Analysis of tumor heterogeneity and cancer gene networks using deep sequencing of MMTV-induced mouse mammary tumors

Cancer develops through a multistep process in which normal cells progress to malignant tumors via the evolution of their genomes as a result of the acquisition of mutations in cancer driver genes. The number, identity and mode of action of cancer driver genes, and how they contribute to tumor evolution is largely unknown. This study deployed...

KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data

Background: Most approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are...

Module-Based Outcome Prediction Using Breast Cancer Compendia

Background. The availability of large collections of microarray datasets (compendia), or knowledge about grouping of genes into pathways (gene sets), is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space....