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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Immunoprofiling as a predictor of patient's response to cancer therapy-promises and challenges
Daniel Bethmann1, Zipei Feng2, Bernard A Fox3
1Martin Luther University Halle-Wittenberg, Institute of Pathology, Halle, Germany; Robert W. Franz Cancer Research Center, Earle A. Chiles Research Institute, Providence Cancer Center, Portland, OR, United States.
Abstract:
Immune cell infiltration is common to many tumors and has been recognized by pathologists for more than 100 years. The application of digital imaging and objective assessment software allowed a concise determination of the type and quantity of immune cells and their location relative to the tumor and, in the case of colon cancer, characterized overall survival better than AJCC TNM staging. Subsequently, expression of PD-L1, by 50% or more tumor cells, identified NSCLC patients with double the response rate to anti-PD-1. Soon, automated staining methods will improve reproducibility of multiplex staining and allow for CLIA standards so that multiplex staining can be used to make clinical decisions. Ultimately, machine-learning algorithms will help interpret data from tissue images and lead to improved delivery of precision medicine.
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