Publications

Publications

Comparing efficiency of micro-RNA and mRNA biomarker liberation with microbubble-enhanced ultrasound exposure.

By:
Contributors: Robert Paproski, PhD
Ultrasound Med Biol. 2014 Sep;40(9):2207-16. doi: 10.1016/j.ultrasmedbio.2014.05.005. Epub 2014 Jul 9.

Abstract

Blood biomarkers are potentially powerful diagnostic tools that are limited clinically by low concentrations, the inability to determine biomarker origin and unknown patient baseline. Recently, ultrasound has been shown to liberate proteins and large mRNA biomarkers, overcoming many of these limitations. We have since demonstrated that adding lipid-stabilized microbubbles elevates mRNA concentration an order of magnitude compared with ultrasound without microbubbles, in vitro. Unfortunately the large size of some mRNA molecules may limit efficiency of release and hinder efficacy as an ultrasound-liberated biomarker. We hypothesize that smaller molecules will be released more efficiently with ultrasound than larger molecules. Although investigation of large libraries of biomarkers should be performed to fully validate this hypothesis, we focus on a small subset of mRNA and micro-RNAs. Specifically, we focus on miR-21 (22 base pairs [bp]), which is upregulated in certain forms of cancer, compared with previously investigated mammaglobin mRNA (502 bp). We also report release of micro-RNA miR-155 (22 bp) and housekeeping rRNA S18 (1869 bp). More than 10 million additional miR-21 copies per 100,000 cells are released with ultrasound-microbubble exposure. The low- molecular-weight miR-21 proved to be liberated 50 times more efficiently than high-molecular-weight mammaglobin mRNA, releasing orders of magnitude more miR-21 than mammaglobin mRNA under comparable conditions.

 PubMed

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Russ Greiner’s Team “PC LEARN”, tied for 1st in the Prostate Cancer DREAM Challenge

Competing with 50 teams from around the world in the Prostate Cancer DREAM Challenge, University of Alberta’s PC LEARN team tied for 1st in one of the 3 sub-challenges
to predict the survival and toxicity of Docetaxel treatment in patients with metastatic castrate resistant prostate cancer!

“The DREAM Challenge was an exciting opportunity for us to apply machine learning to real medical data and possibly to contribute to medical research.” said lead PI and APCaRI member Russ Greiner.

The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. https://www.synapse.org/#!Synapse:syn2813558/wiki/70844

- Russ Greiner