First-generation immune checkpoint inhibitors, including anti–CTLA-4 and anti–programmed death 1 (anti–PD-1) antibodies, have led to major clinical progress, yet resistance frequently leads to treatment failure. Thus, new targets acting on T cells are needed. CD33-related sialic acid–binding immunoglobulin-like lectins (Siglecs) are pattern-recognition immune receptors binding to a range of sialoglycan ligands, which appear to function as self-associated molecular patterns (SAMPs) that suppress autoimmune responses. Siglecs are expressed at very low levels on normal T cells, and these receptors were not until recently considered as interesting targets on T cells for cancer immunotherapy. Here, we show an upregulation of Siglecs, including Siglec-9, on tumor-infiltrating T cells from non–small cell lung cancer (NSCLC), colorectal, and ovarian cancer patients. Siglec-9–expressing T cells coexpressed several inhibitory receptors, including PD-1. Targeting of the sialoglycan-SAMP/Siglec pathway in vitro and in vivo resulted in increased anticancer immunity. T cell expression of Siglec-9 in NSCLC patients correlated with reduced survival, and Siglec-9 polymorphisms showed association with the risk of developing lung and colorectal cancer. Our data identify the sialoglycan-SAMP/Siglec pathway as a potential target for improving T cell activation for immunotherapy.

Interferon-λs 1-4 (IFNL1-4) modulate immunity in the context of infections and autoimmune diseases, through a complex network of induced genes. IFN-λs act by binding to the hetero-dimeric IFN-λ receptor (IFNLR), activating a STAT-phosphorylation dependent signalling cascade, and thereby inducing hundreds of IFN-stimulated genes modulating various immune functions via complex forward and feedback loops. In contrast to the IFN-α signalling cascade, three important differences have been discovered. Firstly, the IFNLR is not ubiquitously expressed; in particular, immune cells show significant variation in the expression levels and susceptibilities to IFN-λs. Secondly, the binding affinities of individual IFN-λs to the IFNLR strongly varies and are generally lower compared to IFN-α binding affinities. Finally, genetic variation in the form of a series of single nucleotide polymorphisms (SNPs) linked to genes involved in the IFN-λ signalling cascade has been described, and associated with the clinical course and treatment outcomes of hepatitis B and C virus infection. The clinical impact of the IFN-λ signalling and the SNP variations may, however, reach far beyond viral hepatitis. Recent publications show important roles for IFN-λs in a broad range of viral infections such as human T-cell leukemia type-1 virus, rotaviruses and influenza virus. Potentially IFN-λ even modulates the course of bacterial colonization and infections as shown for Staphylococcus aureus and Mycobacterium tuberculosis. Although the immunological processes involved in controlling viral and bacterial infections are highly diverse, IFN-λs may interfere at various levels: as an innate immune cytokine with direct antiviral effects; or as a modulator of the IFN-α induced signalling via the suppressor of cytokine signalling 1 (SOCS1) and the Ubiquitin specific peptidase 18 (USP18) inhibitory feedback loops. In addition, the modulation of adaptive immune functions via macrophage and dendritic cell polarization, and subsequent priming, activation, and proliferation of pathogen-specific T- and B-cells may also be important elements associated with infectious diseases outcomes. This review summarizes the emerging details of the IFN-λ immunobiology in the context of the host immune response and viral and bacterial infections.

Background Immune checkpoint inhibiting antibodies were introduced into routine clinical practice for cancer patients. Checkpoint blockade has led to durable remissions in some patients, but may also induce immune-related adverse events (irAEs). Lung cancer patients show an increased risk for complications, when infected with influenza viruses. Therefore, vaccination is recommended. However, the efficacy and safety of influenza vaccination during checkpoint blockade and its influence on irAEs is unclear. Similarly, the influence of vaccinations on T cell-mediated immune reactions in patients during PD-1 blockade remains poorly defined. Methods We vaccinated 23 lung cancer patients and 11 age-matched healthy controls using a trivalent inactivated influenza vaccine to investigate vaccine-induced immunity and safety during checkpoint blockade. Results We did not observe significant differences between patients and healthy controls in vaccine-induced antibody titers against all three viral antigens. Influenza vaccination resulted in protective titers in more than 60% of patients/participants. In cancer patients, the post-vaccine frequency of irAEs was 52.2% with a median time to occurrence of 3.2 months after vaccination. Six of 23 patients (26.1%) showed severe grade 3/4 irAEs. This frequency of irAEs might be higher than the rate previously published in the literature and the rate observed in a non-study population at our institution (all grades 25.5%, grade 3/4 9.8%). Conclusions Although this is a non-randomized trial with a limited number of patients, the increased rate of immunological toxicity is concerning. This finding should be studied in a larger patient population.

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

ChatGPT, GPT-4, and Bard are highly advanced natural language process–based computer programs (chatbots) that simulate and process human conversation in written or spoken form. Recently released by the company OpenAI, ChatGPT was trained on billions of unknown text elements (tokens) and rapidly gained wide attention for its ability to respond to questions in an articulate manner across a wide range of knowledge domains. These potentially disruptive large language model (LLM) technologies have a broad range of conceivable applications in medicine and medical microbiology. In this opinion article, I describe how chatbot technologies work and discuss the strengths and weaknesses of ChatGPT, GPT-4, and other LLMs for applications in the routine diagnostic laboratory, focusing on various use cases for the pre- to post-analytical process.