Using microRNA Networks to Understand Cancer.

Human cancers are characterized by deregulated expression of multiple microRNAs (miRNAs), involved in essential pathways that confer the malignant cells their tumorigenic potential. Each miRNA can regulate hundreds of messenger RNAs (mRNAs), while various miRNAs can control the same mRNA. Additionally, many miRNAs regulate and are regulated by other species of non-coding RNAs, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs). For this reason, it is extremely difficult to predict, study, and analyze the precise role of a single miRNA involved in human cancer, considering the complexity of its connections. Focusing on a single miRNA molecule represents a limited approach. Additional information could come from network analysis, which has become a common tool in the biological field to better understand molecular interactions. In this review, we focus on the main types of networks (monopartite, association networks and bipartite) used for analyzing biological data related to miRNA function. We briefly present the important steps to take when generating networks, illustrating the theory with published examples and with future perspectives of how this approach can help to better select miRNAs that can be therapeutically targeted in cancer.

Altered cancer metabolism in mechanisms of immunotherapy resistance.

Many metabolic alterations, including the Warburg effect, occur in cancer cells that influence the tumor microenvironment, including switching to glycolysis from oxidative phosphorylation, using opportunistic modes of nutrient acquisition, and increasing lipid biosynthesis. The altered metabolic landscape of the tumor microenvironment can suppress the infiltration of immune cells and other functions of antitumor immunity through the production of immune-suppressive metabolites. Metabolic dysregulation in cancer cells further affects the expression of cell surface markers, which interferes with immune surveillance. Immune checkpoint therapies have revolutionized the standard of care for some patients with cancer, but disease in many others is resistant to immunotherapy. Specific metabolic pathways involved in immunotherapy resistance include PI3K-Akt-mTOR, hypoxia-inducible factor (HIF), adenosine, JAK/STAT, and Wnt/Beta-catenin. Depletion of essential amino acids such as glutamine and tryptophan and production of metabolites like kynurenine in the tumor microenvironment also blunt immune cell function. Targeted therapies against metabolic checkpoints could work in synergy with immune checkpoint therapy. This combined strategy could be refined by profiling patients' mutation status before treatment and identifying the optimal sequencing of therapies. This personalized combinatorial approach, which has yet to be explored, may well pave the way for overcoming resistance to immunotherapy.

OncomiR-10b hijacks the small molecule inhibitor linifanib in human cancers.

The pervasive role of microRNAs (miRNAs) in cancer pathobiology drives the introduction of new drug development approaches such as miRNA inhibition. In order to advance miRNA-therapeutics, meticulous screening strategies addressing specific tumor targets are needed. Small molecule inhibitors represent an attractive goal for these strategies. In this study, we devised a strategy to screen for small molecule inhibitors that specifically inhibit, directly or indirectly, miR-10b (SMIRs) which is overexpressed in metastatic tumors. We found that the multi-tyrosine kinase inhibitor linifanib could significantly inhibit miR-10b and reverse its oncogenic function in breast cancer and liver cancer both in vitro and in vivo. In addition, we showed that the efficacy of linifanib to inhibit tyrosine kinases was reduced by high miR-10b levels. When the level of miR-10b is high, it can "hijack" the linifanib and reduce its kinase inhibitory effects in cancer resulting in reduced anti-tumor efficacy. In conclusion, our study describes an effective strategy to screen for small molecule inhibitors of miRNAs. We further propose that miR-10b expression levels, due to the newly described "hijacking" effect, may be used as a biomarker to select patients for linifanib treatment.