A New Cooperation between EZH2 and p38 Proteins Enhances Metastasis in Triple Negative Breast Cancer

The Celina Kleer lab at the University of Michigan Department of Pathology and Rogel Cancer Center has found a new mechanism that fuels metastasis in triple negative breast cancers. In their new study they show that EZH2, a master regulator of cell type identity, known to function through methylation of histones, has a new, unexpected function in aggressive breast cancers. By adding methyl groups to the p38 protein, EZH2 enhances the ability of breast cancer cells to spread to other tissue throughout the body, a process known as metastasis.

Visual Inventory Management Pilot Takes Off at Microbiology Lab

On Thursday, July 15, Scott Marquette, Associate Chief Operating Officer from Michigan Medicine, visited the Clinical Microbiology laboratory at the Department of Pathology. The purpose of the visit was to learn more about the Visual Inventory Management pilot, a new initiative that is intended to help lab staff better organize their tools, resources and assets within the lab setting.

Kristina Martin Receives ASCLS Scientific-Assembly Laboratory Administration Award

The Department of Pathology's Clinical Operations Director Kristina Martin recently received the Scientific-Assembly Laboratory Administration award from the American Society for Clinical Laboratory Science (ASCLS) at the Joint Annual Meeting in Grand Rapids, Michigan last month. The award recognizes outstanding professional achievement of an individual ASCLS member within his or her chosen area of academic, scientific, or vocational interest.

Qualitative Image Analysis Study Shows Excellent Results

A landmark study into quantitative image analysis in ER, PgR, and HER2 in invasive breast carcinoma was recently published in the American Journal of Clinical PathologyDr. Mustafa Yousif, Assistant Professor of Breast Pathology and Informatics, and colleagues conducted a retrospective study of 1,367 invasive breast carcinomas of all histopathology subtypes, for which ER, PgR, and HER2 were analyzed by manual scoring. These were compared to the results obtained using quantitative image analysis (QIA).  QIA uses a form of artificial intelligence (AI) called deep learning to identify specific regions of interest and to interpret that based on programmed algorithms.

Using Artificial Intelligence to predict ERG Gene Fusion in Prostate Cancer

The role of artificial intelligence (AI) in healthcare continues to expand. In a recent issue of BMC CancerDr. Vipulkumar Dadhania (first author) and colleagues published a result of their study Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer. The expert team from the Michigan Center for Translational Pathology developed a deep-learning-based model to predict ERG genomic rearrangements in prostatic adenocarcinomas using only H&E-stained digital slides. Their AI models were accurate at 78.6%-79.7%, depending on the magnification, with a 20x magnification the most accurate. Sensitivity was found to be at 75% across magnification levels (10x, 20x, 40x) and specificity ranged from 81.7% (10x and 40x) to 83.1% (20x).