Stroma-derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer

Shephard, Alex P., Giles, Peter ORCID: https://orcid.org/0000-0003-3143-6854, Mbengue, Mariama, Alraies, Amr, Spary, Lisa K., Kynaston, Howard ORCID: https://orcid.org/0000-0003-1902-9930, Gurney, Mark J. ORCID: https://orcid.org/0000-0003-1119-6638, Falcón-Pérez, Juan M., Royo, Félix, Tabi, Zsuzsanna, Parthimos, Dimitris, Errington, Rachel J., Clayton, Aled and Webber, Jason P. 2021. Stroma-derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer. Journal of Extracellular Vesicles 10 (12) , e12150. 10.1002/jev2.12150

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Abstract

Histological assessment of prostate cancer is the key diagnostic test and can predict disease outcome. This is however an invasive procedure that carries associated risks, hence non-invasive assays to support the diagnostic pathway are much needed. A key feature of disease progression, and subsequent poor prognosis, is the presence of an altered stroma. Here we explored the utility of prostate stromal cell-derived vesicles as indicators of an altered tumour environment. We compared vesicles from six donor-matched pairs of adjacent-normal versus disease-associated primary stromal cultures. We identified 19 differentially expressed transcripts that discriminate disease from normal stromal extracellular vesicles (EVs). EVs isolated from patient serum were investigated for these putative disease-discriminating mRNA. A set of transcripts including Caveolin-1 (CAV1), TMP2, THBS1, and CTGF were found to be successful in discriminating clinically insignificant (Gleason = 6) disease from clinically significant (Gleason > 8) prostate cancer. Furthermore, correlation between transcript expression and progression-free survival suggests that levels of these mRNA may predict disease outcome. Informed by a machine learning approach, combining measures of the five most informative EV-associated mRNAs with PSA was shown to significantly improve assay sensitivity and specificity. An in-silico model was produced, showcasing the superiority of this multi-modal liquid biopsy compared to needle biopsy for predicting disease progression. This proof of concept highlights the utility of serum EV analytics as a companion diagnostic test with prognostic utility, which may obviate the need for biopsy.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Medicine
Additional Information:	This is an open access article under the terms of the Creative Commons Attribution License
Publisher:	Wiley Open Access
ISSN:	2001-3078
Date of First Compliant Deposit:	1 November 2021
Date of Acceptance:	13 September 2021
Last Modified:	23 Jul 2024 16:10
URI:	https://orca.cardiff.ac.uk/id/eprint/145118

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