Soroor Hediyeh-zadeh

Abstract Natural killer (NK) cell activity is essential for initiating antitumor responses and may be linked to immunotherapy success. NK cells and other innate immune components could be exploitable for cancer treatment, which drives the need for tools and methods that identify therapeutic avenues. Here, we extend our gene-set scoring method singscore to investigate NK cell infiltration by applying RNA-seq analysis to samples from bulk tumors. Computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several such tools, then curated the gene list using a comparative analysis of tumors and immune cell types. Using a gene-set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA), we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of NK cell infiltration. Furthermore, these survival effects are enhanced in tumors that show higher expression of genes that encode NK cell stimuli such as the cytokine IL15. Using this signature, we then examine transcriptomic data to identify tumor and stromal components that may influence the penetrance of NK cells into solid tumors. Our results provide evidence that NK cells play a role in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Our computational analysis identifies putative gene targets that may be of therapeutic value for boosting NK cell antitumor immunity.

The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.