GGTyper: genotyping complex structural variants using short-read sequencing data.

MOTIVATION: Complex structural variants (SVs) are genomic rearrangements that involve multiple segments of DNA. They contribute to human diversity and have been shown to cause Mendelian disease. Nevertheless, our abilities to analyse complex SVs are very limited. As opposed to deletions and other canonical types of SVs, there are no established tools that have explicitly been designed for analysing complex SVs. RESULTS: Here, we describe a new computational approach that we specifically designed for genotyping complex SVs in short-read sequenced genomes. Given a variant description, our approach computes genotype-specific probability distributions for observing aligned read pairs with a wide range of properties. Subsequently, these distributions can be used to efficiently determine the most likely genotype for any set of aligned read pairs observed in a sequenced genome. In addition, we use these distributions to compute a genotyping difficulty for a given variant, which predicts the amount of data needed to achieve a reliable call. Careful evaluation confirms that our approach outperforms other genotypers by making reliable genotype predictions across both simulated and real data. On up to 7829 human genomes, we achieve high concordance with population-genetic assumptions and expected inheritance patterns. On simulated data, we show that precision correlates well with our prediction of genotyping difficulty. This together with low memory and time requirements makes our approach well-suited for application in biomedical studies involving small to very large numbers of short-read sequenced genomes. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/kehrlab/Complex-SV-Genotyping.

Spatial Cellular Networks from omics data with SpaCeNet.

Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells, together with the cells' spatial distribution, provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. SpaCeNet is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence relations between captured variables within individual cells and by disentangling these from conditional independence relations between variables of different cells.

IL-2 and TCR stimulation induce expression and secretion of IL-32ß by human T cells.

IL-32 expression is important for pathogen clearance but detrimental in chronic inflammation, autoimmunity, and cancer. T cells are major IL-32 producers in these diseases and key mediators of pathogen and tumor elimination but also autoimmune destruction. However, their contribution to IL-32 biology during immune responses is hardly understood due to several isoforms with divergent inflammatory properties. Here, we identified IL-32ß as the predominant isoform in various T cell subsets of healthy individuals and breast cancer patients with the highest levels detected in intratumoral regulatory T cells. We show that IL-32ß is induced by IL-2 but IL-32ß release requires T Cell Receptor rather than IL2R stimulation. Using inhibitors of protein secretion pathways and serial (ultra)centrifugation of T cell supernatants, we demonstrate that T cells actively secrete IL-32ß unconventionally, as a free protein and, to a minor degree, through exosomes. Thus, our data identify activated T cells as major IL-32ß secretors in health and cancer.

Restricting datasets to classifiable samples augments discovery of immune disease biomarkers.

Immunological diseases are typically heterogeneous in clinical presentation, severity and response to therapy. Biomarkers of immune diseases often reflect this variability, especially compared to their regulated behaviour in health. This leads to a common difficulty that frustrates biomarker discovery and interpretation - namely, unequal dispersion of immune disease biomarker expression between patient classes necessarily limits a biomarker's informative range. To solve this problem, we introduce dataset restriction, a procedure that splits datasets into classifiable and unclassifiable samples. Applied to synthetic flow cytometry data, restriction identifies biomarkers that are otherwise disregarded. In advanced melanoma, restriction finds biomarkers of immune-related adverse event risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. Hence, dataset restriction augments discovery of immune disease biomarkers, increases predictive certainty for classifiable samples and improves multivariate models incorporating biomarkers with a limited informative range. This principle can be directly extended to any classification task.

T cell-mediated elimination of cancer cells by blocking CEACAM6-CEACAM1 interaction.

Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), a cell surface receptor, is expressed on normal epithelial tissue and highly expressed in cancers of high unmet medical need, such as non-small cell lung, pancreatic, and colorectal cancer. CEACAM receptors undergo homo- and heterophilic interactions thereby regulating normal tissue homeostasis and angiogenesis, and in cancer, tumor invasion and metastasis. CEACAM6 expression on malignant plasma cells inhibits antitumor activity of T cells, and we hypothesize a similar function on epithelial cancer cells. The interactions between CEACAM6 and its suggested partner CEACAM1 on T cells were studied. A humanized CEACAM6-blocking antibody, BAY 1834942, was developed and characterized for its immunomodulating effects in co-culture experiments with T cells and solid cancer cells and in comparison to antibodies targeting the immune checkpoints programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), and T cell immunoglobulin mucin-3 (TIM-3). The immunosuppressive activity of CEACAM6 was mediated by binding to CEACAM1 expressed by activated tumor-specific T cells. BAY 1834942 increased cytokine secretion by T cells and T cell-mediated killing of cancer cells. The in vitro efficacy of BAY 1834942 correlated with the degree of CEACAM6 expression on cancer cells, suggesting potential in guiding patient selection. BAY 1834942 was equally or more efficacious compared to blockade of PD-L1, and at least an additive efficacy was observed in combination with anti-PD-1 or anti-TIM-3 antibodies, suggesting an efficacy independent of the PD-1/PD-L1 axis. In summary, CEACAM6 blockade by BAY 1834942 reactivates the antitumor response of T cells. This warrants clinical evaluation.

Common clonal origin of conventional T cells and induced regulatory T cells in breast cancer patients.

Regulatory CD4+ T cells (Treg) prevent tumor clearance by conventional T cells (Tconv) comprising a major obstacle of cancer immune-surveillance. Hitherto, the mechanisms of Treg repertoire formation in human cancers remain largely unclear. Here, we analyze Treg clonal origin in breast cancer patients using T-Cell Receptor and single-cell transcriptome sequencing. While Treg in peripheral blood and breast tumors are clonally distinct, Tconv clones, including tumor-antigen reactive effectors (Teff), are detected in both compartments. Tumor-infiltrating CD4+ cells accumulate into distinct transcriptome clusters, including early activated Tconv, uncommitted Teff, Th1 Teff, suppressive Treg and pro-tumorigenic Treg. Trajectory analysis suggests early activated Tconv differentiation either into Th1 Teff or into suppressive and pro-tumorigenic Treg. Importantly, Tconv, activated Tconv and Treg share highly-expanded clones contributing up to 65% of intratumoral Treg. Here we show that Treg in human breast cancer may considerably stem from antigen-experienced Tconv converting into secondary induced Treg through intratumoral activation.

Entangled three-qubit states without concurrence and three-tangle.

We provide a complete analysis of mixed three-qubit states composed of a Greenberger-Horne-Zeilinger state and a W state orthogonal to the former. We present optimal decompositions and convex roofs for the three-tangle. Further, we provide an analytical method to decide whether or not an arbitrary rank-2 state of three qubits has vanishing three-tangle. These results highlight intriguing differences compared to the properties of two-qubit mixed states, and may serve as a quantitative reference for future studies of entanglement in multipartite mixed states. By studying the Coffman-Kundu-Wootters inequality we find that, while the amounts of inequivalent entanglement types strictly add up for pure states, this "monogamy" can be lifted for mixed states by virtue of vanishing tangle measures.