Asporin is a stromally expressed marker associated with prostate cancer progression.

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.British Journal of Cancer advance online publication, 2 February 2017; doi:10.1038/bjc.2017.15 www.bjcancer.com

Multimodal Biomarkers That Predict the Presence of Gleason Pattern 4: Potential Impact for Active Surveillance

AbstractPurpose:Latent grade group ≥2 prostate cancer can impact the performance of active surveillance protocols. To date, molecular biomarkers for active surveillance have relied solely on RNA or protein. We trained and independently validated multimodal (mRNA abundance, DNA methylation, and/or DNA copy number) biomarkers that more accurately separate grade group 1 from grade group ≥2 cancers.Materials and Methods:Low- and intermediate-risk prostate cancer patients were assigned to training (n=333) and validation (n=202) cohorts. We profiled the abundance of 342 mRNAs, 100 DNA copy number alteration loci, and 14 hypermethylation sites at 2 locations per tumor. Using the training cohort with cross-validation, we evaluated methods for training classifiers of pathological grade group ≥2 in centrally reviewed radical prostatectomies. We trained 2 distinct classifiers, PRONTO-e and PRONTO-m, and validated them in an independent radical prostatectomy cohort.Results:PRONTO-e comprises 353 mRNA and copy number alteration features. PRONTO-m includes 94 clinical, mRNAs, copy number alterations, and methylation features at 14 and 12 loci, respectively. In independent validation, PRONTO-e and PRONTO-m predicted grade group ≥2 with respective true-positive rates of 0.81 and 0.76, and false-positive rates of 0.43 and 0.26. Both classifiers were resistant to sampling error and identified more upgrading cases than a well-validated presurgical risk calculator, CAPRA (Cancer of the Prostate Risk Assessment; P < .001).Conclusions:Two grade group classifiers with superior accuracy were developed by incorporating RNA and DNA features and validated in an independent cohort. Upon further validation in biopsy samples, classifiers with these performance characteristics could refine selection of men for active surveillance, extending their treatment-free survival and intervals between surveillance.Active surveillance (AS) is recommended for men with low- and favorable intermediate–risk prostate cancer.1 Compared to AS for low-risk men, AS for intermediate-risk men would likely benefit from more intensive surveillance to stave off disease progression. Despite increased use of advanced imaging tools, risk calculators, and molecular biomarkers, a third or more of men initially classified as low risk actually have intermediate or higher risk, heralded by subsequent detection of occult Gleason pattern 4.2,3 Strategies to identify such men have limited accuracy. They include attention to traditional risk factors such as age, tumor size and extent, and PSA level, measured by tests such as digital rectal examination, multiparametric (mp) MRI, and biopsy and blood analyses. Despite its increasing use in prostate cancer risk assessment, expert prostate mpMRI is a limited resource with low (circa 59%) sensitivity for intermediate-risk cases.4 A biomarker that more accurately distinguishes between grade group (GG) 1 and GG ≥2 could be helpful in deintensifying AS for men with truly low-risk cancers.Several commercially available and guideline-approved tests use gene (mRNA or protein) expression levels in prostate cancer biopsies to detect adverse pathology (AP; ie, GG ≥3 or nonorgan-confined disease) in the subsequent prostatectomy. However, no existing molecular test has been adopted in current guidelines as standard of care to distinguish between GG1 and GG ≥2 cancers.1,5,6 Despite indications that such tests could be useful,6,7 uptake has been limited, perhaps because of low accuracy, which in turn may derive from limitations in the number and types of molecular features included in each test. Since cardinal molecular features of early prostate carcinogenesis include not only altered gene expression but also DNA methylation events and copy number alterations (CNAs),8-10 we hypothesized that tests combining these features could provide superior performance in separating low-grade (GG1) cancers from their higher-grade (GG ≥2) counterparts.The personalized risk stratification for patients with early prostate cancer (PRONTO) program is a pan-Canadian effort that aims to develop a GG classifier to stratify risk in prostate cancer and achieve technical and clinical validation in statistically powered cohorts. Here, we report the development of 2 candidate classifiers comprising different types of molecular features. These classifiers, developed and independently validated, achieve superior performance by integrating tumor mRNA abundance, DNA copy number, and/or DNA methylation profiles. We demonstrate that these classifiers could add value above and beyond routinely captured clinical data and are remarkably resistant to sampling error. We discuss how adoption of classifiers with these attributes has the potential to improve current AS approaches without increasing patient morbidity. By identifying men at increased risk of occult GG ≥2 cancer, surveillance biopsies could be taken earlier to confirm the presence and extent of Gleason pattern 4 cancer. By confirming GG1 cancers, such biomarkers could identify men for whom it would be safe to forgo MRI or increase the intervals between surveillance biopsies, reducing burdens on health care systems and patients

Genome-wide analysis of AR binding and comparison with transcript expression in primary human fetal prostate fibroblasts and cancer associated fibroblasts.

The androgen receptor (AR) is a transcription factor, and key regulator of prostate development and cancer, which has discrete functions in stromal versus epithelial cells. AR expressed in mesenchyme is necessary and sufficient for prostate development while loss of stromal AR is predictive of prostate cancer progression. Many studies have characterized genome-wide binding of AR in prostate tumour cells but none have used primary mesenchyme or stroma. We applied ChIPseq to identify genomic AR binding sites in primary human fetal prostate fibroblasts and patient derived cancer associated fibroblasts, as well as the WPMY1 cell line overexpressing AR. We identified AR binding sites that were specific to fetal prostate fibroblasts (7534), cancer fibroblasts (629), WPMY1-AR (2561) as well as those common among all (783). Primary fibroblasts had a distinct AR binding profile versus prostate cancer cell lines and tissue, and showed a localisation to gene promoter binding sites 1 kb upstream of the transcriptional start site, as well as non-classical AR binding sequence motifs. We used RNAseq to define transcribed genes associated with AR binding sites and derived cistromes for embryonic and cancer fibroblasts as well as a cistrome common to both. These were compared to several in vivo ChIPseq and transcript expression datasets; which identified subsets of AR targets that were expressed in vivo and regulated by androgens. This analysis enabled us to deconvolute stromal AR targets active in stroma within tumour samples. Taken together, our data suggest that the AR shows significantly different genomic binding site locations in primary prostate fibroblasts compared to that observed in tumour cells. Validation of our AR binding site data with transcript expression in vitro and in vivo suggests that the AR target genes we have identified in primary fibroblasts may contribute to clinically significant and biologically important AR-regulated changes in prostate tissue.</p

BRCA1 can stimulate gene transcription by a unique mechanism

Most familial breast and ovarian cancers have been linked to mutations in the BRCA1 gene. BRCA1 has been shown to affect gene transcription but how it does so remains elusive. Here we show that BRCA1 can stimulate transcription without the requirement for a DNA-tethering function in mammalian and yeast cells. Furthermore, the BRCA1 C-terminal region can stimulate transcription of the p53-responsive promoter, MDM2. Unlike many enhancer-specific activators, non-tethered BRCA1 does not require a functional TATA element to stimulate transcription. Our results suggest that BRCA1 can enhance transcription by a function additional to recruiting the transcriptional machinery to a targeted gene

Significance of coprophagy for the fatty acid profile in body tissues of rabbits fed different diets

Four groups of eight New Zealand hybrid rab- bits were fattened with ad libitum access to the following pelleted experimental diets: ryegrass meal or alfalfa meal fed either alone or with oats meal in a ratio of 1:1. After 25 weeks they were slaughtered and dissected. Fatty acid (FA) profiles of caecotrophs (re-ingested fermentation products of the caecum), perirenal adipose tissue and intramuscular fat in the Musculus quadriceps were deter- mined. With high proportions of branched-chain FA (BFA) and trans FA, and increased proportions of saturated FA relative to the diets, the caecotroph FA profile showed a clear fingerprint of anaerobe microbial lipid metabolism including biohydrogenation. By contrast, the FA profiles of adipose and lean tissue comprised high proportions of polyunsaturated FA (PUFA), whilst BFA and trans FA occurred in much lower proportions compared to the ca- ecotrophs. Thus, coprophagy did not substantially modify the FA composition of the tissues investigated. Use of forage-only diets, compared to the oats supplemented diets, led to extraordinary high proportions of n-3 PUFA (including 18:3 and long-chain n-3) in the fat of adipose (21.3 vs. 6.7%) and lean tissue (15.4 vs. 5.7%). The forage type diet (grass vs. alfalfa) had smaller effects on the FA profiles. Indications of diet effects on endogenous desatu- ration, chain elongation and differential distribution offunctional FA between the two tissues investigated were found