Immune checkpoint blockade (ICB) has revolutionized cancer therapeutics. Desmoplastic malignancies, such as cholangiocarcinoma (CCA), have an abundant tumor immune microenvironment (TIME). However, to date, ICB monotherapy in such malignancies has been ineffective. Herein, we identify tumor-associated macrophages (TAMs) as the primary source of programmed death–ligand 1 (PD-L1) in human and murine CCA. In a murine model of CCA, recruited PD-L1⁺ TAMs facilitated CCA progression. However, TAM blockade failed to decrease tumor progression due to a compensatory emergence of granulocytic myeloid-derived suppressor cells (G-MDSCs) that mediated immune escape by impairing T cell response. Single-cell RNA sequencing (scRNA-Seq) of murine tumor G-MDSCs highlighted a unique ApoE G-MDSC subset enriched with TAM blockade; further analysis of a human scRNA-Seq data set demonstrated the presence of a similar G-MDSC subset in human CCA. Finally, dual inhibition of TAMs and G-MDSCs potentiated ICB. In summary, our findings highlight the therapeutic potential of coupling ICB with immunotherapies targeting immunosuppressive myeloid cells in CCA.

Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit (“QA”), and QIAGEN GeneRead DNA FFPE Kit (“QGR”). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (< 5%), and collectively shared a “C > T|G > A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Background: Scattered tubular-like cells (STCs) proliferate and differentiate to support neighboring injured renal tubular cells during recovery from insults. Renal artery stenosis (RAS) induces renal ischemia and hypertension and leads to loss of kidney function, but whether RAS alters renal endogenous repair mechanisms, such as STCs, remains unknown. We hypothesize that RAS in swine modifies the messenger RNA (mRNA) profile of STCs, blunting their in vitro reparative capacity. Methods: CD24+/CD133+ STCs were isolated from pig kidneys after 10-weeks of RAS or sham (n = 3 each) and their gene cargo analyzed using high-throughput mRNAseq. Expression profiles for upregulated and downregulated mRNAs in RAS-STCs were functionally interpreted by gene ontology analysis. STC activation was assessed by counting the total number of STCs in pig kidney sections using flow cytometry, whereas cell proliferation was assessed in vitro. Results: Of all expressed genes, 1430 genes were upregulated and 315 downregulated in RAS- versus Normal-STCs. Expression of selected candidate genes followed the same fold change directions as the mRNAseq findings. Genes upregulated in RAS-STCs were involved in cell adhesion, extracellular matrix remodeling, and kidney development, whereas those downregulated in RAS-STCs are related to cell cycle and cytoskeleton. The percentage of STCs from dissociated kidney cells was higher in RAS versus Normal pigs, but their proliferation rate was blunted. Conclusions: Renal ischemia and hypertension in swine induce changes in the mRNA profile of STCs, associated with increased STC activation and impaired proliferation. These observations suggest that RAS may alter the reparative capacity of STCs.

Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to 1-dimensional space by using a filter function that is subsequently used to reconstruct the data topology relationships. However, domain context information and prior knowledge have not been considered in current TDA modeling frameworks. Here, we report the development and evaluation of a semisupervised topological analysis (STA) framework that incorporates discrete or continuously labeled data points and selects the most relevant filter functions accordingly. We validate the proposed STA framework with simulation data and then apply it to samples from Genotype-Tissue Expression data and ovarian cancer transcriptome datasets. The graphs generated by STA for these 2 datasets, based on gene expression profiles, are consistent with prior knowledge, thereby supporting the effectiveness of the proposed framework.

Benign breast disease (BBD) is common and confers heterogeneous increases in breast cancer risk; however, risk prediction relies mainly on histopathology a

Richter transformation (RT) represents the development of an aggressive lymphoma in chronic lymphocytic leukemia (CLL). Patients with RT and relapsed CLL have poor outcomes. Yet, the extent of molecular differences between the two entities has not been fully explored. In this pilot study, we conducted RNA-seq and targeted panel sequencing of nodal tissues from 12 patients, including seven with RT and five with CLL. Analysis of RNA-seq data revealed two major clusters, with five RT in cluster C1 and the remaining two RT and all five CLL in C2. Within C2, one of the CLL ultimately developed RT; it showed more similarity to the two RT than to the other CLL in expression profile, suggesting the presence of expression signature for RT prior to the clinical diagnosis. In addition, differentially expressed genes, the majority of which showed higher expression in C1 relative to C2, were enriched in pathways known to be important for CLL pathogenesis or transformation. Deconvolution of the bulk RNA-seq data revealed major differences in cellular composition between the two clusters, notably tumor B cells, macrophages M1, and CD8+ T cells. Furthermore, by targeted sequencing, we identified 51 genes that carried recurrent copy number alterations (CNAs) preferentially occurring in either cluster. Over 80% of these CNAs occurred in C2, mainly gains of 17q12q25 in CLL. Patients in C1 had shorter overall survival (median 11 months) compared to those in C2 (median 36 months). Together, our findings highlight noticeable differences in transcriptomic and genomic alterations between CLL versus RT.