A team of investigators led by researchers at the University of Toronto has identified a set of prostate cancer biomarkers for use with non-invasive liquid biopsies for the identification of aggressive tumors before surgery. This new study—“Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer”—published recently in Nature Communications advances the quest to develop a precise, non-invasive diagnostic tool that can address the global clinical dilemma of over-treating slow-growing, low-risk prostate cancers in men that may never need to be treated.
“We believe we have found a better way that allows us to predict which patients have a slow-growing versus aggressive prostate cancer using non-invasive biomarkers,” explained senior study author Thomas Kislinger, Ph.D., associate professor in the department of medical biophysics at the University of Toronto. “This could eventually help us personalize cancer treatment for these patients.”
The current gold standard for diagnosing prostate cancer is the use of the PSA test and needle biopsies. However, the latter technique may not detect hidden tumors or cancer that has already spread beyond the prostate gland.
“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,” noted lead study author Yunee Kim, Ph.D., who did much of the work as a doctoral candidate in Kislinger’s laboratory.
The researchers used urine samples containing prostatic secretions from 210 patients after they had undergone digital rectal examinations (DRE). The DRE is the standard clinical “first step” to determine the need for further diagnostic testing of the prostate.
“We used targeted proteomics to accurately quantify hundreds of proteins in urine samples (post-DRE) to identify liquid biopsy signatures,” Dr. Kislinger remarked. “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 included a second, independent cohort of 210 patients. Applying computational biology, we used the quantitative data from mass spectrometry to develop the fluid biomarkers for aggressive prostate cancer.”
“We applied machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis,” the authors wrote. “Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.”
Kislinger noted that the current research took four years and involved samples from 300 patients, but that his team is looking toward much larger cohorts in the near future. “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 prostate cancer.”
