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

Alex P. Shephard, Peter Giles, Mariama Mbengue, Amr Alraies, Lisa K. Spary, Howard Kynaston, Mark J. Gurney, Juan M. Falcón‐Pérez, Félix Royo, Zsuzsanna Tabi, Dimitris Parthimos, Rachel J. Errington, Aled Clayton, Jason Webber Orcid Logo

Journal of Extracellular Vesicles, Volume: 10, Issue: 12

Swansea University Author: Jason Webber Orcid Logo

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DOI (Published version): 10.1002/jev2.12150

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 subsequen...

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Published in: Journal of Extracellular Vesicles
ISSN: 2001-3078 2001-3078
Published: Wiley 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57984
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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 vs disease-associated primary stromal cultures. We identified 19 differentially expressed transcripts that discriminate disease from normal stromal EVs. EVs isolated from patient serum were investigated for these putative disease-discriminating mRNA. A set of transcripts including 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 5 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.
Keywords: biomarker; extracellular vesicles; prostate cancer; RNA; stroma
College: Faculty of Medicine, Health and Life Sciences
Funders: Prostate Cancer UK. Grant Number: CDF13-001; Cancer Research Wales; H2020 Marie Skłodowska-Curie Actions (Initial Training Network proEVLifeCycle). Grant Number: 860303
Issue: 12