Functional DNA quantification guides accurate next-generation sequencing mutation detection in formalin-fixed, paraffin-embedded tumor biopsies.

The formalin-fixed, paraffin-embedded (FFPE) biopsy is a challenging sample for molecular assays such as targeted next-generation sequencing (NGS). We compared three methods for FFPE DNA quantification, including a novel PCR assay ('QFI-PCR') that measures the absolute copy number of amplifiable DNA, across 165 residual clinical specimens. The results reveal the limitations of commonly used approaches, and demonstrate the value of an integrated workflow using QFI-PCR to improve the accuracy of NGS mutation detection and guide changes in input that can rescue low quality FFPE DNA. These findings address a growing need for improved quality measures in NGS-based patient testing.

Targeted, high-depth, next-generation sequencing of cancer genes in formalin-fixed, paraffin-embedded and fine-needle aspiration tumor specimens.

Implementation of highly sophisticated technologies, such as next-generation sequencing (NGS), into routine clinical practice requires compatibility with common tumor biopsy types, such as formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration specimens, and validation metrics for platforms, controls, and data analysis pipelines. In this study, a two-step PCR enrichment workflow was used to assess 540 known cancer-relevant variants in 16 oncogenes for high-depth sequencing in tumor samples on either mature (Illumina GAIIx) or emerging (Ion Torrent PGM) NGS platforms. The results revealed that the background noise of variant detection was elevated approximately twofold in FFPE compared with cell line DNA. Bioinformatic algorithms were optimized to accommodate this background. Variant calls from 38 residual clinical colorectal cancer FFPE specimens and 10 thyroid fine-needle aspiration specimens were compared across multiple cancer genes, resulting in an accuracy of 96.1% (95% CI, 96.1% to 99.3%) compared with Sanger sequencing, and 99.6% (95% CI, 97.9% to 99.9%) compared with an alternative method with an analytical sensitivity of 1% mutation detection. A total of 45 of 48 samples were concordant between NGS platforms across all matched regions, with the three discordant calls each represented at <10% of reads. Consequently, NGS of targeted oncogenes in real-life tumor specimens using distinct platforms addresses unmet needs for unbiased and highly sensitive mutation detection and can accelerate both basic and clinical cancer research.

Limits of ligand selectivity from docking to models: in silico screening for A(1) adenosine receptor antagonists.

G protein-coupled receptors (GPCRs) are attractive targets for pharmaceutical research. With the recent determination of several GPCR X-ray structures, the applicability of structure-based computational methods for ligand identification, such as docking, has increased. Yet, as only about 1% of GPCRs have a known structure, receptor homology modeling remains necessary. In order to investigate the usability of homology models and the inherent selectivity of a particular model in relation to close homologs, we constructed multiple homology models for the A(1) adenosine receptor (A(1)AR) and docked ∼2.2 M lead-like compounds. High-ranking molecules were tested on the A(1)AR as well as the close homologs A(2A)AR and A(3)AR. While the screen yielded numerous potent and novel ligands (hit rate 21% and highest affinity of 400 nM), it delivered few selective compounds. Moreover, most compounds appeared in the top ranks of only one model. These findings have implications for future screens.

Stochastic pairwise alignments and scoring methods for comparative protein structure modeling.

Despite recent advances in fold recognition algorithms that identify template structures with distant homology to the target sequence, the quality of the target-template alignment can be a major problem for distantly related proteins in comparative modeling. Here we report for the first time on the use of ensembles of pairwise alignments obtained by stochastic backtracking as a means to improve three-dimensional comparative protein models. In every one of the 35 cases, the ensemble produced by the program probA resulted in alignments that were closer to the structural alignment than those obtained from the optimal alignment. In addition, we examined the lowest energy structure among these ensembles from four different structural assessment methods and compared these with the optimal and structural alignment model. The structural assessment methods consisted of the DFIRE, DOPE, and ProsaII statistical potential energies and the potential energy from the CHARMM protein force field coupled to a Generalized Born implicit solvent model. The results demonstrate that the generation of alignment ensembles through stochastic backtracking using probA combined with one of the statistical potentials for assessing three-dimensional structures can be used to improve comparative models.