Individualized detection of TMPRSS2-ERG fusion status in prostate cancer: a rank-based qualitative transcriptome signature.

BACKGROUND: TMPRSS2-ERG (T2E) fusion is highly related to aggressive clinical features in prostate cancer (PC), which guides individual therapy. However, current fusion prediction tools lacked enough accuracy and biomarkers were unable to be applied to individuals across different platforms due to their quantitative nature. This study aims to identify a transcriptome signature to detect the T2E fusion status of PC at the individual level. METHODS: Based on 272 high-throughput mRNA expression profiles from the Sboner dataset, we developed a rank-based algorithm to identify a qualitative signature to detect T2E fusion in PC. The signature was validated in 1223 samples from three external datasets (Setlur, Clarissa, and TCGA). RESULTS: A signature, composed of five mRNAs coupled to ERG (five ERG-mRNA pairs, 5-ERG-mRPs), was developed to distinguish T2E fusion status in PC. 5-ERG-mRPs reached 84.56% accuracy in Sboner dataset, which was verified in Setlur dataset (n = 455, accuracy = 82.20%) and Clarissa dataset (n = 118, accuracy = 81.36%). Besides, for 495 samples from TCGA, two subtypes classified by 5-ERG-mRPs showed a higher level of significance in various T2E fusion features than subtypes obtained through current fusion prediction tools, such as STAR-Fusion. CONCLUSIONS: Overall, 5-ERG-mRPs can robustly detect T2E fusion in PC at the individual level, which can be used on any gene measurement platform without specific normalization procedures. Hence, 5-ERG-mRPs may serve as an auxiliary tool for PC patient management.

The Heterogeneous Cellular States of Glioblastoma Stem Cells Revealed by Single Cell Analysis.

Glioblastoma stem cells (GSCs) contributed to the progression, treatment resistance, and relapse of glioblastoma (GBM). However, current researches on GSCs were performed usually outside the human tumor microenvironment, ignoring the importance of the cellular states of primary GSCs. In this study, we leveraged single-cell transcriptome sequencing data of 6 independent GBM cohorts from public databases, and combined lineage and stemness features to identify primary GSCs. We dissected the cell states of GSCs and correlated them with the clinical outcomes of patients. As a result, we constructed a cellular hierarchy where GSCs resided at the center. In addition, we identified and characterized 2 different and recurrent GSCs subpopulations: proliferative GSCs (pGSCs) and quiescent GSCs (qGSCs). The pGSCs showed high cell cycle activity, indicating rapid cell division, while qGSCs showed a quiescent state. Then we traced the processes of tumor development by pseudo-time analysis and tumor phylogeny, and found that GSCs accumulated throughout the whole tumor development period. During the process, pGSCs mainly contributed to the early stage and qGSCs were enriched in the later stage. Finally, we constructed an 8-gene prognostic signature reflecting pGSCs activity and found that patients whose tumors were enriched for the pGSC signature had poor clinical outcomes. Our study highlights the primary GSCs heterogeneity and its correlation to tumor development and clinical outcomes, providing the potential targets for GBM treatment.

Decoding the associations between cell functional states in head and neck cancer based on single-cell transcriptome.

OBJECTIVES: Systematically identifying cancer cell functional states, especially their associations, is key to understanding the pathogenesis of cancers. MATERIALS AND METHODS: Here, we systematically identified six cancer-related states, including epithelial-mesenchymal transition (EMT), immune response, epithelial differentiation, stress, G1/S and G2/M phases, in head and neck squamous cell carcinoma (HNSCC) based on single-cell RNA-sequencing (scRNA-seq). RESULTS AND CONCLUSION: We defined the association patterns between these functional states and found the patterns were correlated with the state activity. Particularly, immune response and EMT were negatively, positively, or non-significantly correlated in samples with the highest immune response activity, the lowest activity of the two states, or with the highest EMT activity, respectively. Combining scRNA-seq data of immune cells and four independent HNSCC cohorts, we found the negative relationship between EMT and immune response was correlated with an activated immune microenvironment and a longer survival, while the non-significant relationship was correlated with an immunosuppressed microenvironment and a poor prognosis. Collectively, our results provide insight into the association patterns between functional states in HNSCC, and may facilitate the elucidation of the interactions between cancer cells and immune system during cancer progression.

Dynamic regulatory networks of T cell trajectory dissect transcriptional control of T cell state transition.

T cells exhibit heterogeneous functional states, which correlate with responsiveness to immune checkpoint blockade and prognosis of tumor patients. However, the molecular regulatory mechanisms underlying the dynamic process of T cell state transition remain largely unknown. Based on single-cell transcriptome data of T cells in non-small cell lung cancer, we combined cell states and pseudo-times to propose a pipeline to construct dynamic regulatory networks for dissecting the process of T cell dysfunction. Candidate regulators at different stages were revealed in the process of tumor-infiltrating T cell dysfunction. Through comparing dynamic networks across the T cell state transition, we revealed frequent regulatory interaction rewiring and further refined critical regulators mediating each state transition. Several known regulators were identified, including TCF7, EOMES, ID2, and TOX. Notably, one of the critical regulators, TSC22D3, was frequently identified in the state transitions from the intermediate state to the pre-dysfunction and dysfunction state, exerting diverse roles in each state transition by regulatory interaction rewiring. Moreover, higher expression of TSC22D3 was associated with the clinical outcome of tumor patients. Our study embedded transcription factors (TFs) within the temporal dynamic networks, providing a comprehensive view of dynamic regulatory mechanisms controlling the process of T cell state transition.

Breast cancer prognosis signature: linking risk stratification to disease subtypes.

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

CancerSEA: a cancer single-cell state atlas.

High functional heterogeneity of cancer cells poses a major challenge for cancer research. Single-cell sequencing technology provides an unprecedented opportunity to decipher diverse functional states of cancer cells at single-cell resolution, and cancer scRNA-seq datasets have been largely accumulated. This emphasizes the urgent need to build a dedicated resource to decode the functional states of cancer single cells. Here, we developed CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/ or http://202.97.205.69/CancerSEA/), the first dedicated database that aims to comprehensively explore distinct functional states of cancer cells at the single-cell level. CancerSEA portrays a cancer single-cell functional state atlas, involving 14 functional states (including stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation and quiescence) of 41 900 cancer single cells from 25 cancer types. It allows querying which functional states are associated with the gene (or gene list) of interest in different cancers. CancerSEA also provides functional state-associated PCG/lncRNA repertoires across all cancers, in specific cancers, and in individual cancer single-cell datasets. In summary, CancerSEA provides a user-friendly interface for comprehensively searching, browsing, visualizing and downloading functional state activity profiles of tens of thousands of cancer single cells and the corresponding PCGs/lncRNAs expression profiles.

Discovering Rare Genes Contributing to Cancer Stemness and Invasive Potential by GBM Single-Cell Transcriptional Analysis.

Single-cell RNA sequencing presents the sophisticated delineation of cell transcriptomes in many cancer types and highlights the tumor heterogeneity at higher resolution, which provides a new chance to explore the molecular mechanism in a minority of cells. In this study, we utilized publicly available single-cell RNA-seq data to discover and comprehensively dissect rare genes existing in few glioblastoma (GBM) cells. Moreover, we designed a framework to systematically identify 51 rare protein-coding genes (PCGs) and 47 rare long non-coding RNAs (lncRNAs) in GBM. Patients with high expression levels of rare genes like CYB5R2 and TPPP3 had worse overall survival and disease-free survival, implying their potential implication in GBM progression and prognosis. We found that these rare genes tended to be specifically expressed in GBM cancer stem cells, which emphasized their ability to characterize stem-like cancer cells and implied their contribution to GBM growth. Furthermore, rare genes were enriched in a 17-cell subset, which was located in an individual branch of the pseudotime trajectory of cancer progression and exhibited high cell cycle activity and invasive potential. Our study captures the rare genes highly expressed in few cells, deepens our understanding of special states during GBM tumorigenesis and progression such as cancer stemness and invasion, and proposes potential targets for cancer therapy.

A pan-cancer atlas of cancer hallmark-associated candidate driver lncRNAs.

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non-coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well-known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan-cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in 'Tissue Invasion and Metastasis' in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA-based target therapy.

TIP: A Web Server for Resolving Tumor Immunophenotype Profiling.

: Systematically tracking the tumor immunophenotype is required to understand the mechanisms of cancer immunity and improve clinical benefit of cancer immunotherapy. However, progress in current research is hindered by the lack of comprehensive immune activity resources and easy-to-use tools for biologists, clinicians, and researchers to conveniently evaluate immune activity during the "cancer-immunity cycle." We developed a user-friendly one-stop shop web tool called TIP to comprehensively resolve tumor immunophenotype. TIP has the capability to rapidly analyze and intuitively visualize the activity of anticancer immunity and the extent of tumor-infiltrating immune cells across the seven-step cancer-immunity cycle. Also, we precalculated the pan-cancer immunophenotype for 11,373 samples from 33 The Cancer Genome Atlas human cancers that allow users to obtain and compare immunophenotype of pan-cancer samples. We expect TIP to be useful in a large number of emerging cancer immunity studies and development of effective immunotherapy biomarkers. TIP is freely available for use at http://biocc.hrbmu.edu.cn/TIP/. SIGNIFICANCE: TIP is a one-stop shop platform that can help biologists, clinicians, and researchers conveniently evaluate anticancer immune activity with their own gene expression data.See related commentary by Hirano, p. 6536.

DiseaseEnhancer: a resource of human disease-associated enhancer catalog.

Large-scale sequencing studies discovered substantial genetic variants occurring in enhancers which regulate genes via long range chromatin interactions. Importantly, such variants could affect enhancer regulation by changing transcription factor bindings or enhancer hijacking, and in turn, make an essential contribution to disease progression. To facilitate better usage of published data and exploring enhancer deregulation in various human diseases, we created DiseaseEnhancer (http://biocc.hrbmu.edu.cn/DiseaseEnhancer/), a manually curated database for disease-associated enhancers. As of July 2017, DiseaseEnhancer includes 847 disease-associated enhancers in 143 human diseases. Database features include basic enhancer information (i.e. genomic location and target genes); disease types; associated variants on the enhancer and their mediated phenotypes (i.e. gain/loss of enhancer and the alterations of transcription factor bindings). We also include a feature on our website to export any query results into a file and download the full database. DiseaseEnhancer provides a promising avenue for researchers to facilitate the understanding of enhancer deregulation in disease pathogenesis, and identify new biomarkers for disease diagnosis and therapy.

Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis.

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as 'cell adhesion' and 'cell migration'. Furthermore, they are most significantly correlated with 'tissue invasion and metastasis', a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.