Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.

ABSTRACT: The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the ti

SUBMITTER: Manak MS 

PROVIDER: S-EPMC6407716 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature