Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

Purpose: BRAF V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to BRAFi±MEKi. We investigated mechanisms for this and explored whether genotype affects response to immunotherapy. Experimental Design: Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with BRAFi±MEKi underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti–PD-1 immunotherapy was examined. Results: Baseline tissue and clinical outcome with BRAFi±MEKi were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, −31% vs. −52%, P = 0.154) and shorter progression-free survival (PFS; median, 5.7 vs. 7.1 months, P = 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in PIK3R1 and tumor-suppressor genes. In patients treated with anti–PD-1, V600K (n = 19) had superior outcomes than V600E (n = 84), including response rate (53% vs. 29%, P = 0.059), PFS (median, 19 vs. 2.7 months, P = 0.049), and overall survival (20.4 vs. 11.7 months, P = 0.081). Conclusions: BRAF V600K melanomas appear to benefit less from BRAFi±MEKi than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy.

BackgroundHuman Papillomavirus (HPV) associated oropharyngeal squamous cell carcinoma (OPSCC) is one of the fastest growing cancers in the Western world. When compared to OPSCCs induced by smoking or alcohol, patients with HPV+ OPSCC, have better survival and the mechanisms remain unclear.MethodsThe Cancer Genome Atlas (TCGA) database was examined for genes associated with tissue-resident CD8+ T cells. Multiplex immunohistochemistry (IHC) staining was performed on tumor specimen taken from 35 HPV+ and 27 HPV- OPSCC patients.ResultsTCGA database revealed that the expression of genes encoding CD103 and CD69 were significantly higher in HPV+ head and neck SCCs (HNSCC) than in HPV- HNSCC. Higher expression levels of these two genes were also associated with better overall survival. IHC staining showed that the proportion of CD103+ tumor-resident CD8+ T cells were significantly higher in HPV+ OPSCCs when compared to HPV- OPSCC. This higher level was also associated with both lower risk of loco-regional failure, and better overall survival. Importantly, patients with HPV- OPSCC who had comparable levels of CD103+ tumor-resident CD8+ T cells to those with HPV+ OPSCC demonstrated similar survival as those with HPV+OPSCC.ConclusionOur results show that CD103+ tumor-resident CD8+ T cells are critical for protective immunity in both types of OPSCCs. Our data further suggest that the enhanced local protective immunity provided by tumor-resident T cell responses is the underlying factor driving favorable clinical outcomes in HPV+ OPSCCs over HPV- OPSCCs.

We present Cepo, a method to generate cell-type-specific gene statistics of differentially stable genes from single-cell RNA-sequencing (scRNA-seq) data to define cell identity. Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells, and lineage inference of single cells.Defining cell identity is fundamental to understand the cellular heterogeneity in the population and the cell-type-specific response to environmental signals and experimental perturbations. Exploring cell identity has been enabled by rapid technological advances in genome-wide profiling of the molecular content in single cells 1-3 . This comprehensive lens into the molecular properties unique to each cell type allows defining and predicting cell identities in ways that were previously not feasible using data generated by bulk/population analytics technologies. The most widely used method to define genes associated with cell identity is differential expression (DE) 4,5 . Despite the advances in high-throughput scRNA-seq data that would provide unprecedented resolution of cell identity, none of the current approaches 6 has been evaluated systematically for their attribute and fidelity for defining cell identity genes (CIGs) from scRNAseq dataset of millions of cells and consisting of hundreds of cell types.We developed Cepo (refers to "cell" in Korean), a method to retrieve genes defining cell identity from scRNA-seq data. We propose a biologically motivated metric, differential stability (DS), to identify cell-type specific genes on the premise that stable gene expression is a key indicator of cell identity. Our hypothesis implies that genes marking a cell type should be (1) expressed and (2) stable in its expression level relative to other cell types. We translate the criteria into a computational framework where, using pre-defined celltype labels, we compute cell-type-specific statistics to prioritise genes that are DS against other cell types in all cell-type pair comparisons (Fig. 1a, Online Methods: Cepo implementation).We performed a comprehensive benchmarking of Cepo with several differential analysis methods 6-9 using both simulated and experimental scRNA-seq datasets in a range of biological systems that require knowledge of cell identity. We tested the accuracy and efficacy of the method to detect subtle changes in stability against simulated DS genes with varying stability (Supplementary Fig. 1 and Online Methods). We show that Cepo, followed by Voom, can identify simulated DS genes with the highest accuracy relative to other methods (Fig. 1b and Supplementary Fig. 2, 3).

No abstract