Interfacial recognition of human prostate-specific antigen by immobilized monoclonal antibody: effects of solution conditions and surface chemistry.

The specific recognition between monoclonal antibody (anti-human prostate-specific antigen, anti-hPSA) and its antigen (human prostate-specific antigen, hPSA) has promising applications in prostate cancer diagnostics and other biosensor applications. However, because of steric constraints associated with interfacial packing and molecular orientations, the binding efficiency is often very low. In this study, spectroscopic ellipsometry and neutron reflection have been used to investigate how solution pH, salt concentration and surface chemistry affect antibody adsorption and subsequent antigen binding. The adsorbed amount of antibody was found to vary with pH and the maximum adsorption occurred between pH 5 and 6, close to the isoelectric point of the antibody. By contrast, the highest antigen binding efficiency occurred close to the neutral pH. Increasing the ionic strength reduced antibody adsorbed amount at the silica-water interface but had little effect on antigen binding. Further studies of antibody adsorption on hydrophobic C8 (octyltrimethoxysilane) surface and chemical attachment of antibody on (3-mercaptopropyl)trimethoxysilane/4-maleimidobutyric acid N-hydroxysuccinimide ester-modified surface have also been undertaken. It was found that on all surfaces studied, the antibody predominantly adopted the 'flat on' orientation, and antigen-binding capabilities were comparable. The results indicate that antibody immobilization via appropriate physical adsorption can replace elaborate interfacial molecular engineering involving complex covalent attachments.

Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

Label-free detection of human prostate-specific antigen (hPSA) using film bulk acoustic resonators (FBARs)

Label-free detection of cancer biomarkers using low cost biosensors has promising applications in clinical diagnostics. In this work, ZnO-based thin film bulk acoustic wave resonators (FBARs) with resonant frequency of ∼1.5 GHz and mass sensitivity of 0.015 mg/m2 (1.5 ng/cm2) have been fabricated for their deployment as biosensors. Mouse monoclonal antibody, anti-human prostate-specific antigen (Anti-hPSA) has been used to bind human prostate-specific antigen (hPSA), a model cancer used in this study. Ellipsometry was used to characterize and optimise the antibody adsorption and antigen binding on gold surface. It was found that the best amount of antibody at the gold surface for effective antigen binding is around 1 mg/m2, above or below which resulted in the reduced antigen binding due to either the limited binding sites (below 1 mg/m2) or increased steric effect (above 1 mg/m2). The FBAR data were in good agreement with the data obtained from ellipsometry. Antigen binding experiments using FBAR sensors demonstrated that FBARs have the capability to precisely detect antigen binding, thereby making FBARs an attractive low cost alternative to existing cancer diagnostic sensors. © 2013 Elsevier B.V.