English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present a nonparametric Bayesian method for disease subtype discovery inmulti-dimensional cancer data. Our method can simultaneously analyse a widerange of data types, allowing for both agreement and disagreement between theirunderlying clustering structure. It includes feature selection and infers themost 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 andmicroRNA data. We identify 8 distinct consensus subtypes and study theirprognostic value for death, new tumour events, progression and recurrence. Theconsensus subtypes are prognostic of tumour recurrence (log-rank p-value of$3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This isdriven principally by the methylation data (log-rank p-value of $2.0 \times10^{-3}$) 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 lowlevels of methylation. This subtype has very low rates of tumour recurrence andno new events in 10 years of follow up. We also identify a small geneexpression subtype of 6 patients that shows particularly poor survivaloutcomes. Additionally, we note a consensus subtype that showly a highlydistinctive data signature and suggest that it is therefore a biologicallydistinct subtype of glioblastoma.  The code is available from https://sites.google.com/site/multipledatafusion/

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