Prevention and Management of Bone Metastases in Lung Cancer: A Review

Abstract:Approximately 30 to 40% of patients with advanced lung cancer will develop bone metastases in the course of their disease, resulting in a significant negative impact on both morbidity and survival. Skeletal complications of bone metastases include pain, pathologic fractures, spinal cord compression, and hypercalcemia. Total medical care costs are greater among patients with bone metastases who develop skeletal complications. A randomized phase III trial of the third generation bisphosphonate zoledronic acid has shown clinical benefit in the management of a subgroup of patients with bone metastases from lung cancer. Zoledronic acid treatment was associated with a reduction in both the risk of, and time to, a skeletal-related event. One of the markers of bone resorption, N-telopeptide, is both prognositic for development of skeletal-related events and predictive for benefit from zoledronic acid. In preclinical models, bisphosphonates have also demonstrated antitumor activity and are therefore currently being evaluated in adjuvant trials. Inhibition of the receptor activator of nuclear factor kappa B ligand-RANK pathway can reduce osteoclast-mediated bone resorption, and trials comparing receptor activator of nuclear factor kappa B ligand inhibitors with bisphosphonates are ongoing, including patients with lung cancer. In this article, we review the management of bone metastases and hypercalcemia as well as potential future directions for bone directed therapies in patients with lung cancer

SpectralNET – an application for spectral graph analysis and visualization

BACKGROUND: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. RESULTS: Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). CONCLUSION: SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request

Distinct Biological Network Properties between the Targets of Natural Products and Disease Genes

We show that natural products target proteins with a high number of protein−protein functional interactions (high biological network connectivity) and that these protein targets have higher network connectivity than disease genes. This feature may facilitate disruption of essential biological pathways, resulting in competitor death. This result also suggests that additional sources of small molecules will be required to discover drugs targeting the root causes of human disease in the future.Chemistry and Chemical Biolog

Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries

There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR·uPA, TEAD4·Yap1, and CaVα·CaVβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR·uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4·Yap1. Virtual screening of conformationally restricted compounds targeting uPAR·uPA and TEAD4·Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions

Levels of Matrix Metalloproteinase-9 within Cerebrospinal Fluid in a Rabbit Model of Coccidioidal Meningitis and Vasculitis

Matrix metalloproteinase (MMP)-9 is produced by the central nervous system and inflammatory cells in a variety of inflammatory conditions in both animals and humans. MMP-9 promotes inflammation, breakdown of the blood-brain barrier, and vasculitis. Because vasculitis is seen frequently in patients with coccidioidal meningitis (CM), this study evaluated the presence of MMP-9 within the cerebrospinal fluid (CSF) of rabbits infected intracisternally with Coccidioides immitis arthroconidia. Infected rabbits demonstrated systemic and neurological sequelae to infection, including CSF pleocytosis. Levels of MMP-9 within CSF were assayed by use of zymography and compared with MMP-2 levels, which served as an internal control. Elevated levels of MMP-9 were detectable by day 3, continued to increase through day 10, and declined by day 15 after infection. MMP-9 may contribute to inflammation and vasculitis in this animal model. Future work can focus on evaluation of MMP inhibitors, to gain a better perspective of the role of this MMP in C

Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells

Summary: Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes

Crystal Structure of the Long-Chain Fatty Acid Transporter FadL

The mechanisms by which hydrophobic molecules, such as long-chain fatty acids, enter cells are poorly understood. In Gram-negative bacteria, the lipopolysaccharide layer in the outer membrane is an efficient barrier for fatty acids and aromatic hydrocarbons destined for biodegradation. We report crystal structures of the long-chain fatty acid transporter FadL from Escherichia coli at 2.6 and 2.8 angstrom resolution. FadL forms a 14-stranded β barrel that is occluded by a central hatch domain. The structures suggest that hydrophobic compounds bind to multiple sites in FadL and use a transport mechanism that involves spontaneous conformational changes in the hatch

Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery

Predicting compound activity from phenotypic profiles and chemical structures

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources—chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)—to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6–10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process

Expanding Stereochemical and Skeletal Diversity Using Petasis Reactions and 1,3-Dipolar Cycloadditions

A short and modular synthetic pathway using intramolecular 1,3-dipolar cycloaddition reactions and yielding functionalized isoxazoles, isoxazolines, and isoxazolidines is described. The change in shape of previous compounds and those in this study is quantified and compared using principal moment-of-inertia shape analysis.Chemistry and Chemical Biolog