Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of “stemness subtypes” often result from low cancer cell content in tumor biopsies rather than cancer cell–specific stemness or mesenchymal/EMT characteristics. Significance: Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.

Background: Aberrant interferon signaling is a key element of various diseases, but resolving gene expression signatures that stem from different types of IFNs in tissue samples is still a challenge. Most published IFN signatures comprise genes that are activated by different IFNs: they cannot discriminate type-I (IFN-I) and type-II (IFN-II) IFN stimulation. Most often such signatures were obtained from a single expression dataset that had been obtained in a specific cellular context, and their translatability to other experimental contexts has not been demonstrated. Results: We leveraged multiple RNA-seq datasets of IFN stimulation in a network meta-analysis workflow to obtain IFN gene signatures separating IFN-I and IFN-II. We validated our signatures in bulk and single cell RNA-seq datasets of various cellular contexts demonstrating similar or higher coherence than previously published signatures. Our IFN-II signature is broader applicable than other published signatures as it demonstrates strong performance in detecting IFN-II response in more cell types. In three SLE microarray datasets our IFN-I signature was highly coherent and correlated with disease severity better than most published signatures. In TCGA, our IFN-II signature produced distinct profiles compared to published IFN-I signatures and correlated strongly with published CD8⁺ T cell signatures. In cohorts of three different cancer types, we observed higher signature scores of our IFN-II signature in responders than in non-responders to immune checkpoint inhibitor (ICI) therapy. Conclusions: Our IFN-I and IFN-II response-specific gene expression signatures can inform on complex IFN responses in a more fine-grained way than previously possible. They can be used to assess type I versus II IFN response in gene expression data produced by different technologies, for different diseases and even different cell types in single cell studies. The association of high scores of our IFN-II signature with anti-tumor response to ICIs suggests a role as a biomarker to predict ICI response.

Background: Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches.Results: We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment.Discussion and conclusion: We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.

Multivariate cox proportional hazard analysis association between EMT-related gene expression signature scores and disease free survival and overall survival.

Association of EMT-related gene expression signature scores with tumor purity and fibroblast signatures in TME naïve samples