Glucocorticoid treatment influences prostate cancer cell growth and the tumor microenvironment via altered glucocorticoid receptor signaling in prostate fibroblasts

Despite significant therapeutic advances in recent years, treatment of metastatic prostate cancer (PCa) remains palliative, owing to the inevitable occurrence of drug resistance. There is increasing evidence that epithelial glucocorticoid receptor (GR) signaling and changes in the tumor-microenvironment (TME) play important roles in this process. Since glucocorticoids (GCs) are used as concomitant medications in the course of PCa treatment, it is essential to investigate the impact of GCs on stromal GR signaling in the TME. Therefore, general GR mRNA and protein expression was assessed in radical prostatectomy specimens and metastatic lesions. Elevated stromal GR signaling after GC treatment resulted in altered GR-target gene, soluble protein expression, and in a morphology change of immortalized and primary isolated cancer-associated fibroblasts (CAFs). Subsequently, these changes affected proliferation, colony formation, and 3D-spheroid growth of multiple epithelial PCa cell models. Altered expression of extra-cellular matrix (ECM) and adhesion-related proteins led to an ECM remodeling. Notably, androgen receptor pathway inhibitor treatments did not affect CAF viability. Our findings demonstrate that GC-mediated elevated GR signaling has a major impact on the CAF secretome and the ECM architecture. GC-treated fibroblasts significantly influence epithelial tumor cell growth and must be considered in future therapeutic strategies.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

A novel calcium channel Cavβ2 splice variant with unique properties predominates in the retina

Cavβ subunits are essential for surface expression of voltage-gated calcium channel complexes and crucially modulate biophysical properties like voltage-dependent inactivation. Here, we describe the discovery and characterization of a novel Cavβ2 variant with distinct features that predominates in the retina. We determined spliced exons in retinal transcripts of the Cacnb2 gene, coding for Cavβ2, by RNA-Seq data analysis and quantitative PCR. We cloned a novel Cavβ2 splice variant from mouse retina, which we are calling β2i, and investigated biophysical properties of calcium currents with this variant in a heterologous expression system as well as its intrinsic membrane interaction when expressed alone. Our data showed that β2i predominated in the retina with expression in photoreceptors and bipolar cells. Furthermore, we observed that the β2i N-terminus exhibited an extraordinary concentration of hydrophobic residues, a distinct feature not seen in canonical variants. The biophysical properties resembled known membrane-associated variants, and β2i exhibited both a strong membrane association and a propensity for clustering, which depended on hydrophobic residues in its N-terminus. We considered available Cavβ structure data to elucidate potential mechanisms underlying the observed characteristics but resolved N-terminus structures were lacking and thus, precluded clear conclusions. With this description of a novel N-terminus variant of Cavβ2, we expand the scope of functional variation through N-terminal splicing with a distinct form of membrane attachment. Further investigation of the molecular mechanisms underlying the features of β2i could provide new angles on the way Cavβ subunits modulate Ca2+ channels at the plasma membrane.

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune cell infiltration patterns, which has been linked to therapy sensitivity and resistance. However, full understanding of how immune cell phenotypes vary across different patient subgroups is lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution by integrating 1,283,972 single cells from 556 samples and 318 patients across 29 datasets, including our dataset capturing cells with low mRNA content. We stratify patients into immune-deserted, B cell, T cell, and myeloid cell subtypes. Using bulk samples with genomic and clinical information, we identify cellular components associated with tumor histology and genotypes. We then focus on the analysis of tissue-resident neutrophils (TRNs) and uncover distinct subpopulations that acquire new functional properties in the tissue microenvironment, providing evidence for the plasticity of TRNs. Finally, we show that a TRN-derived gene signature is associated with anti-programmed cell death ligand 1 (PD-L1) treatment failure.

Computational cancer neoantigen prediction: current status and recent advances

Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them.

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Tumors have the ability to acquire immune escape mechanisms.

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Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system.

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The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research.

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A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.

nextNEOpi: a comprehensive pipeline for computational neoantigen prediction

SUMMARY

Somatic mutations and gene fusions can produce immunogenic neoantigens mediating anticancer immune responses. However, their computational prediction from sequencing data requires complex computational workflows to identify tumor-specific aberrations, derive the resulting peptides, infer patients' Human Leukocyte Antigen types and predict neoepitopes binding to them, together with a set of features underlying their immunogenicity. Here, we present nextNEOpi (nextflow NEOantigen prediction pipeline) a comprehensive and fully automated bioinformatic pipeline to predict tumor neoantigens from raw DNA and RNA sequencing data. In addition, nextNEOpi quantifies neoepitope- and patient-specific features associated with tumor immunogenicity and response to immunotherapy.

AVAILABILITY AND IMPLEMENTATION

nextNEOpi source code and documentation are available at https://github.com/icbi-lab/nextNEOpi.

CONTACT

dietmar.rieder@i-med.ac.at or francesca.finotello@uibk.ac.at.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data

SUMMARY

Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.

AVAILABILITY AND IMPLEMENTATION

Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Container-aided integrative QTL and RNA-seq analysis of Collaborative Cross mice supports distinct sex-oriented molecular modes of response in obesity

BACKGROUND

The Collaborative Cross (CC) mouse population is a valuable resource to study the genetic basis of complex traits, such as obesity. Although the development of obesity is influenced by environmental factors, underlying genetic mechanisms play a crucial role in the response to these factors. The interplay between the genetic background and the gene expression pattern can provide further insight into this response, but we lack robust and easily reproducible workflows to integrate genomic and transcriptomic information in the CC mouse population.

RESULTS

We established an automated and reproducible integrative workflow to analyse complex traits in the CC mouse genetic reference panel at the genomic and transcriptomic levels. We implemented the analytical workflow to assess the underlying genetic mechanisms of host susceptibility to diet induced obesity and integrated these results with diet induced changes in the hepatic gene expression of susceptible and resistant mice. Hepatic gene expression differs significantly between obese and non-obese mice, with a significant sex effect, where male and female mice exhibit different responses and coping mechanisms.

CONCLUSION

Integration of the data showed that different genes but similar pathways are involved in the genetic susceptibility and disturbed in diet induced obesity. Genetic mechanisms underlying susceptibility to high-fat diet induced obesity are different in female and male mice. The clear distinction we observed in the systemic response to the high-fat diet challenge and to obesity between male and female mice points to the need for further research into distinct sex-related mechanisms in metabolic disease.

NeoFuse: predicting fusion neoantigens from RNA sequencing data

SUMMARY

Gene fusions can generate immunogenic neoantigens that mediate anticancer immune responses. However, their computational prediction from RNA sequencing (RNA-seq) data requires deep bioinformatics expertise to assembly a computational workflow covering the prediction of: fusion transcripts, their translated proteins and peptides, Human Leukocyte Antigen (HLA) types, and peptide-HLA binding affinity. Here, we present NeoFuse, a computational pipeline for the prediction of fusion neoantigens from tumor RNA-seq data. NeoFuse can be applied to cancer patients' RNA-seq data to identify fusion neoantigens that might expand the repertoire of suitable targets for immunotherapy.

AVAILABILITY AND IMPLEMENTATION

NeoFuse source code and documentation are available under GPLv3 license at https://icbi.i-med.ac.at/NeoFuse/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

New developments in the evolution and application of the WHO/IPCS framework on mode of action/species concordance analysis

The World Health Organization/International Programme on Chemical Safety mode of action/human relevance framework has been updated to reflect the experience acquired in its application and extend its utility to emerging areas in toxicity testing and non-testing methods. The underlying principles have not changed, but the framework’s scope has been extended to enable integration of information at different levels of biological organization and reflect evolving experience in a much broader range of potential applications. Mode of action/species concordance analysis can also inform hypothesis-based data generation and research priorities in support of risk assessment.

The modified framework is incorporated within a roadmap, with feedback loops encouraging continuous refinement of fit-for-purpose testing strategies and risk assessment. Important in this construct is consideration of dose–response relationships and species concordance analysis in weight of evidence. The modified Bradford Hill considerations have been updated and additionally articulated to reflect increasing experience in application for cases where the toxicological outcome of chemical exposure is known.

The modified framework can be used as originally intended, where the toxicological effects of chemical exposure are known, or in hypothesizing effects resulting from chemical exposure, using information on putative key events in established modes of action from appropriate in vitro or in silico systems and other lines of evidence.

This modified mode of action framework and accompanying roadmap and case examples are expected to contribute to improving transparency in explicitly addressing weight of evidence considerations in mode of action/species concordance analysis based on both conventional data sources and evolving methods.