Yun Xiao

One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.

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.

BACKGROUND: Platinum-based concurrent chemoradiotherapy is the standard of care for patients with locoregionally advanced nasopharyngeal carcinoma. Additional gemcitabine and cisplatin induction chemotherapy has shown promising efficacy in phase 2 trials. METHODS: In a parallel-group, multicenter, randomized, controlled, phase 3 trial, we compared gemcitabine and cisplatin as induction chemotherapy plus concurrent chemoradiotherapy with concurrent chemoradiotherapy alone. Patients with locoregionally advanced nasopharyngeal carcinoma were randomly assigned in a 1:1 ratio to receive gemcitabine (at a dose of 1 g per square meter of body-surface area on days 1 and 8) plus cisplatin (80 mg per square meter on day 1), administered every 3 weeks for three cycles, plus chemoradiotherapy (concurrent cisplatin at a dose of 100 mg per square meter every 3 weeks for three cycles plus intensity-modulated radiotherapy) or chemoradiotherapy alone. The primary end point was recurrence-free survival (i.e., freedom from disease recurrence [distant metastasis or locoregional recurrence] or death from any cause) in the intention-to-treat population. Secondary end points included overall survival, treatment adherence, and safety. RESULTS: A total of 480 patients were included in the trial (242 patients in the induction chemotherapy group and 238 in the standard-therapy group). At a median follow-up of 42.7 months, the 3-year recurrence-free survival was 85.3% in the induction chemotherapy group and 76.5% in the standard-therapy group (stratified hazard ratio for recurrence or death, 0.51; 95% confidence interval [CI], 0.34 to 0.77; P = 0.001). Overall survival at 3 years was 94.6% and 90.3%, respectively (stratified hazard ratio for death, 0.43; 95% CI, 0.24 to 0.77). A total of 96.7% of the patients completed three cycles of induction chemotherapy. The incidence of acute adverse events of grade 3 or 4 was 75.7% in the induction chemotherapy group and 55.7% in the standard-therapy group, with a higher incidence of neutropenia, thrombocytopenia, anemia, nausea, and vomiting in the induction chemotherapy group. The incidence of grade 3 or 4 late toxic effects was 9.2% in the induction chemotherapy group and 11.4% in the standard-therapy group. CONCLUSIONS: Induction chemotherapy added to chemoradiotherapy significantly improved recurrence-free survival and overall survival, as compared with chemoradiotherapy alone, among patients with locoregionally advanced nasopharyngeal carcinoma. (Funded by the Innovation Team Development Plan of the Ministry of Education and others; ClinicalTrials.gov number, NCT01872962.).

Abstract 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

Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA-miRNA synergistic network from a new view. Here, we constructed a miRNA-miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.