A Network Biology-Guided Investigation of the Long Noncoding RNAs' Role in Glaucoma.

Glaucoma is a group of eye diseases characterized by progressive harm to the optic nerve that often results in loss of vision and blindness. Recent studies suggest that deregulation in the expression of a particular type of RNAs named long noncoding RNAs (lncRNAs) can impact the development of glaucoma and, hence, are promising targets for drug discovery. LncRNAs are transcripts with a length longer than 200 nucleotides that are not translated into proteins.

A Practical Guideline for MicroRNA Sequencing Data Analysis in Chronic Lymphocytic Leukemia.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression. They have been associated with several diseases and cancers, including chronic lymphocytic leukemia (CLL). CLL is the most common form of adult leukemia, and its pathogenesis is driven by the deletion of miRNAs, such as the miR-15a/16-1 cluster.

E2F1-induced autocrine IL-6 inflammatory loop mediates cancer-immune crosstalk that predicts T cell phenotype switching and therapeutic responsiveness.

Melanoma is a metastatic, drug-refractory cancer with the ability to evade immunosurveillance. Cancer immune evasion involves interaction between tumor intrinsic properties and the microenvironment. The transcription factor E2F1 is a key driver of tumor evolution and metastasis.

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features.

Purpose: This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed skin diseases such as eczema, lymphoma, and melanoma. The novel contribution lies in integrating Radiomic features with GNNs and comparing their efficacy against traditional multi-stain profiles.

Stringent monitoring can decrease mortality of immune checkpoint inhibitor induced cardiotoxicity.

Background: Immune checkpoint inhibitor (ICI)-induced myocarditis is a rare immune-related adverse event (irAE) with a fatality rate of 40%-46%. However, irMyocarditis can be asymptomatic. Thus, improved monitoring, detection and therapy are needed.

Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

Background: Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers.

Methods: We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components.

Identifying biomarkers and novel therapeutic targets in uveal melanoma.

Uveal melanoma (UM) is an orphan cancer despite being the most common eye tumor in adults. Patients often present to skin cancer centers for treatment of metastatic disease although there are significant genetic, biological, and clinical differences from cutaneous melanoma. The treatments most commonly used for metastatic UM are tebentafusp and combined immune checkpoint blockade, both of which yield low response rates and may be accompanied by high treatment costs and significant immune-related toxicities.

Integration of transcriptomics data into agent-based models of solid tumor metastasis.

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression.

Is tebentafusp superior to combined immune checkpoint blockade and other systemic treatments in metastatic uveal melanoma? A comparative efficacy analysis with population adjustment.

Background: Distinct systemic treatments exist for metastatic uveal melanoma. Tebentafusp and combined immune checkpoint blockade (ICB) with ipilimumab plus anti-PD-1 antibodies are the most commonly used treatment options but their comparative efficacy is unclear. The aim of this study is to compare currently available systemic treatments regarding overall survival (OS) and progression-free survival (PFS) with a focus on the comparison of tebentafusp versus combined ICB.

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.

Glaucoma-TrEl: A web-based interactive database to build evidence-based hypotheses on the role of trace elements in glaucoma.

Objective: Glaucoma is a chronic neurological disease that is associated with high intraocular pressure (IOP), causes gradual damage to retinal ganglion cells, and often culminates in vision loss. Recent research suggests that glaucoma is a complex multifactorial disease in which multiple interlinked genes and pathways play a role during onset and development. Also, differential availability of trace elements seems to play a role in glaucoma pathophysiology, although their mechanism of action is unknown.

The Role of MicroRNAs in Cancer Biology and Therapy from a Systems Biology Perspective.

Since the discovery of microRNAs (miRNAs) in Caenorhabditis elegans, our understanding of their cellular function has progressed continuously. Today, we have a good understanding of miRNA-mediated gene regulation, miRNA-mediated cross talk between genes including competing endogenous RNAs, and miRNA-mediated signaling transduction both in normal human physiology and in diseases.Besides, these noncoding RNAs have shown their value for clinical applications, especially in an oncological context.

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.

PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data.

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.

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.

Single-Molecule RNA Sequencing Reveals IFNγ-Induced Differential Expression of Immune Escape Genes in Merkel Cell Polyomavirus-Positive MCC Cell Lines.

Merkel cell carcinoma (MCC) is a rare and highly aggressive cancer, which is mainly caused by genomic integration of the Merkel cell polyomavirus and subsequent expression of a truncated form of its large T antigen. The resulting primary tumor is known to be immunogenic and under constant pressure to escape immune surveillance. Because interferon gamma (IFNγ), a key player of immune response, is secreted by many immune effector cells and has been shown to exert both anti-tumoral and pro-tumoral effects, we studied the transcriptomic response of MCC cells to IFNγ.

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.

Clinical determinants of long-term survival in metastatic uveal melanoma.

This study aimed to identify prognostic factors in patients with metastatic uveal melanoma (UM) that were associated with long-term survival in a real-world setting. A total of 94 patients with metastatic UM were included from German skin cancer centers and the German national skin cancer registry (ADOReg). Data were analyzed for the response to treatment, progression-free survival, and overall survival (OS).

Plasma-derived extracellular vesicles discriminate type-1 allergy subjects from non-allergic controls.

Background: Allergies are on the rise globally, with an enormous impact on affected individuals' quality of life as well as health care resources. They cause a wide range of symptoms, from slightly inconvenient to potentially fatal immune reactions. While allergies have been described and classified phenomenologically, there is an unmet need for easily accessible biomarkers to stratify the severity of clinical symptoms.

IL-33-induced metabolic reprogramming controls the differentiation of alternatively activated macrophages and the resolution of inflammation.

Alternatively activated macrophages (AAMs) contribute to the resolution of inflammation and tissue repair. However, molecular pathways that govern their differentiation have remained incompletely understood. Here, we show that uncoupling protein-2-mediated mitochondrial reprogramming and the transcription factor GATA3 specifically controlled the differentiation of pro-resolving AAMs in response to the alarmin IL-33.