Enzyme trafficking and coclustering precede and accurately predict human breast cancer recurrences: an interdisciplinary review

Although great effort has been expended to understand cancer's origins, less attention has been given to the primary cause of cancer deaths-cancer recurrences and their sequelae. This interdisciplinary review addresses mechanistic features of aggressive cancer by studying metabolic enzyme patterns within ductal carcinoma in situ (DCIS) of the breast lesions. DCIS lesions from patients who did or did not experience a breast cancer recurrence were compared. Several proteins, including phosphoSer226-glucose transporter type 1, phosphofructokinase type L and phosphofructokinase/fructose 2,6-bisphosphatase type 4 are found in nucleoli of ductal epithelial cells in samples from patients who will not subsequently recur, but traffic to the cell periphery in samples from patients who will experience a cancer recurrence. Large coclusters of enzymes near plasmalemmata will enhance product formation because enzyme concentrations in clusters are very high while solvent molecules and solutes diffuse through small channels. These structural changes will accelerate aerobic glycolysis. Agglomerations of pentose phosphate pathway and glutathione synthesis enzymes enhance GSH formation. As aggressive cancer lesions are incomplete at early stages, they may be unrecognizable. We have found that machine learning provides superior analyses of tissue images and may be used to identify biomarker patterns associated with recurrent and nonrecurrent patients with high accuracy. This suggests a new prognostic test to predict patients with DCIS who are likely to recur and those who are at low risk for recurrence. Mechanistic interpretations provide a deeper understanding of anticancer drug action and suggest that aggressive metastatic cancer cells are sensitive to reductive chemotherapy.

Automated summary

While great effort has been made to understand the origins of cancer, less attention has been given to the primary cause of cancer deaths - cancer recurrences and their consequences. This interdisciplinary review explores the mechanisms of aggressive cancer by studying metabolic enzyme patterns in ductal carcinoma in situ (DCIS) of the breast lesions. The study found that machine learning can accurately identify biomarker patterns associated with cancer recurrence, providing a new prognostic test to predict the likelihood of recurrence in patients with DCIS.