Cellanyx Publishes Details of Novel, First-in-Class Live Tumor Cell Phenotypic Test to Risk-Stratify Aggressive Vs. Indolent Disease Risk in Solid Tumors
BEVERLY, Mass.--(BUSINESS WIRE)-- Cellanyx and clinical collaborators today published the first studies demonstrating the potential of a novel, live cancer cell phenotypic test to predict adverse pathology and allow risk stratification of patients with solid tumors such as prostate and breast cancer. This first-in-class, live tumor cell phenotypic test is designed to provide actionable information on cancer aggressiveness to support shared clinical decision-making. The technology and preliminary clinical results are described today in the Advance On-line issue of Nature Biomedical Engineering.
“There is an urgent need for more precise cancer risk stratification tools,” said David Albala, MD, Chief of Urology at Crouse Hospital (Syracuse, NY) and an author on the paper. “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 percent, reported in these initial validation studies suggest great promise as a risk stratification tool.” He added that additional studies are needed to demonstrate the test’s utility in clinical settings alongside established methods. Dr. Albala is a member of the Cellanyx Scientific Advisory Board (SAB).
The Nature Biomedical Engineering paper reports the initial results from the analytical validation of the test from a multicenter, blinded clinical study of prostate tissue samples from patients who had undergone radical prostatectomies. The authors additionally describe preliminary results from a separate proof-of-principle study in breast cancer patients undergoing surgery (mastectomy or lumpectomy).
Predictive metrics from the live tumor cell phenotypic analysis were compared with the conventional post-surgical adverse pathology findings to establish the sensitivity and specificity of the Cellanyx test. The results showed that the Cellanyx generated superior results in predicting adverse pathologies with sensitivity and specificity of more than 80 percent (ROC >80 percent) and separated patients into distinct, quantifiable groups based on predicted adverse pathology features.
“The challenge in risk stratification of solid tumors is due in large part to tumor heterogeneity,” commented Grannum R Sant, MD, Professor of Urology at Tufts University, Chairman of Cellanyx’s SAB and a board member. “The Cellanyx technology addresses tumor heterogeneity by employing for the first time innovative phenotypic biomarkers in live single cancer cells. Advanced machine vision and machine vision learning techniques, generated quantitative risk stratification scores by objectively selecting and prioritizing a large number of phenotypic biomarkers. The results demonstrate the promise and potential of a truly personalized approach to cancer risk stratification.”
Technology for Live Tumor Cell Phenotypic Analysis
The live tumor cell phenotypic test described in the paper 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 cell culture methodology was published in Urology in July 2017.
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.
“We have extended these preliminary results to other tumor types, including kidney, bladder and lung cancer and we are advancing the work in prostate cancer with the goal of demonstrating clinical validation and utility in men undergoing diagnostic prostate biopsies,” said Ashok Chander, PhD, co-founder of Cellanyx and an author on the paper. He noted that the test, which is being developed as a laboratory-developed test (LDT), has been designed to readily integrate into clinical and clinical laboratory workflow and generate a result in 72-96 hours.
The publication is titled, “Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.”
Cellanyx is developing a proprietary living cell phenotypic cancer testing platform to aid clinical decision-making. The company technology provides quantitative, actionable assessments of individual cancer cells in biopsy samples using multiple phenotypic biochemical and biophysical markers of tumor aggressiveness and metastatic potential. Cellanyx has demonstrated clinical proof-of-concept with its lead test in development, a test to improve risk stratification in men with prostate cancer. Learn more at www.cellanyx.com.
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