ABSTRACT
Prostate tumours are highly variable in their response to therapies, but clinically available prognostic factors can explain only a fraction of this heterogeneity. Here we analysed 200 whole-genome sequences and 277 additional whole-exome sequences from localized, non-indolent prostate tumours with similar clinical risk profiles, and carried out RNA and methylation analyses in a subset. These tumours had a paucity of clinically actionable single nucleotide variants, unlike those seen in metastatic disease. Rather, a significant proportion of tumours harboured recurrent non-coding aberrations, large-scale genomic rearrangements, and alterations in which an inversion repressed transcription within its boundaries. Local hypermutation events were frequent, and correlated with specific genomic profiles. Numerous molecular aberrations were prognostic for disease recurrence, including several DNA methylation events, and a signature comprised of these aberrations outperformed well-described prognostic biomarkers. We suggest that intensified treatment of genomically aggressive localized prostate cancer may improve cure rates.
Subject(s)
Genome, Human/genetics , Genomics , Mutation , Prostatic Neoplasms/genetics , Prostatic Neoplasms/pathology , Chromothripsis , DNA Copy Number Variations , DNA Methylation , Exome/genetics , Humans , Male , Neoplasm Metastasis/genetics , Prognosis , Prostatic Neoplasms, Castration-Resistant/genetics , Prostatic Neoplasms, Castration-Resistant/pathology , RecurrenceABSTRACT
BACKGROUND: Quantitative real-time PCR (qPCR) is the "gold-standard" technique for measuring mRNA abundances. To correctly compare samples and generate biologically valid results, qPCR data usually require comprehensive normalization to account for sample content variation between reactions. The most common normalization approaches use one or more endogenous controls (reference or house-keeping genes) to adjust the measured levels of experimental genes appropriately. Ideal reference genes are those that display minimal variation across experimental conditions, and thus can vary widely across different biological systems. In particular, toxicogenomic studies of transcriptionally-disruptive toxins, like 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), require careful consideration of reference genes. RESULTS: We examined seven candidate reference genes in 199 mice varying in genotype and time/dose of TCDD exposure. We assessed gene-stability in four ways: (1) the variance of the raw Cq values across biological replicates, (2) the fold-change from basal mRNA levels following treatment, (3) the inter- and intra-group stability evaluated using the NormFinder algorithm, (4) the comparative ΔCq method for each candidate gene. Univariate analyses showed Hprt and Eef1a1 are the two most stable individual reference genes. It has been suggested that using multiple genes would produce a more consistent normalization factor; multivariate analysis was performed using NormFinder. In general, stability increased with the number of genes used, but specific gene-combinations synergized. CONCLUSIONS: We have validated seven reference genes for use in analyzing mRNA abundances in mouse models of TCDD toxicity. The use of multiple reference genes increases stability, providing more consistent normalization and more reliable results. The number of reference genes used should be maximized, based on experimental capabilities (platform, sample availability, etc.). Our results show the benefit of validating reference genes using multiple methods prior to generating large biological datasets.