Classification of prostate cancer using a protease activity nanosensor library
© 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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Proceedings of the National Academy of Sciences
2021
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| Online Access: | https://hdl.handle.net/1721.1/135831 |
| _version_ | 1826201080617238528 |
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| author | Dudani, Jaideep S Ibrahim, Maria Kirkpatrick, Jesse Warren, Andrew D Bhatia, Sangeeta N |
| author_facet | Dudani, Jaideep S Ibrahim, Maria Kirkpatrick, Jesse Warren, Andrew D Bhatia, Sangeeta N |
| author_sort | Dudani, Jaideep S |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T11:46:00Z |
| format | Article |
| id | mit-1721.1/135831 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:46:00Z |
| publishDate | 2021 |
| publisher | Proceedings of the National Academy of Sciences |
| record_format | dspace |
| spelling | mit-1721.1/1358312021-10-28T04:11:33Z Classification of prostate cancer using a protease activity nanosensor library Dudani, Jaideep S Ibrahim, Maria Kirkpatrick, Jesse Warren, Andrew D Bhatia, Sangeeta N © 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. 2021-10-27T20:29:32Z 2021-10-27T20:29:32Z 2018 2019-09-16T13:50:37Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135831 en 10.1073/PNAS.1805337115 Proceedings of the National Academy of Sciences of the United States of America 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. application/pdf Proceedings of the National Academy of Sciences PNAS |
| spellingShingle | Dudani, Jaideep S Ibrahim, Maria Kirkpatrick, Jesse Warren, Andrew D Bhatia, Sangeeta N Classification of prostate cancer using a protease activity nanosensor library |
| title | Classification of prostate cancer using a protease activity nanosensor library |
| title_full | Classification of prostate cancer using a protease activity nanosensor library |
| title_fullStr | Classification of prostate cancer using a protease activity nanosensor library |
| title_full_unstemmed | Classification of prostate cancer using a protease activity nanosensor library |
| title_short | Classification of prostate cancer using a protease activity nanosensor library |
| title_sort | classification of prostate cancer using a protease activity nanosensor library |
| url | https://hdl.handle.net/1721.1/135831 |
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