Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues.

The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy. Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.

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.

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.

Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer.

BACKGROUND: Crosstalk between neoplastic and stromal cells fosters prostate cancer (PCa) progression and dissemination. Insight in cell-to-cell communication networks provides new therapeutic avenues to mold processes that contribute to PCa tumor microenvironment (TME) alterations. Here we performed a detailed characterization of PCa tumor endothelial cells (TEC) to delineate intercellular crosstalk between TEC and the PCa TME. METHODS: TEC isolated from 67 fresh radical prostatectomy (RP) specimens underwent multi-omic ex vivo characterization as well as orthogonal validation of both TEC functions and key markers by immunohistochemistry (IHC) and immunofluorescence (IF). To identify cell-cell interaction targets in TEC, we performed single-cell RNA sequencing (scRNA-seq) in four PCa patients who underwent a RP to catalogue cellular TME composition. Targets were cross-validated using IHC, publicly available datasets, cell culture expriments as well as a PCa xenograft mouse model. RESULTS: Compared to adjacent normal endothelial cells (NEC) bulk RNA-seq analysis revealed upregulation of genes associated with tumor vasculature, collagen modification and extracellular matrix remodeling in TEC. PTGIR, PLAC9, CXCL12 and VDR were identified as TEC markers and confirmed by IF and IHC in an independent patient cohort. By scRNA-seq we identified 27 cell (sub)types, including endothelial cells (EC) with arterial, venous and immature signatures, as well as angiogenic tip EC. A focused molecular analysis revealed that arterial TEC displayed highest CXCL12 mRNA expression levels when compared to all other TME cell (sub)populations and showed a negative prognostic role. Receptor-ligand interaction analysis predicted interactions between arterial TEC derived CXCL12 and its cognate receptor CXCR4 on angiogenic tip EC. CXCL12 was in vitro and in vivo validated as actionable TEC target by highlighting the vessel number- and density- reducing activity of the CXCR4-inhibitor AMD3100 in murine PCa as well as by inhibition of TEC proliferation and migration in vitro. CONCLUSIONS: Overall, our comprehensive analysis identified novel PCa TEC targets and highlights CXCR4/CXCL12 interaction as a potential novel target to interfere with tumor angiogenesis in PCa.

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.

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.