Classification of prostate cancer using a protease activity nanosensor library
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8954.full.pdf
Description
Published version
Size
1.43 MB
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Unknown
Checksum (MD5)
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Author(s)
Dudani, Jaideep S
Ibrahim, Maria
Kirkpatrick, Jesse
Warren, Andrew D
Bhatia, Sangeeta N
Date Issued
August 2018
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Version
Final published version
Abstract
© 2018 National Academy of Sciences. All Rights Reserved. Improved biomarkers are needed for prostate cancer, as the current gold standards have poor predictive value. Tests for circulating prostate-specific antigen (PSA) levels are susceptible to various noncancer comorbidities in the prostate and do not provide prognostic information, whereas physical biopsies are invasive, must be performed repeatedly, and only sample a fraction of the prostate. Injectable biosensors may provide a new paradigm for prostate cancer biomarkers by querying the status of the prostate via a noninvasive readout. Proteases are an important class of enzymes that play a role in every hallmark of cancer; their activities could be leveraged as biomarkers. We identified a panel of prostate cancer proteases through transcriptomic and proteomic analysis. Using this panel, we developed a nanosensor library that measures protease activity in vitro using fluorescence and in vivo using urinary readouts. In xenograft mouse models, we applied this nanosensor library to classify aggressive prostate cancer and to select predictive substrates. Last, we coformulated a subset of nanosensors with integrin-targeting ligands to increase sensitivity. These targeted nanosensors robustly classified prostate cancer aggressiveness and outperformed PSA. This activity-based nanosensor library could be useful throughout clinical management of prostate cancer, with both diagnostic and prognostic utility.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Biological Engineering
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Howard Hughes Medical Institute
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/PNAS.1805337115
