Metastasis―the development of tumor growth at a secondary site―is responsible for the majority of cancer-related deaths. It occurs when the primary tumor site sheds cancerous cells which are then circulated through the body via blood vessels or lymph nodes. These become seeds for eventual tumor growth at a secondary location in the body.
Detection of these very rare cells, known as circulating tumor cells or CTCs, is important for early prognosis of serious disease as well as to monitor the effectiveness of treatment. Currently, there is only one method for CTC detection approved by the U.S. Food & Drug Administration (FDA), CellSearch, which is used to diagnose breast, colorectal and prostate cancer.
Results from a recent study―a collaboration between Lehigh University, Lehigh Valley Cancer Institute, and Pennsylvania State University―demonstrate the potential for a new method of detecting circulating tumor cells. Unlike existing methods, which rely on an expensive and time-consuming process that involves labelling antibodies with fluorescence, this technique uses a powerful label-free detection method. Developed by Yaling Liu, a faculty member in Lehigh’s Department of Bioengineering and in the Department of Mechanical Engineering and Mechanics, in collaboration with Xiaolei Huang, faculty member in Penn State’s College of Information Sciences and Technology, the technique applies a machine learning algorithm to bright field microscopy images of cells detected in patient blood samples containing white blood cells and CTCs.
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