Clinical Utility of Microarray-Based Gene Expression Profiling in the Diagnosis and Subclassification of Leukemia: Report From the International Microarray Innovations in Leukemia Study Group

The Microarray Innovations in Leukemia study assessed the clinical utility of gene expression profiling as a single test to subtype leukemias into conventional categories of myeloid and lymphoid malignancies. METHODS: The investigation was performed in 11 laboratories across three continents and included 3,334 patients. An exploratory retrospective stage I study was designed for biomarker discovery and generated whole-genome expression profiles from 2,143 patients with leukemias and myelodysplastic syndromes. The gene expression profiling-based diagnostic accuracy was further validated in a prospective second study stage of an independent cohort of 1,191 patients. RESULTS: On the basis of 2,096 samples, the stage I study achieved 92.2% classification accuracy for all 18 distinct classes investigated (median specificity of 99.7%). In a second cohort of 1,152 prospectively collected patients, a classification scheme reached 95.6% median sensitivity and 99.8% median specificity for 14 standard subtypes of acute leukemia (eight acute lymphoblastic leukemia and six acute myeloid leukemia classes, n = 693). In 29 (57%) of 51 discrepant cases, the microarray results had outperformed routine diagnostic methods. CONCLUSION: Gene expression profiling is a robust technology for the diagnosis of hematologic malignancies with high accuracy. It may complement current diagnostic algorithms and could offer a reliable platform for patients who lack access to today's state-of-the-art diagnostic work-up. Our comprehensive gene expression data set will be submitted to the public domain to foster research focusing on the molecular understanding of leukemias

Activated oncogenic pathways and therapeutic targets in extranodal nasal-type NK/T cell lymphoma revealed by gene expression profiling

We performed comprehensive genome-wide gene expression profiling (GEP) of extranodal nasal-type natural killer/T-cell lymphoma (NKTL) using formalin-fixed, paraffin-embedded tissue (n = 9) and NK cell lines (n = 5) in comparison with normal NK cells, with the objective of understanding the oncogenic pathways involved in the pathogenesis of NKTL and to identify potential therapeutic targets. Pathway and network analysis of genes differentially expressed between NKTL and normal NK cells revealed significant enrichment for cell cycle-related genes and pathways, such as PLK1, CDK1, and Aurora-A. Furthermore, our results demonstrated a pro-proliferative and anti-apoptotic phenotype in NKTL characterized by activation of Myc and nuclear factor kappa B (NF-kappa B), and deregulation of p53. In corroboration with GEP findings, a significant percentage of NKTLs (n = 33) overexpressed c-Myc (45.4%), p53 (87.9%), and NF-kappa B p50 (67.7%) on immunohistochemistry using a tissue microarray containing 33 NKTL samples. Notably, overexpression of survivin was observed in 97% of cases. Based on our findings, we propose a model of NKTL pathogenesis where deregulation of p53 together with activation of Myc and NF-kappa B, possibly driven by EBV LMP-1, results in the cumulative up-regulation of survivin. Down-regulation of survivin with Terameprocol (EM-1421, a survivin inhibitor) results in reduced cell viability and increased apoptosis in tumour cells, suggesting that targeting survivin may be a potential novel therapeutic strategy in NKTL. Copyright (C) 2011 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage

Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage

An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in LEukemia study prephase

Gene expression profiling has the potential to enhance current methods for the diagnosis of haematological malignancies. Here, we present data on 204 analyses from an international standardization programme that was conducted in 11 laboratories as a prephase to the Microarray Innovations in LEukemia (MILE) study. Each laboratory prepared two cell line samples, together with three replicate leukaemia patient lysates in two distinct stages: (i) a 5-d course of protocol training, and (ii) independent proficiency testing. Unsupervised, supervised, and r(2) correlation analyses demonstrated that microarray analysis can be performed with remarkably high intra-laboratory reproducibility and with comparable quality and reliability.

Do general practitioners change how they use the computer during consultations with a significant psychological component?

objectives: To describe the patterns of computer use during patient visits to family doctors and to determine whether doctors alter their pattern of computer use in consultations which have significant psychological content.

Computer simulating a trial of a load-bearing implant: example of an intramedullary prosthesis

Computational modelling is becoming ever more important for obtaining regulatory approval for new medical devices. An accepted approach is to infer performance in a population from an analysis conducted for an idealised or ‘average’ patient; we present here a method for predicting the performance of an orthopaedic implant when released into a population—effectively simulating a clinical trial. Specifically we hypothesise that an analysis based on a method for predicting the performance in a population will lead to different conclusions than an analysis based on an idealised or ‘average’ patient. To test this hypothesis we use a finite element model of an intramedullary implant in a bone whose size and remodelling activity is different for each individual in the population. We compare the performance of a low Young’s modulus implant (View the MathML source) to one with a higher Young’s modulus (200 GPa). Cyclic loading is applied and failure is assumed when the migration of the implant relative to the bone exceeds a threshold magnitude. The analysis for an idealised of ‘average’ patient predicts that the lower modulus device survives longer whereas the analysis simulating a clinical trial predicts no statistically-significant tendency (p=0.77) for the low modulus device to perform better. It is concluded that population-based simulations of implant performance–simulating a clinical trial–present a very valuable opportunity for more realistic computational pre-clinical testing of medical devices.