Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

Mo, Fan; Lin, Dong; Takhar, Mandeep; Ramnarine, Varune Rohan; Dong, Xin; Bell, Robert H.; Volik, Stanislav V.; Wang, Kendric; Xue, Hui; Wang, Yuwei; Haegert, Anne; Anderson, Shawn; Brahmbhatt, Sonal; Erho, Nicholas; Wang, Xinya; Gout, Peter W.; Morris, James; Karnes, R. Jeffrey; Den, Robert B.; Klein, Eric A.; Schaeffer, Edward M.; Ross, Ashley E.; Ren, Shancheng; Şahinalp, S. Cenk; Li, Yingrui; Xu, Xun; Wang, Jun; Wang, Jian; Gleave, Martin; Davicioni, Elai; Sun, Yinghao; Wang, Yuzhuo; Collins, Colin C.

doi:10.1016/j.eururo.2017.02.038

Cited by 64 publications

(62 citation statements)

References 31 publications

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“…Our data expand beyond previous evidence suggesting the clinical significance of stroma in PCa. Our results of fibroblast‐rich stroma predicting poor survival in hormone‐naive PCa is in line with Mo et al who showed that high VIM gene expression predicts metastatic potential in primary PCa. Moreover, we show that fibroblast‐rich stroma associated with CRPC, an observation supported by Thalmann et al who showed that human cancer‐associated fibroblasts may confer castration resistance of LNCaP cell line in vivo, suggesting that fibroblasts may have an active role in the development of CRPC.…”

Section: Discussionsupporting

confidence: 92%

“…Olumi et al described a dramatic tumor‐promoting effect of carcinoma‐associated fibroblasts in PCa, and tumor stroma has been shown to be associated with aggressive disease features and shorter time to biochemical recurrence using needle biopsy as well as prostatectomy samples . Moreover, stromal gene expression analysis in prostatectomy samples revealed signatures specific for metastatic potential of PCa …”

Section: Introductionmentioning

confidence: 99%

“…11,12 Moreover, stromal gene expression analysis in prostatectomy samples revealed signatures specific for metastatic potential of PCa. 13 Vimentin (VIM, VIM) and alpha-smooth muscle actin (aSMA, ACTA2) have been widely used for the classification of the different stromal cell types. VIM is expressed in fibroblasts and myofibroblasts, whereas aSMA is expressed in smooth muscle and in myofibroblasts.…”

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confidence: 99%

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Fibroblast as a critical stromal cell type determining prognosis in prostate cancer

et al. 2019

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Background Tumor stroma associates with prostate cancer (PCa) progression, but its specific cellular composition and association to patient survival outcome have not been characterized. Methods We analyzed stromal composition in human PCa using multiplex immunohistochemistry and quantitative, high‐resolution image analysis in two retrospective, formalin‐fixed paraffin embedded observational clinical cohorts (Cohort I, n = 117; Cohort II, n = 340) using PCa‐specific mortality as outcome measurement. Results A high proportion of fibroblasts associated with aggressive disease and castration‐resistant prostate cancer (CRPC). In a multivariate analysis, increase in fibroblast proportion predicted poor cancer‐specific outcome independently in the two clinical cohorts studied. Conclusions Fibroblasts were the most important cell type in determining prognosis in PCa and associated with CRPC. Thus, the stromal composition could be critically important in developing diagnostic and therapeutic approaches to aggressive prostate cancer.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Fibroblast as a critical stromal cell type determining prognosis in prostate cancer

et al. 2019

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“…We evaluate performance of DGS against five competitor methods: (Cuzick et al, 2011), (Penney et al, 2011), (Erho et al, 2013), (Mo et al, 2017), and (Tyekucheva et al, 2017). Area Under the receiver operating characteristic Curve (AUC), with the Delong method (DeLong et al, 1988;Robin et al, 2011) for confidence interval calculation, was used to compare the genomic signature's ability to predict metastasis in five validation datasets.…”

Section: Clinical and Biological Evaluationmentioning

confidence: 99%

“…Erho et al (2013) identified a gene expression based signature to predict early metastasis following radical prostatectomy using a random forest. Moreover, it was recently demonstrated that gene expression signatures derived from the stroma or microenvironment of the primary tumor are predictive of the metastatic progression of PCa as well (Mo et al, 2017;Tyekucheva et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Deep Genomic Signature for early metastasis prediction in prostate cancer

Sharifi-Noghabi

Liu

Erho

et al. 2018

Preprint

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Motivation: For Prostate Cancer (PCa) patients, timing and intensity of the therapy is adjusted based on their prognosis. This can be predicted from clinical/pathological information and, recently, gene expression signatures. One major challenge in developing such signatures is that all of them are based on cohorts which have limited number of patients with complete clinical outcomes (labelled), especially for slow progressing cancers such as PCa. This poses a challenge to the model development in conjunction with high dimensionality of the transcriptomic data. Results: In this study we aim to exploit the previously untapped potential of a large cohort (n=15,136) with genomic data but no clinical outcome (unlabelled), to improve the performance of the genomic classifiers for predicting PCa metastasis. We propose, Deep Genomic Signature (DGS), based on Denoising Auto-Encoder (DAE) for feature extraction and selection. In order to capture information from the unlabelled and labelled data we train two DAEs separately and apply transfer learning to bridge the gap between them. We show that DGS captures information from these cohorts that can be utilized to build a logistic regression model to predict metastasis. Results on five validation cohorts indicate that this classifier, which is based on high weight genes in the DAEs, outperforms state-of-the-art signatures for metastatic PCa in terms of prediction accuracy. Survival analysis demonstrate the clinical utility of our signature which adds information to the well-established clinical factors and state-of-the-art signatures. Furthermore, pathway analysis reveals that the signature discovered by our DGS captures the hallmarks of PCa metastasis. Availability of the implemented codes and supplementary materials: https://github.com/hosseinshn/Deep-Genomic-Signature

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

Shephard

Giles

Mbengue

et al. 2021

J of Extracellular Vesicle

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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.

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Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

Cited by 64 publications

References 31 publications

Fibroblast as a critical stromal cell type determining prognosis in prostate cancer

Fibroblast as a critical stromal cell type determining prognosis in prostate cancer

Deep Genomic Signature for early metastasis prediction in prostate cancer

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

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