Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness.

To the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy.

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

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.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4).

Objective: To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens.

Methods: Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP.

Rapid and Short-term Extracellular Matrix-mediated In Vitro Culturing of Tumor and Nontumor Human Primary Prostate Cells From Fresh Radical Prostatectomy Tissue.

Objective: To culture prostate cells from fresh biopsy core samples from radical prostatectomy (RP) tissue. Further, given the genetic heterogeneity of prostate cells, the ability to culture single cells from primary prostate tissue may be of importance toward enabling single-cell characterization of primary prostate tissue via molecular and cellular phenotypic biomarkers.

Methods: A total of 260 consecutive tissue samples from RPs were collected between October 2014 and January 2016, transported at 4°C in serum-free media to an off-site central laboratory, dissociated, and cultured.

α-Actinin links extracellular matrix rigidity-sensing contractile units with periodic cell-edge retractions.

During spreading and migration, the leading edges of cells undergo periodic protrusion-retraction cycles. The functional purpose of these cycles is unclear. Here, using submicrometer polydimethylsiloxane pillars as substrates for cell spreading, we show that periodic edge retractions coincide with peak forces produced by local contractile units (CUs) that assemble and disassemble along the cell edge to test matrix rigidity.