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.

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.

Genomic predictors of interindividual differences in response to DNA damaging agents.

Human lymphoblastoid cells derived from different healthy individuals display considerable variation in their transcription profiles. Here we show that such variation in gene expression underlies interindividual susceptibility to DNA damaging agents. The results demonstrate the massive differences in sensitivity across a diverse cell line panel exposed to an alkylating agent.

A simple and sensitive erythroblast scoring system to identify fetal cells in maternal blood.

Objectives: Nucleated red blood cells (NRBCs) of fetal origin appear to have distinguishable characteristics from that of maternal NRBCs in both nuclear morphology and properties of hemoglobin staining. However, these differences have yet to be quantified. Our aim was to develop an erythroblast scoring system using four distinct phenotypic parameters (nuclear roundness, nuclear morphology, gamma hemoglobin staining intensity, and peripheral brightness of the stained cytoplasm) to address this issue.