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

Low rates of Pseudomonas aeruginosa misidentification in isolates from cystic fibrosis patients

Pseudomonas aeruginosa is an important cause of pulmonary infection in cystic fibrosis (CF). Its correct identification ensures effective patient management and infection control strategies. However, little is known about how often CF sputum isolates are falsely identified as P. aeruginosa. We used P. aeruginosa-specific duplex real-time PCR assays to determine if 2,267 P. aeruginosa sputum isolates from 561 CF patients were correctly identified by 17 Australian clinical microbiology laboratories. Misidentified isolates underwent further phenotypic tests, amplified rRNA gene restriction analysis, and partial 16S rRNA gene sequence analysis. Participating laboratories were surveyed on how they identified P. aeruginosa from CF sputum. Overall, 2,214 (97.7%) isolates from 531 (94.7%) CF patients were correctly identified as P. aeruginosa. Further testing with the API 20NE kit correctly identified only 34 (59%) of the misidentified isolates. Twelve (40%) patients had previously grown the misidentified species in their sputum. Achromobacter xylosoxidans (n = 21), Stenotrophomonas maltophilia (n = 15), and Inquilinus limosus (n = 4) were the species most commonly misidentified as P. aeruginosa. Overall, there were very low rates of P. aeruginosa misidentification among isolates from a broad cross section of Australian CF patients. Additional improvements are possible by undertaking a culture history review, noting colonial morphology, and performing stringent oxidase, DNase, and colistin susceptibility testing for all presumptive P. aeruginosa isolates. Isolates exhibiting atypical phenotypic features should be evaluated further by additional phenotypic or genotypic identification techniques.

Sorting out mix-ups. The provenance of tissue sections may be confirmed by PCR using microsatellite markers

Standard identification systems usually ensure that biopsy material is correctly associated with a given patient. Sometimes, as when a tumor is unexpectedly found, the provenance (proof of origin) of a tissue sample may be questioned; the tissue may have been mislabelled or contaminated with tissue from another patient. Techniques used to confirm tissue provenance include comparing either tissue markers of gender or ABO blood groups; however, these methods have weak confirmatory power. Recently, the use of DNA-based polymerase chain reaction (PCR) techniques has been reported. Paired, formalin-fixed, paraffin-embedded, 10 microns tissue sections were selected from 17 patients, 8 of whom had carcinoma, either by dividing a biopsy section, using sequential biopsies, or sequential biopsy and autopsy tissue. The resulting 36 samples were coded before analysis. In two additional cases, 1-mm fragments of tumor from one patient were included in the tissue block of benign tissue from another patient, the tumor fragments were identified on hematoxylin-and-eosin-stained sections, separately scraped off the glass slide, and analyzed. Tissue from two clinical cases, one of suspected mislabelling and one with a suspected carry-over of malignant tissue were also investigated. Short tandem repeat sequences (STR) or microsatellites, are 2-5 base pair repeats that vary in their repeat number between individuals. This variation (polymorphism) can be assessed using a PCR. A panel of markers of 3 STRs; ACPP, INT 2, and CYP 19 (on chromosomes 3, 11, and 15, respectively) were used. DNA was isolated from the samples after xylene deparaffinization and proteinase digestion, and was then amplified in a radioactive PCR using primers selected to give a product size ranging from 136-178 bases. Amplified products were electrophoresed on denaturing polyacrylamide gels, dried, and autoradiographed. DNA segments were successfully extracted from all samples but one, which was fixed in Bouin's fluid. By comparing allele sizes from the panel, all tissue pairs (other than the Bouin's pair) were successfully matched, the 1-mm tumor fragments were correctly assigned, and the two clinical problems were solved. STRs are highly informative and robust markers, well suited to PCR of small portions of tissue sections, and are an effective method to confirm the provenance of benign and malignant biopsy and autopsy material.

The thinking doctor: clinical decision making in contemporary medicine

Diagnostic errors are responsible for a significant number of adverse events. Logical reasoning and good decision-making skills are key factors in reducing such errors, but little emphasis has traditionally been placed on how these thought processes occur, and how errors could be minimised. In this article, we explore key cognitive ideas that underpin clinical decision making and suggest that by employing some simple strategies, physicians might be better able to understand how they make decisions and how the process might be optimised.

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

Background: Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.

Methods: On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.

Results: Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with ‘low cancer-risk’ characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring ‘high cancer-risk” characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest ‘high cancer- risk’ cluster were different than those contributing to the classifiers for the ‘low cancer-risk’ clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.

Conclusions: The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs. © 2013 Emmert-Streib et al; licensee BioMed Central Ltd.

Can diagnostic triage by general practitioners or rheumatology nurses improve the positive predictive value of referrals to early arthritis clinics?

Objectives: To determine whether diagnostic triage by general practitioners (GPs) or rheumatology nurses (RNs) can improve the positive predictive value of referrals to early arthritis clinics (EACs).

Methods: Four GPs and two RNs were trained in the assessment of early in?ammatory arthritis (IA) by four visits to an EAC supervised by hospital rheumatologists. Patients referred to one of three EACs were recruited for study and assessed independently by a GP, an RN and one of six rheumatologists. Each assessor was asked to record their clinical ?ndings and whether they considered the patient to have IA. Each was then asked to judge the appropriateness of the referral according to predetermined guidelines. The rheumatologists had been shown previously to have a satisfactory level of agreement in the assessment of IA.

Results: Ninety-six patients were approached and all consented to take part in the study. In 49 cases (51%), the rheumatologist judged that the patient had IA and that the referral was appropriate. The assessments of GPs and RNs were compared with those of the rheumatologists. Levels of agreement were measured using the kappa value, where 1.0 represents total unanimity. The kappa value was
0.77 for the GPs when compared with the rheumatologists and 0.79 for the RNs. Signi?cant stiffness in the morning or after rest and objective joint swelling were the most important clinical features enabling the GPs and RNs to discriminate between IA and non-IA conditions.

Conclusion: Diagnostic triage by GPs or RNs improved the positive predictive value of referrals to an EAC with a degree of accuracy approaching that of a group of experienced rheumatologists.

A novel diagnostic test detects a low frequency of the hemicentin Gln5345Arg variant among Northern Irish age related macular degeneration patients.

Purpose: Age related macular degeneration (AMD) is a common cause of severe vision loss. Identification of genes involved in AMD will facilitate early detection and ultimately help to identify pathways for treatment for this disorder. The A16,263G mutation in the HEMICENTIN-1 gene produces a non-conservative substitution of arginine for glutamine at codon 5345 which has been implicated in familial AMD. The aim of this study is to develop a rapid diagnostic assay for the detection of this mutation and to evaluate its frequency in a sample of AMD patients. Methods: A primer probe set was designed from exon 104 of the HEMICENTIN-1 gene to differentiate between mutant and wild type alleles. A region spanning the mutation was amplified by PCR using a LightCycler (Roche Diagnostic). The mutation was then detected by melt curve analysis of the hybrid formed between the PCR product and a specific fluorescent probe. The frequency of the mutation within the Northern Ireland population was evaluated by assaying 508 affected AMD patients, 25 possibly affected and 163 controls. Results: This assay clearly discriminates between the A16,263G mutant and wild type HEMICENTIN-1 alleles. The wild type sequence has a single base mismatch with the probe which decreases the stability of the hybrid, resulting in a lower TM (TM=51.27 °C) than that observed for the perfectly matched mutant allele (TM=59.9 °C). The mutant allele was detected in only one of the 696 subjects, an affected AMD patient. Conclusions: We describe a rapid assay for the genotyping of the Gln5345Arg mutation using real-time fluorescence PCR to facilitate rapid processing of samples through combined amplification and detection steps. These characteristics are suitable for a clinical setting where high throughput diagnostic procedures are required. The frequency of this mutation within the Northern Ireland population has been estimated at 0.2%, concurring with previous findings that this mutation is a rare variant associated with AMD. A rapid diagnostic assay will facilitate a reliable and convenient evaluation of the frequency of the Gln5345Arg mutation and its association with AMD within other populations.

RHESSI and SOHO CDS Observations of Explosive Chromospheric Evaporation

Simultaneous observations of explosive chromospheric evaporation are presented using data from the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) and the Coronal Diagnostic Spectrometer (CDS) on board the Solar and Heliospheric Observatory. For the first time, cospatial imaging and spectroscopy have been used to observe explosive evaporation within a hard X-ray emitting region. RHESSI X-ray images and spectra were used to determine the flux of nonthermal electrons accelerated during the impulsive phase of an M2.2 flare. When we assumed a thick-target model, the injected electron spectrum was found to have a spectral index of similar to 7.3, a low-energy cutoff of similar to 20 keV, and a resulting flux of >= 4 x10(10) ergs cm(-2) s(-1). The dynamic response of the atmosphere was determined using CDS spectra; we found a mean upflow velocity of 230 +/- 38 km s(-1) in Fe (XIX) (592.23 angstrom) and associated downflows of 36 +/- 16 and 43 +/- 22 km s(-1) at chromospheric and transition region temperatures, respectively, relative to an averaged quiet- Sun spectra. The errors represent a 1 j dispersion. The properties of the accelerated electron spectrum and the corresponding evaporative velocities were found to be consistent with the predictions of theory.

Error sensitive historical GIS: Identifying areal interpolation errors in time series data

Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.

Computerised Diagnostic Decision Support System for the Classification of Pre-Invasive Cervical Squamous Lesions.

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) 1, CIN II, and CIN M. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN 1, CIN H, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with K statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control. (C) 2003 Elsevier Inc. All rights reserved.