The potential of tumour microenvironment markers to stratify the risk of recurrence in prostate cancer patients.
Thomas GevaertYves-Rémi Van EyckeThomas Vanden BroeckHein Van PoppelIsabelle SalmonSandrine RoriveTim MuilwijkFrank ClaessensDirk De RidderSteven JoniauChristine DecaesteckerPublished in: PloS one (2020)
The tumour micro-environment (TME) plays a crucial role in the onset and progression of prostate cancer (PCa). Here we studied the potential of a selected panel of TME-markers to predict clinical recurrence (CLR) in PCa. Patient cohorts were matched for the presence or absence of CLR 5 years post-prostatectomy. Tissue micro-arrays (TMA) were composed with both prostate non-tumour (PNT) and PCa tissue and subsequently processed for immunohistochemistry (IHC). The IHC panel included markers for cancer activated fibroblasts (CAFs), blood vessels and steroid hormone receptors ((SHR): androgen receptor (AR), progesterone receptor (PR) and estrogen receptor (ER)). Stained slides were digitalised, selectively annotated and analysed for percentage of marker expression with standardized and validated image analysis algorithms. A univariable analysis identified several TME markers with significant impact on CR: expression of CD31 (vascular marker) in PNT stroma, expression of alpha smooth muscle actin (αSMA) in PCa stroma, and PR expression ratio between PCa stroma and PNT stroma. A multivariable model, which included CD31 expression (vascular marker) in PNT stroma and PR expression ratio between PCa stroma and PNT stroma, could significantly stratify patients for CLR, with the identification of a low risk and high-risk subgroup. If validated and confirmed in an independent prospective series, this subgroup might have clinical potential for PCa patient stratification.
Keyphrases
- poor prognosis
- prostate cancer
- estrogen receptor
- binding protein
- smooth muscle
- stem cells
- chronic kidney disease
- machine learning
- long non coding rna
- end stage renal disease
- case report
- benign prostatic hyperplasia
- climate change
- phase iii
- ejection fraction
- extracellular matrix
- breast cancer cells
- robot assisted
- minimally invasive
- data analysis
- high density
- endoplasmic reticulum
- bioinformatics analysis