No abstract

Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance (1H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows >90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of >90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins.

BackgroundConstitutively active androgen receptor variants (AR-V) lacking the ligand binding domain (LBD) may promote the development of castration-resistant prostate cancer (CRPC). The expression of AR-Vs in the clinically most important metastatic site, the bone, has, however, not been well documented. Our aim was therefore to compare levels of AR-Vs in hormone-naive (HN) and CRPC bone metastases in comparison to primary PC and non-malignant prostate tissue, as well as in relation to AR protein expression, whole-genome transcription profiles and patient survival.Methodology/Principal FindingsHormone-naïve (n = 10) and CRPC bone metastases samples (n = 30) were obtained from 40 patients at metastasis surgery. Non-malignant and malignant prostate samples were acquired from 13 prostatectomized men. Levels of full length AR (ARfl) and AR-Vs termed AR-V1, AR-V7, and AR-V567es mRNA were measured with RT-PCR and whole-genome transcription profiles with an Illumina Beadchip array. Protein levels were examined by Western blotting and immunohistochemistry. Transcripts for ARfl, AR-V1, and AR-V7 were detected in most primary tumors and metastases, and levels were significantly increased in CRPC bone metastases. The AR-V567es transcript was detected in 23% of the CRPC bone metastases only. A sub-group of CRPC bone metastases expressed LBD-truncated AR proteins at levels comparable to the ARfl. Detectable AR-V567es and/or AR-V7 mRNA in the upper quartile, seen in 1/3 of all CRPC bone metastases, was associated with a high nuclear AR immunostaining score, disturbed cell cycle regulation and short survival.Conclusions/SignificanceExpression of AR-Vs is increased in CRPC compared to HN bone metastases and associated with a particularly poor prognosis. Further studies are needed to test if patients expressing such AR-Vs in their bone metastases benefit more from drugs acting on or down-stream of these AR-Vs than from therapies inhibiting androgen synthesis.

No abstract

There is a clear case for drug treatments to be selected according to the characteristics of an individual patient, in order to improve efficacy and reduce the number and severity of adverse drug reactions. However, such personalization of drug treatments requires the ability to predict how different individuals will respond to a particular drug/dose combination. After initial optimism, there is increasing recognition of the limitations of the pharmacogenomic approach, which does not take account of important environmental influences on drug absorption, distribution, metabolism and excretion. For instance, a major factor underlying inter-individual variation in drug effects is variation in metabolic phenotype, which is influenced not only by genotype but also by environmental factors such as nutritional status, the gut microbiota, age, disease and the co- or pre-administration of other drugs. Thus, although genetic variation is clearly important, it seems unlikely that personalized drug therapy will be enabled for a wide range of major diseases using genomic knowledge alone. Here we describe an alternative and conceptually new 'pharmaco-metabonomic' approach to personalizing drug treatment, which uses a combination of pre-dose metabolite profiling and chemometrics to model and predict the responses of individual subjects. We provide proof-of-principle for this new approach, which is sensitive to both genetic and environmental influences, with a study of paracetamol (acetaminophen) administered to rats. We show pre-dose prediction of an aspect of the urinary drug metabolite profile and an association between pre-dose urinary composition and the extent of liver damage sustained after paracetamol administration.

Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance (1H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows >90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of >90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins.

No abstract

This article describes the applicability of multivariate projection techniques, such as principal-component analysis (PCA) and partial least-squares (PLS) projections to latent structures, to the large-volume high-density data structures obtained within genomics, proteomics, and metabonomics. PCA and PLS, and their extensions, derive their usefulness from their ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. Three examples are used as illustrations: the first example is a genomics data set and involves modeling of microarray data of cell cycle-regulated genes in the microorganism Saccharomyces cerevisiae. The second example contains NMR-metabonomics data, measured on urine samples of male rats treated with either of the drugs chloroquine or amiodarone. The third and last data set describes sequence-function classification studies in a set of G-protein-coupled receptors using hierarchical PCA.