Cancer is an individual disease—unique in how it develops and behaves in every patient. Systematic characterization of cancer genomes has revealed a staggering complexity and heterogeneity of aberrations among individuals. More recently appreciated that intra-tumor heterogeneity is of critical importance, each tumor harboring sub-populations that vary in clinically important phenotypes such as drug sensitivity. We use genomic technologies to track tumor response to drug and develop computational machine learning algorithms to piece together an understanding of this data deluge towards personalized cancer care. We methods focus on questions such as (1) Identify the genetic determinants of cancer and drug resistance. (2) Model how these aberrations lead tumor networks to go awry, arming the cancer with ability to abnormally grow, metastasize and evade drugs. (3) Understand what part of the tumor network to target by identifying tumor vulnerabilities and potential synergy of drug combinations. (4) Characterize tumor heterogeneity, including drug resistant and tumor initiating subpopulations. Treatment that is based not only on understanding which components go wrong, but also how these go wrong in each individual patient, will improve cancer therapeutics.
Dana Pe'er is associate professor at the Departments of Biological Sciences and Systems Biology at Columbia University. She received a Ph.D in Computer Science from the Hebrew University of Jerusalem and continued her post-doctoral studies with George Church at Harvard Medical School. In particular, we study how genetic variation alters regulatory network function, subsequently phenotype in health and disease.