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Immunotherapy for metastatic colorectal cancer is effective only for mismatch repair-deficient tumors with high microsatellite instability that demonstrate immune infiltration, suggesting that tumor cells can determine their immune microenvironment. To understand this cross-talk, we analyzed the transcriptome of 91,103 unsorted single cells from 23 Korean and 6 Belgian patients. Cancer cells displayed transcriptional features reminiscent of normal differentiation programs, and genetic alterations that apparently fostered immunosuppressive microenvironments directed by regulatory T cells, myofibroblasts and myeloid cells. Intercellular network reconstruction supported the association between cancer cell signatures and specific stromal or immune cell populations. Our collective view of the cellular landscape and intercellular interactions in colorectal cancer provide mechanistic information for the design of efficient immuno-oncology treatment strategies.

Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell transcriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions., Understanding the mechanisms that lead to lung adenocarcinoma metastasis is important for identifying new therapeutics. Here, the authors document the changes in the transcriptome of human lung adenocarcinoma using single-cell sequencing and link cancer cell signatures to immune cell dynamics.

Background Controlled turnover of proteins as mediated by the ubiquitin proteasome system (UPS) is an important element in plant defense against environmental and pathogen stresses. E3 ligases play a central role in subjecting proteins to hydrolysis by the UPS. Recently, it has been demonstrated that a specific class of E3 ligases termed the U-box ligases are directly associated with the defense mechanisms against abiotic and biotic stresses in several plants. However, no studies on U-box E3 ligases have been performed in one of the important staple crops, barley. Results In this study, we identified 67 putative U-box E3 ligases from the barley genome and expressed sequence tags (ESTs). Similar to Arabidopsis and rice U-box E3 ligases, most of barley U-box E3 ligases possess evolutionary well-conserved domain organizations. Based on the domain compositions and arrangements, the barley U-box proteins were classified into eight different classes. Along with this new classification, we refined the previously reported classifications of U-box E3 ligase genes in Arabidopsis and rice. Furthermore, we investigated the expression profile of 67 U-box E3 ligase genes in response to drought stress and pathogen infection. We observed that many U-box E3 ligase genes were specifically up-and-down regulated by drought stress or by fungal infection, implying their possible roles of some U-box E3 ligase genes in the stress responses. Conclusion This study reports the classification of U-box E3 ligases in barley and their expression profiles against drought stress and pathogen infection. Therefore, the classification and expression profiling of barley U-box genes can be used as a platform to functionally define the stress-related E3 ligases in barley. Electronic supplementary material The online version of this article (10.1186/s12864-019-5696-z) contains supplementary material, which is available to authorized users.

Cancer stem-like cells (CSLCs) contribute to the initiation and recurrence of tumors and to their resistance to conventional therapies. In this study, small interfering RNA (siRNA)-based screening of ∼4800 druggable genes in 3-dimensional CSLC cultures in comparison to 2-dimensional bulk cultures of U87 glioma cells revealed 3 groups of genes essential for the following: survival of the CSLC population only, bulk-cultured population only, or both populations. While diverse biologic processes were associated with siRNAs reducing the bulk-cultured population, CSLC-eliminating siRNAs were enriched in a few functional categories, such as lipid metabolism, protein metabolism, and gene expression. Interestingly, siRNAs that selectively reduced CSLC only were found to target genes for cholesterol and unsaturated fatty acid synthesis. The lipidomic profile of CSLCs revealed increased levels of monounsaturated lipids. Pharmacologic blockage of these target pathways reduced CSLCs, and this effect was eliminated by addition of downstream metabolite products. The present CSLC-sensitive target categories provide a useful resource that can be exploited for the selective elimination of CSLCs.-Song, M., Lee, H., Nam, M.-H., Jeong, E., Kim, S., Hong, Y., Kim, N., Yim, H. Y., Yoo, Y.-J., Kim, J. S., Kim, J.-S., Cho, Y.-Y., Mills, G. B., Kim, W.-Y., Yoon, S. Loss-of-function screens of druggable targetome against cancer stem-like cells.

Purpose In multiple myeloma, extramedullary progression is associated with treatment resistance and a high mortality rate. To understand the molecular mechanisms controlling the devastating progression of myeloma, we applied single-cell RNA-sequencing (RNA-seq) to myeloma in the bone marrow and myelomatous pleural effusions or ascites. Experimental design Bone marrow or extramedullary myeloma samples were collected from 15 patients and subjected to single-cell RNA-seq. The single-cell transcriptome data of malignant plasma cells and the surrounding immune microenvironment were analyzed. Results Comparisons of single-cell transcriptomes revealed the systematic activation of proliferation, antigen presentation, proteasomes, glycolysis, and oxidative phosphorylation pathways in extramedullary myeloma cells. The myeloma cells expressed multiple combinations of growth factors and receptors, suggesting autonomous and pleiotropic growth potential at the single-cell level. Comparisons of the tumor microenvironment revealed the presence of cytotoxic T lymphocytes and natural killer (NK) cells in both the bone marrow and extramedullary ascites, demonstrating a gene-expression phenotype indicative of functional compromise. In parallel, isolated myeloma cells persistently expressed class I MHC molecules and upregulated inhibitory molecules for cytotoxic T and NK cells. Conclusions These data suggest that myeloma cells are equipped with specialized immune evasion mechanisms in cytotoxic microenvironments. Taken together, single-cell transcriptome analysis revealed transcriptional programs associated with aggressive myeloma progression that support autonomous cell proliferation and immune evasion.

Background Cell surface proteins have provided useful targets and biomarkers for advanced cancer therapies. The recent clinical success of antibody-drug conjugates (ADCs) highlights the importance of finding selective surface antigens for given cancer subtypes. We thus attempted to develop stand-alone software for the analysis of the cell surface transcriptome of patient cancer samples and to prioritize lineage- and/or mutation-specific over-expression markers in cancer cells. Results A total of 519 genes were selected as surface proteins, and their expression was profiled in 14 cancer subtypes using patient sample transcriptome data. Lineage/mutation-oriented analysis was used to identify subtype-specific surface markers with statistical confidence. Experimental validation confirmed the unique over-expression of predicted surface markers (MUC4, MSLN, and SLC7A11) in lung cancer cells at the protein level. The differential cell surface gene expression of cell lines may differ from that of tissue samples due to the absence of the tumor microenvironment. Conclusions In the present study, advanced 3D models of lung cell lines successfully reproduced the predicted patterns, demonstrating the physiological relevance of cell line-based 3D models in validating surface markers from patient tumor data. Also QSurface software is freely available at http://compbio.sookmyung.ac.kr/~qsurface. Electronic supplementary material The online version of this article (10.1186/s12918-018-0541-6) contains supplementary material, which is available to authorized users.

Although a large collection of cancer cell lines are useful surrogates for patient samples, the physiological relevance of observed molecular phenotypes in cell lines remains controversial. Because transcriptome data are a representative set of molecular phenotypes in cancers, we systematically analyzed the discrepancy of global gene expression profiles between patient samples and cell lines in breast cancers. While the majority of genes exhibited general consistency between patient samples and cell lines, the expression of genes in the categories of extracellular matrix, collagen trimers, receptor activity, catalytic activity and transporter activity were significantly up-regulated only in tissue samples. Genes in the extracellular matrix, particularly collagen trimers, showed a wide variation of expression in tissue, but minimal expression and variation in cell lines. Further analysis of tissue samples exclusively revealed that collagen genes exhibited a cancer stage-dependent expressional variation based on their supramolecular structure. Prognostic collagen biomarkers associated with survival rate were also readily predicted from tissue-oriented transcriptome analysis. This study presents the limitations of cell lines and the exclusive features of tissue samples in terms of functional categories of the cancer transcriptome.