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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
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last_indexed 2021-11-19T04:24:57Z
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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&gt;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. 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spelling v2 57984 2021-09-20 Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer 25d1a26f9b8bb556bd9412080e40351d 0000-0003-4772-3014 Jason Webber Jason Webber true false 2021-09-20 BMS 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. Journal Article Journal of Extracellular Vesicles 10 12 Wiley 2001-3078 2001-3078 biomarker; extracellular vesicles; prostate cancer; RNA; stroma 1 10 2021 2021-10-01 10.1002/jev2.12150 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University Prostate Cancer UK. Grant Number: CDF13-001; Cancer Research Wales; H2020 Marie Skłodowska-Curie Actions (Initial Training Network proEVLifeCycle). Grant Number: 860303 2024-01-19T12:19:18.7008390 2021-09-20T15:29:53.2083397 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Alex P. Shephard 1 Peter Giles 2 Mariama Mbengue 3 Amr Alraies 4 Lisa K. Spary 5 Howard Kynaston 6 Mark J. Gurney 7 Juan M. Falcón‐Pérez 8 Félix Royo 9 Zsuzsanna Tabi 10 Dimitris Parthimos 11 Rachel J. Errington 12 Aled Clayton 13 Jason Webber 0000-0003-4772-3014 14 57984__21248__9836f0c7e0314c80ac02b212b1cfdb5f.pdf 57984.pdf 2021-10-20T16:17:12.8279293 Output 4588406 application/pdf Version of Record true © 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License true eng http://creativecommons.org/licenses/by/4.0/
title Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
spellingShingle Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
Jason Webber
title_short Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
title_full Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
title_fullStr Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
title_full_unstemmed Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
title_sort Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
author_id_str_mv 25d1a26f9b8bb556bd9412080e40351d
author_id_fullname_str_mv 25d1a26f9b8bb556bd9412080e40351d_***_Jason Webber
author Jason Webber
author2 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
format Journal article
container_title Journal of Extracellular Vesicles
container_volume 10
container_issue 12
publishDate 2021
institution Swansea University
issn 2001-3078
2001-3078
doi_str_mv 10.1002/jev2.12150
publisher Wiley
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
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description 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.
published_date 2021-10-01T12:19:17Z
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