Members of the University of Michigan Department of Pathology and Michigan Center for Translational Pathology, in collaboration with the Clinical Proteomic Tumor Analysis Consortium, recently published a large study on clear cell renal cell carcinomas (ccRCCs), which represent about 75% of the RCC cases and account for the most RCC-associated deaths.
The diagnosis of metastatic prostatic cancer (MPC) by fine needle aspiration (FNA) can usually be rendered by examining the cells’ shape and appearance under a microscope (its cytomorphology) and through features seen in immunohistochemical (IHC) testing.
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.
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 Pathology. Dr. 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.
The role of artificial intelligence (AI) in healthcare continues to expand. In a recent issue of BMC Cancer, Dr. 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).
MLabs, and the Department of Pathology laboratories at Michigan Medicine, recognized leaders for advanced diagnostic testing, are now participating in the NCI-Molecular Analysis for Therapy Choice (NCI-MATCH) trial.