• Raul Rabadan
  • Professor
  • Columbia University

Curriculum Vitae

Education

Autonoma University, Madrid, Spain MSc 06/1998 Theoretical Physics.
Autonoma University, Madrid, Spain PhD 01/2001 Theoretical Physics.

Professional Experience

2001-2003: Fellow at the Theoretical Physics Unit at CERN, European Organization for Nuclear Research, in Geneva, Switzerland.
2003-2006: Theoretical Physics Group of the School for Natural Sciences at the Institute for Advanced Study, Princeton, NJ.
2006-2008: Member of The Simons Center for Systems Biology led by A. J. Levine at the Institute for Advanced Study (IAS) in Princeton, NJ.
2008-2014: Assistant Professor, Department of Biomedical Informatics, Center for Computational Biology and Bioinformatics, Columbia University, New York, NY.
2014-2017: Associate Professor, Department of Systems Biology, Department of Biomedical Informatics, Center for Computational Biology and Bioinformatics, Columbia University, New York, NY.
2015-present: Director of the Columbia University Center for Topology of Cancer Evolution and Heterogeneity, Columbia University, New York.
2017-present: Director of the Mathematical Genomics Program at Columbia University, New York.
2017-present: Professor, Department of Systems Biology, Department of Biomedical Informatics, Center for Computational Biology and Bioinformatics, Columbia University, New York, NY.

Research Interests

My main interests are in understanding the dynamics of biological systems by studying large collections of genomes. In that line, I have become very active in reconstructing the main routes of evolution in tumors, the role of clonal heterogeneity and finding alterations that drive tumor evolution and relapse to therapy. My work has been mostly focused on hematological malignancies and brain tumors, but the techniques developed has been applied in a large variety of tumors. My study in the genetics of glioblastoma has been focused on the identification of novel driver genes at diagnosis using genomic information in cross sectional data and large scale clinical information. Of recent interest is to elucidate the role of non-coding RNAs, in particular in the initiation of tumors. My lab has developed computational tools to reconstruct non-coding RNAs associated to diverse regulatory mechanisms in the cell.

Honors & Awards

2013: Stewart Trust Fellow.
2014: Harold and Golden Lamport Award.
2018: Phillip A. Sharp Award.

Publications

a) J. Wang, E. Cazzato, E. Ladewig, V. Frattini, D.S. Rosenbloom, S. Zairis, F. Abate, Z. Liu, O.Elliott, YJ. Shin, JK. Lee, IH. Lee, WY. Park, M. Eoli, A. Blumberg, A. Lasorella, DH. Nam, G. Finocchiaro, A. Iavarone, R. Rabadan, Clonal Evolution of Glioblastoma under Therapy, Nature Genetics 2016 Jul;48(7):768-76 [PMC5627776].
b) R. D. Melamed, K.J. Emmett, C. Madubata, A. Rzhetsky, R. Rabadan. Genetic similarity between cancers and comorbid Mendelian diseases identifies candidate driver genes Nature Communications 2015 Apr 30; 6:7033 [PMC4416231].
c) V. Frattini, V.Trifonov, J. M. Chan, A. Castano, M. Lia, F. Abate, S. T. Keir, A. X. Ji, P. Zoppoli, F. Niola, C. Danussi, I. Dolgalev, P Porrati, S Pellegatta, A. Heguy, G. Gupta, D. J. Pisapia, P. Canoll, J. N. Bruce, R. E. McLendon, H. Yan, K. Aldape, G. Finocchiaro, T. Mikkelsen, G.G. Privé, D. D. Bigner, A. Lasorella*, R. Rabadan*, A. Iavarone*. The integrated landscape of driver genomic alterations in glioblastoma. Nature Genetics 2013 Oct; 45(10):114. * Co-last Authors. [PMC3799953].

Abstract

Clonal heterogeneity and evolution of brain tumors

Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies. This proposition, however, is complicated by the high degree of spatial and clonal heterogeneity and rapid evolution of many tumors. Glioblastoma (GBM), the most common and aggressive primary brain tumor, constitutes one of these complex tumors where current therapies minimally extend life expectancy. To better understand how GBM evolves and the role of clonal heterogeneity, we analyzed multi-sector and longitudinal genomic and transcriptomic data from hundreds of patients. The branching pattern together with estimates of evolutionary rates suggest that clonal diversification precedes tumor growth and that the relapse associated clone typically preexisted years before diagnosis. Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, while geographically separated multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells shows that therapeutic response is associated to genetic similarity, and multifocal tumors, enriched with PIK3CA mutations, have a highly heterogeneous drug response pattern. This work illustrates the current challenges and hopes of genomic-based cancer approaches to complex tumors.