BackgroundAnalysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies (‘omics’) and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis.ResultsWe developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO’s main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA.ConclusionsPROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

BackgroundAnalysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis. ResultsWe developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and for stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA. ConclusionsPROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers.PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

AKT2, AKT3), RB pathway (RB1 and CDK2) and cellular differentiation (K8). The expression profiles were investigated by realtime qPCR in formalin-fixed and paraffin-embedded tissues, and correlated to immunohistochemical-based molecular classes, namely luminal A, luminal B, Her2 +and TN. The study was approved by the Ethical Committee of the University of Trieste. Results and discussions In our cohort lymph node involvement resulted to be related to the contribution of several genes at the primary tumour tissue level. Some of those genes resulted to be more expressed in LN negative BC, such as PIK3B, RB1 and AKT3, while some others were more expressed in LN positive BC, such as HER2 and AKT1. Our results show higher expression levels of PIK3B and AKT3 in less aggressive BC and higher expression levels of AKT1 in more aggressive BC highlighting the complex regulation of that pathway in BC. Shorter cancer specific survival was recorded in patients expressing higher levels of AKT1 and AKT2. Furthermore, better cancer specific survival was recorded in luminal A BC patients expressing higher levels of AKT3 (p=0.005 in LNand p=0.01 in LN+). Conclusion By comparing gene expression in lymph node negative and lymph node positive breast cancers, we found that AKT3 is an independent favourable prognostic factor for luminal A BC patients. Our results showed that a high expression level of AKT3, but not AKT1 and AKT2 was associated to better outcome and longer cancer specific patients' survival in those patients who display the luminal A molecular class irrespective of lymph node involvement.