Efficient Genotyping of KRAS Mutant Non-Small Cell Lung Cancer Using a Multiplexed Droplet Digital PCR Approach.

Droplet digital PCR (ddPCR) can be used to detect low frequency mutations in oncogene-driven lung cancer. The range of KRAS point mutations observed in NSCLC necessitates a multiplex approach to efficient mutation detection in circulating DNA. Here we report the design and optimisation of three discriminatory ddPCR multiplex assays investigating nine different KRAS mutations using PrimePCR™ ddPCR™ Mutation Assays and the Bio-Rad QX100 system. Together these mutations account for 95% of the nucleotide changes found in KRAS in human cancer. Multiplex reactions were optimised on genomic DNA extracted from KRAS mutant cell lines and tested on DNA extracted from fixed tumour tissue from a cohort of lung cancer patients without prior knowledge of the specific KRAS genotype. The multiplex ddPCR assays had a limit of detection of better than 1 mutant KRAS molecule in 2,000 wild-type KRAS molecules, which compared favourably with a limit of detection of 1 in 50 for next generation sequencing and 1 in 10 for Sanger sequencing. Multiplex ddPCR assays thus provide a highly efficient methodology to identify KRAS mutations in lung adenocarcinoma.

Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a diagnostic RING trial.

AIMS: Mutation detection accuracy has been described extensively; however, it is surprising that pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples has not been systematically assessed in clinical context. We designed a RING trial to (i) investigate pre-PCR variability, (ii) correlate pre-PCR variation with EGFR/BRAF mutation testing accuracy and (iii) investigate causes for observed variation. METHODS: 13 molecular pathology laboratories were recruited. 104 blinded FFPE curls including engineered FFPE curls, cell-negative FFPE curls and control FFPE tissue samples were distributed to participants for pre-PCR processing and mutation detection. Follow-up analysis was performed to assess sample purity, DNA integrity and DNA quantitation. RESULTS: Rate of mutation detection failure was 11.9%. Of these failures, 80% were attributed to pre-PCR error. Significant differences in DNA yields across all samples were seen using analysis of variance (p

A new generation of companion diagnostics: cobas BRAF, KRAS and EGFR mutation detection tests

The cobas® (Roche) portfolio of companion diagnostics in oncology currently has three assays CE-marked for in vitro diagnostics. Two of these (EGFR and BRAF) are also US FDA-approved. These assays detect clinically relevant mutations that are correlated with response (BRAF, EGFR) or lack of response (KRAS) to targeted therapies such as selective mutant BRAF inhibitors in malignant melanoma, tyrosine kinases inhibitor in non-small cell lung cancer and anti-EGFR monoclonal antibodies in colorectal cancer, respectively. All these assays are run on a single platform using DNA extracted from a single 5 µm section of a formalin-fixed paraffin-embedded tissue block. The assays provide an ‘end-to-end’ solution from extraction of DNA to automated analysis and report on the cobas z 480. The cobas tests have shown robust and reproducible performance, with high sensitivity and specificity and low limit of detection, making them suitable as companion diagnostics for clinical use.

Heteroduplex analysis of VDJ amplified segments from rearranged IgH genes for clonality assessments in B-cell non-Hodgkin's lymphoma. A comparison between different strategies.

BACKGROUND AND OBJECTIVE: The main difficulty of PCR-based clonality studies for B-cell lymphoproliferative disorders (B-LPD) is discrimination between monoclonal and polyclonal PCR products, especially when there is a high background of polyclonal B cells in the tumor sample. Actually, PCR-based methods for clonality assessment require additional analysis of the PCR products in order to discern between monoclonal and polyclonal samples. Heteroduplex analysis represents an attractive approach since it is easy to perform and avoids the use of radioactive substrates or expensive equipment. DESIGN AND METHODS: We studied the sensitivity and specificity of heteroduplex PCR analysis for monoclonal detection in samples from 90 B-cell non Hodgkin's lymphoma (B-NHL) patients and in 28 individuals without neoplastic B-cell disorders (negative controls). Furthermore, in 42 B-NHL and in the same 28 negative controls, we compared heteroduplex analysis vs the classical PCR technique. We also compared ethidium bromide (EtBr) vs. silver nitrate (AgNO(3)) staining as well as agarose vs. polyacrylamide gel electrophoresis (PAGE). RESULTS: Using two pair consensus primers sited at VH (FR3 and FR2) and at JH, 91% of B-NHL samples displayed monoclonal products after heteroduplex PCR analysis using PAGE and AgNO(3) staining. Moreover, no polyclonal sample showed a monoclonal PCR product. By contrast, false positive results were obtained when using agarose (5/28) and PAGE without heteroduplex analysis: 2/28 and 8/28 with EtBr and AgNO(3) staining, respectively. In addition, false negative results only appeared with EtBr staining: 13/42 in agarose, 4/42 in PAGE without heteroduplex analysis and 7/42 in PAGE after heteroduplex analysis. INTERPRETATION AND CONCLUSIONS: We conclude that AgNO(3) stained PAGE after heteroduplex analysis is the most suitable strategy for detecting monoclonal rearrangements in B-NHL samples because it does not produce false-positive results and the risk of false-negative results is very low.

Enzyme-induced Aggregation of Plasmonic Nanoparticles for the Development of Label-free and Ultrasensitive Detection of Bacterial DNA

Sensitive detection of pathogens is critical to ensure the safety of food supplies and to prevent bacterial disease infection and outbreak at the first onset. While conventional techniques such as cell culture, ELISA, PCR, etc. have been used as the predominant detection workhorses, they are however limited by either time-consuming procedure, complicated sample pre-treatment, expensive analysis and operation, or inability to be implemented at point-of-care testing. Here, we present our recently developed assay exploiting enzyme-induced aggregation of plasmonic gold nanoparticles (AuNPs) for label-free and ultrasensitive detection of bacterial DNA. In the experiments, AuNPs are first functionalized with specific, single-stranded RNA probes so that they exhibit high stability in solution even under high electrolytic condition thus exhibiting red color. When bacterial DNA is present in a sample, a DNA-RNA heteroduplex will be formed and subsequently prone to the RNase H cleavage on the RNA probe, allowing the DNA to liberate and hybridize with another RNA strand. This continuously happens until all of the RNA strands are cleaved, leaving the nanoparticles ‘unprotected’. The addition of NaCl will cause the ‘unprotected’ nanoparticles to aggregate, initiating a colour change from red to blue. The reaction is performed in a multi-well plate format, and the distinct colour signal can be discriminated by naked eye or simple optical spectroscopy. As a result, bacterial DNA as low as pM could be unambiguously detected, suggesting that the enzyme-induced aggregation of AuNPs assay is very easy to perform and sensitive, it will significantly benefit to development of fast and ultrasensitive methods that can be used for disease detection and diagnosis.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background
It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
Results
In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
Conclusions
Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system