New developments in prostate cancer biomarkers

Martin, Neil E.

doi:10.1097/cco.0000000000000279

Cited by 14 publications

(14 citation statements)

References 45 publications

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“…Our nine-gene signature associates robustly with PC recurrence. Currently, there are 3 sets of mRNA-based signatures to evaluate PC progression: the 17-gene Oncotype DX (GPS) [34] , the 31-gene signature of CCP (cell cycle progression; Prolaris) [35] , and the 22-gene Decipher signature [5] , [6] , [7] , [36] . Adding to this resource is our novel nine-gene genomic signature.…”

Section: Discussionmentioning

confidence: 99%

“…The treatment is palliative, and patients eventually develop CRPC. Although the recent developments have resulted in three sets of mRNA-based signatures, Oncotype DX (Genomic Prostate Score/GPS), Prolaris, and Decipher (Genomic Classifier), that evaluate the risk of PC progression [5] , [6] , [7] , our ability to stratify PCs with high risk of progression remains poor. It is therefore critical to improve our understanding of factors contributing to PC recurrence, metastasis, and CRPC.…”

Section: Introductionmentioning

confidence: 99%

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Overexpression of MUC1 and Genomic Alterations in Its Network Associate with Prostate Cancer Progression

Lin

Kapoor

et al. 2017

Neoplasia

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We investigate the association of MUC1 with castration-resistant prostate cancer (CRPC), bone metastasis, and PC recurrence. MUC1 expression was studied in patient-derived bone metastasis and CRPCs produced by prostate-specific PTEN−/− mice and LNCaP xenografts. Elevations in MUC1 expression occur in CRPC. Among nine patients with hormone-naïve bone metastasis, eight express MUC1 in 61% to 100% of PC cells. Utilizing cBioPortal PC genomic data, we organized a training (n = 300), testing (n = 185), and validation (n = 194) cohort. Using the Cox model, a nine-gene signature was derived, including eight genes from a MUC1-related network (APC, CTNNB1/β-catenin, GALNT10, GRB2, LYN, SIGLEC1, SOS1, and ZAP70) and FAM84B. Genomic alterations in these genes reduce disease-free survival (DFS) in the training (P = .00161), testing (P = .00699), entire (training + testing, P = 5.557e-5), and a validation cohort (P = 3.326e-5). The signature independently predicts PC recurrence [hazard ratio (HR) = 1.731; 95% confidence interval (CI): 1.104-2.712; P = .0167] after adjusting for known clinical factors and stratifies patients with high risk of PC recurrence using the median (HR 2.072; 95% CI: 1.245-3.450, P = .0051) and quartile 3 (HR 3.707, 95% CI: 1.949-7.052, P = 6.51e-5) scores. Several novel β-catenin mutants are identified in PCs leading to a rapid onset of death and recurrence. Genomic alterations in APC and CTNNB1/β-catenin reduce DFS in two independent PC cohorts (n = 485, P = .0369; n = 84, P = .0437). The nine-gene signature also associates with reductions in overall survival (P = .0458) and DFS (P = .0163) in melanoma patients (n = 367). MUC1 upregulation is associated with CRPC and bone metastasis. A nine-gene signature derived from a MUC1 network predicts PC recurrence.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Overexpression of MUC1 and Genomic Alterations in Its Network Associate with Prostate Cancer Progression

Lin

Kapoor

et al. 2017

Neoplasia

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“…The 22‐gene Decipher predicts metastasis following RP (Erho et al ., 2013; Karnes et al ., 2013; Klein et al ., 2016). While these and other biomarkers assist decision making and thus improve patient management, their clinical application requires further validation (Lamy et al ., 2017; Martin, 2016; McGrath et al ., 2016; Patel and Gnanapragasam, 2016; Ross et al ., 2016; Zhuang and Johnson, 2016). There is a clear need to improve our ability to stratify PCs with high risk of recurrence following RP.…”

Section: Introductionmentioning

confidence: 99%

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

Jiang

Mei

et al. 2018

Molecular Oncology

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We report here numerous novel genes and multiple new signatures which robustly predict prostate cancer (PC) recurrence. We extracted 696 differentially expressed genes relative to a reported PC signature from the TCGA dataset (n = 492) and built a 15‐gene signature (SigMuc1NW) using Elastic‐net with 10‐fold cross‐validation through analyzing their expressions at 1.5 standard deviation/SD below and 2 SD above a population mean. SigMuc1NW predicts biochemical recurrence (BCR) following surgery with 56.4% sensitivity, 72.6% specificity, and 63.24 median months disease free (MMDF) (P = 1.12e‐12). The prediction accuracy is improved with the use of SigMuc1NW's cutpoint (P = 3e‐15) and is further enhanced (sensitivity 67%, specificity 75.7%, MMDF 45.2, P = 0) when all 15 genes were analyzed through their cutpoints instead of their SDs. These genes individually associate with BCR using either SD or cutpoint as the cutoff points. Eight of 15 genes are individual risk factors after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. Eleven of 15 genes are novel to PC. SigMuc1NW discriminates BCR with time‐dependent AUC (tAUC) values of 76.6% at 11.5 months (76.6%–11.5 m), 73.8%‐22.3 m, 78.5%‐32.1 m, and 76.4%–48.4 m. SigMuc1NW is correlated with adverse features of PC, high Gleason scores (odds ratio/OR 1.48, P < 2e‐16), and advanced tumor stages (OR 1.33, P = 4.37e‐13). SigMuc1NW remains an independent risk factor of BCR (HR 2.44, 95% CI 1.53–3.87, P = 1.62e‐4) after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. In an independent PC (MSKCC) cohort (n = 140), these 15 genes were altered in PC vs normal tissue, metastatic PCs vs primary PCs, and recurrent PCs vs nonrecurrent PCs. Importantly, a 10‐gene subsignature SigMuc1NW1 predicts BCR in MSKCC (P = 3.11e‐15) and TCGA (P = 3.13e‐12); SigMuc1NW1 discriminates BCR at 18.4 m with tAUC as 82.5%. Collectively, our analyses support SigMuc1NW as a novel and robust signature in predicting BCR of PC.

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“…However, there is a limited ability to distinguish lethal from indolent disease, resulting in many patients opting for intervention and risking side effects and over-treatment. To address this, several genomic, transcript and immunohistochemical biomarkers have been assessed for their abilities to identify progressive tumour subtypes and aid treatment choice between intervention and surveillance (Martin, 2016). There is considerable inter- and intra- tumour heterogeneity which limits the efficacy of predictive tests and most focus upon the ability to detect tumour subclones associated with progression, which may be present at low frequency in biopsy samples.…”

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confidence: 99%

Asporin is a stromally expressed marker associated with prostate cancer progression

et al. 2017

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Background:Prostate cancer shows considerable heterogeneity in disease progression and we propose that markers expressed in tumour stroma may be reliable predictors of aggressive tumour subtypes.Methods:We have used Kaplan–Meier, univariate and multivariate analysis to correlate the expression of Asporin (ASPN) mRNA and protein with prostate cancer progression in independent cohorts. We used immunohistochemistry and H scoring to document stromal localisation of ASPN in a tissue microarray and mouse prostate cancer model, and correlated expression with reactive stroma, defined using Masson Trichrome staining. We used cell cultures of primary prostate cancer fibroblasts treated with serum-free conditioned media from prostate cancer cell lines to examine regulation of ASPN mRNA in tumour stromal cells.Results:We observed increased expression of ASPN mRNA in a data set derived from benign vs tumour microdissected tissue, and a correlation with biochemical recurrence using Kaplan–Meier and Cox proportional hazard analysis. ASPN protein localised to tumour stroma and elevated expression of ASPN was correlated with decreased time to biochemical recurrence, in a cohort of 326 patients with a median follow up of 9.6 years. Univariate and multivariate analysis demonstrated that ASPN was correlated with progression, as were Gleason score, and clinical stage. Additionally, ASPN expression correlated with the presence of reactive stroma, suggesting that it may be a stromal marker expressed in response to the presence of tumour cells and particularly with aggressive tumour subtypes. We observed expression of ASPN in the stroma of tumours induced by p53 inhibition in a mouse model of prostate cancer, and correlation with neuroendocrine marker expression. Finally, we demonstrated that ASPN transcript expression in normal and cancer fibroblasts was regulated by conditioned media derived from the PC3, but not LNCaP, prostate cancer cell lines.Conclusions:Our results suggest that ASPN is a stromally expressed biomarker that correlates with disease progression, and is observed in reactive stroma. ASPN expression in stroma may be part of a stromal response to aggressive tumour subtypes.

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New developments in prostate cancer biomarkers

Cited by 14 publications

References 45 publications

Overexpression of MUC1 and Genomic Alterations in Its Network Associate with Prostate Cancer Progression

Overexpression of MUC1 and Genomic Alterations in Its Network Associate with Prostate Cancer Progression

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

Asporin is a stromally expressed marker associated with prostate cancer progression

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