Uncovering the In Vivo Proxisome Using Proximity‐Tagging Methods

The development of new approaches is critical to gain further insights into biological processes that cannot be obtained by existing methods or technologies. The detection of protein–protein interaction is often challenging, especially for weak and transient interactions or for membrane proteins. Over the last decade, several proximity‐tagging methodologies have been developed to explore protein interactions in living cells. Among those, the most efficient are based on protein partner modification, such as biotinylation or pupylation. Such technologies are based on engineered variants of enzymes like peroxidases or ligases that release reactive molecules, in the presence of specific substrates, that bind surrounding proteins. Fusing a protein of interest (POI) to these enzymes allows the definition of an unbiased “proxisome,” that is, all of the proteins in interaction or in close vicinity of the POI. Here, the different proximity‐labeling tools available are described and comprehensive comparison to discuss advantages and limitations is provided.     

The area between ROC curves,a non-parametric method to evaluate a biomarker for patient treatment selection

Treatment selection markers are generally sought for when the benefit of an innovative treatment in comparison with a reference treatment is considered, and this benefit is suspected to vary according to the characteristics of the patients. Classically, such quantitative markers are detected through testing a marker-by-treatment interaction in a parametric regression model. Most alternative methods rely on modeling the risk of event occurrence in each treatment arm or the benefit of the innovative treatment over the marker values, but with assumptions that may be difficult to verify. Herein, a simple non-parametric approach is proposed to detect and assess the general capacity of a quantitative marker for treatment selection when no overall difference in efficacy could be demonstrated between two treatments in a clinical trial. This graphical method relies on the area between treatment-arm-specific receiver operating characteristic curves (ABC), which reflects the treatment selection capacity of the marker. A simulation study assessed the inference properties of the ABC estimator and compared them with other parametric and non-parametric indicators. The simulations showed that the estimate of the ABC had low bias, power comparable to parametric indicators, and that its confidence interval had a good coverage probability (better than the other non-parametric indicator in some cases). Thus, the ABC is a good alternative to parametric indicators. The ABC method was applied to data of the PETACC-8 trial that investigated FOLFOX4 versus FOLFOX4 + cetuximab in stage III colon adenocarcinoma. It enabled the detection of a treatment selection marker: the DDR2 gene.