Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor prognosis and a high likelihood of recurrence. Residual disease after therapy is a key predictor of recurrence often driven by intra-tumoral metabolic heterogeneity. Accumulating evidence indicates that tumors shift between glycolysis and oxidative pathways relying on alternative substrates such as fatty acid oxidation (FAO) to sustain growth and resist therapy. To investigate spatially distinct metabolic profiles within tumors we developed a novel in vivo imaging method that leverages exogenous fluorophores to concurrently quantify oxidative phosphorylation and two key substrates that drive it: glucose and fat. We first developed an unmixing method to differentiate spectrally overlapping fluorophores. Next we validated a concurrent imaging scheme where each fluorophore is injected into a living animal and imaged through a window chamber. Finally we investigated heterogeneity and spatial relationships between metabolic endpoints. We validated our unmixing method in scattering phantoms. We validated our imaging system in vivo; we saw no significant difference in fluorophore measurements when all three were present versus the gold standard. Normal tissues displayed predominantly oxidative phosphorylation. Tumor tissues relied on both glucose (low mitochondrial metabolism suggesting glycolysis) and fatty acids (high mitochondrial metabolism suggesting FAO). A single metabolic phenotype was uniformly distributed across normal tissues; tumors were more heterogeneous. These findings underscore the presence of simultaneous yet compartmentalized metabolic pathways within a single tumor. This study establishes a validated imaging strategy for quantifying metabolic heterogeneity in living tumors enabling deeper insights into the metabolic underpinnings of TNBC.
File list:
1. Figure 1 Raw Data and Scripts
2. Figure 2 Scripts
3. Figure 4 Script
4. Figure SI3 Script
5. Figure 2 3 4 and SI Raw Data
File list:
1. Figure 1 Raw Data and Scripts
2. Figure 2 Scripts
3. Figure 4 Script
4. Figure SI3 Script
5. Figure 2 3 4 and SI Raw Data