A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma.

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting senescent cells, so-called senolytics, constitute a promising avenue for improved cancer treatment in combination with targeted therapies. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCell-based integration of bulk and single-cell RNA-seq data identifies biological processes perturbed during senescence and predicts 90 new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis.

Glioblastoma multiforme (GBM), a grade IV glioma, is a challenging disease for patients and clinicians, with an extremely poor prognosis. These tumours manifest a high molecular heterogeneity, with limited therapeutic options for patients. Since GBM is a rare disease, sufficient statistically strong evidence is often not available to explore the roles of lesser-known GBM proteins. We present a network-based approach using centrality measures to explore some key, topologically strategic proteins for the analysis of GBM. Since network-based analyses are sensitive to changes in network topology, we analysed nine different GBM networks, and show that small but well-curated networks consistently highlight a set of proteins, indicating their likely involvement in the disease. We propose 18 novel candidates which, based on differential expression, mutation analysis, and survival analysis, indicate that they may play a role in GBM progression. These should be investigated further for their functional roles in GBM, their clinical prognostic relevance, and their potential as therapeutic targets.

Construction and contextualization approaches for protein-protein interaction networks.

Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.

Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

Topological network measures for drug repositioning.

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

Modulation of in vitro phagocytic uptake and immunogenicity potential of modified Herceptin®-conjugated PLGA-PEG nanoparticles for drug delivery.

There is an increasing interest in engineered nanoparticle (NP) conjugates for targeted and controlled drug delivery. However, the practical applications of these NP delivery vehicles remain constrained because of their reactivity with the body's immune system defenses resulting in undesirable off-target effects. In this study, poly(D,L lactide-co-glycolide) (PLGA)-b-polyethylene glycol (PEG) NPs conjugated to different quantities of the commercial antibody Herceptin® meant to target HER2-positive breast cancer cells were studied for their immune cell uptake and immunogenic properties (using murine macrophages and human dendritic cells). We further modified the Herceptin®-NP conjugates with short PEG linkers with an aim to increase their biocompatibility. The 50% Herceptin®-NP conjugate group with short PEG modification to Herceptin® showed the best reduction in immune cell uptake by 82% along with the reduction by >50% for proinflammatory cytokine response (TNF-α and IL-6). In conclusion, optimum Herceptin® coverage with improved hydrophilic profile results in reduced phagocytic uptake and immunogenicity of engineered NP-antibody conjugates, potentially minimizing their undesirable off-target effects as a drug delivery vehicle.

Herceptin conjugated PLGA-PHis-PEG pH sensitive nanoparticles for targeted and controlled drug delivery.

A dual functional nano-scaled drug carrier, comprising of a targeting ligand and pH sensitivity, has been made in order to increase the specificity and efficacy of the drug delivery system. The nanoparticles are made of a tri-block copolymer, poly(d,l lactide-co-glycolide) (PLGA)-b-poly(l-histidine) (PHis)-b-polyethylene glycol (PEG), via nano-precipitation. To provide the nanoparticle feature of endolysosomal escape and pH sensitivity, poly(l-histidine) was chosen as a proton sponge polymer. Herceptin, which specifically binds to HER2 antigen, was conjugated to the nanoparticles through click chemistry. The nanoparticles were characterized via dynamic light scattering (DLS) and transmission electron microscopy (TEM). Both methods showed the sizes of about 100nm with a uniform size distribution. The pH sensitivity was assessed by drug releases and size changes at different pH conditions. As pH decreased from 7.4 to 5.2, the drug release rate accelerated and the size significantly increased. During in vitro tests against human breast cancer cell lines, MCF-7 and SK-BR-3 showed significantly increased uptake for Herceptin-conjugated nanoparticles, as compared to non-targeted nanoparticles. Herceptin-conjugated pH-sensitive nanoparticles showed the highest therapeutic effect, and thus validated the efficacy of a combined approach of pH sensitivity and active targeting.