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Automated cell type identification is a key computational challenge in single‐cell RNA‐sequencing (scRNA‐seq) data. To capitalise on the large collection of well‐annotated scRNA‐seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single‐cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state‐of‐the‐art methodology in automated cell type identification from scRNA‐seq data. Synopsis scClassify is a multiscale classification framework based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. scClassify performs multiscale cell type classification based on cell type hierarchies constructed from single or multiple reference datasets. It implements a post‐hoc clustering procedure for discovering novel cell types from cells that are unassigned due to the absence of their types in the reference data. It enables the estimation of the number of cells required in a reference dataset to accurately discriminate a given cell type in a cell type hierarchy. Application to large atlas datasets such as Tabula Muris demonstrates its ability to refine cell types and identify cells from sub‐populations. Graphical Abstract scClassify is a multiscale classification framework based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.

Among patients with advanced renal-cell carcinoma, overall survival and progression-free survival were longer among patients who received pembrolizumab and axitinib than among those who received sunitinib, and more patients in the pembrolizumab–axitinib group than in the sunitinib group had a response.

The first interim analysis of the KEYNOTE-426 study showed superior efficacy of pembrolizumab plus axitinib over sunitinib monotherapy in treatment-naive, advanced renal cell carcinoma. The exploratory analysis with extended follow-up reported here aims to assess long-term efficacy and safety of pembrolizumab plus axitinib versus sunitinib monotherapy in patients with advanced renal cell carcinoma. In the ongoing, randomised, open-label, phase 3 KEYNOTE-426 study, adults (≥18 years old) with treatment-naive, advanced renal cell carcinoma with clear cell histology were enrolled in 129 sites (hospitals and cancer centres) across 16 countries. Patients were randomly assigned (1:1) to receive 200 mg pembrolizumab intravenously every 3 weeks for up to 35 cycles plus 5 mg axitinib orally twice daily or 50 mg sunitinib monotherapy orally once daily for 4 weeks per 6-week cycle. Randomisation was done using an interactive voice response system or integrated web response system, and was stratified by International Metastatic Renal Cell Carcinoma Database Consortium risk status and geographical region. Primary endpoints were overall survival and progression-free survival in the intention-to-treat population. Since the primary endpoints were met at the first interim analysis, updated data are reported with nominal p values. This study is registered with ClinicalTrials.gov, NCT02853331. Between Oct 24, 2016, and Jan 24, 2018, 861 patients were randomly assigned to receive pembrolizumab plus axitinib (n=432) or sunitinib monotherapy (n=429). With a median follow-up of 30·6 months (IQR 27·2–34·2), continued clinical benefit was observed with pembrolizumab plus axitinib over sunitinib in terms of overall survival (median not reached with pembrolizumab and axitinib vs 35·7 months [95% CI 33·3–not reached] with sunitinib); hazard ratio [HR] 0·68 [95% CI 0·55–0·85], p=0·0003) and progression-free survival (median 15·4 months [12·7–18·9] vs 11·1 months [9·1–12·5]; 0·71 [0·60–0·84], p<0·0001). The most frequent (≥10% patients in either group) treatment-related grade 3 or worse adverse events were hypertension (95 [22%] of 429 patients in the pembrolizumab plus axitinib group vs 84 [20%] of 425 patients in the sunitinib group), alanine aminotransferase increase (54 [13%] vs 11 [3%]), and diarrhoea (46 [11%] vs 23 [5%]). No new treatment-related deaths were reported since the first interim analysis. With extended study follow-up, results from KEYNOTE-426 show that pembrolizumab plus axitinib continues to have superior clinical outcomes over sunitinib. These results continue to support the first-line treatment with pembrolizumab plus axitinib as the standard of care of advanced renal cell carcinoma. Merck Sharp & Dohme Corp, a subsidiary of Merck & Co, Inc.

A phase 2 trial showed improved progression-free survival for atezolizumab plus bevacizumab versus sunitinib in patients with metastatic renal cell carcinoma who express programmed death-ligand 1 (PD-L1). Here, we report results of IMmotion151, a phase 3 trial comparing atezolizumab plus bevacizumab versus sunitinib in first-line metastatic renal cell carcinoma. In this multicentre, open-label, phase 3, randomised controlled trial, patients with a component of clear cell or sarcomatoid histology and who were previously untreated, were recruited from 152 academic medical centres and community oncology practices in 21 countries, mainly in Europe, North America, and the Asia-Pacific region, and were randomly assigned 1:1 to either atezolizumab 1200 mg plus bevacizumab 15 mg/kg intravenously once every 3 weeks or sunitinib 50 mg orally once daily for 4 weeks on, 2 weeks off. A permuted-block randomisation (block size of 4) was applied to obtain a balanced assignment to each treatment group with respect to the stratification factors. Study investigators and participants were not masked to treatment allocation. Patients, investigators, independent radiology committee members, and the sponsor were masked to PD-L1 expression status. Co-primary endpoints were investigator-assessed progression-free survival in the PD-L1 positive population and overall survival in the intention-to-treat (ITT) population. This trial is registered with ClinicalTrials.gov, number NCT02420821. Of 915 patients enrolled between May 20, 2015, and Oct 12, 2016, 454 were randomly assigned to the atezolizumab plus bevacizumab group and 461 to the sunitinib group. 362 (40%) of 915 patients had PD-L1 positive disease. Median follow-up was 15 months at the primary progression-free survival analysis and 24 months at the overall survival interim analysis. In the PD-L1 positive population, the median progression-free survival was 11·2 months in the atezolizumab plus bevacizumab group versus 7·7 months in the sunitinib group (hazard ratio [HR] 0·74 [95% CI 0·57–0·96]; p=0·0217). In the ITT population, median overall survival had an HR of 0·93 (0·76–1·14) and the results did not cross the significance boundary at the interim analysis. 182 (40%) of 451 patients in the atezolizumab plus bevacizumab group and 240 (54%) of 446 patients in the sunitinib group had treatment-related grade 3–4 adverse events: 24 (5%) in the atezolizumab plus bevacizumab group and 37 (8%) in the sunitinib group had treatment-related all-grade adverse events, which led to treatment-regimen discontinuation. Atezolizumab plus bevacizumab prolonged progression-free survival versus sunitinib in patients with metastatic renal cell carcinoma and showed a favourable safety profile. Longer-term follow-up is necessary to establish whether a survival benefit will emerge. These study results support atezolizumab plus bevacizumab as a first-line treatment option for selected patients with advanced renal cell carcinoma. F Hoffmann–La Roche Ltd and Genentech Inc.

Cytoreductive nephrectomy has been the standard of care in metastatic renal-cell carcinoma for 20 years, supported by randomized trials and large, retrospective studies. However, the efficacy of targeted therapies has challenged this standard. We assessed the role of nephrectomy in patients with metastatic renal-cell carcinoma who were receiving targeted therapies. In this phase 3 trial, we randomly assigned, in a 1:1 ratio, patients with confirmed metastatic clear-cell renal-cell carcinoma at presentation who were suitable candidates for nephrectomy to undergo nephrectomy and then receive sunitinib (standard therapy) or to receive sunitinib alone. Randomization was stratified according to prognostic risk (intermediate or poor) in the Memorial Sloan Kettering Cancer Center prognostic model. Patients received sunitinib at a dose of 50 mg daily in cycles of 28 days on and 14 days off every 6 weeks. The primary end point was overall survival. A total of 450 patients were enrolled from September 2009 to September 2017. At this planned interim analysis, the median follow-up was 50.9 months, with 326 deaths observed. The results in the sunitinib-alone group were noninferior to those in the nephrectomy-sunitinib group with regard to overall survival (stratified hazard ratio for death, 0.89; 95% confidence interval, 0.71 to 1.10; upper boundary of the 95% confidence interval for noninferiority, ≤1.20). The median overall survival was 18.4 months in the sunitinib-alone group and 13.9 months in the nephrectomy-sunitinib group. No significant differences in response rate or progression-free survival were observed. Adverse events were as anticipated in each group. Sunitinib alone was not inferior to nephrectomy followed by sunitinib in patients with metastatic renal-cell carcinoma who were classified as having intermediate-risk or poor-risk disease. (Funded by Assistance Publique-Hôpitaux de Paris and others; CARMENA ClinicalTrials.gov number, NCT00930033 .).

Background The advancement of single-cell sequencing has progressed our ability to solve biological questions. Cell type annotation is of vital importance to this process, allowing for the analysis and interpretation of enormous single-cell datasets. At present, however, manual cell annotation which is the predominant approach remains limited by both speed and the requirement of expert knowledge. Methods To address these challenges, we constructed a hierarchically organized marker map through manually curating over 26,000 cell marker entries from about 7000 publications. We then developed WISE, a weighted and integrated gene set enrichment method, to integrate the prevalence of canonical markers and ordered differentially expressed genes of specific cell types in the marker map. Benchmarking analysis suggested that our method outperformed state-of-the-art methods. Results By integrating the marker map and WISE, we developed a user-friendly and convenient web server, ACT ( http://xteam.xbio.top/ACT/ or http://biocc.hrbmu.edu.cn/ACT/ ), which only takes a simple list of upregulated genes as input and provides interactive hierarchy maps, together with well-designed charts and statistical information, to accelerate the assignment of cell identities and made the results comparable to expert manual annotation. Besides, a pan-tissue marker map was constructed to assist in cell assignments in less-studied tissues. Applying ACT to three case studies showed that all cell clusters were quickly and accurately annotated, and multi-level and more refined cell types were identified. Conclusions We developed a knowledge-based resource and a corresponding method, together with an intuitive graphical web interface, for cell type annotation. We believe that ACT, emerging as a powerful tool for cell type annotation, would be widely used in single-cell research and considerably accelerate the process of cell type identification.

Background Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. Results We curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap, an easy-to-use single-cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. Additionally, the scMayoMapDatabase can be integrated with other tools and further improve their performance. Conclusions scMayoMap and scMayoMapDatabase will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.

Background Clear cell renal cell carcinoma (CCRCC) is characterized by a highly metastatic potential. The stromal communication between stem cells and cancer cells critically influences metastatic dissemination of cancer cells. Methods The effect of exosomes isolated from cancer stem cells (CSCs) of CCRCC patients on the progress of epithelial-mesenchymal transition (EMT) and lung metastasis of CCRCC cells were examined. Results CSCs exosomes promoted proliferation of CCRCC cells and accelerated the progress of EMT. Bioactive miR-19b-3p transmitted to cancer cells by CSC exosomes induced EMT via repressing the expression of PTEN. CSCs exosomes derived from CCRCC patients with lung metastasis produced the strongest promoting effect on EMT. Notably, CD103 + CSC exosomes were enriched in tumor cells and in lung as well, highlighting the organotropism conferred by CD103. In addition, CD103 + exosomes were increased in blood samples from CCRCC patients with lung metastasis. Conclusions CSC exosomes transported miR-19b-3p into CCRCC cells and initiated EMT promoting metastasis. CD103 + acted to guide CSC exosomes to target cancer cells and organs, conferring the higher metastatic capacity of CCRCC to lungs, suggesting CD103 + exosomes as a potential metastatic diagnostic biomarker. Graphical abstract ᅟ

Background Tumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types. Results We compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8 + T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number. Conclusions Our analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.