Prostate cancer is the most common cancer among men in the United States (1). Its prevalence and categorization challenges mean that there is a growing need for more rapid and accurate tests to improve diagnosis. Although many researchers have turned to ctDNA to track cancer progression, it lacks the ability to trace transcriptomic changes specific to the evolution of prostate cancer. Now, researchers from Cedars-Sinai Medical Center have created a scoring assay using nanotechnology to quantify disease-relevant mRNAs and more accurately establish which disease stage (2).
The EV Digital Scoring Assay isolates extracellular vesicles (EV) from blood samples to determine a patient’s prostate cancer status. EVs, which are shed by most cells, contain genetic materials including miRNA, mRNA, and fragments of DNA – making them versatile and promising candidates for metastasis detection. The assay device is composed of an “EV Click Chip” – a system that purifies extracellular vesicles for analysis – and a digital PCR that quantifies a panel of 11 mRNA markers relevant to prostate cancer. Metastasis scores were then calculated from the expression of the 11 genes.
The device was used on a sample of 40 prostate cancer patients and exhibited the ability to distinguish metastatic from localized prostate cancer with greater sensitivity and rapidity than alternative ultracentrifugation methods. The assay also successfully detected micrometastases that current imaging modalities could not identify.
Concluding that the EV Digital Scoring Assay successfully used EVs to characterize gene expression, the researchers expressed their hope that it will one day be used alongside prostate-specific antigen tests to offer greater insight into disease progression. The assay also holds promise for determining individualized treatment methods for prostate cancer patients and helping them avoid unnecessary treatments.
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References
- CDC, “Prostate Cancer Statistics” (2022). Available at: http://bit.ly/3EvTyLf.
- J Wang et al., Nano Today, 48, 101746 (2023). PMID: 36711067.