Clustering subtypes of breast cancer by combining immunohistochemistry profiles and metabolism characteristics measured using FDG PET/CT.

Background: The aim of this study was to investigate the effect of combining immunohistochemical profiles and metabolic information to characterize breast cancer subtypes.

Methods: This retrospective study included 289 breast tumors from 284 patients who underwent preoperative  F-fluorodeoxyglucose (FDG) positron emission tomography/ computed tomography (PET/CT). Molecular subtypes of breast cancer were classified as Hormonal, HER2, Dual (a combination of both Hormonal and HER2 features), and triple-negative (TN). Histopathologic findings and immunohistochemical results for Ki-67, EGFR, CK 5/6, and p53 were also analyzed. The maximum standardized uptake value (SUV) measured from FDG PET/CT was used to evaluate tumoral glucose metabolism.

Results: Overall, 182, 24, 47, and 36 tumors were classified as Hormonal, HER2, Dual, and TN subtypes, respectively. Molecular profiles of tumor aggressiveness and the tumor SUV revealed a gradual increase from the Hormonal to the TN type. The tumor SUV was significantly correlated with tumor size, expression levels of p53, Ki-67, and EGFR, and nuclear grade (all p < 0.001). In contrast, the tumor SUV was negatively correlated with the expression of estrogen receptors (r = - 0.234, p < 0.001) and progesterone receptors (r = - 0.220, p < 0.001). Multiple linear regression analysis revealed that histopathologic markers explained tumor glucose metabolism (adjusted R-squared value 0.238, p < 0.001). Tumor metabolism can thus help define breast cancer subtypes with aggressive/adverse prognostic features.

Conclusions: Metabolic activity measured using FDG PET/CT was significantly correlated with the molecular alteration profiles of breast cancer assessed using immunohistochemical analysis. Combining molecular markers and metabolic information may aid in the recognition and understanding of tumor aggressiveness in breast cancer and be helpful as a prognostic marker.