Mutational signature and prognosis in adenocarcinoma of the bladder.

Adenocarcinoma of the bladder is a rare urinary bladder carcinoma with limited therapy options due to lack of molecular characterization. Here, we aimed to reveal the mutational and transcriptomic landscapes of adenocarcinoma of the bladder and assess any relationship with prognosis. Between February 2015 and June 2021, a total of 23 patients with adenocarcinoma of the bladder were enrolled. These included 16 patients with primary bladder adenocarcinomas and seven patients with urachal adenocarcinoma. Whole exome sequencing (16 patients), whole genome sequencing (16 patients), bulk RNA sequencing (RNA-seq) (19 patients), and single-cell RNA-seq (5 patients) were conducted for the specimens. Correlation analysis, survival analysis, and t-tests were also performed. Prevalent T>A substitutions were observed among somatic mutations, and major trinucleotide contexts included 5'-CTC-3' and 5'-CTG-3'. This pattern was mainly contributed by COSMIC signature 22 related to chemical carcinogen exposure (probably aristolochic acid), which has not been reported in bladder adenocarcinoma. Moreover, genes with copy number changes were also enriched in the KEGG term 'chemical carcinogenesis'. Transcriptomic analysis suggested high immune cell infiltration and luminal-like features in the majority of samples. Interestingly, a small fraction of samples with an APOBEC-derived mutational signature exhibited a higher risk of disease progression compared with samples with only a chemical carcinogen-related signature, confirming the molecular and prognostic heterogeneity of bladder adenocarcinoma. This study presents mutational and transcriptomic landscapes of bladder adenocarcinoma, and indicates that a chemical carcinogen-related mutational signature may be related to a better prognosis compared with an APOBEC signature in adenocarcinoma of the bladder. © 2024 The Pathological Society of Great Britain and Ireland.

Single-cell RNA sequencing reveals the heterogeneity and intercellular communication of hepatic stellate cells and macrophages during liver fibrosis.

Uncontrolled and excessive progression of liver fibrosis is thought to be the prevalent pathophysiological cause of liver cirrhosis and hepatocellular cancer, and there are currently no effective antifibrotic therapeutic options available. Intercellular communication and cellular heterogeneity in the liver are involved in the progression of liver fibrosis, but the exact nature of the cellular phenotypic changes and patterns of interregulatory remain unclear. Here, we performed single-cell RNA sequencing on nonparenchymal cells (NPCs) isolated from normal and fibrotic mouse livers. We identified eight main types of cells, including endothelial cells, hepatocytes, dendritic cells, B cells, natural killer/T (NK/T) cells, hepatic stellate cells (HSCs), cholangiocytes and macrophages, and revealed that macrophages and HSCs exhibit the most variance in transcriptional profile. Further analyses of HSCs and macrophage subpopulations and ligand-receptor interaction revealed a high heterogeneity characterization and tightly interregulated network of these two groups of cells in liver fibrosis. Finally, we uncovered a profibrotic Thbs1+ macrophage subcluster, which expands in mouse and human fibrotic livers, activating HSCs via PI3K/AKT/mTOR signaling pathway. Our findings decode unanticipated insights into the heterogeneity of HSCs and macrophages and their intercellular crosstalk at a single-cell level, and may provide potential therapeutic strategies in liver fibrosis.

Aberrant alteration of peripheral B lymphocyte subsets in hepatocellular carcinoma patients.

Although B lymphocytes are widely known to participate in the immune response, the conclusive roles of B lymphocyte subsets in the antitumor immune response have not yet been determined. Single-cell data from GEO datasets were first analyzed, and then a B cell flow cytometry panel was used to analyze the peripheral blood of 89 HCC patients and 33 healthy controls recruited to participate in our research. Patients with HCC had a higher frequency of B10 cells and a lower percentage of MZB cells than healthy controls. And the changes in B cell subsets might occur at an early stage. Moreover, the frequency of B10 cells decreased after surgery. Positively correlated with B10 cells, the elevated IL-10 level in HCC serum may be a new biomarker in HCC identification. For the first time, our results suggest that altered B cell subsets are associated with the development and prognosis of HCC. Increased B10 cell percentage and IL-10 in HCC patients suggest they might augment the development of liver tumors. Hence, B cell subsets and related cytokines may have predictive value in HCC patients and could be potential targets for immunotherapy in HCC.

Regionwise Generative Adversarial Image Inpainting for Large Missing Areas.

Recently, deep neural networks have achieved promising performance for in-filling large missing regions in image inpainting tasks. They have usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur, and other artifacts. Moreover, most inpainting approaches cannot handle well the case of a large contiguous missing area. To address these problems, we propose a generic inpainting framework capable of handling incomplete images with both contiguous and discontiguous large missing areas. We pose this in an adversarial manner, deploying regionwise operations in both the generator and discriminator to separately handle the different types of regions, namely, existing regions and missing ones. Moreover, a correlation loss is introduced to capture the nonlocal correlations between different patches, and thus, guide the generator to obtain more information during inference. With the help of regionwise generative adversarial mechanism, our framework can restore semantically reasonable and visually realistic images for both discontiguous and contiguous large missing areas. Extensive experiments on three widely used datasets for image inpainting task have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, on the large contiguous and discontiguous missing areas.

Simulation the potential distribution of Dendrolimus houi and its hosts, Pinus yunnanensis and Cryptomeria fortunei, under climate change in China.

Due to climate change, it is significant to explore the impact of rising temperatures on the distribution of Dendrolimus houi Lajonquiere (Lepidoptera) and its host plants, Pinus yunnanensis and Cryptomeria fortunei, and to simulate their suitable future distribution areas in order to provide a theoretical basis for the monitoring of, and early warning about, D. houi and the formulation of effective prevention and control policies. Based on the known distribution areas of, and relevant climate data for, D. houi, P. yunnanensis, and C. fortunei, their suitable habitat in China was predicted using the ENMeval data package in order to adjust the maximum entropy (MaxEnt) model parameters. The results showed that the regularization multiplier was 0.5 when the feature combination was LQHPT, with a MaxEnt model of lowest complexity and excellent prediction accuracy. The main climate variable affecting the geographical distribution of D. houi, P. yunnanensis, and C. fortunei is temperature, specifically including isothermality, temperature seasonality, maximum temperature of warmest month, minimum temperature of warmest month, average temperature of coldest quarter. The potential suitable distribution areas for P. yunnanensis and D. houi were similar under climate change, mainly distributed in southwest China, while C. fortunei was mainly distributed in southeast China. Under different future-climate scenarios, the areas suitable for the three species will increase, except for P. yunnanensis in the 2070s under Shared Socioeconomic Pathway 5-8.5. With climate change, all three species were found to have a tendency to migrate to higher latitudes and higher altitudes. The centroids of the areas suitable for P. yunnanensis and D. houi will migrate to the northwest and the centroids of the areas suitable for C. fortunei will migrate to the northeast.

Accurate tumor clonal structures require single-cell analysis.

Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.

Simulation of the potential distribution of rare and endangered Satyrium species in China under climate change.

Satyrium is an endangered and rare genus of plant that has various pharmacodynamic functions. In this study, optimized MaxEnt models were used in analyzing potential geographical distributions under current and future climatic conditions (the 2050s and 2070s) and dominant environmental variables influencing their geographic distribution. The results provided reference for implementation of long-term conservation and management approaches for the species. The results showed that the area of the total suitable habitat for Satyrium ciliatum (S. ciliatum) in China is 32.51 × 104 km2, the total suitable habitat area for Satyrium nepalense (S. nepalense) in China is 61.76 × 104 km2, and the area of the total suitable habitat for Satyrium yunnanense (S. yunnanense) in China is 89.73 × 104 km2 under current climatic conditions. The potential suitable habitat of Satyrium is mainly distributed in Southwest China. The major environmental variables influencing the geographical distribution of S. ciliatum were isothermality (bio3), temperature seasonality (bio4), and mean temperature of coldest quarter (bio11). Environmental variables such as isothermality (bio3), temperature seasonality (bio4), and precipitation of coldest quarter (bio19) affected the geographical distribution of S. nepalense; and environmental variables such as isothermality (bio3), temperature seasonality (bio4), and lower temperature of coldest month (bio6) affected the geographical distribution of S. yunnanense. The distribution range of Satyrium was extended as global warming increased, showing emissions of greenhouse gases with lower concentration (SSP1-2.6) and higher concentration (SSP5-8.5). According to the study, the distribution of suitable habitat will shift with a change to higher elevation areas and higher latitude areas in the future.

Exposure to aristolochic acid I is associated with poor prognosis of liver cancer patients.

The aristolochic acids (AAs), derived from Aristolochia and Asarum species used widely in herbal medicines, are closely associated with liver cancer. The major AA derivatives are aristolochic acid I (AAI) and II (AAII), which can bind DNA covalently to form AA-DNA adducts after metabolic activation in vivo. Among all these AA-DNA adducts, 7-(deoxyadenosine-N6-yl) aristolactam I (dA-AL-I) is the most abundant and persistent DNA lesion in patients. However, the direct evidence indicating AA exposure in human liver cancer is still missing. Here, we analyzed dA-AL-I adduct, the direct biomarker of AAI exposure, by ultra-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UPLC-TQ/MS) in 209 liver cancer patients. Also, DNA samples from mice treated with/without AAI were used as positive and negative controls. dA-AL-I adduct was present in 110 of 209 (52.6%) patients, indicating that these patients were exposed to AAI prior to their clinical investigations and also had a worse prognosis. The relative high AA exposure rate and worse prognosis in our cohort of patients emphasize the significance to increase public awareness to avoid the use of herbal medicine containing AAs or their derivatives.

Transductive Relation-Propagation With Decoupling Training for Few-Shot Learning.

Few-shot learning, aiming to learn novel concepts from one or a few labeled examples, is an interesting and very challenging problem with many practical advantages. Existing few-shot methods usually utilize data of the same classes to train the feature embedding module and in a row, which is unable to learn adapting to new tasks. Besides, traditional few-shot models fail to take advantage of the valuable relations of the support-query pairs, leading to performance degradation. In this article, we propose a transductive relation-propagation graph neural network (GNN) with a decoupling training strategy (TRPN-D) to explicitly model and propagate such relations across support-query pairs, and empower the few-shot module the ability of transferring past knowledge to new tasks via the decoupling training. Our few-shot module, namely TRPN, treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intraclass commonality and interclass uniqueness. Through relation propagation, the model could generate the discriminative relation embeddings for support-query pairs. To the best of our knowledge, this is the first work that decouples the training of the embedding network and the few-shot graph module with different tasks, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.

Clonal evolution in liver cancer at single-cell and single-variant resolution.

Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic diversity and treatment resistance, and such heterogeneity has only been characterized at single-cell sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship.

Immune suppressive landscape in the human esophageal squamous cell carcinoma microenvironment.

Cancer immunotherapy has revolutionized cancer treatment, and it relies heavily on the comprehensive understanding of the immune landscape of the tumor microenvironment (TME). Here, we obtain a detailed immune cell atlas of esophageal squamous cell carcinoma (ESCC) at single-cell resolution. Exhausted T and NK cells, regulatory T cells (Tregs), alternatively activated macrophages and tolerogenic dendritic cells are dominant in the TME. Transcriptional profiling coupled with T cell receptor (TCR) sequencing reveal lineage connections in T cell populations. CD8 T cells show continuous progression from pre-exhausted to exhausted T cells. While exhausted CD4, CD8 T and NK cells are major proliferative cell components in the TME, the crosstalk between macrophages and Tregs contributes to potential immunosuppression in the TME. Our results indicate several immunosuppressive mechanisms that may be simultaneously responsible for the failure of immuno-surveillance. Specific targeting of these immunosuppressive pathways may reactivate anti-tumor immune responses in ESCC.