Relocation of phosphofructokinases within epithelial cells is a novel event preceding breast cancer recurrence that accurately predicts patient outcomes

Although recurrent cancers can become life threatening, little is known about the intracellular events required for cancer recurrences. Due to this lack of mechanistic information, there is no test to predict cancer recurrences or nonrecurrences during early stages of disease. In this retrospective study, we use ductal carcinoma in situ of the breast as a framework to better understand the mechanism of cancer recurrences using patient outcomes as the physiological observable. Conventional pathology slides were labeled with anti-phosphofructokinase type L (PFKL) and anti-phosphofructokinase/fructose-2,6-bisphosphatase type 4 (PFKFB4) reagents. PFKL and PFKFB4 were found in ductal epithelial cell nucleoli from DCIS samples of women who did not experience a cancer recurrence. In contrast, PFKL and PFKFB4 may be found near the plasma membrane in samples from patients who will develop recurrent cancer. With the use of machine learning to predict patient outcomes, holdout studies of individual patient micrographs for the three biomarkers PFKL, PFKFB4, and phosphorylated glucose transporter 1 (GLUT1) demonstrated 38.6% true negatives, 49.5% true positives, 11.9% false positives, and 0% false negatives (n = 101). A subpopulati on of recurrent samples demonstrated PFKL, PFKFB4, and phosphorylated GLUT1 accumulation at the apical surface of epithelial cells, suggesting that carbohydrates can be harvested from the ducts' luminal spaces as an energy source. We suggest that PFK isotype patterns are metabolic switches representing key mechanistic steps of recurrences. Furthermore, PFK enzyme patterns within epithelial cells contribute to an accurate diagnostic test to classify DCIS patients as high or low recurrence risk.

Automated summary

This study analyzed the differences in PFKL and PFKFB4 distribution within ductal epithelial cells from breast cancer samples, showing potential for predicting cancer recurrence risk. Machine learning was utilized to accurately predict patient outcomes based on these biomarkers. The PFK enzyme patterns within epithelial cells were identified as metabolic switches representing key mechanistic steps of cancer recurrences.