Introduction:
Inhibitors of BRAF and MEK have dramatically improved outcomes for patients with melanoma, yet many still succumb to metastatic disease, underscoring the need for deeper biological insights. Interestingly, in clinical cohorts, high BMI has been associated with superior responses to BRAF/MEK inhibitors (BRAF/MEKi). To explore the mechanisms underlying this paradoxical phenomenon, we employed melanoma mouse models to investigate the metabolic reprogramming associated with both dietary and therapeutic interventions.
Methods:
Using human (A375, immunocompromised) and murine (YUMMER1.7 PV1, immune-competent) melanoma models, we assessed the effects of a normal diet (ND) versus a high-fat diet (HFD) under BRAF/MEKi (Dabrafenib/Trametinib) or vehicle treatment. The study employed a comprehensive metabolomics analysis pipeline developed in R to normalize data, perform differential metabolomics analysis, and conduct Metabolite Set Enrichment Analysis (MSEA).
Our R workflow first evaluated normalization methods using Relative Log Expression (RLE) plots to select the optimal approach for minimizing technical variability. We then applied the limma package for differential analysis, identifying metabolites significantly altered between key conditions, such as ND vs. HFD and BRAF/MEKi vs. vehicle treatments. Finally, MSEA was used to interpret the biological significance of these changes by identifying impacted metabolic pathways.
Results:
Normalization Optimization: RLE plots provided a comparison across various normalization methods, to select the most effective strategy for reducing technical noise and enhancing data quality.
Differential Metabolomics Analysis: Limma-based differential analysis revealed significant metabolite changes in response to both dietary and therapeutic interventions. Metabolites associated with glycolysis and lipid metabolism were particularly impacted, consistent with clinical observations of enhanced BRAF/MEKi efficacy in high-BMI patients.
Metabolite Set Enrichment Analysis: MSEA highlighted pathways related to glucose metabolism and lipid biosynthesis, which were significantly affected by the interaction between diet and BRAF/MEKi treatment.
Conclusions:
This R-based workflow offers a robust approach to metabolomics data analysis, addressing steps such as normalization, differential analysis, and pathway enrichment. By integrating RLE plots, limma for differential analysis, and MSEA, this workflow provides a comprehensive tool for understanding metabolic reprogramming in melanoma models. Our results emphasize the importance of dietary factors in modulating tumor metabolism, particularly in enhancing the response to targeted therapies.