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A methylated DNA classifier to identify prostate cancer with over 90% sensitivity and specificity

Background

High prevalence and low risk of progression have made it difficult to effectively screen and manage prostate cancer. Currently, elevated serum prostate‑specific antigen (PSA) testing is the gold standard screening tool for prostate cancer. Men with a positive PSA test undergo an invasive prostate biopsy to obtain a definitive diagnosis. However, PSA testing has a false positive rate of over 75% and a positive predictive value of approximately 25%.

In the United States alone, 4.7 million men are identified each year as having elevated PSA levels. Of these men, 1.3 million are recommended to undergo biopsy for prostate cancer, resulting in an estimated annual cost of $2.5 billion. Approximately two-thirds of men undergoing prostate biopsy have no tumor diagnosed due to the high false positive rates of serum PSA. These unnecessary biopsies come at a substantial cost to the healthcare system while also causing anxiety and trauma in patients. This invasive procedure exposes men to the risk of serious complications, including bleeding, infection, urinary incontinence, and erectile dysfunction.

Thus there is a significant need for a more accurate noninvasive biomarker to improve diagnosis and reduce the number of unnecessary biopsies for prostate cancer. As a result, numerous studies have been conducted to investigate the use of messenger RNA, microRNA, genetic mutations, and prostate‑specific proteins as potential biomarkers. However, prostate cancers typically do not possess many informative biomarkers in these categories.

Instead, aberrant DNA methylation may be a superior substrate for biomarker discovery in early prostate cancer. In contrast to the above cancer‑specific DNA methylation alterations are highly prevalent in prostate cancer. While research in this area is growing, most research efforts to date have focused broadly on describing the overall epigenetic landscape of prostate cancer. Few studies have validated their results or focused on making methylation‑based classifiers for clinically important outcomes.

Technology Overview

Queen’s researchers used real‑time methylation‑specific PCR (qMSP) assays targeting 15 DNA methylation alterations which had been validated in multiple previous reports. Each locus was tested individually and in combination with other loci to determine its accuracy as part of a compact multigene biomarker, as multigene biomarkers are superior to single‑gene tests.

The GAS6/GSTP1/HAPLN3 was the best multigene model, showing sensitivity of 94% and specificity of 93% in the validation dataset¹ (Figure 1). The three‑gene model also showed a significant improvement over univariate approaches, as none of the univariate classifiers had both sensitivity and specificity of above 90%.