Next Generation Weight Loss Drugs for the Prevention of Cancer?

Background: Western populations are losing the battle over healthy weight management, and excess body weight is a notable cancer risk factor at the population level. There is ongoing interest in pharmacological interventions aimed at promoting weight loss, including GLP-1 receptor agonists (GLP-1RA), which may be a useful tool to stem the rising tide of obesity-related cancers. Purpose: To investigate the potential of next generation weight loss drugs (NGWLD) like GLP-1RA in population-level chemoprevention.Research Design: We used the OncoSim microsimulation tool to estimate the population-level reductions in obesity and the potentially avoidable obesity-related cancers in Canada over the next 25 years.Results: We estimated a total of 71 281 preventable cancers by 2049, with 36 235 and 35 046 cancers prevented for females and males, respectively. Among the 327 254 total projected cancer cases in 2049, 1.3% are estimated to be preventable through intervention with NGWLD.Conclusions: Pharmacologic intervention is not the ideal solution for the obesity-related cancer crisis. However, these agents and subsequent generations provide an additional tool to rapidly reduce body weight and adiposity in populations that have been extremely challenging to reduce weight with standard diet and exercise approaches. Additional research is needed around approaches to prevent initial weight gain and maintain long-term weight loss.

Eatomics: Shiny Exploration of Quantitative Proteomics Data.

Quantitative proteomics data are becoming increasingly more available, and as a consequence are being analyzed and interpreted by a larger group of users. However, many of these users have less programming experience. Furthermore, experimental designs and setups are getting more complicated, especially when tissue biopsies are analyzed. Luckily, the proteomics community has already established some best practices on how to conduct quality control, differential abundance analysis and enrichment analysis. However, an easy-to-use application that wraps together all steps for the exploration and flexible analysis of quantitative proteomics data is not yet available. For Eatomics, we utilize the R Shiny framework to implement carefully chosen parts of established analysis workflows to (i) make them accessible in a user-friendly way, (ii) add a multitude of interactive exploration possibilities, and (iii) develop a unique experimental design setup module, which interactively translates a given research hypothesis into a differential abundance and enrichment analysis formula. In this, we aim to fulfill the needs of a growing group of inexperienced quantitative proteomics data analysts. Eatomics may be tested with demo data directly online via https://we.analyzegenomes.com/now/eatomics/ or with the user's own data by installation from the Github repository at https://github.com/Millchmaedchen/Eatomics.