Abstract PL04-04: Novel approaches for personalizing treatments: From nutrition to cancer
Authors: Segal Eran 
Published: July 2017

Abstract
Elevated blood glucose levels are rapidly increasing in the general population, resulting in a sharp incline in the prevalence of prediabetes and impaired glucose tolerance and eventual development of type 2 diabetes mellitus. Dietary intake is considered a central determinant of glucose levels, with high postmeal glucose levels affecting weight gain, obesity, hunger, and energy dips and being associated with increased risk of cardiovascular disease, cancer, and overall mortality. However, despite their importance, existing dietary methods for controlling postmeal glucose levels have limited efficacy. By continuously monitoring week-long glucose levels in over 1,000 people, we found high variability in the response of different people to identical meals, suggesting that generic population-wide dietary recommendations have limited utility and are ineffective in achieving proper glycemic control. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glucose responses to real-life meals. Moreover, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses in a cohort of prediabetics and consistent alterations to gut microbiota configuration. These results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. As another example of personalized medicine approaches that we are developing, I present our work on functional profiling of a library of over 10,000 variants that we generated for the tumor suppressor p53, the most frequently mutated gene in human cancers. Remarkably, the mutational effects observed in large-scale in-vitro assays with this library correspond to p53 mutation recurrence in patients and provide many novel insights on adverse and benign variants, protein structure, and evolutionary conservation. Apart from gaining comprehensive insights into the effects of the p53 "mutome," our results may lead to better understanding of patients' response to treatment based on their p53 sequence, potentially contributing to the development of novel patient-specific therapeutics. Finally, I also present our studies of the mechanisms driving recurrent postdieting obesity in which we identified an intestinal microbiome signature that persists after successful dieting of obese mice. This microbiome signature contributes to faster weight regain and metabolic aberrations upon re-exposure to obesity-promoting conditions and transmits the accelerated weight regain phenotype upon interanimal transfer. Notably, a microbiome-based machine-learning algorithm enabled personalized prediction of the extent of postdieting weight regain. We further find that the microbiome contributes to diminished postdieting flavonoid levels and reduced energy expenditure and demonstrate that flavonoid-based "postbiotic" intervention ameliorates excessive secondary weight gain. These results thus highlight a possible microbiome contribution to accelerated postdieting weight regain and suggest that microbiome-targeting approaches may help to diagnose and treat this common disorder.
Citation Format: Eran Segal. Novel approaches for personalizing treatments: From nutrition to cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr PL04-04. doi:10.1158/1538-7445.AM2017-PL04-04