Breast cancer is a complex heterogeneous disease for which a great deal of transcriptomic data is available. This expression data has allowed us to classify these cancers into at least five molecular subtypes, Luminal A, Luminal B, Her2, Normal-like and Basal. These different breast cancer sub-types are treated differently in the clinic and have different survival outcomes. Within each of these subtypes it is important to identify which patients are at risk of developing a more aggressive phenotype so as to tailor the level of clinical intervention. Prognostic markers are used to asses the risk of patients having good or poor clinical outcome.

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BreastMark is an algorithm we have developed that has allowed us to identify subsets of genes/miRNAs that are associated with disease progression in breast cancer and its subtypes i.e. a set of putative prognostic markers. This algorithm integrates gene expression microarrays which frequently also contain miRNA expression information, and detailed clinical data to correlate clinical outcome with differential gene/miRNA expression levels. This algorithm integrates gene expression and survival data from 26 datasets on 12 different microarray platforms corresponding to ~17,000 genes in up to 4,738 samples. It also allows us to examine the prognostic potential of 341 microRNAs. The accompanying manuscript is published and available here.


Molecular Therapeutics for Cancer, Ireland (MTCI) is a Science Foundation Ireland-funded Strategic Research Cluster which aims to discover and develop new anti-cancer drugs. There is an urgent need for improved drug treatments for cancer, which is emerging as the leading cause of mortality in Ireland and other western countries. Traditional cancer chemotherapy has resulted in improved outcomes for some types of cancer, but remains a generally unsatisfactory form of treatment, with low rates of cure, and prominent side-effects.