Browsing Research Groups by Title
Now showing items 73-75 of 75
Vaccination with HPV16 L2E6E7 fusion protein in GPI-0100 adjuvant elicits protective humoral and cell-mediated immunity.A vaccine comprising human papillomavirus type 16 (HPV16) L2, E6 and E7 in a single tandem fusion protein (termed TA-CIN) has the potential advantages of both broad cross-protection against HPV transmission through induction of L2 antibodies able to cross neutralize different HPV types and of therapy by stimulating T cell responses targeting HPV16 early proteins. However, patients vaccinated with TA-CIN alone develop weak HPV neutralizing antibody and E6/E7-specific T cell responses. Here we test TA-CIN formulated along with the adjuvant GPI-0100, a semi-synthetic quillaja saponin analog that was developed to promote both humoral and cellular immune responses. Subcutaneous administration to mice of TA-CIN (20 microg) with 50microg GPI-0100, three times at biweekly intervals, elicited high titer HPV16 neutralizing serum antibody, robust neutralizing titers for other HPV16-related types, including HPV31 and HPV58, and neutralized to a lesser extent other genital mucosatropic papillomaviruses like HPV18, HPV45, HPV6 and HPV11. Notably, vaccination with TA-CIN in GPI-0100 protected mice from cutaneous HPV16 challenge as effectively as HPV16 L1 VLP without adjuvant. Formulation of TA-CIN with GPI-0100 enhanced the production of E7-specific, interferon gamma producing CD8(+) T cell precursors by 20-fold. Vaccination with TA-CIN in GPI-0100 also completely prevented tumor growth after challenge with 5x10(4) HPV16-transformed TC-1 tumor cells, whereas vaccination with TA-CIN alone delayed tumor growth. Furthermore, three monthly vaccinations with 125 microg of TA-CIN and 1000 microg GPI-0100 were well tolerated by pigtail macaques and induced both HPV16 E6/E7-specific T cell responses and serum antibodies that neutralized all HPV types tested.
A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis.
X:Map: annotation and visualization of genome structure for Affymetrix exon array analysis.Affymetrix exon arrays aim to target every known and predicted exon in the human, mouse or rat genomes, and have reporters that extend beyond protein coding regions to other areas of the transcribed genome. This combination of increased coverage and precision is important because a substantial proportion of protein coding genes are predicted to be alternatively spliced, and because many non-coding genes are known also to be of biological significance. In order to fully exploit these arrays, it is necessary to associate each reporter on the array with the features of the genome it is targeting, and to relate these to gene and genome structure. X:Map is a genome annotation database that provides this information. Data can be browsed using a novel Google-maps based interface, and analysed and further visualized through an associated BioConductor package. The database can be found at http://xmap.picr.man.ac.uk.