Checkpoint inhibitors (CI) instigate anticancer immunity in many neoplastic diseases, albeit only in a fraction of patients. The clinical success of cyclophosphamide (C)-based haploidentical stem-cell transplants indicates that this drug may re-orchestrate the immune system. Using models of triple-negative breast cancer (TNBC) with different intratumoral immune contexture, we demonstrate that a combinatorial therapy of intermittent C, CI, and vinorelbine (V), activates antigen presenting cells (APC), and abrogates local and metastatic tumour growth by a T-cell-related effect. Single-cell transcriptome analysis of >50,000 intratumoral immune cells after therapy treatment showed a gene signature suggestive of a change resulting from exposure to a mitogen, ligand, or antigen for which it is specific, as well as APC-toT cell adhesion. This transcriptional program also increased intratumoral tcf1+ stem-like CD8+ T cells and altered the balance between terminally and progenitorexhausted T cells favoring the latter. Overall, our data support the clinical investigation of this therapy in TNBC. Statement of Significance A combinatorial therapy in mouse models of breast cancer increases checkpoint inhibition by activating antigen presenting cells, enhancing intratumoral tcf1+ stem-like CD8+ T-cells, and increasing progenitor exhausted CD8+ T-cells. of combinatorial therapies (2). The clinical success of cyclophosphamide (C)-based haploidentical stem cell transplants in hematological malignancies indicates that this drug has the potential to reorchestrate the immune system against cancer cells (3). Along a similar way, C is administered before CART cell infusion to improve their clinical efficacy (4). We have previously found that low-dose, daily C, in association with V was able to improve the Research.

BackgroundThe introduction of pathology tissue-chromatin immunoprecipitation (PAT-ChIP), a technique allowing chromatin immunoprecipitation (ChIP) from formalin-fixed paraffin-embedded (FFPE) tissues, has extended the application of chromatin studies to clinical patient samples. However, extensive crosslinking introduced during routine tissue fixation of clinical specimens may hamper the application of PAT-ChIP to genome-wide studies (PAT-ChIP-Seq) from archived tissue samples. The reduced efficiency in chromatin extraction from over-fixed formalin archival samples is the main hurdle to overcome, especially when low abundant epigenetic marks (e.g., H3K4me3) are investigated.ResultsWe evaluated different modifications of the original PAT-ChIP protocol to improve chromatin isolation from FFPE tissues. With this aim, we first made extensive usage of a normal human colon specimen fixed at controlled conditions (24 h, 48 h, and 72 h) to mimic the variability of tissue fixation that is most frequently found in archived samples. Different conditions of chromatin extraction were tested applying either diverse sonication protocols or heat-mediated limited reversal of crosslinking (LRC). We found that, if compared with canonical PAT-ChIP protocol, LRC strongly increases chromatin extraction efficiency, especially when 72-h fixed FFPE samples are used. The new procedure, that we named enhanced PAT-ChIP (EPAT-ChIP), was then applied at genome-wide level using an archival sample of invasive breast carcinoma to investigate H3K4me3, a lowly abundant histone modification, and H3K27me3 and H3K27ac, two additional well-known histone marks.ConclusionsEPAT-ChIP procedure improves the efficiency of chromatin isolation from FFPE samples allowing the study of long time-fixed specimens (72 h), as well as the investigation of low distributed epigenetic marks (e.g., H3K4me3) and the analysis of multiple histone marks from low amounts of starting material. We believe that EPAT-ChIP will facilitate the application of chromatin studies to archived pathology samples, thus contributing to extend the current understanding of cancer epigenomes and enabling the identification of clinically useful tumor biomarkers.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0576-y) contains supplementary material, which is available to authorized users.

SummaryInduced pluripotent stem cell (iPSC)-derived hematopoietic cells represent a highly attractive source for cell and gene therapy. Given the longevity, plasticity, and self-renewal potential of distinct macrophage subpopulations, iPSC-derived macrophages (iPSC-Mφ) appear of particular interest in this context. We here evaluated the airway residence, plasticity, and therapeutic efficacy of iPSC-Mφ in a murine model of hereditary pulmonary alveolar proteinosis (herPAP). We demonstrate that single pulmonary macrophage transplantation (PMT) of 2.5–4 × 106 iPSC-Mφ yields efficient airway residence with conversion of iPSC-Mφ to an alveolar macrophage (AMφ) phenotype characterized by a distinct surface marker and gene expression profile within 2 months. Moreover, PMT significantly improves alveolar protein deposition and other critical herPAP disease parameters. Thus, our data indicate iPSC-Mφ as a source of functional macrophages displaying substantial plasticity and therapeutic potential that upon pulmonary transplantation will integrate into the lung microenvironment, adopt an AMφ phenotype and gene expression pattern, and profoundly ameliorate pulmonary disease phenotypes.

Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny-and Electron-based standalone desktop application for macOS and Windows, which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user.Through an interactive and intuitive graphical interface, users can i) explore similarities and heterogeneity between samples and cells clusters in 2D or 3D projections such as t-SNE or UMAP, ii) display the expression level of single genes or genes sets of interest, iii) browse tables of most expressed genes and marker genes for each sample and cluster.We provide a simple example to show how Cerebro can be used and which are its capabilities.Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists which facilitates effective interaction to shorten the gap between analysis and interpretation of the data. AvailabilityCerebro and example data sets are available at https://github.com/romanhaa/Cerebro. Similarly, the R packages cerebroApp and cerebroPrepare R packages are available at