Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning. (October 2018)

• Record Type: Journal Article
• Authors: Manak, Michael; Varsanik, Jonathan; Hogan, Brad; Whitfield, Matt; Su, Wendell; Joshi, Nikhil; Steinke, Nicolai; Min, Andrew; Berger, Delaney; Saphirstein, Robert; Dixit, Gauri; Meyyappan, Thiagarajan; Chu, Hui-May; Knopf, Kevin; Albala, David; Sant, Grannum; Chander, Ashok
• Is Part Of: Nature biomedical engineering. Volume 2:Number 10(2018)
• Page Start: 761
• Page End: 772
• Publication Date: 2018-10
• Subjects: Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1038/s41551-018-0285-z
• Languages: English
• ISSNs: 2157-846X

Abstract

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 time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients. An assay that uses machine-learning algorithms on phenotypic-biomarker data from live primary cells predicts post-surgical adverse pathology in prostate-cancer and breast cancer tissue samples from patients.