Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Félix Sanchez-Garcia, Patricia Villagrasa, Junji Matsui, Dylan Kotliar, Verónica Castro, Uri David Akavia, Bo Juen Chen, Laura Saucedo-Cuevas, Ruth Rodriguez Barrueco, David Llobet-Navas, Jose M. Silva, Dana Pe'Er

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

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.

Original languageEnglish
Pages (from-to)1461-1475
Number of pages15
JournalCell
Volume159
Issue number6
DOIs
StatePublished - 4 Dec 2014

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