PROJECT SUMMARY Cancer-associated cachexia (CAC) is characterized by the involuntary loss of skeletal muscle and adipose tissue mass. This multifactorial syndrome is driven by the combination of reduced food intake and altered systemic metabolism. Approximately half of all cancer patients present with complications associated with cachexia, leading to increased mortality, functional impairment, and reduced quality of life. Effective therapies to treat cancer-associated cachexia are currently lacking, with treatment focused on nutritional support and symptom mitigation. Resolution of cachexia is optimally achieved through treatment of the underlying disease. Targeting the metabolic alterations that drive cachexia to identify novel drug targets remains an area of active inquiry. Treatment of cachexia has remained elusive due to disease heterogeneity. Emerging evidence has suggested there are multiple subtypes of this complex disease, which may explain the lack of consensus in the criteria used to describe cancer-associated cachexia. Additionally, these definitions are further muddied in the context of obesity, which can confound diagnoses of cachexia based upon body-mass index and hide loss of skeletal muscle mass. While recent work has described the catabolic changes in metabolic organs such as skeletal muscle, adipose tissue, and liver that occur with cachexia, as well as identified inflammatory mediators that can drive these processes, further studies are needed to more comprehensively identify systemic physiological changes that occur in CAC. This innovative proposal will address major gaps in our understanding of CAC: 1) Do common cancer mutations modulate the development of CAC? 2) How do dietary interventions impact the course of CAC? Prior studies have been limited by lack of tractable, in vivo models of CAC that develop in an autochthonous setting. Using genetically engineered mouse models, we identify a novel model of CAC that is driven by tumor genetics and dietary intervention. Based upon characteristics of this model, we will identify novel potential drug targets specific to subsets of cancer-associated cachexia.