug discovery studies on tailored therapies, are still lacking. In this review, the latest neuroblastoma 3D in vitro models are presented and their applicability for a more accurate prediction of therapy outcomes is discussed.The killer-cell immunoglobulin-like receptor (KIR) proteins evolve to fight viruses and mediate the body's reaction to pregnancy. These roles provide selection pressure for variation at both the structural/haplotype and base/allele levels. At the same time, the genes have evolved relatively recently by tandem duplication and therefore exhibit very high sequence similarity over thousands of bases. These variation-homology patterns make it impossible to interpret KIR haplotypes from abundant short-read genome sequencing data at population scale using existing methods. Here, we developed an efficient computational approach for in silico KIR probe interpretation (KPI) to accurately interpret individual's KIR genes and haplotype-pairs from KIR sequencing reads. We designed synthetic 25-base sequence probes by analyzing previously reported haplotype sequences, and we developed a bioinformatics pipeline to interpret the probes in the context of 16 KIR genes and 16 haplotype structures. We demonstrated its accuracy on a synthetic data set as well as a real whole genome sequences from 748 individuals from The Genome of the Netherlands (GoNL). The GoNL predictions were compared with predictions from SNP-based predictions. Our results show 100% accuracy rate for the synthetic tests and a 99.6% family-consistency rate in the GoNL tests. Agreement with the SNP-based calls on KIR genes ranges from 72%-100% with a mean of 92%; most differences occur in genes KIR2DS2, KIR2DL2, KIR2DS3, and KIR2DL5 where KPI predicts presence and the SNP-based interpretation predicts absence. Overall, the evidence suggests that KPI's accuracy is 97% or greater for both KIR gene and haplotype-pair predictions, and the presence/absence genotyping leads to ambiguous haplotype-pair predictions with 16 reference KIR haplotype structures. KPI is free, open, and easily executable as a Nextflow workflow supported by a Docker environment at https//github.com/droeatumn/kpi.Solid organ transplant recipients (SOTRs) are at increased risk for many infections, whether viral, bacterial, or fungal, due to immunosuppressive therapy to prevent organ rejection. The same immune defects that render transplanted patients susceptible to infection dampen their immune response to vaccination. Therefore, it is vital to identify immune defects to vaccination in transplant recipients and methods to obviate them. These methods can include alternative vaccine composition, dosage, adjuvants, route of administration, timing, and re-vaccination strategies. Systems biology is a relatively new field of study, which utilizes high throughput means to better understand biological systems and predict outcomes. Systems biology approaches have been used to help obtain a global picture of immune responses to infections and vaccination (i.e. systems vaccinology), but little work has been done to use systems biology to improve vaccine efficacy in immunocompromised patients, particularly SOTRs, thus far. Etrumadenant cell line Systems vaccinology approaches may hold key insights to vaccination in this vulnerable population.