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

ncStem: a comprehensive resource of curated and predicted ncRNAs in cancer stemness

Cancer stemness plays an important role in cancer initiation and progression, and is the major cause of tumor invasion, metastasis, recurrence, and poor prognosis. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play a critical role in regulating cancer stemness. Here, we developed the ncStem database to record manually curated and predicted ncRNAs associated with cancer stemness. In total, ncStem contains 645 experimentally verified entries, including 159 long non-coding RNAs (lncRNAs), 254 microRNAs (miRNAs), 39 circular RNAs (circRNAs), and 5 other ncRNAs. The detailed information of each entry includes the ncRNA name, ncRNA identifier, disease, reference, expression direction, tissue, species, and so on. In addition, ncStem also provides computationally predicted cancer stemness-associated ncRNAs for 33 TCGA cancers, which were prioritized using the random walk with restart (RWR) algorithm based on regulatory and co-expression networks. The total predicted cancer stemness-associated ncRNAs included 11 132 lncRNAs and 972 miRNAs. Moreover, ncStem provides tools for functional enrichment analysis, survival analysis, and cell location interrogation for cancer stemness-associated ncRNAs. In summary, ncStem provides a platform to retrieve cancer stemness-associated ncRNAs, which may facilitate research on cancer stemness and offer potential targets for cancer treatment. Database URL: http://www.nidmarker-db.cn/ncStem/index.html.

MicroRNAs participate in the regulation of apoptosis and oxidative stress-related gene expression in rabbits infected with Lagovirus europaeus GI.1 and GI.2 genotypes

MicroRNAs (miRs) are a group of small, 17-25 nucleotide, non-coding RNA that regulate gene expression at the post-transcriptional level. To date, little is known about the molecular signatures of regulatory interactions between miRs and apoptosis and oxidative stress in viral diseases. Lagovirus europaeus is a virus that causes severe disease in rabbits (Oryctolagus cuniculus) called Rabbit Hemorrhagic Disease (RHD) and belongs to the Caliciviridae family, Lagovirus genus. Within Lagovirus europaeus associated with RHD, two genotypes (GI.1 and GI.2) have been distinguished, and the GI.1 genotype includes four variants (GI.1a, GI.1b, GI.1c, and GI.1d). The study aimed to assess the expression of miRs and their target genes involved in apoptosis and oxidative stress, as well as their potential impact on the pathways during Lagovirus europaeus-two genotypes (GI.1 and GI.2) infection of different virulences in four tissues (liver, lung, kidneys, and spleen). The expression of miRs and target genes related to apoptosis and oxidative stress was determined using quantitative real-time PCR (qPCR). In this study, we evaluated the expression of miR-21 (PTEN, PDCD4), miR-16b (Bcl-2, CXCL10), miR-34a (p53, SIRT1), and miRs-related to oxidative stress-miR-122 (Bach1) and miR-132 (Nfr-2). We also examined the biomarkers of both processes (Bax, Bax/Bcl-2 ratio, Caspase-3, PARP) and HO-I as biomarkers of oxidative stress. Our report is the first to present the regulatory effects of miRs on apoptosis and oxidative stress genes in rabbit infection with Lagovirus europaeus-two genotypes (GI.1 and GI.2) in four tissues (liver, lungs, kidneys, and spleen). The regulatory effect of miRs indicates that, on the one hand, miRs can intensify apoptosis (miR-16b, miR-34a) in the examined organs in response to a viral stimulus and, on the other hand, inhibit (miR-21), which in both cases may be a determinant of the pathogenesis of RHD and tissue damage. Biomarkers of the Bax and Bax/Bcl-2 ratio promote more intense apoptosis after infection with the Lagovirus europaeus GI.2 genotype. Our findings demonstrate that miR-122 and miR-132 regulate oxidative stress in the pathogenesis of RHD, which is associated with tissue damage. The HO-1 biomarker in the course of rabbit hemorrhagic disease indicates oxidative tissue damage. Our findings show that miR-21, miR-16b, and miR-34a regulate three apoptosis pathways. Meanwhile, miR-122 and miR-132 are involved in two oxidative stress pathways.

Recent progress in the immunotherapy of hepatocellular carcinoma: Non-coding RNA-based immunotherapy may improve the outcome

Hepatocellular carcinoma (HCC) is the second most lethal cancer and a leading cause of cancer-related mortality worldwide. Immune checkpoint inhibitors (ICIs) significantly improved the prognosis of HCC; however, the therapeutic response remains unsatisfactory in a substantial proportion of patients or needs to be further improved in responders. Herein, other methods of immunotherapy, including vaccine-based immunotherapy, adoptive cell therapy, cytokine delivery, kynurenine pathway inhibition, and gene delivery, have been adopted in clinical trials. Although the results were not encouraging enough to expedite their marketing. A major proportion of human genome is transcribed into non-coding RNAs (ncRNAs). Preclinical studies have extensively investigated the roles of ncRNAs in different aspects of HCC biology. HCC cells reprogram the expression pattern of numerous ncRNAs to decrease the immunogenicity of HCC, exhaust the cytotoxic and anti-cancer function of CD8 + T cells, natural killer (NK) cells, dendritic cells (DCs), and M1 macrophages, and promote the immunosuppressive function of T Reg cells, M2 macrophages, and myeloid-derived suppressor cells (MDSCs). Mechanistically, cancer cells recruit ncRNAs to interact with immune cells, thereby regulating the expression of immune checkpoints, functional receptors of immune cells, cytotoxic enzymes, and inflammatory and anti-inflammatory cytokines. Interestingly, prediction models based on the tissue expression or even serum levels of ncRNAs could predict response to immunotherapy in HCC. Moreover, ncRNAs markedly potentiated the efficacy of ICIs in murine models of HCC. This review article first discusses recent advances in the immunotherapy of HCC, then dissects the involvement and potential application of ncRNAs in the immunotherapy of HCC.

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

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

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

Dendritic cells (DCs) can be used for therapeutic vaccination against cancer. The success of this therapy depends on efficient tumor-antigen presentation to cytotoxic T lymphocytes (CTLs) and the induction of durable CTL responses by the DCs. Therefore, simulation of such a biological system by computational modeling is appealing because it can improve our understanding of the molecular mechanisms underlying CTL induction by DCs and help identify new strategies to improve therapeutic DC vaccination for cancer. Here, we developed a multi-level model accounting for the life cycle of DCs during anti-cancer immunotherapy. Specifically, the model is composed of three parts representing different stages of DC immunotherapy - the spreading and bio-distribution of intravenously injected DCs in human organs, the biochemical reactions regulating the DCs' maturation and activation, and DC-mediated activation of CTLs. We calibrated the model using quantitative experimental data that account for the activation of key molecular circuits within DCs, the bio-distribution of DCs in the body, and the interaction between DCs and T cells. We showed how such a data-driven model can be exploited in combination with sensitivity analysis and model simulations to identify targets for enhancing anti-cancer DC vaccination. Since other previous works show how modeling improves therapy schedules and DC dosage, we here focused on the molecular optimization of the therapy. In line with this, we simulated the effect in DC vaccination of the concerted modulation of combined intracellular regulatory processes and proposed several possibilities that can enhance DC-mediated immunogenicity. Taken together, we present a comprehensive time-resolved multi-level model for studying DC vaccination in melanoma. Although the model is not intended for personalized patient therapy, it could be used as a tool for identifying molecular targets for optimizing DC-based therapy for cancer, which ultimately should be tested in in vitro and in vivo experiments.

A disease network-based deep learning approach for characterizing melanoma

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

Efficient antisense inhibition reveals microRNA-155 to restrain a late-myeloid inflammatory programme in primary human phagocytes

A persisting obstacle in human immunology is that blood-derived leukocytes are notoriously difficult to manipulate at the RNA level. Therefore, our knowledge about immune-regulatory RNA-networks is largely based on tumour cell-line and rodent knockout models, which do not fully mimic human leukocyte biology. Here, we exploit straightforward cell penetrating peptide (CPP) chemistry to enable efficient loss-of-function phenotyping of regulatory RNAs in primary human blood-derived cells. The classical CPP octaarginine (R8) enabled antisense peptide-nucleic-acid (PNA) oligomer delivery into nearly 100% of human blood-derived macrophages without apparent cytotoxicity even up to micromolar concentrations. In a proof-of-principle experiment, we successfully de-repressed the global microRNA-155 regulome in primary human macrophages using a PNA-R8 oligomer, which phenocopies a CRISPR-Cas9 induced gene knockout. Interestingly, although it is often believed that fairly high concentrations (μM) are needed to achieve antisense activity, our PNA-R8 was effective at 200 nM. RNA-seq characterized microRNA-155 as a broad-acting riboregulator, feedback restraining a late myeloid differentiation-induced pro-inflammatory network, comprising MyD88-signalling and ubiquitin-proteasome components. Our results highlight the important role of the microRNA machinery in fine-control of blood-derived human phagocyte immunity and open the door for further studies on regulatory RNAs in difficult-to-transfect primary human immune cells.