title: Integration of genomic data enables selective discovery of breast cancer drivers.
abstract: Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer.
authors.affiliation: Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA; Department of Computer Science, Columbia University, New York, NY 10027, USA.
authors.forename: Félix
authors.initials: F
authors.lastname: Sanchez-Garcia
authors.affiliation: Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA.
authors.forename: Patricia
authors.initials: P
authors.lastname: Villagrasa
authors.affiliation: Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA.
authors.forename: Junji
authors.initials: J
authors.lastname: Matsui
authors.affiliation: Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA.
authors.forename: Dylan
authors.initials: D
authors.lastname: Kotliar
authors.affiliation: Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA.
authors.forename: Verónica
authors.initials: V
authors.lastname: Castro
authors.affiliation: Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA.
authors.forename: Uri-David
authors.initials: UD
authors.lastname: Akavia
authors.affiliation: Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA.
authors.forename: Bo-Juen
authors.initials: BJ
authors.lastname: Chen
authors.affiliation: Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Saucedo-Cuevas
authors.affiliation: Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA.
authors.forename: Ruth
authors.initials: R
authors.lastname: Rodriguez Barrueco
authors.affiliation: Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA.
authors.forename: David
authors.initials: D
authors.lastname: Llobet-Navas
authors.affiliation: Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA. Electronic address: jose.silva@mssm.edu.
authors.forename: Jose M
authors.initials: JM
authors.lastname: Silva
authors.affiliation: Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA. Electronic address: dpeer@biology.columbia.edu.
authors.forename: Dana
authors.initials: D
authors.lastname: Pe'er
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D045744:Cell Line, Tumor; D056915:DNA Copy Number Variations; D005260:Female; D055106:Genome-Wide Association Study; D006801:Humans; D016688:Mice, Inbred NOD; D016513:Mice, SCID; D034622:RNA Interference