B cell lymphoma 6 promotes hepatocellular carcinoma progression by inhibiting tumor infiltrating CD4+T cell cytotoxicity through ESM1.

Immunotherapy exhibited potential effects for advanced hepatocellular carcinoma, unfortunately, the clinical benefits are often countered by cancer adaptive immune suppressive response. Uncovering the mechanism how cancer cells evade immune surveillance would help to develop new immunotherapy approaches and combination therapy. In this article, by analyzing the transcriptional factors which modulate the differentially expressed genes between T cell infiltration high group and low group, we identified oncoprotein B cell lymphoma 6 (BCL6) suppresses the infiltration and activation of tumor infiltrating T lymphocytes, thus correlated with poorer clinical outcome. By using antibody deletion experiment, we further demonstrated that CD4+T cells but not CD8+T cells are the main lymphocyte population suppressed by Bcl6 to promote HCC development. Mechanistically, BCL6 decreases cancer cell expression of pro-inflammatory cytokines and T lymphocyte chemokines such as IL6, IL1F6, and CCL5. Moreover, BCL6 upregulates Endothelial cell-specific molecule 1 (ESM1) to inhibit T lymphocyte recruitment and activation possibly through ICAM-1/LFA-1 signaling pathway. Our findings uncovered an unappreciated paracrine mechanism how cancer cell-derived BCL6 assists cancer cell immune evasion, and highlighted the role of CD4+T cells in HCC immune surveillance.

Spatial heterogeneity of tumor microenvironment influences the prognosis of clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is an immunologically and histologically diverse tumor. However, how the structural heterogeneity of tumor microenvironment (TME) affects cancer progression and treatment response remains unclear. Hence, we characterized the TME architectures of ccRCC tissues using imaging mass cytometry (IMC) and explored their associations with clinical outcome and therapeutic response. METHODS: Using IMC, we profiled the TME landscape of ccRCC and paracancerous tissue by measuring 17 markers involved in tissue architecture, immune cell and immune activation. In the ccRCC tissue, we identified distinct immune architectures of ccRCC tissue based on the mix score and performed cellular neighborhood (CN) analysis to subdivide TME phenotypes. Moreover, we assessed the relationship between the different TME phenotypes and ccRCC patient survival, clinical features and treatment response. RESULTS: We found that ccRCC tissues had higher levels of CD8+ T cells, CD163- macrophages, Treg cells, endothelial cells, and fibroblasts than paracancerous tissues. Immune infiltrates in ccRCC tissues distinctly showed clustered and scattered patterns. Within the clustered pattern, we identified two subtypes with different clinical outcomes based on CN analysis. The TLS-like phenotype had cell communities resembling tertiary lymphoid structures, characterized by cell-cell interactions of CD8+ T cells-B cells and GZMB+CD8+ T cells-B cells, which exhibited anti-tumor features and favorable outcomes, while the Macrophage/T-clustered phenotype with macrophage- or T cell-dominated cell communities had a poor prognosis. Patients with scattered immune architecture could be further divided into scattered-CN-hot and scattered-CN-cold phenotypes based on the presence or absence of immune CNs, but both had a better prognosis than the macrophage/T-clustered phenotype. We further analyzed the relationship between the TME phenotypes and treatment response in five metastatic ccRCC patients treated with sunitinib, and found that all three responders were scattered-CN-hot phenotype while both non-responders were macrophage/T-clustered phenotype. CONCLUSION: Our study revealed the structural heterogeneity of TME in ccRCC and its impact on clinical outcome and personalized treatment. These findings highlight the potential of IMC and CN analysis for characterizing TME structural units in cancer research.

Single-cell transcriptomics provides insights into the origin and microenvironment of human oesophageal high-grade intraepithelial neoplasia.

BACKGROUND: High-grade intraepithelial neoplasia (HIN) is the precursor of oesophageal squamous cell carcinoma. The molecular and functional properties of HIN are determined by intrinsic origin cells and the extrinsic microenvironment. Yet, these factors are poorly understood. METHODS: We performed single-cell RNA sequencing of cells from HINs and adjacent tissues from the human oesophagus. We analysed the heterogeneity of basal layer cells and confirmed it using immunostaining. Aneuploid cells in HIN were studied using primary cell culture combined with karyotype analysis. We reconstructed the lineage relationship between tumour and normal populations based on transcriptome similarity. Integration analysis was applied to our epithelial data and published invasive cancer data, and results were confirmed by immunostaining and 3D organoid functional experiments. We also analysed the tumour microenvironment of HIN. RESULTS: The basal layer contained two cell populations: KRT15high STMN1low and KRT15high STMN1high cells, which were located mainly in the interpapillary and papillary zones, respectively. The KRT15high STMN1low population more closely resembled stem cells and transcriptome similarity revealed that HIN probably originated from these slow-cycling KRT15high STMN1low cells. 3D Organoid experiments and RNA-sequencing showed that basal-cell features and the differentiation ability of the normal epithelium were largely retained in HIN, but may change dramatically in tumour invasion stage. Moreover, the tumour microenvironment of HIN was characterised by both inflammation and immunosuppression. CONCLUSIONS: Our study provides a comprehensive single-cell transcriptome landscape of human oesophageal HIN. Our findings on the origin cells and unique microenvironment of HIN will allow for the development of strategies to block tumour progression and even prevent cancer initiation.

CD9, a potential leukemia stem cell marker, regulates drug resistance and leukemia development in acute myeloid leukemia.

BACKGROUND: Leukemia stem cells (LSCs) are responsible for the initiation, progression, and relapse of acute myeloid leukemia (AML). Therefore, a therapeutic strategy targeting LSCs is a potential approach to eradicate AML. In this study, we aimed to identify LSC-specific surface markers and uncover the underlying mechanism of AML LSCs. METHODS: Microarray gene expression data were used to investigate candidate AML-LSC-specific markers. CD9 expression in AML cell lines, patients with AML, and normal donors was evaluated by flow cytometry (FC). The biological characteristics of CD9-positive (CD9+) cells were analyzed by in vitro proliferation, chemotherapeutic drug resistance, migration, and in vivo xenotransplantation assays. The molecular mechanism involved in CD9+ cell function was investigated by gene expression profiling. The effects of alpha-2-macroglobulin (A2M) on CD9+ cells were analyzed with regard to proliferation, drug resistance, and migration. RESULTS: CD9, a cell surface protein, was specifically expressed on AML LSCs but barely detected on normal hematopoietic stem cells (HSCs). CD9+ cells exhibit more resistance to chemotherapy drugs and higher migration potential than do CD9-negative (CD9-) cells. More importantly, CD9+ cells possess the ability to reconstitute human AML in immunocompromised mice and promote leukemia growth, suggesting that CD9+ cells define the LSC population. Furthermore, we identified that A2M plays a crucial role in maintaining CD9+ LSC stemness. Knockdown of A2M impairs drug resistance and migration of CD9+ cells. CONCLUSION: Our findings suggest that CD9 is a new biomarker of AML LSCs and is a promising therapeutic target.

mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies.

Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available. Therefore, we developed an R software named mrMLM v4.0.2. This software integrates mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO methods developed by our lab. There are four components in mrMLM v4.0.2, including dataset input, parameter setting, software running, and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0.2, built upon Shiny, is also available. To confirm the correctness of the aforementioned programs, all the methods in mrMLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets. The results confirm the superior performance of mrMLM v4.0.2 to other methods currently available. False positive rates are effectively controlled, albeit with a less stringent significance threshold. mrMLM v4.0.2 is publicly available at BioCode (https://bigd.big.ac.cn/biocode/tools/BT007077) or R (https://cran.r-project.org/web/packages/mrMLM.GUI/index.html) as an open-source software.

Methodological implementation of mixed linear models in multi-locus genome-wide association studies.

The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with ≤ 0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.

Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies.

Genome-wide association study (GWAS) entails examining a large number of single nucleotide polymorphisms (SNPs) in a limited sample with hundreds of individuals, implying a variable selection problem in the high dimensional dataset. Although many single-locus GWAS approaches under polygenic background and population structure controls have been widely used, some significant loci fail to be detected. In this study, we used an iterative modified-sure independence screening (ISIS) approach in reducing the number of SNPs to a moderate size. Expectation-Maximization (EM)-Bayesian least absolute shrinkage and selection operator (BLASSO) was used to estimate all the selected SNP effects for true quantitative trait nucleotide (QTN) detection. This method is referred to as ISIS EM-BLASSO algorithm. Monte Carlo simulation studies validated the new method, which has the highest empirical power in QTN detection and the highest accuracy in QTN effect estimation, and it is the fastest, as compared with efficient mixed-model association (EMMA), smoothly clipped absolute deviation (SCAD), fixed and random model circulating probability unification (FarmCPU), and multi-locus random-SNP-effect mixed linear model (mrMLM). To further demonstrate the new method, six flowering time traits in Arabidopsis thaliana were re-analyzed by four methods (New method, EMMA, FarmCPU, and mrMLM). As a result, the new method identified most previously reported genes. Therefore, the new method is a good alternative for multi-locus GWAS.

Mapping small-effect and linked quantitative trait loci for complex traits in backcross or DH populations via a multi-locus GWAS methodology.

Composite interval mapping (CIM) is the most widely-used method in linkage analysis. Its main feature is the ability to control genomic background effects via inclusion of co-factors in its genetic model. However, the result often depends on how the co-factors are selected, especially for small-effect and linked quantitative trait loci (QTL). To address this issue, here we proposed a new method under the framework of genome-wide association studies (GWAS). First, a single-locus random-SNP-effect mixed linear model method for GWAS was used to scan each putative QTL on the genome in backcross or doubled haploid populations. Here, controlling background via selecting markers in the CIM was replaced by estimating polygenic variance. Then, all the peaks in the negative logarithm P-value curve were selected as the positions of multiple putative QTL to be included in a multi-locus genetic model, and true QTL were automatically identified by empirical Bayes. This called genome-wide CIM (GCIM). A series of simulated and real datasets was used to validate the new method. As a result, the new method had higher power in QTL detection, greater accuracy in QTL effect estimation, and stronger robustness under various backgrounds as compared with the CIM and empirical Bayes methods.