Computer Model Uses Enzyme Organization to Predict Early Breast Cancer Recurrence

Computer Model Uses Enzyme Organization to Predict Early Breast Cancer Recurrence
The distinctive organization of enzymes in early breast ductal cancer can be used to correctly predict recurrences in 91% of cases, with 4% false negatives, a new study has found.  The diagnostic tool may assist with the early identification of patients at high risk of cancer recurrence while reducing overdiagnosis and treatment of low-risk patients. The study, "Spatial locations of certain enzymes and transporters within preinvasive ductal epithelial cells predict human breast cancer recurrences," was published in the American Journal of Physiology-Cell Physiology. Ductal carcinoma in situ (DCIS) is an early-stage, non-metastatic form of breast cancer. While DCIS is noninvasive and highly treatable, it carries the risk of recurrence as metastatic (or invasive) breast cancer. Identification of DCIS patients who are at high risk for recurrence remains a challenge as available genomic tests that screen a patient’s complete genetic information have thus far failed to predict patient outcomes. "Typically, patients with pre-invasive cancers, such as DCIS, are treated very aggressively," Howard Petty, PhD, study co-author and professor at the University of Michigan, said in a university press release. "In the case of DCIS, this means partial or total mastectomies ... but we know from other work that more than half of these patients will not experience invasive disease."  Researchers at the university now hypothesized that the spatial organization of enzymes within breast cancer cells could be a better predictor for patient outcomes than genomics or the levels of certain enzymes. Several classes of enzymes were of interest, namely pentose phosphate pathway (PPP) enzymes, glutathione synthesis (GSH) enzymes, and RhoA enzymes, all of which are involved in can
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