Miércoles 7 de diciembre de 2016,  

• Salón Diego Bricio Hernández, CIMAT, Guanajuato.
  13.00-14.00, Analysis of cancer genomic data using computational algebraic topology.
  Javier Arsuaga, University of California, Davis.
  Resumen

  Genomic technologies measure thousands of molecular signals with the goal of understanding essential biological processes. In cancer these molecular signals have been used to characterize disease subtypes, cancer pathways as well as subsets of patients with specific prognostic factors. This large amount of information however is so complex that new mathematical methods are required for further analyses. Computational homology provides such a method. We have developed a new homology based supervised method that identifies significant copy number changes in the tumor genome. This method associates a set of point clouds to any given profile and uses β0 of the surfaces to detect frequent copy number changes and β1 to further analyze the structure of the copy number changes. We applied this method to a set of breast cancer patients with known molecular subtype. The analysis using β0 confirmed previously reported copy number changes and found three new significant changes in the basal subtype: 1p, 2p and 14q. The analysis using β1 identified multiple co-occurring amplifications. I will discuss those related with the ERBB2/HER2 subtype (17q12, 17q21.2 and 17q21.33). The talk will end discussing possible extensions of this approach.