Journal article 1051 views 57 downloads
Stroma‐derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer
Journal of Extracellular Vesicles, Volume: 10, Issue: 12
Swansea University Author:
Jason Webber
-
PDF | Version of Record
© 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License
Download (4.38MB)
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...
| 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 |
| 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 |