Clinical validation of a genetic model to estimate the risk of developing choroidal neovascular age-related macular degeneration

Predictive tests for estimating the risk of developing late-stage neovascular age-related macular degeneration (AMD) are subject to unique challenges. AMD prevalence increases with age, clinical phenotypes are heterogeneous and control collections are prone to high false-negative rates, as many control subjects are likely to develop disease with advancing age. Risk prediction tests have been presented previously, using up to ten genetic markers and a range of self-reported non-genetic variables such as body mass index (BMI) and smoking history. In order to maximise the accuracy of prediction for mainstream genetic testing, we sought to derive a test comparable in performance to earlier testing models but based purely on genetic markers, which are static through life and not subject to misreporting. We report a multicentre assessment of a larger panel of single nucleotide polymorphisms (SNPs) than previously analysed, to improve further the classification performance of a predictive test to estimate the risk of developing choroidal neovascular (CNV) disease. We developed a predictive model based solely on genetic markers and avoided inclusion of self-reported variables (eg smoking history) or non-static factors (BMI, education status) that might otherwise introduce inaccuracies in calculating individual risk estimates. We describe the performance of a test panel comprising 13 SNPs genotyped across a consolidated collection of four patient cohorts obtained from academic centres deemed appropriate for pooling. We report on predictive effect sizes and their classification performance. By incorporating multiple cohorts of homogeneous ethnic origin, we obtained >80 per cent power to detect differences in genetic variants observed between cases and controls. We focused our study on CNV, a subtype of advanced AMD associated with a severe and potentially treatable form of the disease. Lastly, we followed a two-stage strategy involving both test model development and test model validation to present estimates of classification performance anticipated in the larger clinical setting. The model contained nine SNPs tagging variants in the regulators of complement activation (RCA) locus spanning the complement factor H (CFH), complement factor H-related 4 (CFHR4), complement factor H-related 5 (CFHR5) and coagulation factor XIII B subunit (F13B) genes; the four remaining SNPs targeted polymorphisms in the complement component 2 (C2), complement factor B (CFB), complement component 3 (C3) and age-related maculopathy susceptibility protein 2 (ARMS2) genes. The pooled sample size (1,132 CNV cases, 822 controls) allowed for both model development and model validation to confirm the accuracy of risk prediction. At the validation stage, our test model yielded 82 per cent sensitivity and 63 per cent specificity, comparable with metrics reported with earlier testing models that included environmental risk factors. Our test had an area under the curve of 0.80, reflecting a modest improvement compared with tests reported with fewer SNPs

Circulating protein biomarkers of pharmacodynamic activity of sunitinib in patients with metastatic renal cell carcinoma: modulation of VEGF and VEGF-related proteins

<p>Abstract</p> <p>Background</p> <p>Sunitinib malate (SUTENT^®) is an oral, multitargeted tyrosine kinase inhibitor, approved multinationally for the treatment of advanced RCC and of imatinib-resistant or – intolerant GIST. The purpose of this study was to explore potential biomarkers of sunitinib pharmacological activity via serial assessment of plasma levels of four soluble proteins from patients in a phase II study of advanced RCC: VEGF, soluble VEGFR-2 (sVEGFR-2), placenta growth factor (PlGF), and a novel soluble variant of VEGFR-3 (sVEGFR-3).</p> <p>Methods</p> <p>Sunitinib was administered at 50 mg/day on a 4/2 schedule (4 weeks on treatment, 2 weeks off treatment) to 63 patients with metastatic RCC after failure of first-line cytokine therapy. Predose plasma samples were collected on days 1 and 28 of each cycle and analyzed via ELISA.</p> <p>Results</p> <p>At the end of cycle 1, VEGF and PlGF levels increased >3-fold (relative to baseline) in 24/54 (44%) and 22/55 (40%) cases, respectively (P < 0.001). sVEGFR-2 levels decreased ≥ 30% in 50/55 (91%) cases and ≥ 20% in all cases (P < 0.001) during cycle 1, while sVEGFR-3 levels were decreased ≥ 30% in 48 of 55 cases (87%), and ≥ 20% in all but 2 cases. These levels tended to return to near-baseline after 2 weeks off treatment, indicating that these effects were dependent on drug exposure. Overall, significantly larger changes in VEGF, sVEGFR-2, and sVEGFR-3 levels were observed in patients exhibiting objective tumor response compared with those exhibiting stable disease or disease progression (P < 0.05 for each analyte; analysis not done for PlGF).</p> <p>Conclusion</p> <p>Sunitinib treatment in advanced RCC patients leads to modulation of plasma levels of circulating proteins involved in VEGF signaling, including soluble forms of two VEGF receptors. This panel of proteins may be of value as biomarkers of the pharmacological and clinical activity of sunitinib in RCC, and of angiogenic processes in cancer and other diseases.</p

Cloning and DNA Sequence Analysis of bmpC, a Gene Encoding a Potential Membrane Lipoprotein of Borrelia Burgdorferi

Immunoscreening of a lambda gt11 genomic library of Borrelia burgdorferi expressed in Escherichia coli permitted detection of a clone containing a partial sequence of a B. burgdorferi gene encoding a protein with significant homology to TmpC of Treponema pallidum. Subsequent cloning and DNA sequence analysis revealed an open reading frame encoding a protein with 353 amino acid residues. The open reading frame is preceded by putative promoter sequences and a ribosome binding site, and is initiated with a TTG. The putative protein shares 26% identity with TmpC, contains a signal peptidase II sequence, and is also homologous to the gene products of the recently described bmpA and bmpB of B. burgdorferi. This gene has been designated bmpC. Additional sequencing and restriction analysis indicate that it is located at approximately 400 kbp on the chromosomal map of B. burgdorferi, immediately upstream of bmpA and bmpB