Results of a risk stratification study designed to demonstrate the ability of a new live tumor cell phenotypic biomarker test to identify patients with low and intermediate grade prostate cancer at risk of aggressive disease, were published in Urology. 
Prostate cancer is the most common cancer in men in the United States, with an estimated 164,690 news cases diagnosed in 2018, according to the American Cancer Society. Although most men do not die of the disease, there will be estimated 39,430 deaths from prostate cancer in 2018. The inability of current tests to provide risk stratification of aggressive disease in low and intermediate grade patients leads to missed diagnoses, and inadequate treatment.
Risk stratification in prostate cancer – distinguishing clinically significant cancer from indolent disease – remains a major challenge in men with low and intermediate grade disease
The biomarker test was developed by Cellanyx, a company developing a proprietary living cell phenotypic cancer testing platform to aid clinical decision-making
“There is an urgent need for more precise cancer risk stratification tools,” noted David Albala, MD, Chief of Urology at Crouse Hospital (Syracuse, NY) and “In the study, which analyzed tissue collected from radical prostatectomy specimens, the live tumor cell phenotypic test predicted specific post-surgical adverse pathology features – the gold standard of prostate cancer clinical diagnosis – with a high degree of sensitivity and specificity.”
Albala, is a member of the Cellanyx Scientific Advisory Board (SAB) and one of the authors of the paper.
Low and intermediate Gleason
Current risk assessments are based on analysis of formalin-fixed tissue or genomic analysis of selected genes. These methods, such as Gleason scoring for prostate cancer, lack precision in low and intermediate risk patients, leading to missed aggressive tumors, as well as over-diagnosis and over treatment of indolent disease. The high sensitivity and specificity with the phenotypic test, both exceeding 80%, reported in initial validation studies suggest great promise as a risk stratification tool.
“Significantly, the test identified subgroups of prostate cancer patients within established low and intermediate Gleason and Prostate Cancer Grading Group (PGGC) tumor grades who had a higher risk based on adverse pathology features, such as positive surgical margins, lymph node involvement, and extra-prostatic extension. These initial clinical results suggest considerable potential of this phenotypic test as a risk stratification tool for prostate cancer patients with low and intermediate grade disease. The results will need to be confirmed in future studies in prostate cancer patients at the time of initial biopsy.”
“Risk stratification in prostate cancer – distinguishing clinically significant cancer from indolent disease – remains a major challenge in men with low and intermediate grade disease,” Albala said.
“A subset of these patients may develop aggressive disease and we currently lack sufficiently precise, personalized risk stratification tools to distinguish between indolent and potentially aggressive disease,” he added.
“Tumor heterogeneity and risk stratification are major challenges in the contemporary management of prostate cancer,” said Grannum R Sant, MD, a co-author, Professor of Urology at Tufts University, Chairman of Cellanyx’s SAB and a board member.
“This Cellanyx proof of concept study of the first in class, live single cell phenotypic biomarker platform is a major contribution to personalized oncology. If these findings are confirmed in a planned prostate needle biopsy trial, this phenotypic biomarker test will significantly augment Gleason, Grade Group and formalin-fixed tissue genomic analysis in risk stratification of Gleason 6 and Gleason 7 (3+4, 4+3) prostate cancer.”
The study was a multi-center, blinded, prospective trial that evaluated fresh prostate tissue samples taken from 251 men undergoing radical prostatectomy, of which 237 samples were successfully cultured and analyzed. The samples were evaluated in a central laboratory where they were tested, on a specially coated microfluidic chip and analyzed for phenotypic biomarkers in individual cells using machine vision and machine learning algorithms. The predictions of specific adverse pathology features were then compared to the actual post-surgical pathology reported findings following data un-blinding.
“The machine vision learning and intelligent algorithms developed by our team allowed objective prioritization and scoring of phenotypic biomarkers for each cell, actionable scores for predicting adverse pathological features that clinicians can use to risk-stratify patients,” said Ashok Chander, PhD, an author on the paper and co-founder of Cellanyx.
The novel test accurately predicted post-radical prostatectomy adverse pathology features with an area under the curve (AUC) through receive operating characteristics analysis of greater than 0.85. The test distinguished among low and intermediate grade cancers (Gleason 3+3, 3+4 and 4+3 and PCGG 1, 2 and 3) with high precision (AUC >0.80).
The live tumor cell phenotypic test is built around several key technology components and is enabled by a novel proprietary extra-cellular matrix (ECM) formulation that allows rapid culturing of primary human tumor cells. The ability to culture primary tumor cells for analysis overcomes a major barrier to single cell tumor analysis.
The test employs microfluidics technology, and automated live-cell and fixed-cell confocal microscopy to access several hundred cellular phenotypic biomarkers. The live- (or fixed) cell images of several hundred single cells are analyzed by machine vision algorithms and the biomarker data are objectively prioritized and quantified by intelligent machine learning algorithms.
The intelligent machine-learning algorithm objectively prioritizes and scores the biomarkers for each cell and generates actionable scores for prediction of a number of adverse pathological features
The biomarker test is being developed as a Laboratory Developed Test that can be run in any CLIA laboratory. The patient sample characteristics and acquisition were designed to fit seamlessly into the workflow of the urologist.
Albala D, Manak MS, Varsanik JS, Rashid HH, Mouraviev V, Zappala SM, Ette E, et al. 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). Urology. 2018 Oct 10. pii: S0090-4295(18)31073-2. doi: 10.1016/j.urology.2018.09.032. [Pubmed][Article]
Last Editorial Review: November 30, 2018
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