A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche. Issue 7 (27th February 2019)

• Record Type: Journal Article
• Authors: Oliver, C. Ryan; Altemus, Megan A.; Westerhof, Trisha M.; Cheriyan, Hannah; Cheng, Xu; Dziubinski, Michelle; Wu, Zhifen; Yates, Joel; Morikawa, Aki; Heth, Jason; Castro, Maria G.; Leung, Brendan M.; Takayama, Shuichi; Merajver, Sofia D.
• Is Part Of: Lab on a chip. Volume 19:Issue 7(2019)
• Page Start: 1162
• Page End: 1173
• Publication Date: 2019-02-27
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/c8lc01387j
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastasize to the brain. Abstract : Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastasize to the brain. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood–brain niche (μBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.