Last modified 2 years ago Last modified on 08/27/15 15:54:27


  • Genomics of cancer. We have developed a sequencing analysis pipeline that we are currently using to characterize different tumor samples, in terms of their somatic mutations and cancer susceptibility genes, in a number of experimental systems, including clinical samples and samples from genetically modified mouse models. Our aim is to identify chemoresistance and metastasis associated mutations.
  • Algorithms for the development of genome-based personalized cancer treatments. We will integrate mutational analysis data with clinical phenotype information and develop machine-learning algorithms to identify complex patterns in patient-mutational profiles, which can be then used to stratify patients for prognosis and to design treatment strategies.
  • Integrative network biology of cancer. The high number and the intrapatient and intratumor heterogeneity of identified somatic mutations make the task of explaining the links between genetic alterations and carcinogenesis challenging. By using a combination of high-throughput experimental methods, determining a probabilistic interactome, and applying network optimization techniques, we can select candidate driver mutations and identify the pathways that link these genetic alterations to cancer progression.


To identify driver mutations:

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To analyze ChIP-seq experiments: