Functional requirement of alternative splicing in epithelial-mesenchymal transition of pancreatic circulating tumor.

Circulating tumor cells (CTCs) that undergo epithelial-to-mesenchymal transition (EMT) can provide valuable information regarding metastasis and potential therapies. However, current studies on the EMT overlook alternative splicing. Here, we used single-cell full-length transcriptome data and mRNA sequencing of CTCs to identify stage-specific alternative splicing of partial EMT and mesenchymal states during pancreatic cancer metastasis. We classified definitive tumor and normal epithelial cells via genetic aberrations and demonstrated dynamic changes in the epithelial-mesenchymal continuum in both epithelial cancer cells and CTCs. We provide the landscape of alternative splicing in CTCs at different stages of EMT, uncovering cell-type-specific splicing patterns and splicing events in cell surface proteins suitable for therapies. We show that MBNL1 governs cell fate through alternative splicing independently of changes in gene expression and affects the splicing pattern during EMT. We found a high frequency of events that contained multiple premature termination codons and were enriched with C and G nucleotides in close proximity, which influence the likelihood of stop codon readthrough and expand the range of potential therapeutic targets. Our study provides insights into the EMT transcriptome's dynamic changes and identifies potential diagnostic and therapeutic targets in pancreatic cancer.

A multifactorial analysis of FAP to regulate gastrointestinal cancers progression.

Background: Fibroblast activation protein (FAP) is a cell-surface serine protease that has both dipeptidyl peptidase as well as endopeptidase activities and could cleave substrates at post-proline bond. Previous findings showed that FAP was hard to be detected in normal tissues but significantly up-regulated in remodeling sites like fibrosis, atherosclerosis, arthritis and embryonic tissues. Though increasing evidence has demonstrated the importance of FAP in cancer progression, no multifactorial analysis has been developed to investigate its function in gastrointestinal cancers until now. Methods: By comprehensive use of datasets from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), scTIME Portal and Human Protein Atlas (HPA), we evaluated the carcinogenesis potential of FAP in gastrointestinal cancers, analyzing the correlation between FAP and poor outcomes, immunology in liver, colon, pancreas as well as stomach cancers. Then liver cancer was selected as example to experimentally validate the pro-tumor and immune regulative role of FAP in gastrointestinal cancers. Results: FAP was abundantly expressed in gastrointestinal cancers, such as LIHC, COAD, PAAD and STAD. Functional analysis indicated that the highly-expressed FAP in these cancers could affect extracellular matrix organization process and interacted with genes like COL1A1, COL1A2, COL3A1 and POSTN. In addition, it was also observed that FAP was positively correlated to M2 macrophages infiltration across these cancers. To verify these findings in vitro, we used LIHC as example and over-expressed FAP in human hepatic stellate LX2 cells, a main cell type that produce FAP in tumor tissues, and then investigate its role on LIHC cells as well as macrophages. Results showed that the medium from FAP-over-expressed LX2 cells could significantly promote the motility of MHCC97H and SK-Hep1 LIHC cells, increase the invasion of THP-1 macrophages and induce them into pro-tumor M2 phenotype. Conclusion: In summary, we employed bioinformatic tools and experiments to perform a comprehensive analysis about FAP. Up-regulation of FAP in gastrointestinal cancers was primarily expressed in fibroblasts and contributes to tumor cells motility, macrophages infiltration and M2 polarization, revealing the multifactorial role of FAP in gastrointestinal cancers progression.

N-glycosylation by N-acetylglucosaminyltransferase IVa enhances the interaction of integrin ß1 with vimentin and promotes hepatocellular carcinoma cell motility.

N-glycosylation has been revealed to be tightly associated with cancer metastasis. As a key transferase that catalyzes the formation of ß1,4 N-acetylglucosamine (ß1,4GlcNAc) branches on the mannose core of N-glycans, N-acetylglucosaminyltransferase IVa (GnT-IVa) has been reported to be involved in hepatocellular carcinoma (HCC) metastasis by forming N-glycans; however, the underlying mechanisms are largely unknown. In the current study, we found that GnT-IVa was upregulated in HCC tissues and positively correlated with worse outcomes in HCC patients. We found that GnT-IVa could promote tumor growth in mice; notably, this effect was attenuated after mutating the enzymatic site (D445A) of GnT-IVa, suggesting that GnT-IVa regulated HCC progression by forming ß1,4GlcNAc branches. To mechanistically investigate the role of GnT-IVa in HCC, we conducted GSEA and GO functional analysis as well as in vitro experiments. The results showed that GnT-IVa could enhance HCC cell migration, invasion and adhesion ability and increase ß1,4GlcNAc branch glycans on integrin ß1 (ITGB1), a tumor-associated glycoprotein that is closely involved in cell motility by interacting with vimentin. Interruption of ß1,4GlcNAc branch glycan modification on ITGB1 could suppress the interaction of ITGB1 with vimentin and inhibit cell motility. These results revealed that GnT-IVa could promote HCC cell motility by affecting the biological functions of ITGB1 through N-glycosylation. In summary, our results revealed that GnT-IVa is highly expressed in HCC and can form ß1,4GlcNAc branches on ITGB1, which are essential for interactions with vimentin to promote HCC cell motility. These findings not only proposed a novel mechanism for GnT-IVa in HCC progression but also revealed the significance of N-glycosylation on ITGB1 during the process, which may provide a novel target for future HCC therapy.

Identification of shared characteristics in tumor-infiltrating T cells across 15 cancers.

Tumor-infiltrating T cells are essential players in tumor immunotherapy. Great progress has been achieved in the investigation of T cell heterogeneity. However, little is well known about the shared characteristics of tumor-infiltrating T cells across cancers. In this study, we conduct a pan-cancer analysis of 349,799 T cells across 15 cancers. The results show that the same T cell types had similar expression patterns regulated by specific transcription factor (TF) regulons across cancers. Multiple T cell type transition paths were consistent in cancers. We found that TF regulons associated with CD8+ T cells transitioned to terminally differentiated effector memory (Temra) or exhausted (Tex) states were associated with patient clinical classification. We also observed universal activated cell-cell interaction pathways of tumor-infiltrating T cells in all cancers, some of which specifically mediated crosstalk in certain cell types. Moreover, consistent characteristics of TCRs in the aspect of variable and joining region genes were found across cancers. Overall, our study reveals common features of tumor-infiltrating T cells in different cancers and suggests future avenues for rational, targeted immunotherapies.

DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development.

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

A pan-cancer analysis of alternative splicing of splicing factors in 6904 patients.

Great progress has been made in the investigation on mutation and expression of splicing factor. However, little is known on the role of alternative splicing of splicing factors across cancers. Here, we reported a pan-cancer analysis of alternative splicing of splicing factors spanning 6904 patients across 16 cancer types, and identified 167 splicing factors with implications regulating cancer-specific splicing patterns through alternative splicing. Furthermore, we found that abnormal splicing events of splicing factors could serve as potential common regulators for alternative splicing in different cancers. In addition, we developed a splicing-derived neoepitopes database (ASPNs), which provided the corresponding putative alternative splicing-derived neoepitopes of 16 cancer types. Our results suggested that alternative splicing of splicing factors involved in the pre-RNA splicing process was common across cancer types and may represent an underestimated hallmark of tumorigenesis.

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma.

BACKGROUD: Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options. METHODS: The corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model. RESULTS: Here, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/ . When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients. CONCLUSIONS: Basing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy.

Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing.

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.