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Epithelial ovarian cancer (EOC) has a 5-year relative survival of 50%, partly because markers of early-stage disease are not available in current clinical diagnostics. The aim of the present study was to investigate whether EOC is associated with transcriptional profiles in blood collected up to 7 years before diagnosis. For this, we used RNA-stabilized whole blood, which contains circulating immune cells, from a sample of EOC cases from the population-based Norwegian Women and Cancer (NOWAC) postgenome cohort. We explored case-control differences in gene expression in all EOC (66 case-control pairs), as well as associations between gene expression and metastatic EOC (56 pairs), serous EOC (45 pairs, 44 of which were metastatic), and interval from blood sample collection to diagnosis (≤3 or >3 years; 34 and 31 pairs, respectively). Lastly, we assessed differential expression of genes associated with EOC in published functional genomics studies that used blood samples collected from newly diagnosed women. After adjustment for multiple testing, this nested case-control study revealed no significant case-control differences in gene expression in all EOC (false discovery rate q>0.96). With the exception of a few probes, the log 2 fold change values obtained in gene-wise linear models were below ±0.2. P-values were lowest in analyses of metastatic EOC (80% of which were serous EOC). No common transcriptional profile was indicated by interval to diagnosis; when comparing the 100 genes with the lowest p-values in gene-wise tests in samples collected ≤3 and >3 years before EOC diagnosis, no overlap in these genes was observed. Among 86 genes linked to ovarian cancer in previous publications, our data contained expression values for 42, and of these, tests of LIME1 , GPR162 , STAB1 , and SKAP1 , resulted in unadjusted p<0.05. Although limited by sample size, our findings indicated less variation in blood gene expression between women with similar tumor characteristics.

Previous studies have indicated a synergistic effect between radiotherapy and immunotherapy. A better understanding of how this combination affects the immune system can help to clarify its role in the treatment of metastatic cancer. We performed T cell receptor (TCR) sequencing on 46 sequentially collected samples from 15 patients with stage IV non‐small cell lung cancer, receiving stereotactic body radiotherapy combined with a programmed cell death ligand‐1 (PD‐L1) inhibitor. TCR repertoire diversity was assessed using Rényi diversity curves and the Shannon diversity index. TCR clones were tracked over time. We found decreasing or stable diversity in the best responders, and an increase in diversity at progression in patients with an initial response. Expansion of TCR clones was more often seen in responders. Several patients also developed new clones of high abundance. This seemed to be more related to radiotherapy than to immune checkpoint blockade. In summary, we observed similar dynamics in the TCR repertoire as have been described with immunotherapy alone. In addition, the occurrence of new unique clones of high abundance after radiotherapy may indicate that radiotherapy functions as a personalized cancer vaccine. Radiotherapy and immunotherapy have been proposed to work synergistically in the treatment of cancer. We used T cell receptor (TCR) sequencing to monitor the immunological response to combined stereotactic radiotherapy and PD‐L1 blockade in patients with metastatic non‐small cell lung cancer. Specifically, we investigated possible mechanisms of action and the potential of the TCR repertoire as a dynamic biomarker.

It is increasingly clear that an extraordinarily diverse range of clinically important conditions—including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers—leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person’s B cells (antibodies) and T cells. We refer to these as the immunome . Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML. The proliferation of molecular biology and bioinformatics tools necessary to generate huge quantities of immune receptor data has not been matched by frameworks that allow easy data analysis. The authors present immuneML, an open-source collaborative ecosystem for machine learning analysis of adaptive immune receptor repertoires.

In both septic and aseptic inflammation, N-formyl peptides may enter the circulation and induce a systemic inflammatory response syndrome similar to that observed during septic shock. The inflammatory response is brought about by the binding of N-formyl peptide to formyl peptide receptors (FPRs), specific signaling receptors expressed on myeloid as well as non-myeloid cells involved in the inflammatory process. N-formyl peptides conjugated with fluorochromes, such as fluorescein isothiocyanate (FITC) are increasingly experimentally used to identify tissues involved in inflammation. Hypothesizing that the process of FITC-conjugation may transfer formyl peptide to a ligand that is efficiently cleared from the circulation by the natural powerful hepatic scavenging regime we studied the biodistribution of intravenously administered FITC-fNLPNTL (Fluorescein-isothiocyanate- N-Formyl-Nle-Leu-Phe-Nle-Tyr-Lys) in mice. Our findings can be summarized as follows: i) In contrast to unconjugated fNLPNTL, FITC-fNLPNTL was rapidly taken up in the liver; ii) Mouse and human liver sinusoidal endothelial cells (LSECs) and hepatocytes express formyl peptide receptor 1 (FRP1) on both mRNA (PCR) and protein (Western blot) levels; iii) Immunohistochemistry showed that mouse and human liver sections expressed FRP1 in LSECs and hepatocytes; and iv) Uptake of FITC-fNLPNTL could be largely blocked in mouse and human hepatocytes by surplus-unconjugated fNLPNTL, thereby suggesting that the hepatocytes in both species recognized FITC-fNLPNTL and fNLPNTL as indistinguishable ligands. This was in contrast to the mouse and human LSECs, in which the uptake of FITC-fNLPNTL was mediated by both FRP1 and a scavenger receptor, specifically expressed on LSECs. Based on these results we conclude that a significant proportion of FITC-fNLPNTL is taken up in LSECs via a scavenger receptor naturally expressed in these cells. This calls for great caution when using FITC-fNLPNTL and other chromogen-conjugated formyl peptides as a probe to identify cells in a liver engaged in inflammation. Moreover, our finding emphasizes the role of the liver as an important neutralizer of otherwise strong inflammatory signals such as formyl peptides.

Humoral immunity is divided into the cellular B cell and protein-level antibody responses. High-throughput sequencing has advanced our understanding of both these fundamental aspects of B cell immunology as well as aspects pertaining to vaccine and therapeutics biotechnology. Although the protein-level serum and mucosal antibody repertoire make major contributions to humoral protection, the sequence composition and dynamics of antibody repertoires remain underexplored. This limits insight into important immunological and biotechnological parameters such as the number of antigen-specific antibodies, which are for example, relevant for pathogen neutralization, microbiota regulation, severity of autoimmunity, and therapeutic efficacy. High-resolution mass spectrometry (MS) has allowed initial insights into the antibody repertoire. We outline current challenges in MS-based sequence analysis of antibody repertoires and propose strategies for their resolution. Recent advances in high-resolution mass spectrometry (MS) instruments and liquid chromatography (LC)-MS/MS data analysis software have enabled novel insights into the serum and mucosal antibody repertoire.There is a lack of standardization and benchmarking of antibody repertoire proteomics (Ab-seq) in both experimental and analytical pipelines.Knowledge of the sequence composition and dynamics of the antibody repertoire remains limited due to the complexity of antibody biology as well as Ab-seq workflow-related technological, experimental, and computational challenges that hinder the development of vaccines, antibody therapeutics, and immunodiagnostics.Newly developed strategies can improve Ab-seq workflows and technology.

Background Formyl peptide receptor 1 (FPR1) is a G protein-coupled receptor mainly expressed by the cells of myeloid origin, where it mediates the innate immune response to bacterial formylated peptides. High expression of FPR1 has been detected in various cancers but the function of FPR1 in tumorigenesis is poorly understood. Methods Expression of FPR1 in neuroblastoma cell lines and primary tumors was studied using RT-PCR, western blotting, immunofluorescence and immunohistochemistry. Calcium mobilization assays and western blots with phospho-specific antibodies were used to assess the functional activity of FPR1 in neuroblastoma. The tumorigenic capacity of FPR1 was assessed by xenografting of neuroblastoma cells expressing inducible FPR1 shRNA, FPR1 cDNA or control shRNA in nude mice. Results FPR1 is expressed in neuroblastoma primary tumors and cell lines. High expression of FPR1 corresponds with high-risk disease and poor patient survival. Stimulation of FPR1 in neuroblastoma cells using fMLP, a selective FPR1 agonist, induced intracellular calcium mobilization and activation of MAPK/Erk, PI3K/Akt and P38-MAPK signal transduction pathways that were inhibited by using Cyclosporin H, a selective receptor antagonist for FPR1. shRNA knock-down of FPR1 in neuroblastoma cells conferred a delayed xenograft tumor development in nude mice, whereas an ectopic overexpression of FPR1 promoted augmented tumorigenesis in nude mice. Conclusion Our data demonstrate that FPR1 is involved in neuroblastoma development and could represent a therapy option for the treatment of neuroblastoma.

Enniatins (ENNs) and beauvericin (BEA) are cyclic hexadepsipeptide fungal metabolites which have demonstrated antibiotic, antimycotic, and insecticidal activities. The substantial toxic potentials of these mycotoxins are associated with their ionophoric molecular properties and relatively high lipophilicities. ENNs occur extensively in grain and grain-derived products and are considered a food safety issue by the European Food Safety Authority (EFSA). The tolerable daily intake and maximum levels for ENNs in humans and animals remain unestablished due to key toxicological and toxicokinetic data gaps, preventing full risk assessment. Aiming to find critical data gaps impeding hazard characterization and risk evaluation, this review presents a comprehensive summary of the existing information from in vitro and in vivo studies on toxicokinetic characteristics and cytotoxic, genotoxic, immunotoxic, endocrine, reproductive and developmental effects of the most prevalent ENN analogues (ENN A, A1, B, B1) and BEA. The missing information identified showed that additional studies on ENNs and BEA have to be performed before sufficient data for an in-depth hazard characterisation of these mycotoxins become available.

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.

There is a large body of evidence demonstrating long-lasting protective effect of each full-term pregnancy (FTP) on the development of breast cancer (BC) later in life, a phenomenon that could be related to both hormonal and immunological changes during pregnancies. In this work, we studied the pregnancy-associated differences in peripheral blood gene expression profiles between healthy women and women diagnosed with BC in a prospective design. Using an integrated system epidemiology approach, we modeled BC incidence as a function of parity in the Norwegian Women and Cancer (NOWAC) cohort (165,000 women) and then tested the resulting mathematical model using gene expression profiles in blood in a nested case-control study (460 invasive case-control pairs) of women from the NOWAC postgenome cohort. Lastly, we undertook a gene set enrichment analysis for immunological gene sets. A linear trend fitted the dataset precisely showing an 8% decrease in risk of BC for each FTP, independent of stratification on other risk factors and lasting for decades after a woman's last FTP. Women with six children demonstrated 48% reduction in the incidence of BC compared to nulliparous. When we looked at gene expression, we found that 756 genes showed linear trends in cancer-free controls (false discovery rate [FDR] 5%), but this was not the case for any of the genes in BC cases. Gene set enrichment analysis of immunologic gene sets (C7 collection in Molecular Signatures Database) revealed 215 significantly enriched human gene sets (FDR 5%). We found marked differences in gene expression and enrichment profiles of immunologic gene sets between BC cases and healthy controls, suggesting an important protective effect of the immune system on BC risk.