Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting.

Epigenetic markers in circulating cell-free DNA as prognostic markers for survival of castration-resistant prostate cancer patients.

BACKGROUND: Noninvasive biomarkers to guide personalized treatment for castration-resistant prostate cancer (CRPC) are needed. In this study, we analyzed hypermethylation patterns of two genes (GSTP1 and APC) in plasma cell-free DNA (cfDNA) of CRPC patients. The aim of this study was to analyze the cfDNA concentrations and levels of the epigenetic markers and to assess the value of these biomarkers for prognosis. METHODS: In this prospective study, patients were included before starting new treatment after developing CRPC. The blood samples were collected prior to start of the treatment and at three time points thereafter. cfDNA was extracted from 1.5 mL of plasma and before performing a methylation-specific PCR, bisulfate modification was carried out. RESULTS: The median levels of cfDNA, GSTP1, and APC copies in the baseline samples of CRPC patients (n = 47) were higher than in controls (n = 30). In the survival analysis, the group with baseline marker levels below median had significant less PCa-related deaths (P-values <0.02) and did not reach the median survival point. The survival distributions for the groups were statistically significant for the cfDNA concentration, GSTP1 and APC copies, as well as PSA combined with GSTP1 + APC (P-values <0.03). Furthermore, there were strong positive correlations between PSA and marker response after starting treatment (P-values <0.04). CONCLUSIONS: In conclusion, this study showed the kinetics of methylated cfDNA (GSTP1 and APC) in plasma of CRPC patients after starting treatment. Furthermore, the value of the markers before treatment is prognostic for overall survival. These results are promising for developing a test to guide treatment-decision-making for CRPC patients.

Molecular diagnostics for congenital hearing loss including 15 deafness genes using a next generation sequencing platform.

BACKGROUND: Hereditary hearing loss (HL) can originate from mutations in one of many genes involved in the complex process of hearing. Identification of the genetic defects in patients is currently labor intensive and expensive. While screening with Sanger sequencing for GJB2 mutations is common, this is not the case for the other known deafness genes (> 60). Next generation sequencing technology (NGS) has the potential to be much more cost efficient. Published methods mainly use hybridization based target enrichment procedures that are time saving and efficient, but lead to loss in sensitivity. In this study we used a semi-automated PCR amplification and NGS in order to combine high sensitivity, speed and cost efficiency. RESULTS: In this proof of concept study, we screened 15 autosomal recessive deafness genes in 5 patients with congenital genetic deafness. 646 specific primer pairs for all exons and most of the UTR of the 15 selected genes were designed using primerXL. Using patient specific identifiers, all amplicons were pooled and analyzed using the Roche 454 NGS technology. Three of these patients are members of families in which a region of interest has previously been characterized by linkage studies. In these, we were able to identify two new mutations in CDH23 and OTOF. For another patient, the etiology of deafness was unclear, and no causal mutation was found. In a fifth patient, included as a positive control, we could confirm a known mutation in TMC1. CONCLUSIONS: We have developed an assay that holds great promise as a tool for screening patients with familial autosomal recessive nonsyndromal hearing loss (ARNSHL). For the first time, an efficient, reliable and cost effective genetic test, based on PCR enrichment, for newborns with undiagnosed deafness is available.

Applying massive parallel sequencing to molecular diagnosis of Marfan and Loeys-Dietz syndromes.

The Marfan (MFS) and Loeys-Dietz (LDS) syndromes are caused by mutations in the fibrillin-1 (FBN1) and Transforming Growth Factor Beta Receptor 1 and 2 (TGFBR1 and TGFBR2) genes, respectively. With the current conventional mutation screening technologies, analysis of this set of genes is time consuming and expensive. We have tailored a cost-effective and reliable mutation discovery strategy using multiplex PCR followed by Next Generation Sequencing (NGS). In a first stage, genomic DNA from five MFS or LDS patient samples with previously identified mutations and/or polymorphisms in FBN1 and TGFBR1 and 2 were analyzed and revealed all expected variants. In a second stage, we validated the technique on 87 samples from MFS patients fulfilling the Ghent criteria. This resulted in the identification of 75 FBN1 mutations, of which 67 were unique. Subsequent Multiplex Ligation-dependent Probe Amplification (MLPA) analysis of the remaining negative samples identified four large deletions/insertions. Finally, Sanger sequencing identified a missense mutation in FBN1 exon 1 that was not included in the NGS workflow. In total, there was an overall mutation identification rate of 92%, which is in agreement with data published previously. We conclude that multiplex PCR of all coding exons of FBN1 and TGFBR1/2 followed by NGS analysis and MLPA is a robust strategy for time- and cost-effective identification of mutations.