Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells

Inflammatory Rheumatic Disorders and Bone

Inflammatory joint diseases such as rheumatoid arthritis, as well as other rheumatic conditions, such as systemic lupus erythematosus (SLE) and ankylosing spondylitis, comprise a heterogeneous group of joint disorders that are all associated with extra-articular side effects, including bone loss and fractures. The concept of osteoimmunology is based on growing insights into the links between the immune system and bone. The pathogenesis of osteoporosis in these patients is multifactorial. We have, more or less as an example, described this extensively for patients with SLE. High disease activity (inflammation) and immobility are common factors that substantially increase fracture risk in these patients, on top of the background fracture risk based on, among other factors, age, body mass index, and gender. Although no fracture reduction has been shown in intervention studies in patients with inflammatory rheumatic diseases, we present treatment options that might be useful for clinicians who are treating these patients

Prevalence of congenital heart defects and persistent pulmonary hypertension of the neonate with Down syndrome

The aim of this study was to assess the prevalence of congenital heart defects (CHDs) and persistent pulmonary hypertension of the neonate (PPHN) in children with Down syndrome (DS) and to assess its impact on neonatal factors. It was a prospective study of a birth cohort of children with DS born between 2003 and 2006 registered by the Dutch Paediatric Surveillance Unit (DPSU). A CHD occurred in 43% of 482 children with trisomy 21. Atrioventricular septal defect was found in 54%, ventricular septal defect in 33.3% and patent ductus arteriosus in 5.8%. The incidence of PPHN in DS was 5.2%, which is significantly higher than the general population (p < 0.001). The reported mortality in newborns with DS was overall 3.3% and was still significant higher in children with a CHD versus no CHD (5.8% versus 1.5%) (p = 0.008). The presence of CHD in children with DS had no influence on their birth weight, mean gestational age and Apgar score. In neonates with DS, we found not only a 43% prevalence of CHD, but also a high incidence of PPHN at 5.2%. Early recognition of the cardiac condition of neonates with DS seems justified

Colorful Niches of Phytoplankton Shaped by the Spatial Connectivity in a Large River Ecosystem: A Riverscape Perspective

Large rivers represent a significant component of inland waters and are considered sentinels and integrators of terrestrial and atmospheric processes. They represent hotspots for the transport and processing of organic and inorganic material from the surrounding landscape, which ultimately impacts the bio-optical properties and food webs of the rivers. In large rivers, hydraulic connectivity operates as a major forcing variable to structure the functioning of the riverscape, and–despite increasing interest in large-river studies–riverscape structural properties, such as the underwater spectral regime, and their impact on autotrophic ecological processes remain poorly studied. Here we used the St. Lawrence River to identify the mechanisms structuring the underwater spectral environment and their consequences on pico- and nanophytoplankton communities, which are good biological tracers of environmental changes. Our results, obtained from a 450 km sampling transect, demonstrate that tributaries exert a profound impact on the receiving river’s photosynthetic potential. This occurs mainly through injection of chromophoric dissolved organic matter (CDOM) and non-algal material (tripton). CDOM and tripton in the water column selectively absorbed wavelengths in a gradient from blue to red, and the resulting underwater light climate was in turn a strong driver of the phytoplankton community structure (prokaryote/eukaryote relative and absolute abundances) at scales of many kilometers from the tributary confluence. Our results conclusively demonstrate the proximal impact of watershed properties on underwater spectral composition in a highly dynamic river environment characterized by unique structuring properties such as high directional connectivity, numerous sources and forms of carbon, and a rapidly varying hydrodynamic regime. We surmise that the underwater spectral composition represents a key integrating and structural property of large, heterogeneous river ecosystems and a promising tool to study autotrophic functional properties. It confirms the usefulness of using the riverscape approach to study large-river ecosystems and initiate comparison along latitudinal gradients

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail