Exosomes derived from B16F0 melanoma cells alter the transcriptome of cytotoxic T cells that impacts mitochondrial respiration.

While recent clinical studies demonstrate the promise of cancer immunotherapy, a barrier for broadening the clinical benefit is identifying how tumors locally suppress cytotoxic immunity. As an emerging mode of intercellular communication, exosomes secreted by malignant cells can deliver a complex payload of coding and noncoding RNA to cells within the tumor microenvironment. Here, we quantified the RNA payload within tumor-derived exosomes and the resulting dynamic transcriptomic response to cytotoxic T cells upon exosome delivery to better understand how tumor-derived exosomes can alter immune cell function. Exosomes derived from B16F0 melanoma cells were enriched for a subset of coding and noncoding RNAs that did not reflect the abundance in the parental cell. Upon exosome delivery, RNAseq revealed the dynamic changes in the transcriptome of CTLL2 cytotoxic T cells. In analyzing transiently coexpressed gene clusters, pathway enrichment suggested that the B16F0 exosomal payload altered mitochondrial respiration, which was confirmed independently, and upregulated genes associated with the Notch signaling pathway. Interestingly, exosomal miRNA appeared to have no systematic effect on downregulating target mRNA levels. DATABASES: Gene expression data are available in the GEO database under the accession SuperSeries number GSE102951.

Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016.