Osteoblast and monocyte responses to 444 ferritic stainless steel intended for a Magneto-Mechanically Actuated Fibrous Scaffold

The rationale behind this work is to design an implant device, based on a ferromagnetic material, with the potential to deform in vivo promoting osseointegration through the growth of a healthy periprosthetic bone structure. One of the primary requirements for such a device is that the material should be non-inflammatory and non-cytotoxic. In the study described here, we assessed the short-term cellular response to 444 ferritic stainless steel; a steel, with a very low interstitial content and a small amount of strong carbide-forming elements to enhance intergranular corrosion resistance. Two different human cell types were used: (i) foetal osteoblasts and (ii) monocytes. Austenitic stainless steel 316L, currently utilised in many commercially available implant designs, and tissue culture plastic were used as the control surfaces. Cell viability, proliferation and alkaline phosphatase activity were measured. In addition, cells were stained with alizarin red and fluorescently-labelled phalloidin and examined using light, fluorescence and scanning electron microscopy. Results showed that the osteoblast cells exhibited a very similar degree of attachment, growth and osteogenic differentiation on all surfaces. Measurement of lactate dehydrogenase activity and tumour necrosis factor alpha protein released from human monocytes indicated that 444 stainless steel did not cause cytotoxic effects or any significant inflammatory response. Collectively, the results suggest that 444 ferritic stainless steel has the potential to be used in advanced bone implant designs. © 2011 Elsevier Ltd.

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/