Dimitris Anastassiou, Director, Genomic Information Systems Laboratory, Columbia University
Thursday, November 15, 2012, 3:00 PM to 4:00 PM
Location: 305 Physical Sciences Building
Hosted By Artem Sokolov, Biomolecular Engineering
Anastassiou's team won the recent Sage Bionetworks DREAM Breast Cancer Prognosis Challenge.
Mining gene expression profiles has proven valuable for identifying signatures serving as surrogates of cancer phenotypes. However, the similarities of such signatures across different cancer types have not been strong enough to conclude that they represent a universal biological mechanism shared among multiple cancer types. Here we present a computational method for generating signatures using an iterative process that converges to one of several precise attractors defining signatures representing biomolecular events, such as cell transdifferentiation or the presence of an amplicon. By analyzing rich gene expression datasets from different cancer types, we identified many such biomolecular events, some of which are universally present in all tested cancer types in nearly identical form. Although the method is unsupervised, we show that it often leads to attractors with strong phenotypic associations. We present severalsuch multi-cancer attractors focusing on three that are prominent and sharply defined in all cases: a mesenchymal transition attractor strongly associated with tumor stage; a mitotic chromosomal instability attractor strongly associated with tumor grade; and a lymphocyte-specific attractor. These attractors proved to be strongly prognostic in the recent Sage Bionetworks DREAM Breast Cancer Prognosis Challenge.
Source: http://cbse.soe.ucsc.edu/events/event/2397
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