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BackgroundNumerous risk prediction algorithms based on conventional risk factors for Coronary Heart Disease (CHD) are available but provide only modest discrimination. The inclusion of genetic information may improve clinical utility.MethodsWe tested the use of two gene scores (GS) in the prospective second Northwick Park Heart Study (NPHSII) of 2775 healthy UK men (284 cases), and Pakistani case-control studies from Islamabad/Rawalpindi (321 cases/228 controls) and Lahore (414 cases/219 controls). The 19-SNP GS included SNPs in loci identified by GWAS and candidate gene studies, while the 13-SNP GS only included SNPs in loci identified by the CARDIoGRAMplusC4D consortium.ResultsIn NPHSII, the mean of both gene scores was higher in those who went on to develop CHD over 13.5 years of follow-up (19-SNP p=0.01, 13-SNP p=7x10-3). In combination with the Framingham algorithm the GSs appeared to show improvement in discrimination (increase in area under the ROC curve, 19-SNP p=0.48, 13-SNP p=0.82) and risk classification (net reclassification improvement (NRI), 19-SNP p=0.28, 13-SNP p=0.42) compared to the Framingham algorithm alone, but these were not statistically significant. When considering only individuals who moved up a risk category with inclusion of the GS, the improvement in risk classification was statistically significant (19-SNP p=0.01, 13-SNP p=0.04). In the Pakistani samples, risk allele frequencies were significantly lower compared to NPHSII for 13/19 SNPs. In the Islamabad study, the mean gene score was higher in cases than controls only for the 13-SNP GS (2.24 v 2.34, p=0.04). There was no association with CHD and either score in the Lahore study.ConclusionThe performance of both GSs showed potential clinical utility in European men but much less utility in subjects from Pakistan, suggesting that a different set of risk loci or SNPs may be required for risk prediction in the South Asian population.

Molecular diagnostic tests, based on the detection and identification of nucleic acids in human biological samples, are increasingly employed in the diagnosis of infectious diseases and may be of future benefit to CF microbiology services. Our growing understanding of the complex polymicrobial nature of CF airway infection has highlighted current and likely future shortcomings in standard diagnostic practices. Failure to detect fastidious or slow growing microbes and misidentification of newly emerging pathogens could potentially be addressed using culture-independent molecular technologies with high target specificity. This review considers existing molecular diagnostic tests in the context of the key requirements for an envisaged CF microbiology focussed assay. The issues of assay speed, throughput, detection of multiple pathogens, data interpretation and antimicrobial susceptibility testing are discussed.

The Hedgehog pathway is one of the major driver pathways in pancreatic ductal adenocarcinoma. This study investigated prognostic importance of Hedgehog signaling pathway in pancreatic cancer patients who underwent a radical resection. Tumors and adjacent non-neoplastic pancreatic tissues were obtained from 45 patients with histologically verified pancreatic cancer. The effect of experimental taxane chemotherapy on the expression of Hedgehog pathway was evaluated in vivo using a mouse xenograft model prepared using pancreatic cancer cell line Paca-44. Mice were treated by experimental Stony Brook Taxane SB-T-1216. The transcript profile of 34 Hedgehog pathway genes in patients and xenografts was assessed using quantitative PCR. The Hedgehog pathway was strongly overexpressed in pancreatic tumors and upregulation of SHH, IHH, HHAT and PTCH1 was associated with a trend toward decreased patient survival. No association of Hedgehog pathway expression with KRAS mutation status was found in tumors. Sonic hedgehog ligand was overexpressed, but all other downstream genes were downregulated by SB-T-1216 treatment in vivo. Suppression of HH pathway expression in vivo by taxane-based chemotherapy suggests a new mechanism of action for treatment of this aggressive tumor.

Musculoskeletal diseases such as rheumatoid arthritis are complex multifactorial disorders that are chronic in nature and debilitating for patients. A number of drug families are available to clinicians to manage these disorders but few tests exist to target these to the most responsive patients. As a consequence, drug failure and switching to drugs with alternate modes of action is common. In parallel, a limited number of laboratory tests are available which measure biological indicators or 'biomarkers' of disease activity, autoimmune status, or joint damage. There is a growing awareness that assimilating the fields of drug selection and diagnostic tests into 'companion diagnostics' could greatly advance disease management and improve outcomes for patients. This review aims to highlight: the current applications of biomarkers in rheumatology with particular focus on companion diagnostics; developments in the fields of proteomics, genomics, microbiomics, imaging and bioinformatics and how integration of these technologies into clinical practice could support therapeutic decisions.