Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

Abstract

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential ( https://www.synmirapy.net/dc-optimization ). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

“…This method allowed us to prioritize genes that are significantly differentially expressed and have high connectivity as well as strong regulatory effects on other dysregulated and densely connected genes in the cancer networks. Previously, we have used the method to identify microRNAs for improving the effectiveness of cancer immunotherapy [53] . Biologically, the identified genes in cancer networks could be functionally important and potentially linked to tumorigenesis and metastasis [49] , [54] , [55] , therefore having potential as prognostic markers and therapeutic targets for cancer.…”

Artificial intelligence in cancer target identification and drug discovery

“…For example, Lai et al 199 deployed an integrated approach that combined network-based algorithms and RNA sequencing data to delineate miRNA-based strategies that enhanced DC (dendritic cell)-elicited immune responses. First, the authors performed RNA sequencing to obtain the protein-coding genes and miRNAs in relation to standard DCs.…”

Multi-Level Computational Modeling of Anti-Cancer Dendritic Cell Vaccination Utilized to Select Molecular Targets for Therapy Optimization

“…In the current version of the model, the intracellular signaling module is centered on the activation of the NF-κB signaling pathway, which is known to be pivotal in the maturation and activation of DCs. However, other regulatory pathways also play an important role in DC vaccination, and these pathways can crosstalk with each other forming a large regulatory network (Lai et al, 2021). Including these pathways into an intracellular module requires access to time-series data of their activation in DCs.…”

“…So far, most of the published models have considered only cell-to-cell communications through direct contact or the secretion of cytokines and chemokines. However, intracellular biochemical networks that are crucial for regulating cell function can be tuned to improve immunotherapies (Lai et al, 2021). Therefore, integrating them into multi-level computational models offers the opportunity to simulate and analyze molecular events that can determine the efficiency of anti-cancer immunotherapies.…”