Liquid Biopsy Biomarkers for Aggressive Prostate Cancer Discovered

The new research advances the quest to develop a precise, noninvasive diagnostic tool that can address over-treatment of slow-growing, low-risk PC.

"We believe we have found a better way that allows us to predict which patients have a slow-growing versus aggressive PC using non-invasive biomarkers. This could eventually help us personalize cancer treatment for these patients," said principal investigator Prof. Dr. Thomas Kislinger, of Princess Margaret Cancer Centre, University Health Network (UHN; Toronto, Canada), and of University of Toronto.

"A fluid-based biomarker would be ideal … to spare patients with indolent (slow-growing) disease from unnecessary procedures, while identifying and treating those who would benefit from treatment intensification," said co-lead author Dr. Yunee Kim.

The Kislinger team – in collaboration with Paul Boutros, Ontario Institute for Cancer Research (Canada), and O. John Semmes, Eastern Virginia Medical School (Norfolk, VA, USA) – used urine samples containing prostatic secretions from 210 patients after they had undergone digital rectal examinations (DRE, the standard clinical "first step" to determine need for further diagnostic testing of the prostate). The research took four years and involved samples from almost 300 patients.

"We used targeted proteomics to accurately quantify hundreds of proteins in urine samples (post-DRE) to identify liquid biopsy signatures. The first round of research involved 80 patients and quantified 150 proteins that were then narrowed down to 34 for further investigation. The next round involved a second, independent cohort of 210 patients,” said Dr. Kislinger, "Applying computational biology, we used the quantitative data from mass spectrometry to develop the fluid biomarkers for aggressive PC." He added, "The next step will be further studies with urine samples from 1,000 international patients to validate if the biomarkers identified have broader clinical utilities in PC."

The study, by Kim Y, Jeon J, et al, was published online ahead of print June 28, 2016, in the journal Nature Communications.

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