Abstract 4347: A novel live cell diagnostic platform measuring phenotypic biomarkers using objective algorithmic analysis enables further risk stratification for intermediate-risk prostate cancer patients
Authors: Michael S. Manak, Wendell R. Su, Andrew Min, Brad J. Hogan, Matthew J. Whitfield, Jonathan S. Varsanik, Delaney Berger, Mani Foroohar, Kimberly M. Rieger-Christ, Travis B. Sullivan, Naveen Kella, Ray Hernandez, Vladimir Mouraviev, Kevin B. Knopf, Hani H. Rashid, David M. Albala, Grannum R. Sant, Ashok C. Chander
Journal: Cancer Research (2015)

Abstract Due to the inconsistencies of existing molecular, genomic, and pathophysiologic markers for patient risk stratification, effective prostate cancer diagnostics and treatment remains a challenge in clinical practice. Therefore, the development of a diagnostic platform that differentiates cancer patients who have clinically significant disease from those who have a low risk of progression is an important area of interest. In this study, we tested a diagnostic platform that combines a scalable microfluidic device, an automated live cell assay, and objective machine vision algorithms to measure phenotypic biomarkers [defined here as functional biophysical and molecular biomarkers], which evaluate both local growth and metastatic potential of prostate cancer. An analytical validation study was performed on fresh prostate cancer samples (n = 100) obtained at the time of radical prostatectomy (RP). The diagnostic platform enables: 1) growth of patient cells ex vivo on extra cellular matrix formulations supporting adhesion/survival for 72 hours 2) high-throughput imaging of multiple phenotypic biomarkers such as morphology, cytoskeleton dynamics, and protein subcellular localization &amp; modification states and 3) objective quantification of biomarkers via machine vision analysis. Patient samples were imaged over a three hour period capturing live-cell biophysical biomarkers. After three hours cells were fixed and stained for molecular biomarkers. Machine vision technology was then utilized to analyze phenotypic biomarkers to yield specific metrics that quantified local tumor growth (Oncogenic Potential-OPs) and invasive potential of the tumor to other tissues (Metastatic Potential- MPs) that correlated with RP specimen pathologic findings. Analysis of quantified phenotypic biomarkers distinguished normal cells from cancer cells. The OP and MP metrics demonstrated statistical significance in distinguishing Gleason 6 (low-risk) from Gleason 7 (intermediate-risk) prostate cancer with 80% sensitivity and 80% specificity and concordance with relevant RP pathology findings. Specifically, OP and MP derived from defined phenotypic biomarker metrics, demonstrated the ability to differentiate Gleason 6 and 7 scores and correlated with, 1) seminal vesicle invasion, 2) positive RP surgical margins, 3) vascular invasion, and 4) lymph node involvement. This novel functional-live-cell diagnostic platform allows for the measurement of a biomarker panel that further stratifies patients to improve prostate cancer treatment, clinical decision-making, further risk stratification of intermediate prostate cancer populations, and potentially predict actionable pathological findings leading to improved treatment outcomes for prostate cancer patients. Citation Format: Michael S. Manak, Wendell R. Su, Andrew Min, Brad J. Hogan, Matthew J. Whitfield, Jonathan S. Varsanik, Delaney Berger, Mani Foroohar, Kimberly M. Rieger-Christ, Travis B. Sullivan, Naveen Kella, Ray Hernandez, Vladimir Mouraviev, Kevin B. Knopf, Hani H. Rashid, David M. Albala, Grannum R. Sant, Ashok C. Chander. A novel live cell diagnostic platform measuring phenotypic biomarkers using objective algorithmic analysis enables further risk stratification for intermediate-risk prostate cancer patients. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4347. doi:10.1158/1538-7445.AM2015-4347