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Only in the past decade tremendous advances have been made in understanding prostate cancer genomics and consequently in applying new treatment strategies. As options regarding treatments are increasing so are the challenges in selecting the right treatment option for each patient and not the least, understanding the optimal time-point and sequence of applying available treatments. Critically, without reliable methods that enable sequential monitoring of evolving genotypes in individual patients, we will never reach effective personalised driven treatment approaches. This review focuses on the clinical implications of prostate cancer genomics and the potential of cfDNA in facilitating treatment management., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs)., Methods: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection., Results: Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments., Conclusions: AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .

Androgen receptor (AR) gene aberrations are rare in prostate cancer before primary hormone treatment but emerge with castration resistance. To determine AR gene status using a minimally invasive assay that could have broad clinical utility, we developed a targeted next-generation sequencing approach amenable to plasma DNA, covering all AR coding bases and genomic regions that are highly informative in prostate cancer. We sequenced 274 plasma samples from 97 castration-resistant prostate cancer patients treated with abiraterone at two institutions. We controlled for normal DNA in patients' circulation and detected a sufficiently high tumor DNA fraction to quantify AR copy number state in 217 samples (80 patients). Detection of AR copy number gain and point mutations in plasma were inversely correlated, supported further by the enrichment of nonsynonymous versus synonymous mutations in AR copy number normal as opposed to AR gain samples. Whereas AR copy number was unchanged from before treatment to progression and no mutant AR alleles showed signal for acquired gain, we observed emergence of T878A or L702H AR amino acid changes in 13% of tumors at progression on abiraterone. Patients with AR gain or T878A or L702H before abiraterone (45%) were 4.9 and 7.8 times less likely to have a ≥50 or ≥90% decline in prostate-specific antigen (PSA), respectively, and had a significantly worse overall [hazard ratio (HR), 7.33; 95% confidence interval (CI), 3.51 to 15.34; P = 1.3 × 10(-9)) and progression-free (HR, 3.73; 95% CI, 2.17 to 6.41; P = 5.6 × 10(-7)) survival. Evaluation of plasma AR by next-generation sequencing could identify cancers with primary resistance to abiraterone., (Copyright © 2015, American Association for the Advancement of Science.)

Purpose: We evaluated whether next-generation sequencing (NGS) of circulating cell-free DNA (cfDNA) could be used for patient selection and as a tumor clone response biomarker in patients with advanced cancers participating in early-phase clinical trials of targeted drugs., Experimental Design: Plasma samples from patients with known tumor mutations who completed at least two courses of investigational targeted therapy were collected monthly, until disease progression. NGS was performed sequentially on the Ion Torrent PGM platform., Results: cfDNA was extracted from 39 patients with various tumor types. Treatments administered targeted mainly the PI3K-AKT-mTOR pathway (n = 28) or MEK (n = 7). Overall, 159 plasma samples were sequenced with a mean sequencing coverage achieved of 1,685X across experiments. At trial initiation (C1D1), 23 of 39 (59%) patients had at least one mutation identified in cfDNA (mean 2, range 1-5). Out of the 44 mutations identified at C1D1, TP53, PIK3CA and KRAS were the top 3 mutated genes identified, with 18 (41%), 9 (20%), 8 (18%) different mutations, respectively. Out of these 23 patients, 13 received a targeted drug matching their tumor profile. For the 23 patients with cfDNA mutation at C1D1, the monitoring of mutation allele frequency (AF) in consecutive plasma samples during treatment with targeted drugs demonstrated potential treatment associated clonal responses. Longitudinal monitoring of cfDNA samples with multiple mutations indicated the presence of separate clones behaving discordantly. Molecular changes at cfDNA mutation level were associated with time to disease progression by RECIST criteria., Conclusions: Targeted NGS of cfDNA has potential clinical utility to monitor the delivery of targeted therapies., (©2015 American Association for Cancer Research.)

Prostate cancer is very common in elderly men in developed countries. Unravelling the molecular and biological processes that contribute to tumor development and progressive growth, including its heterogeneity, is a challenging task. The fusion of the genes ERG and TMPRSS2 is the most frequent genomic alteration in prostate cancer. ERG is an oncogene that encodes a member of the family of ETS transcription factors. At lower frequency, other members of this gene family are also rearranged and overexpressed in prostate cancer. TMPRSS2 is an androgen-regulated gene that is preferentially expressed in the prostate. Most of the less frequent ETS fusion partners are also androgen-regulated and prostate-specific. During the last few years, novel concepts of the process of gene fusion have emerged, and initial experimental results explaining the function of the ETS genes ERG and ETV1 in prostate cancer have been published. In this review, we focus on the most relevant ETS gene fusions and summarize the current knowledge of the role of ETS transcription factors in prostate cancer. Finally, we discuss the clinical relevance of TMRPSS2-ERG and other ETS gene fusions in prostate cancer.

Background: The molecular basis of the clinical heterogeneity of prostate cancer (PCa) is not well understood., Objective: The purpose of our study was to identify and characterize genes in a clinically relevant gene expression signature in a subgroup of primary PCa positive for transmembrane protease, serine 2 (TMPRSS2)-v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG)., Design, Setting, and Participants: We studied gene expression profiles by unsupervised hierarchical clustering in 48 primary PCas from patients with a long clinical follow-up. Results were correlated with clinical outcome and validated in an independent patient cohort. Selected genes from a defined classifier were tested in vitro for biologic properties., Intervention: Initial treatment of primary tumors was radical prostatectomy., Outcome Measurements and Statistical Analysis: Associations between clinical and histopathologic variables were evaluated by the Pearson χ(2) test, Mann-Whitney U test, or Kruskal-Wallis test, where appropriate. The log-rank test or Breslow method was used for statistical analysis of Kaplan-Meier survival curves., Results and Limitations: Most tumors that overexpressed ERG clustered separately from other primary PCas. No differences in any clinical end points between ERG-positive and ERG-negative cancers were detected. Importantly, within the ERG-positive samples, two subgroups were identified, which differed significantly in prostate-specific antigen recurrence-free survival, and cancer-specific and overall survival. From our findings, we defined a gene expression classifier of 36 genes. In a second, completely independent tumor set, the classifier also distinguished ERG-positive subgroups with different clinical outcome. In both patient cohorts, the classifier was not predictive in ERG-negative tumors. Biologic processes regulated by genes in the classifier included cell adhesion and bone remodeling. Tumor growth factor-β signaling was indicated as the main differing signaling pathway between the two ERG subgroups. In vitro biologic assays of two selected genes from the classifier (inhibin, beta A [INHBA] and cadherin 11, type 2, OB-cadherin (osteoblast) [CDH11]) supported a functional role in PCa progression. Possible multifocality and limited number of PCa samples can be limitations of the study., Conclusions: The classifier identified can contribute to prediction of tumor progression in ERG-positive primary prostate tumors and might be instrumental in therapy decisions., (Copyright © 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.)