A Meta-Analysis of Retinoblastoma Copy Numbers Refines the List of Possible Driver Genes Involved in Tumor Progression

BackgroundWhile RB1 loss initiates retinoblastoma development, additional somatic copy number alterations (SCNAs) can drive tumor progression. Although SCNAs have been identified with good concordance between studies at a cytoband resolution, accurate identification of single genes for all recurrent SCNAs is still challenging. This study presents a comprehensive meta-analysis of genome-wide SCNAs integrated with gene expression profiling data, narrowing down the list of plausible retinoblastoma driver genes.MethodsWe performed SCNA profiling of 45 primary retinoblastoma samples and eight retinoblastoma cell lines by high-resolution microarrays. We combined our data with genomic, clinical and histopathological data of ten published genome-wide SCNA studies, which strongly enhanced the power of our analyses (N = 310).ResultsComprehensive recurrence analysis of SCNAs in all studies integrated with gene expression data allowed us to reduce candidate gene lists for 1q, 2p, 6p, 7q and 13q to a limited gene set. Besides the well-established driver genes RB1 (13q-loss) and MYCN (2p-gain) we identified CRB1 and NEK7 (1q-gain), SOX4 (6p-gain) and NUP205 (7q-gain) as novel retinoblastoma driver candidates. Depending on the sample subset and algorithms used, alternative candidates were identified including MIR181 (1q-gain) and DEK (6p gain). Remarkably, our study showed that copy number gains rarely exceeded change of one copy, even in pure tumor samples with 100% homozygosity at the RB1 locus (N = 34), which is indicative for intra-tumor heterogeneity. In addition, profound between-tumor variability was observed that was associated with age at diagnosis and differentiation grades.InterpretationSince focal alterations at commonly altered chromosome regions were rare except for 2p24.3 (MYCN), further functional validation of the oncogenic potential of the described candidate genes is now required. For further investigations, our study provides a refined and revised set of candidate retinoblastoma driver genes.     

Comparative Cost-Effectiveness of Drugs in Early versus Late Stages of Cancer; Review of the Literature and a Case Study in Breast Cancer

Background

Many oncological drugs that are being used in the adjuvant setting were first submitted for reimbursement in the metastatic stage, with differences in incremental cost-effectiveness ratios (ICERs) in both settings having potential implications for reimbursement and pricing. The aim of this study is to identify a possible trend in the cost-effectiveness for the early/adjuvant and late/metastatic stages of oncological drugs through review and case study.

Methods

We reviewed pairs of cost-effectiveness analyses of the same oncological drug in different stages for Scotland and the Netherlands. The case study in this report was directed at trastuzumab in the Dutch situation. Using a simplified Markov model, the cost-effectiveness in early and late stage of breast cancer was calculated and compared to the findings from the review.

Results

Comparable studies were found for cetuximab, bortezomib and bosutinib. Treatments in the late stage were found to be more expensive per QALY by a factor ranging from 1.5 to 12. The case study provided a similar result; late stage treatment was more expensive by a factor 10. Using, for example, a threshold of €80,000/QALY, the early stage of cetuximab, bosutinib and trastuzumab are deemed cost-effective, while their compared late stage is lifted over the threshold and potentially considered not cost-effective.

Conclusion

ICERs of oncological drugs used in different stages are more unfavourable in the late stage than in the early stage. Applying a reasonable threshold may result in early stage treatment being deemed cost-effective while late stage potentially not. Authorities should be aware of this when assessing oncological drugs and interpreting the corresponding ICERs, in the situation where oncological drugs are generally most submitted for reimbursement in the late stage initially.     

Fibrin-fiber architecture influences cell spreading and differentiation

ABSTRACT

The mechanical and structural properties of the extracellular matrix (ECM) play an important role in regulating cell fate. The natural ECM has a complex fibrillar structure and shows nonlinear mechanical properties, which are both difficult to mimic synthetically. Therefore, systematically testing the influence of ECM properties on cellular behavior is very challenging. In this work we show two different approaches to tune the fibrillar structure and mechanical properties of fibrin hydrogels. Addition of extra thrombin before gelation increases the protein density within the fibrin fibers without significantly altering the mechanical properties of the resulting hydrogel. On the other hand, by forming a composite hydrogel with a synthetic biomimetic polyisocyanide network the protein density within the fibrin fibers decreases, and the mechanics of the composite material can be tuned by the PIC/fibrin mass ratio. The effect of the changes in gel structure and mechanics on cellular behavior are investigated, by studying human mesenchymal stem cell (hMSC) spreading and differentiation on these gels. We find that the trends observed in cell spreading and differentiation cannot be explained by the bulk mechanics of the gels, but correlate to the density of the fibrin fibers the gels are composed of. These findings strongly suggest that the microscopic properties of individual fibers in fibrous networks play an essential role in determining cell behavior.     

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.     

Accounting for the nested nature of genetic variation across levels of organization improves our understanding of biodiversity and community ecology

Recent work has demonstrated that the presence or abundance of specific genotypes, populations, species and phylogenetic clades may influence community and ecosystem properties such as resilience or productivity. Many ecological studies, however, use simple linear models to test for such relationships, including species identity as the predictor variable and some measured trait or function as the response variable without accounting for the nestedness of genetic variation across levels of organization. This omission may lead to incorrect inference about which source of variation influences community and ecosystem properties. Here, we explicitly compare this common approach to alternative ways of modeling variation in trait data, using simulated trait data and empirical results of common‐garden trials using multiple levels of genetic variation within Eucalyptus, Populus and Picea. We show that: 1) when nested variation is ignored, an incorrect conclusion of species effect is drawn in up to 20% of cases; 2) overestimation of the species effect increases – up to 60% in some scenarios – as the nested term explains more of the variation; and 3) the sample sizes needed to overcome these potential problems associated with aggregating nested hierarchical variation may be impractically large. In common‐garden trials, incorporating nested models increased explanatory power twofold for mammal browsing rate in Eucalyptus, threefold for leaf area in Populus, and tenfold for branch number in Picea. Thoroughly measuring intraspecific variation and characterizing hierarchical genetic variation beyond the species level has implications for developing more robust theory in community ecology, managing invaded natural systems, and improving inference in biodiversity–ecosystem functioning research. Synthesis Until recently, ecologists acknowledged the ubiquity of within‐species trait variation, but paid scant attention to how much it affects communities and ecosystems. Here, the authors used simulated trait data and common‐garden studies to demonstrate that we ignore intraspecific trait variation at our peril. In both simulated and experimental systems, in many cases ignoring intraspecific variation led to incorrect statistical inferences and inflated the effect size of species identity. This study shows that ecologists must characterize hierarchically nested genetic and phenotypic variation to fully understand the links between individual traits, community structure and ecosystem functioning.     

When and where plant‐soil feedback may promote plant coexistence: a meta‐analysis

Plant‐soil feedback (PSF) theory provides a powerful framework for understanding plant dynamics by integrating growth assays into predictions of whether soil communities stabilise plant–plant interactions. However, we lack a comprehensive view of the likelihood of feedback‐driven coexistence, partly because of a failure to analyse pairwise PSF, the metric directly linked to plant species coexistence. Here, we determine the relative importance of plant evolutionary history, traits, and environmental factors for coexistence through PSF using a meta‐analysis of 1038 pairwise PSF measures. Consistent with eco‐evolutionary predictions, feedback is more likely to mediate coexistence for pairs of plant species (1) associating with similar guilds of mycorrhizal fungi, (2) of increasing phylogenetic distance, and (3) interacting with native microbes. We also found evidence for a primary role of pathogens in feedback‐mediated coexistence. By combining results over several independent studies, our results confirm that PSF may play a key role in plant species coexistence, species invasion, and the phylogenetic diversification of plant communities.